fix(skills): harden slash skill activation across chat channels (#3466)

* support slash skill activation

* format slash skill activation

* Preserve slash skill activation with uploads

* Address slash skill review feedback

* Address slash skill follow-up review

* Fix lazy slash skill storage resolution

* Keep slash skill activation out of system prompt

* Address slash skill review issues

* fix: harden slash skill command handling

* feat(frontend): add slash skill autocomplete

* fix: address slash skill review feedback

* fix: preserve slash skill text for IM uploads

.agent/skills/smoke-test/SKILL.md

@@ -59,7 +59,7 @@ smoke-test/
 2. **Check pnpm** - Package manager
 3. **Check uv** - Python package manager
 4. **Check nginx** - Reverse proxy
-5. **Check required ports** - Confirm that ports 2026, 3000, 8001, and 2024 are not occupied
+5. **Check required ports** - Confirm that ports 2026, 3000, and 8001 are not occupied
 
 **Docker mode environment check** (if Docker is selected):
 1. **Check whether Docker is installed** - Run `docker --version`
@@ -93,17 +93,17 @@ smoke-test/
 ### Phase 5: Service Health Check
 
 **Local mode health check**:
-1. **Check process status** - Confirm that LangGraph, Gateway, Frontend, and Nginx processes are all running
+1. **Check process status** - Confirm that Gateway, Frontend, and Nginx processes are all running
 2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
 3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
-4. **Check LangGraph service** - Verify the availability of relevant endpoints
+4. **Check LangGraph-compatible API** - Verify the `/api/langgraph/*` route exposed by Gateway
 5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
 
 **Docker mode health check** (when using Docker):
 1. **Check container status** - Run `docker ps` and confirm that all containers are running
 2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
 3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
-4. **Check LangGraph service** - Verify the availability of relevant endpoints
+4. **Check LangGraph-compatible API** - Verify the `/api/langgraph/*` route exposed by Gateway
 5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
 
 ### Optional Functional Verification
@@ -135,7 +135,7 @@ smoke-test/
 
 The following warnings can appear during smoke testing and do not block a successful result:
 - Feishu/Lark SSL errors in Gateway logs (certificate verification failure) can be ignored if that channel is not enabled
-- Warnings in LangGraph logs about missing methods in the custom checkpointer, such as `adelete_for_runs` or `aprune`, do not affect the core functionality
+- Warnings in Gateway logs about missing methods in the custom checkpointer, such as `adelete_for_runs` or `aprune`, do not affect the core functionality
 
 ## Key Tools
 

.agent/skills/smoke-test/references/SOP.md

@@ -138,7 +138,6 @@ This document describes the detailed operating steps for each phase of the DeerF
    lsof -i :2026  # Main port
    lsof -i :3000  # Frontend
    lsof -i :8001  # Gateway
-   lsof -i :2024  # LangGraph
    ```
 
 **Success Criteria**: All ports are free, or they are occupied only by DeerFlow-related processes.
@@ -258,7 +257,7 @@ This document describes the detailed operating steps for each phase of the DeerF
 **Steps**:
 1. Run `make dev-daemon` (background mode)
 
-**Description**: This command starts all services (LangGraph, Gateway, Frontend, Nginx).
+**Description**: This command starts all services (Gateway embedded runtime, Frontend, Nginx).
 
 **Notes**:
 - `make dev` runs in the foreground and stops with Ctrl+C
@@ -272,7 +271,6 @@ This document describes the detailed operating steps for each phase of the DeerF
 **Steps**:
 1. Wait 90-120 seconds for all services to start completely
 2. You can monitor startup progress by checking these log files:
-   - `logs/langgraph.log`
    - `logs/gateway.log`
    - `logs/frontend.log`
    - `logs/nginx.log`
@@ -316,11 +314,10 @@ This document describes the detailed operating steps for each phase of the DeerF
 **Steps**:
 1. Run the following command to check processes:
    ```bash
-   ps aux | grep -E "(langgraph|uvicorn|next|nginx)" | grep -v grep
+   ps aux | grep -E "(uvicorn|next|nginx)" | grep -v grep
    ```
 
 **Success Criteria**: Confirm that the following processes are running:
-- LangGraph (`langgraph dev`)
 - Gateway (`uvicorn app.gateway.app:app`)
 - Frontend (`next dev` or `next start`)
 - Nginx (`nginx`)
@@ -356,10 +353,11 @@ curl http://localhost:2026/health
 
 ---
 
-#### 5.1.4 Check LangGraph Service
+#### 5.1.4 Check LangGraph-compatible API
 
 **Steps**:
-1. Visit relevant LangGraph endpoints to verify availability
+1. Visit `http://localhost:2026/api/langgraph/assistants/lead_agent` to verify Gateway's LangGraph-compatible API route is reachable.
+2. A `401` response is acceptable when authentication is enabled and no session cookie is provided.
 
 ---
 
@@ -373,7 +371,6 @@ curl http://localhost:2026/health
    - `deer-flow-nginx`
    - `deer-flow-frontend`
    - `deer-flow-gateway`
-   - `deer-flow-langgraph` (if not in gateway mode)
 
 ---
 
@@ -406,10 +403,11 @@ curl http://localhost:2026/health
 
 ---
 
-#### 5.2.4 Check LangGraph Service
+#### 5.2.4 Check LangGraph-compatible API
 
 **Steps**:
-1. Visit relevant LangGraph endpoints to verify availability
+1. Visit `http://localhost:2026/api/langgraph/assistants/lead_agent` to verify Gateway's LangGraph-compatible API route is reachable.
+2. A `401` response is acceptable when authentication is enabled and no session cookie is provided.
 
 ---
 

.agent/skills/smoke-test/references/troubleshooting.md

@@ -254,7 +254,6 @@ Processes exit quickly after running `make dev-daemon`.
 **Solutions**:
 1. Check log files:
    ```bash
-   tail -f logs/langgraph.log
    tail -f logs/gateway.log
    tail -f logs/frontend.log
    tail -f logs/nginx.log
@@ -367,24 +366,7 @@ Errors appear in `gateway.log`.
    uv sync
    ```
 
-4. Confirm that the LangGraph service is running normally (if not in gateway mode)
+4. Confirm that the Gateway process is running normally.
-
----
-
-### Issue: LangGraph Fails to Start
-
-**Symptoms**:
-Errors appear in `langgraph.log`.
-
-**Solutions**:
-1. Check LangGraph logs:
-   ```bash
-   tail -f logs/langgraph.log
-   ```
-
-2. Check config.yaml
-3. Check whether Python dependencies are complete
-4. Confirm that port 2024 is not occupied
 
 ---
 
@@ -519,7 +501,7 @@ Accessing `/health` returns an error or times out.
 
 2. Confirm that config.yaml exists and has valid formatting
 3. Check whether Python dependencies are complete
-4. Confirm that the LangGraph service is running normally
+4. Confirm that the Gateway process is running normally.
 
 **Solutions** (Docker mode):
 1. Check gateway container logs:
@@ -529,7 +511,7 @@ Accessing `/health` returns an error or times out.
 
 2. Confirm that config.yaml is mounted correctly
 3. Check whether Python dependencies are complete
-4. Confirm that the LangGraph service is running normally
+4. Confirm that the Gateway process is running normally.
 
 ---
 
@@ -539,7 +521,7 @@ Accessing `/health` returns an error or times out.
 
 #### View All Service Processes
 ```bash
-ps aux | grep -E "(langgraph|uvicorn|next|nginx)" | grep -v grep
+ps aux | grep -E "(uvicorn|next|nginx)" | grep -v grep
 ```
 
 #### View Service Logs
@@ -548,7 +530,6 @@ ps aux | grep -E "(langgraph|uvicorn|next|nginx)" | grep -v grep
 tail -f logs/*.log
 
 # View specific service logs
-tail -f logs/langgraph.log
 tail -f logs/gateway.log
 tail -f logs/frontend.log
 tail -f logs/nginx.log

.agent/skills/smoke-test/scripts/check_local_env.sh

@@ -65,7 +65,7 @@ if ! command -v lsof >/dev/null 2>&1; then
     echo "  Install lsof and rerun this check"
     all_passed=false
 else
-    for port in 2026 3000 8001 2024; do
+    for port in 2026 3000 8001; do
         if lsof -i :$port >/dev/null 2>&1; then
             echo "⚠  Port $port is already in use:"
             lsof -i :$port | head -2

.agent/skills/smoke-test/scripts/deploy_local.sh

@@ -54,7 +54,6 @@ echo "=========================================="
 echo ""
 echo "🌐 Access URL: http://localhost:2026"
 echo "📋 View logs:"
-echo "   - logs/langgraph.log"
 echo "   - logs/gateway.log"
 echo "   - logs/frontend.log"
 echo "   - logs/nginx.log"

.agent/skills/smoke-test/scripts/health_check.sh

@@ -76,12 +76,11 @@ if [ "$mode" = "docker" ]; then
         all_passed=false
     fi
 else
-    summary_hint="logs/{langgraph,gateway,frontend,nginx}.log"
+    summary_hint="logs/{gateway,frontend,nginx}.log"
     print_step "1. Checking local service ports..."
     check_listen_port "Nginx" 2026
     check_listen_port "Frontend" 3000
     check_listen_port "Gateway" 8001
-    check_listen_port "LangGraph" 2024
 fi
 echo ""
 
@@ -104,8 +103,8 @@ else
 fi
 echo ""
 
-echo "5. Checking LangGraph service..."
+echo "5. Checking LangGraph-compatible Gateway API..."
-check_http_status "LangGraph service" "http://localhost:2024/" "200|301|302|307|308|404"
+check_http_status "LangGraph-compatible Gateway API" "http://localhost:2026/api/langgraph/assistants/lead_agent" "200|401"
 echo ""
 
 echo "=========================================="

.agent/skills/smoke-test/templates/report.docker.template.md

@@ -78,7 +78,7 @@
 - [x] Container status - {{status_containers}}
 - [x] Frontend service - {{status_frontend}}
 - [x] API Gateway - {{status_api_gateway}}
-- [x] LangGraph service - {{status_langgraph}}
+- [x] LangGraph-compatible Gateway API - {{status_langgraph}}
 
 **Phase Status**: {{stage5_status}}
 
@@ -147,7 +147,6 @@ Commit Message: {{git_commit_message}}
 | deer-flow-nginx | {{nginx_status}} | {{nginx_uptime}} |
 | deer-flow-frontend | {{frontend_status}} | {{frontend_uptime}} |
 | deer-flow-gateway | {{gateway_status}} | {{gateway_uptime}} |
-| deer-flow-langgraph | {{langgraph_status}} | {{langgraph_uptime}} |
 
 ---
 

.agent/skills/smoke-test/templates/report.local.template.md

@@ -80,7 +80,7 @@
 - [x] Process status - {{status_processes}}
 - [x] Frontend service - {{status_frontend}}
 - [x] API Gateway - {{status_api_gateway}}
-- [x] LangGraph service - {{status_langgraph}}
+- [x] LangGraph-compatible Gateway API - {{status_langgraph}}
 
 **Phase Status**: {{stage5_status}}
 
@@ -152,7 +152,7 @@ Commit Message: {{git_commit_message}}
 | Nginx | {{nginx_status}} | {{nginx_endpoint}} |
 | Frontend | {{frontend_status}} | {{frontend_endpoint}} |
 | Gateway | {{gateway_status}} | {{gateway_endpoint}} |
-| LangGraph | {{langgraph_status}} | {{langgraph_endpoint}} |
+| Gateway LangGraph API | {{langgraph_status}} | {{langgraph_endpoint}} |
 
 ---
 
@@ -166,7 +166,7 @@ Commit Message: {{git_commit_message}}
 
 ### If the Test Fails
 1. [ ] Review references/troubleshooting.md for common solutions
-2. [ ] Check local logs: `logs/{langgraph,gateway,frontend,nginx}.log`
+2. [ ] Check local logs: `logs/{gateway,frontend,nginx}.log`
 3. [ ] Verify configuration file format and content
 4. [ ] If needed, fully reset the environment: `make stop && make clean && make install && make dev-daemon`
 

.env.example

@@ -21,6 +21,7 @@ INFOQUEST_API_KEY=your-infoquest-api-key
 # DEEPSEEK_API_KEY=your-deepseek-api-key
 # NOVITA_API_KEY=your-novita-api-key  # OpenAI-compatible, see https://novita.ai
 # MINIMAX_API_KEY=your-minimax-api-key  # OpenAI-compatible, see https://platform.minimax.io
+# STEPFUN_API_KEY=your-stepfun-api-key  # OpenAI-compatible, see https://platform.stepfun.com
 # VLLM_API_KEY=your-vllm-api-key  # OpenAI-compatible
 # FEISHU_APP_ID=your-feishu-app-id
 # FEISHU_APP_SECRET=your-feishu-app-secret

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,159 @@
+name: 🐛 Bug report
+description: Report something that isn't working so maintainers can reproduce and fix it.
+title: "[bug] "
+labels: ["bug"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to file a bug. A clear, reproducible report is the
+        single biggest factor in how fast it gets fixed.
+
+        Please fill in every required field — especially **reproduction steps** and **logs**.
+
+  - type: checkboxes
+    id: preflight
+    attributes:
+      label: Before you start
+      options:
+        - label: I searched [existing issues](https://github.com/bytedance/deer-flow/issues?q=is%3Aissue) and this is not a duplicate.
+          required: true
+        - label: I can reproduce this on the latest `main`.
+          required: false
+
+  - type: input
+    id: summary
+    attributes:
+      label: Problem summary
+      description: One sentence describing the bug.
+      placeholder: e.g. make dev fails to start the gateway service
+    validations:
+      required: true
+
+  - type: dropdown
+    id: areas
+    attributes:
+      label: Affected area(s)
+      description: Which part of DeerFlow does this touch? Select all that apply.
+      multiple: true
+      options:
+        - Frontend (UI / Next.js)
+        - Backend API (gateway / endpoints / SSE)
+        - Agents / LangGraph (graph, prompts, langgraph.json)
+        - Sandbox / Docker
+        - Skills
+        - MCP
+        - Config / setup (make, config.yaml, env)
+        - Docs
+        - Not sure
+    validations:
+      required: true
+
+  - type: textarea
+    id: actual
+    attributes:
+      label: What happened?
+      description: The actual behavior. Include the key error lines verbatim.
+      placeholder: When I do X, I expected Y but I got Z.
+    validations:
+      required: true
+
+  - type: textarea
+    id: expected
+    attributes:
+      label: Expected behavior
+      placeholder: What did you expect to happen instead?
+    validations:
+      required: true
+
+  - type: textarea
+    id: reproduce
+    attributes:
+      label: Steps to reproduce
+      description: Exact commands and sequence. Minimal steps that reliably reproduce the problem.
+      placeholder: |
+        1. make check
+        2. make install
+        3. make dev
+        4. ...
+    validations:
+      required: true
+
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant logs
+      description: Paste key lines from logs (for example `logs/gateway.log`, `logs/frontend.log`). Redact secrets.
+      render: shell
+    validations:
+      required: true
+
+  - type: dropdown
+    id: run_mode
+    attributes:
+      label: How are you running DeerFlow?
+      options:
+        - Local (make dev)
+        - Docker (make docker-start)
+        - CI
+        - Other
+    validations:
+      required: true
+
+  - type: dropdown
+    id: os
+    attributes:
+      label: Operating system
+      options:
+        - macOS
+        - Linux
+        - Windows
+        - Other
+    validations:
+      required: true
+
+  - type: input
+    id: platform_details
+    attributes:
+      label: Platform details
+      description: Architecture and shell, if relevant.
+      placeholder: e.g. arm64, zsh
+
+  - type: input
+    id: python_version
+    attributes:
+      label: Python version
+      placeholder: e.g. Python 3.12.9
+
+  - type: input
+    id: node_version
+    attributes:
+      label: Node.js version
+      placeholder: e.g. v22.11.0
+
+  - type: input
+    id: pnpm_version
+    attributes:
+      label: pnpm version
+      placeholder: e.g. 10.26.2
+
+  - type: input
+    id: uv_version
+    attributes:
+      label: uv version
+      placeholder: e.g. 0.7.20
+
+  - type: textarea
+    id: git_info
+    attributes:
+      label: Git state
+      description: Output of `git branch --show-current` and the latest commit SHA.
+      placeholder: |
+        branch: feature/my-branch
+        commit: abcdef1
+
+  - type: textarea
+    id: additional
+    attributes:
+      label: Additional context
+      description: Screenshots, related issues, config snippets (redacted), or anything else that helps triage.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,11 @@
+blank_issues_enabled: false
+contact_links:
+  - name: 💬 Questions & usage help
+    url: https://github.com/bytedance/deer-flow/discussions/categories/q-a
+    about: "How do I use X? Why does Y behave like that? Ask in Discussions — it gets answered faster and stays searchable."
+  - name: 💡 Ideas & proposals
+    url: https://github.com/bytedance/deer-flow/discussions/categories/ideas
+    about: Have a half-formed idea? Float it in Discussions before opening a formal feature request.
+  - name: 🔒 Report a security vulnerability
+    url: https://github.com/bytedance/deer-flow/security/policy
+    about: Do not open a public issue for security problems. Follow the security policy instead.

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -0,0 +1,67 @@
+name: 💡 Feature request
+description: Propose a new capability or an improvement to an existing one.
+title: "[feat] "
+labels: ["enhancement"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for the suggestion. For non-trivial features, please open a
+        [Discussion](https://github.com/bytedance/deer-flow/discussions/categories/ideas)
+        first to align on scope before writing code.
+
+  - type: checkboxes
+    id: preflight
+    attributes:
+      label: Before you start
+      options:
+        - label: I searched [existing issues](https://github.com/bytedance/deer-flow/issues?q=is%3Aissue) and this is not a duplicate.
+          required: true
+
+  - type: textarea
+    id: problem
+    attributes:
+      label: Problem / motivation
+      description: What problem does this solve? What is painful today, or what does it unblock?
+      placeholder: "I'm always frustrated when ..."
+    validations:
+      required: true
+
+  - type: textarea
+    id: solution
+    attributes:
+      label: Proposed solution
+      description: Describe the change from a user's / caller's perspective.
+    validations:
+      required: true
+
+  - type: dropdown
+    id: areas
+    attributes:
+      label: Affected area(s)
+      description: Which part of DeerFlow would this touch? Select all that apply.
+      multiple: true
+      options:
+        - Frontend (UI / Next.js)
+        - Backend API (gateway / endpoints / SSE)
+        - Agents / LangGraph (graph, prompts, langgraph.json)
+        - Sandbox / Docker
+        - Skills
+        - MCP
+        - Config / setup
+        - Docs
+        - Not sure
+    validations:
+      required: true
+
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Alternatives considered
+      description: Other approaches you weighed and why you discarded them.
+
+  - type: textarea
+    id: additional
+    attributes:
+      label: Additional context
+      description: Mockups, links, related issues, or anything else that helps.

.github/ISSUE_TEMPLATE/runtime-information.yml

@@ -1,128 +0,0 @@
-name: Runtime Information
-description: Report runtime/environment details to help reproduce an issue.
-title: "[runtime] "
-labels:
-  - needs-triage
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for sharing runtime details.
-        Complete this form so maintainers can quickly reproduce and diagnose the problem.
-
-  - type: input
-    id: summary
-    attributes:
-      label: Problem summary
-      description: Short summary of the issue.
-      placeholder: e.g. make dev fails to start gateway service
-    validations:
-      required: true
-
-  - type: textarea
-    id: expected
-    attributes:
-      label: Expected behavior
-      placeholder: What did you expect to happen?
-    validations:
-      required: true
-
-  - type: textarea
-    id: actual
-    attributes:
-      label: Actual behavior
-      placeholder: What happened instead? Include key error lines.
-    validations:
-      required: true
-
-  - type: dropdown
-    id: os
-    attributes:
-      label: Operating system
-      options:
-        - macOS
-        - Linux
-        - Windows
-        - Other
-    validations:
-      required: true
-
-  - type: input
-    id: platform_details
-    attributes:
-      label: Platform details
-      description: Add architecture and shell if relevant.
-      placeholder: e.g. arm64, zsh
-
-  - type: input
-    id: python_version
-    attributes:
-      label: Python version
-      placeholder: e.g. Python 3.12.9
-
-  - type: input
-    id: node_version
-    attributes:
-      label: Node.js version
-      placeholder: e.g. v23.11.0
-
-  - type: input
-    id: pnpm_version
-    attributes:
-      label: pnpm version
-      placeholder: e.g. 10.26.2
-
-  - type: input
-    id: uv_version
-    attributes:
-      label: uv version
-      placeholder: e.g. 0.7.20
-
-  - type: dropdown
-    id: run_mode
-    attributes:
-      label: How are you running DeerFlow?
-      options:
-        - Local (make dev)
-        - Docker (make docker-dev)
-        - CI
-        - Other
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduce
-    attributes:
-      label: Reproduction steps
-      description: Provide exact commands and sequence.
-      placeholder: |
-        1. make check
-        2. make install
-        3. make dev
-        4. ...
-    validations:
-      required: true
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant logs
-      description: Paste key lines from logs (for example logs/gateway.log, logs/frontend.log).
-      render: shell
-    validations:
-      required: true
-
-  - type: textarea
-    id: git_info
-    attributes:
-      label: Git state
-      description: Share output of git branch and latest commit SHA.
-      placeholder: |
-        branch: feature/my-branch
-        commit: abcdef1
-
-  - type: textarea
-    id: additional
-    attributes:
-      label: Additional context
-      description: Add anything else that might help triage.

.github/labels.yml

@@ -0,0 +1,119 @@
+# Declarative label source of truth for DeerFlow.
+#
+# This file is the single source of truth for repository labels used by the
+# auto-labeling workflows (.github/workflows/pr-labeler.yml, pr-triage.yml,
+# issue-triage.yml). Auto-labelers can only apply labels that already exist,
+# so every label referenced by a workflow MUST be declared here.
+#
+# Apply with:  uv run --with pyyaml python scripts/sync_labels.py [--repo OWNER/NAME]
+# CI keeps it in sync via .github/workflows/label-sync.yml (runs on changes here).
+#
+# Sync is additive/update-only: it creates or updates the labels listed below
+# and never deletes labels that are not listed.
+#
+# Color = 6-digit hex without the leading '#'.
+
+labels:
+  # ── Type ─────────────────────────────────────────────────────────────────
+  # Mostly GitHub defaults; declared here so colors/descriptions stay stable
+  # and so issue templates can rely on them existing.
+  - name: bug
+    color: d73a4a
+    description: Something isn't working
+  - name: enhancement
+    color: a2eeef
+    description: New feature or request
+  - name: documentation
+    color: 0075ca
+    description: Improvements or additions to documentation
+  - name: question
+    color: d876e3
+    description: Further information is requested
+
+  # ── Area (auto, by changed paths — see .github/labeler.yml) ───────────────
+  # Mirrors the "Surface area" section of the pull request template.
+  - name: "area:frontend"
+    color: c5def5
+    description: Next.js frontend under frontend/
+  - name: "area:backend"
+    color: c5def5
+    description: Gateway / runtime / core backend under backend/
+  - name: "area:agents"
+    color: c5def5
+    description: Agents, subagents, graph wiring, prompts, langgraph.json
+  - name: "area:sandbox"
+    color: c5def5
+    description: Sandboxed execution and docker/
+  - name: "area:skills"
+    color: c5def5
+    description: Skills under skills/ or the skills harness
+  - name: "area:mcp"
+    color: c5def5
+    description: Model Context Protocol integration
+  - name: "area:ci"
+    color: c5def5
+    description: GitHub Actions, CI config, repo tooling
+  - name: "area:docs"
+    color: c5def5
+    description: Documentation and Markdown only
+  - name: "area:deps"
+    color: c5def5
+    description: Dependency manifests / lockfiles
+
+  # ── Size (auto, by additions + deletions — see pr-triage.yml) ─────────────
+  - name: "size/XS"
+    color: "009900"
+    description: PR changes < 20 lines
+  - name: "size/S"
+    color: 77bb00
+    description: PR changes 20-100 lines
+  - name: "size/M"
+    color: eebb00
+    description: PR changes 100-300 lines
+  - name: "size/L"
+    color: ee9900
+    description: PR changes 300-700 lines
+  - name: "size/XL"
+    color: ee5500
+    description: PR changes 700+ lines
+
+  # ── Risk (auto, by changed paths — see pr-triage.yml) ─────────────────────
+  - name: "risk:low"
+    color: 0e8a16
+    description: "Low risk: docs / i18n / assets only"
+  - name: "risk:medium"
+    color: fbca04
+    description: "Medium risk: regular code changes"
+  - name: "risk:high"
+    color: b60205
+    description: "High risk: backend API, agents, sandbox, auth, deps, CI"
+
+  # ── Priority (manual) ─────────────────────────────────────────────────────
+  - name: P0
+    color: b60205
+    description: Critical priority
+  - name: P1
+    color: d93f0b
+    description: Major priority
+  - name: P2
+    color: e99695
+    description: Normal priority
+
+  # ── Status (auto + manual) ────────────────────────────────────────────────
+  - name: needs-triage
+    color: fef2c0
+    description: Awaiting maintainer triage
+  - name: needs-validation
+    color: d4c5f9
+    description: Touches front/back contract surface; needs real-path validation
+  - name: skip-validation
+    color: cccccc
+    description: "Maintainer override: do not auto-add needs-validation on this PR"
+  - name: reviewing
+    color: 5319e7
+    description: A maintainer is reviewing this PR
+
+  # ── Contributor ───────────────────────────────────────────────────────────
+  - name: first-time-contributor
+    color: c2e0c6
+    description: First contribution to this repository — be welcoming

.github/pull_request_template.md

@@ -59,3 +59,17 @@ Fixes #
        Frontend:  cd frontend && pnpm format && pnpm lint && pnpm typecheck && BETTER_AUTH_SECRET=local-dev-secret pnpm build && make test
        Frontend E2E (if you touched frontend/): cd frontend && make test-e2e -->
 
+
+## AI assistance
+
+<!-- DeerFlow is an AI project — most PRs here use AI coding tools, and that's
+     welcome. Disclosing it just helps reviewers calibrate how closely to read the
+     diff. Please fill all three; don't delete the section. -->
+
+**Tool(s) used:** <!-- e.g. Claude Code, Cursor, GitHub Copilot, Codex, Windsurf, or "none" -->
+
+**How you used it:** <!-- e.g. "generated the module from a spec", "autocomplete only",
+     "AI wrote tests, I wrote the impl". A prompt or conversation link is great too. -->
+
+- [ ] I've read and understand every line of this change and take responsibility for it — it's not unreviewed AI output.
+

.github/workflows/label-sync.yml

@@ -0,0 +1,38 @@
+name: Label Sync
+
+# Keeps repository labels in sync with the declarative source of truth
+# (.github/labels.yml). Runs whenever that file changes on main, and can be
+# triggered manually. Additive/update-only — never deletes labels.
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - ".github/labels.yml"
+      - "scripts/sync_labels.py"
+      - ".github/workflows/label-sync.yml"
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  issues: write
+
+concurrency:
+  group: label-sync
+  cancel-in-progress: false
+
+jobs:
+  sync:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+
+      - name: Sync labels
+        run: uv run --with pyyaml python scripts/sync_labels.py
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GH_REPO: ${{ github.repository }}

.github/workflows/replay-e2e.yml

@@ -0,0 +1,108 @@
+name: Replay E2E (front-back contract)
+
+# Guards the front-back contract via record/replay (no API key in CI):
+#   Layer 1 — backend golden: replay a recorded trace through the real gateway,
+#             assert the SSE event sequence matches the committed golden.
+#   Layer 2 — full-stack render: real Next.js frontend + real gateway (replay
+#             model) + Chromium; assert the replayed turns render in the browser.
+# Triggered by changes on EITHER side of the contract so a backend change can no
+# longer pass without the frontend-facing checks running.
+
+on:
+  push:
+    branches: ["main"]
+    paths:
+      - "frontend/**"
+      - "backend/app/gateway/**"
+      - "backend/packages/harness/**"
+      - "backend/tests/fixtures/replay/**"
+      - "backend/tests/replay_provider.py"
+      - "backend/tests/_replay_fixture.py"
+      - "backend/tests/seed_runs_router.py"
+      - "backend/tests/test_replay_golden.py"
+      - "backend/scripts/run_replay_gateway.py"
+      - ".github/workflows/replay-e2e.yml"
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+    paths:
+      - "frontend/**"
+      - "backend/app/gateway/**"
+      - "backend/packages/harness/**"
+      - "backend/tests/fixtures/replay/**"
+      - "backend/tests/replay_provider.py"
+      - "backend/tests/_replay_fixture.py"
+      - "backend/tests/seed_runs_router.py"
+      - "backend/tests/test_replay_golden.py"
+      - "backend/scripts/run_replay_gateway.py"
+      - ".github/workflows/replay-e2e.yml"
+
+concurrency:
+  group: replay-e2e-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  backend-replay-golden:
+    name: Layer 1 — backend golden (no API key)
+    if: github.event_name != 'pull_request' || github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+      - uses: actions/checkout@v6
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+      - name: Install backend dependencies
+        working-directory: backend
+        run: uv sync --group dev
+      - name: Replay golden (backend SSE contract)
+        working-directory: backend
+        run: PYTHONPATH=. uv run pytest tests/test_replay_golden.py -v
+
+  fullstack-replay-render:
+    name: Layer 2 — full-stack render (no API key)
+    if: github.event_name != 'pull_request' || github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+    timeout-minutes: 25
+    steps:
+      - uses: actions/checkout@v6
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+      - name: Install backend dependencies (replay gateway)
+        working-directory: backend
+        run: uv sync --group dev
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+      - name: Enable Corepack
+        run: corepack enable
+      - name: Use pinned pnpm version
+        run: corepack prepare pnpm@10.26.2 --activate
+      - name: Install frontend dependencies
+        working-directory: frontend
+        run: pnpm install --frozen-lockfile
+      - name: Install Playwright Chromium
+        working-directory: frontend
+        run: npx playwright install chromium --with-deps
+      - name: Full-stack replay render (DOM assertions are the gate)
+        working-directory: frontend
+        run: pnpm exec playwright test -c playwright.real-backend.config.ts
+      - name: Upload report + render artifact
+        uses: actions/upload-artifact@v4
+        if: ${{ !cancelled() }}
+        with:
+          name: replay-render
+          path: |
+            frontend/playwright-report/
+            frontend/test-results/
+          retention-days: 7

.github/workflows/triage.yml

@@ -0,0 +1,223 @@
+name: Triage
+
+# One workflow for all event-driven PR/issue labeling. Replaces the former
+# pr-labeler / pr-triage / issue-triage workflows (and drops actions/labeler).
+#
+# Design notes:
+#   * All jobs are pure-metadata: they read changed-file lists / PR fields / the
+#     review payload via the API and write labels. PR code is NEVER checked out
+#     or executed, so pull_request_target is safe here.
+#   * Each job only reconciles labels in namespaces IT owns
+#     (area:* / size/* / risk:* / needs-validation). It never touches labels
+#     applied by maintainers or other tools (bug, priority, etc.). first-time-
+#     contributor and reviewing are add-only.
+#   * State is read LIVE (listFiles + listLabelsOnIssue) at run time, not from
+#     the (stale) event payload, so rapid synchronize events converge instead
+#     of thrashing.
+
+on:
+  pull_request_target:
+    types: [opened, synchronize, reopened, ready_for_review]
+  pull_request_review:
+    types: [submitted]
+  issues:
+    types: [opened]
+
+permissions:
+  contents: read
+  pull-requests: write
+  issues: write
+
+jobs:
+  # ── PR: area / size / risk / needs-validation / first-time ─────────────────
+  pr-labels:
+    if: github.event_name == 'pull_request_target' && github.event.pull_request.draft == false
+    runs-on: ubuntu-latest
+    concurrency:
+      group: triage-pr-${{ github.event.pull_request.number }}
+      cancel-in-progress: true
+    steps:
+      - name: Apply PR labels from live state
+        uses: actions/github-script@v8
+        with:
+          script: |
+            const pr = context.payload.pull_request;
+            const { owner, repo } = context.repo;
+            const num = pr.number;
+
+            // ---- live changed files ----
+            const files = await github.paginate(github.rest.pulls.listFiles, {
+              owner, repo, pull_number: num, per_page: 100,
+            });
+            const paths = files.map(f => f.filename);
+            const m = (re) => paths.some(p => re.test(p));
+
+            // ---- area: replaces .github/labeler.yml (path -> area) ----
+            const AREA_RULES = [
+              ['area:frontend', [/^frontend\//]],
+              ['area:backend',  [/^backend\/app\//, /^backend\/packages\/harness\/deerflow\/(runtime|persistence|config|tools|guardrails|tracing|models|utils|uploads)\//]],
+              ['area:agents',   [/^backend\/packages\/harness\/deerflow\/(agents|subagents|reflection)\//, /(^|\/)langgraph\.json$/, /^backend\/.*\/prompts\//]],
+              ['area:sandbox',  [/^docker\//, /^backend\/packages\/harness\/deerflow\/sandbox\//, /(^|\/)Dockerfile$/]],
+              ['area:skills',   [/^skills\//, /^backend\/packages\/harness\/deerflow\/skills\//, /^frontend\/src\/core\/skills\//]],
+              ['area:mcp',      [/^backend\/packages\/harness\/deerflow\/mcp\//, /^frontend\/src\/core\/mcp\//]],
+              ['area:ci',       [/^\.github\//, /^scripts\//]],
+              ['area:docs',     [/^docs\//, /\.mdx?$/]],
+              ['area:deps',     [/(^|\/)(pyproject\.toml|uv\.lock|package\.json|pnpm-lock\.yaml)$/]],
+            ];
+            const areaLabels = AREA_RULES
+              .filter(([, res]) => res.some(re => m(re)))
+              .map(([label]) => label);
+
+            // ---- size: additions+deletions, excluding lockfiles/snapshots ----
+            const EXCLUDE_SIZE = /(^|\/)(uv\.lock|pnpm-lock\.yaml|package-lock\.json)$|\.snap$/;
+            const churn = files
+              .filter(f => !EXCLUDE_SIZE.test(f.filename))
+              .reduce((s, f) => s + (f.additions || 0) + (f.deletions || 0), 0);
+            const sizeLabel =
+              churn < 20 ? 'size/XS' :
+              churn < 100 ? 'size/S' :
+              churn < 300 ? 'size/M' :
+              churn < 700 ? 'size/L' : 'size/XL';
+
+            // ---- risk ----
+            const docsOnly = paths.length > 0 && paths.every(p =>
+              /\.(md|mdx|txt)$/i.test(p) || p.startsWith('docs/') ||
+              /\.(png|jpe?g|gif|svg|webp|ico)$/i.test(p));
+            const highRisk =
+              m(/^backend\/app\/gateway\//) ||
+              m(/^backend\/packages\/harness\/deerflow\/(agents|subagents|sandbox)\//) ||
+              m(/(^|\/)langgraph\.json$/) ||
+              m(/(^|\/)(auth|authz|security)/i) ||
+              m(/(pyproject\.toml|uv\.lock|package\.json|pnpm-lock\.yaml)$/) ||
+              m(/^docker\//) ||
+              m(/^\.github\/workflows\//);
+            const riskLabel = docsOnly ? 'risk:low' : (highRisk ? 'risk:high' : 'risk:medium');
+
+            // ---- needs-validation: front/back contract surface ----
+            const contract =
+              m(/^backend\/app\/gateway\//) ||
+              m(/^backend\/packages\/harness\/deerflow\/(agents|subagents)\//) ||
+              m(/(^|\/)langgraph\.json$/) ||
+              m(/^frontend\/src\/core\/(api|threads|messages)\//);
+
+            // ---- live current labels (NOT the stale event payload) ----
+            const current = (await github.paginate(github.rest.issues.listLabelsOnIssue, {
+              owner, repo, issue_number: num, per_page: 100,
+            })).map(l => l.name);
+            const hasSkip = current.includes('skip-validation');
+
+            // Reconcile ONLY namespaces we own; never touch others.
+            const owned = (n) =>
+              n.startsWith('area:') || n.startsWith('size/') ||
+              n.startsWith('risk:') || n === 'needs-validation';
+            const desired = new Set([...areaLabels, sizeLabel, riskLabel]);
+            if (contract && !hasSkip) desired.add('needs-validation');
+
+            const toRemove = current.filter(n => owned(n) && !desired.has(n));
+            const toAdd = [...desired].filter(n => !current.includes(n));
+
+            // first-time-contributor: add-only, on opened, real users only.
+            if (context.payload.action === 'opened' &&
+                pr.user.type === 'User' &&
+                ['FIRST_TIME_CONTRIBUTOR', 'FIRST_TIMER'].includes(pr.author_association) &&
+                !current.includes('first-time-contributor')) {
+              toAdd.push('first-time-contributor');
+            }
+
+            for (const name of toRemove) {
+              try {
+                await github.rest.issues.removeLabel({ owner, repo, issue_number: num, name });
+              } catch (e) {
+                if (e.status !== 404) throw e;
+              }
+            }
+            if (toAdd.length) {
+              await github.rest.issues.addLabels({ owner, repo, issue_number: num, labels: toAdd });
+            }
+            core.info(`area=[${areaLabels.join(',')}] ${sizeLabel} ${riskLabel} churn=${churn} ` +
+              `validation=${desired.has('needs-validation')} ` +
+              `(+${toAdd.join(',') || '-'} / -${toRemove.join(',') || '-'})`);
+
+  # ── PR: reviewing label on a maintainer's human review ─────────────────────
+  reviewing:
+    if: github.event_name == 'pull_request_review'
+    runs-on: ubuntu-latest
+    concurrency:
+      group: triage-review-${{ github.event.pull_request.number }}
+      cancel-in-progress: false
+    steps:
+      - name: Add reviewing label for maintainer reviews
+        uses: actions/github-script@v8
+        with:
+          script: |
+            const { owner, repo } = context.repo;
+            const num = context.payload.pull_request.number;
+            const review = context.payload.review;
+            const assoc = review.author_association;     // payload field; no API call
+            const type = review.user && review.user.type;
+
+            // author_association is NONE for every automated reviewer
+            // (Copilot, CodeRabbit, Codex, Sourcery, ...), so this allowlist
+            // drops them all without a denylist — and never calls the
+            // collaborators API that 404s on "Copilot is not a user".
+            // user.type === 'User' guards the rare bot-added-as-collaborator case.
+            if (!['OWNER', 'MEMBER', 'COLLABORATOR'].includes(assoc) || type !== 'User') {
+              core.info(`reviewer ${review.user && review.user.login} assoc=${assoc} type=${type}; skipping.`);
+              return;
+            }
+
+            const labels = (await github.paginate(github.rest.issues.listLabelsOnIssue, {
+              owner, repo, issue_number: num, per_page: 100,
+            })).map(l => l.name);
+            if (labels.includes('reviewing')) {
+              core.info('Already labeled reviewing; skipping.');
+              return;
+            }
+            try {
+              await github.rest.issues.addLabels({
+                owner, repo, issue_number: num, labels: ['reviewing'],
+              });
+              core.info('Added "reviewing".');
+            } catch (e) {
+              if (e.status === 403) core.info('No permission to label (expected on some fork PRs).');
+              else throw e;
+            }
+
+  # ── Issue: needs-triage on every new issue ────────────────────────────────
+  issue-triage:
+    if: github.event_name == 'issues'
+    runs-on: ubuntu-latest
+    concurrency:
+      group: triage-issue-${{ github.event.issue.number }}
+      cancel-in-progress: false
+    steps:
+      - name: Add needs-triage label
+        uses: actions/github-script@v8
+        with:
+          script: |
+            const { owner, repo } = context.repo;
+            const issue_number = context.payload.issue.number;
+
+            // Read live labels (not the event payload) so labels added at creation
+            // time via the API or by another automation are seen — consistent with
+            // the live-state reads in the PR jobs above.
+            const current = (await github.paginate(github.rest.issues.listLabelsOnIssue, {
+              owner, repo, issue_number, per_page: 100,
+            })).map(l => l.name);
+            if (current.includes('needs-triage')) {
+              core.info('Issue already has needs-triage; nothing to do.');
+              return;
+            }
+            // Self-heal: create the label if it does not exist yet.
+            try {
+              await github.rest.issues.createLabel({
+                owner, repo, name: 'needs-triage', color: 'fef2c0',
+                description: 'Awaiting maintainer triage',
+              });
+            } catch (e) {
+              if (e.status !== 422) throw e; // 422 = already exists
+            }
+            await github.rest.issues.addLabels({
+              owner, repo, issue_number, labels: ['needs-triage'],
+            });
+            core.info(`Added needs-triage to #${issue_number}.`);

CONTRIBUTING.md

@@ -287,6 +287,21 @@ Nginx (port 2026) ← Unified entry point
    git push origin feature/your-feature-name
    ```
 
+## AI assistance disclosure
+
+DeerFlow is an AI project and we welcome AI-assisted contributions. To help
+reviewers calibrate how closely to read a change, **every pull request must
+complete the "AI assistance" section of the
+[PR template](.github/pull_request_template.md)**:
+
+- which tool(s) you used (or `none`),
+- how you used them, and
+- a confirmation that a human has read, understands, and takes responsibility
+  for the change.
+
+Please don't delete the section. PRs that ignore it may be asked to fill it in
+before review.
+
 ## Testing
 
 ```bash

Makefile

@@ -89,36 +89,7 @@ install:
 
 # Pre-pull sandbox Docker image (optional but recommended)
 setup-sandbox:
-	@echo "=========================================="
+	@$(RUN_WITH_GIT_BASH) ./scripts/setup-sandbox.sh
-	@echo "  Pre-pulling Sandbox Container Image"
-	@echo "=========================================="
-	@echo ""
-	@IMAGE=$$(grep -A 20 "# sandbox:" config.yaml 2>/dev/null | grep "image:" | awk '{print $$2}' | head -1); \
-	if [ -z "$$IMAGE" ]; then \
-		IMAGE="enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest"; \
-		echo "Using default image: $$IMAGE"; \
-	else \
-		echo "Using configured image: $$IMAGE"; \
-	fi; \
-	echo ""; \
-	if command -v container >/dev/null 2>&1 && [ "$$(uname)" = "Darwin" ]; then \
-		echo "Detected Apple Container on macOS, pulling image..."; \
-		container image pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
-	fi; \
-	if command -v docker >/dev/null 2>&1; then \
-		echo "Pulling image using Docker..."; \
-		if docker pull "$$IMAGE"; then \
-			echo ""; \
-			echo "✓ Sandbox image pulled successfully"; \
-		else \
-			echo ""; \
-			echo "⚠ Failed to pull sandbox image (this is OK for local sandbox mode)"; \
-		fi; \
-	else \
-		echo "✗ Neither Docker nor Apple Container is available"; \
-		echo "  Please install Docker: https://docs.docker.com/get-docker/"; \
-		exit 1; \
-	fi
 
 # Start all services in development mode (with hot-reloading)
 dev:
@@ -148,7 +119,6 @@ stop:
 clean: stop
 	@echo "Cleaning up..."
 	@-rm -rf backend/.deer-flow 2>/dev/null || true
-	@-rm -rf backend/.langgraph_api 2>/dev/null || true
 	@-rm -rf logs/*.log 2>/dev/null || true
 	@echo "✓ Cleanup complete"
 

README.md

@@ -585,6 +585,8 @@ A standard Agent Skill is a structured capability module — a Markdown file tha
 
 Skills are loaded progressively — only when the task needs them, not all at once. This keeps the context window lean and makes DeerFlow work well even with token-sensitive models.
 
+Users can explicitly activate an enabled skill for a single turn by starting the request with `/skill-name`, for example `/data-analysis analyze uploads/foo.csv`. DeerFlow loads that skill's `SKILL.md` as hidden current-turn context while leaving the base prompt limited to skill metadata. Slash activation respects disabled skills, custom-agent skill whitelists, and existing channel commands such as `/new` and `/help`.
+
 When you install `.skill` archives through the Gateway, DeerFlow accepts standard optional frontmatter metadata such as `version`, `author`, and `compatibility` instead of rejecting otherwise valid external skills.
 
 Tools follow the same philosophy. DeerFlow comes with a core toolset — web search, web fetch, file operations, bash execution — and supports custom tools via MCP servers and Python functions. Swap anything. Add anything.

backend/.gitignore

@@ -24,5 +24,10 @@ config.yaml
 # Langgraph
 .langgraph_api
 
+# Sandbox runtime working dir — pre-created and excluded from uvicorn reload
+# (scripts/serve.sh, docker/dev-entrypoint.sh). Anchored so it does not match
+# the source package backend/packages/harness/deerflow/sandbox/.
+/sandbox/
+
 # Claude Code settings
 .claude/settings.local.json

backend/CLAUDE.md

@@ -122,10 +122,14 @@ Blocking-IO runtime gate (`tests/blocking_io/`):
   `tests/support/detectors/blocking_io_runtime.py`). Any sync blocking IO
   call whose stack passes through DeerFlow business code while running on
   the asyncio event loop raises `BlockingError` and fails the test.
-- Two regression anchors live there: `test_skills_load.py` (locks the
+- Regression anchors live there: `test_skills_load.py` (locks the
   `asyncio.to_thread` offload around `LocalSkillStorage.load_skills`, fix
-  for #1917) and `test_sqlite_lifespan.py` (locks the offload around
+  for #1917); `test_sqlite_lifespan.py` (locks the offload around
-  SQLite path resolution plus `ensure_sqlite_parent_dir`, fix for #1912).
+  SQLite path resolution plus `ensure_sqlite_parent_dir`, fix for #1912);
+  `test_jsonl_run_event_store.py` (locks `JsonlRunEventStore`'s async
+  API offloading its file IO via `asyncio.to_thread`, fix #3084); and
+  `test_uploads_middleware.py` (locks `UploadsMiddleware.abefore_agent`
+  offloading the uploads-directory scan off the event loop).
 - `test_gate_smoke.py` is a meta-test asserting the gate actually catches
   unoffloaded blocking IO and that the `@pytest.mark.allow_blocking_io`
   opt-out works.
@@ -188,7 +192,7 @@ from deerflow.config import get_app_config
 
 ### Middleware Chain
 
-Lead-agent middlewares are assembled in strict append order across `packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py` (`build_lead_runtime_middlewares`) and `packages/harness/deerflow/agents/lead_agent/agent.py` (`_build_middlewares`):
+Lead-agent middlewares are assembled in strict append order across `packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py` (`build_lead_runtime_middlewares`) and `packages/harness/deerflow/agents/lead_agent/agent.py` (`build_middlewares`):
 
 1. **ThreadDataMiddleware** - Creates per-thread directories under the user's isolation scope (`backend/.deer-flow/users/{user_id}/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); resolves `user_id` via `get_effective_user_id()` (falls back to `"default"` in no-auth mode); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local thread directory
 2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
@@ -198,16 +202,17 @@ Lead-agent middlewares are assembled in strict append order across `packages/har
 6. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
 7. **SandboxAuditMiddleware** - Audits sandboxed shell/file operations for security logging before tool execution continues
 8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
-9. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
+9. **SkillActivationMiddleware** - Detects strict `/skill-name task` syntax on the latest real user message, resolves only enabled and runtime-allowed skills, reads `SKILL.md` from trusted skill storage, injects the skill body as hidden current-turn model context, and records a `middleware:skill_activation` audit event with skill name, category, path, and content hash
-10. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
+10. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
-11. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional); subagent usage is cached by `tool_call_id` only while token usage is enabled and merged back into the dispatching AIMessage by message position rather than message id
+11. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
-12. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
+12. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional); subagent usage is cached by `tool_call_id` only while token usage is enabled and merged back into the dispatching AIMessage by message position rather than message id
-13. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
+13. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
-14. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
+14. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
-15. **DeferredToolFilterMiddleware** - Hides deferred tool schemas from the bound model until tool search is enabled (optional)
+15. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
-16. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
+16. **DeferredToolFilterMiddleware** - Hides deferred (MCP) tool schemas from the bound model using a build-time deferred-name set + catalog hash, reading per-thread promotions from `ThreadState.promoted` (hash-scoped, no ContextVar); a tool becomes bound on subsequent turns after `tool_search` returns its schema (optional, if `tool_search.enabled`)
-17. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
+17. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
-18. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
+18. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
+19. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
 
 ### Configuration System
 
@@ -219,17 +224,9 @@ Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** direc
 
 **Config Caching**: `get_app_config()` caches the parsed config, but automatically reloads it when the resolved config path changes or the file's mtime increases. This keeps Gateway and LangGraph reads aligned with `config.yaml` edits without requiring a manual process restart.
 
-**Config Hot-Reload Boundary**: Gateway dependencies route through `get_app_config()` on every request, so per-run fields like `models[*].max_tokens`, `summarization.*`, `title.*`, `memory.*`, `subagents.*`, `tools[*]`, and the agent system prompt pick up `config.yaml` edits on the next message. `AppConfig` is intentionally **not** cached on `app.state` — `lifespan()` keeps a local `startup_config` variable for one-shot bootstrap work (logging level, channels, `langgraph_runtime` engines) and passes it explicitly to `langgraph_runtime(app, startup_config)`. Infrastructure fields are **restart-required**:
+**Config Hot-Reload Boundary**: Gateway dependencies route through `get_app_config()` on every request, so per-run fields like `models[*].max_tokens`, `summarization.*`, `title.*`, `memory.*`, `subagents.*`, `tools[*]`, and the agent system prompt pick up `config.yaml` edits on the next message. `AppConfig` is intentionally **not** cached on `app.state` — `lifespan()` keeps a local `startup_config` variable for one-shot bootstrap work and passes it to `langgraph_runtime(app, startup_config)`.
 
-| Field | Why a restart is required |
+Infrastructure fields are **restart-required**. The authoritative list lives in `packages/harness/deerflow/config/reload_boundary.py::STARTUP_ONLY_FIELDS` and is mirrored by the standardised `"startup-only:"` prefix on the corresponding `Field(description=...)` in `AppConfig`, so IDE hover on those fields surfaces the reason inline (no need to context-switch into this table). Currently registered: `database`, `checkpointer`, `run_events`, `stream_bridge`, `sandbox`, `log_level`, `channels`. Adding a new restart-required field requires updating the registry; drift is pinned by `tests/test_reload_boundary.py`.
-|---|---|
-| `database.*` | `init_engine_from_config()` runs once during `langgraph_runtime()` startup; the SQLAlchemy engine holds the connection pool. |
-| `checkpointer.*` (including SQLite WAL/journal settings) | `make_checkpointer()` binds the persistent checkpointer once at startup. |
-| `run_events.*` | `make_run_event_store()` selects memory- vs. SQL-backed implementation at startup. |
-| `stream_bridge.*` | `make_stream_bridge()` constructs the bridge object once. |
-| `sandbox.use` | `get_sandbox_provider()` caches the provider singleton (`_default_sandbox_provider`); a new class path takes effect only on next process start. |
-| `log_level` | `apply_logging_level()` is called only in `app.py` startup; it mutates the root logger's level, and `get_app_config()` returning a fresh `AppConfig` does not retrigger it. |
-| `channels.*` IM platform credentials | `start_channel_service()` is invoked once during startup; live channels are not rebuilt on config change. |
 
 Configuration priority:
 1. Explicit `config_path` argument
@@ -267,7 +264,7 @@ CORS is same-origin by default when requests enter through nginx on port 2026. S
 | **Uploads** (`/api/threads/{id}/uploads`) | `POST /` - upload files (auto-converts PDF/PPT/Excel/Word); `GET /list` - list; `DELETE /{filename}` - delete |
 | **Threads** (`/api/threads/{id}`) | `DELETE /` - remove DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
 | **Artifacts** (`/api/threads/{id}/artifacts`) | `GET /{path}` - serve artifacts; active content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) are always forced as download attachments to reduce XSS risk; `?download=true` still forces download for other file types |
-| **Suggestions** (`/api/threads/{id}/suggestions`) | `POST /` - generate follow-up questions; rich list/block model content is normalized before JSON parsing |
+| **Suggestions** (`/api/threads/{id}/suggestions`) | `POST /` - generate follow-up questions; rich list/block model content is normalized and inline reasoning (`<think>...</think>`, including unclosed/truncated blocks from reasoning models like MiniMax-M3) is stripped before JSON parsing |
 | **Thread Runs** (`/api/threads/{id}/runs`) | `POST /` - create background run; `POST /stream` - create + SSE stream; `POST /wait` - create + block; `GET /` - list runs; `GET /{rid}` - run details; `POST /{rid}/cancel` - cancel; `GET /{rid}/join` - join SSE; `GET /{rid}/messages` - paginated messages `{data, has_more}`; `GET /{rid}/events` - full event stream; `GET /../messages` - thread messages with feedback; `GET /../token-usage` - aggregate tokens |
 | **Feedback** (`/api/threads/{id}/runs/{rid}/feedback`) | `PUT /` - upsert feedback; `DELETE /` - delete user feedback; `POST /` - create feedback; `GET /` - list feedback; `GET /stats` - aggregate stats; `DELETE /{fid}` - delete specific |
 | **Runs** (`/api/runs`) | `POST /stream` - stateless run + SSE; `POST /wait` - stateless run + block; `GET /{rid}/messages` - paginated messages by run_id `{data, has_more}` (cursor: `after_seq`/`before_seq`); `GET /{rid}/feedback` - list feedback by run_id |
@@ -277,6 +274,7 @@ CORS is same-origin by default when requests enter through nginx on port 2026. S
 - When a persistent `RunStore` is configured, `get()` and `list_by_thread()` hydrate historical runs from the store. In-memory records win for the same `run_id` so task, abort, and stream-control state stays attached to active local runs.
 - `cancel()` and `create_or_reject(..., multitask_strategy="interrupt"|"rollback")` persist interrupted status through `RunStore.update_status()`, matching normal `set_status()` transitions.
 - Store-only hydrated runs are readable history. If the current worker has no in-memory task/control state for that run, cancellation APIs can return 409 because this worker cannot stop the task.
+- `POST /wait` (both thread-scoped and `/api/runs/wait`) drains the stream bridge via `wait_for_run_completion()` instead of bare `await record.task`, so it honours the run's `on_disconnect` setting and cancels the background run on real client disconnect rather than returning a stale checkpoint (issue #3265).
 
 Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runtime, all other `/api/*` → Gateway REST APIs.
 
@@ -308,6 +306,7 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
 **Concurrency**: `MAX_CONCURRENT_SUBAGENTS = 3` enforced by `SubagentLimitMiddleware` (truncates excess tool calls in `after_model`), 15-minute timeout
 **Flow**: `task()` tool → `SubagentExecutor` → background thread → poll 5s → SSE events → result
 **Events**: `task_started`, `task_running`, `task_completed`/`task_failed`/`task_timed_out`
+**Deferred MCP tools** (if `tool_search.enabled`): `SubagentExecutor._build_initial_state` assembles deferral after policy filtering via the shared `assemble_deferred_tools` (fail-closed), appends the `tool_search` tool, injects the `<available-deferred-tools>` section into the subagent's `SystemMessage`, and threads the setup to `_create_agent`, which attaches `DeferredToolFilterMiddleware` through `build_subagent_runtime_middlewares(deferred_setup=...)`. Subagents thus withhold full MCP schemas until promotion, same as the lead agent; each task run gets a fresh `ThreadState` so promotion is isolated per run
 
 ### Tool System (`packages/harness/deerflow/tools/`)
 
@@ -342,7 +341,7 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
 - **Cache invalidation**: Detects config file changes via mtime comparison
 - **Transports**: stdio (command-based), SSE, HTTP
 - **OAuth (HTTP/SSE)**: Supports token endpoint flows (`client_credentials`, `refresh_token`) with automatic token refresh + Authorization header injection
-- **Runtime updates**: Gateway API saves to extensions_config.json; LangGraph detects via mtime
+- **Runtime updates**: Gateway API saves to extensions_config.json; the Gateway-embedded runtime detects changes via mtime
 
 ### Skills System (`packages/harness/deerflow/skills/`)
 
@@ -350,6 +349,7 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
 - **Format**: Directory with `SKILL.md` (YAML frontmatter: name, description, license, allowed-tools)
 - **Loading**: `load_skills()` recursively scans `skills/{public,custom}` for `SKILL.md`, parses metadata, and reads enabled state from extensions_config.json
 - **Injection**: Enabled skills listed in agent system prompt with container paths
+- **Slash activation**: `/skill-name task` loads that enabled skill's `SKILL.md` for the current model call only. The resolver rejects leading whitespace, missing separators, reserved channel commands (`/new`, `/help`, `/bootstrap`, `/status`, `/models`, `/memory`), disabled skills, and skills outside a custom agent's whitelist.
 - **Installation**: `POST /api/skills/install` extracts .skill ZIP archive to custom/ directory
 
 ### Model Factory (`packages/harness/deerflow/models/factory.py`)
@@ -369,7 +369,7 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
 
 ### IM Channels System (`app/channels/`)
 
-Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the DeerFlow agent via the LangGraph Server.
+Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the DeerFlow agent via Gateway's LangGraph-compatible API.
 
 
 **Architecture**: Channels communicate with Gateway through the `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side. The internal SDK client injects process-local internal auth plus a matching CSRF cookie/header pair so Gateway accepts state-changing thread/run requests from channel workers without relying on browser session cookies.
@@ -495,7 +495,7 @@ Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` me
   - `"messages-tuple"` — per-chunk update: for AI text this is a **delta** (concat per `id` to rebuild the full message); tool calls and tool results are emitted once each
   - `"custom"` — forwarded from `StreamWriter`
   - `"end"` — stream finished (carries cumulative `usage` counted once per message id)
-- Agent created lazily via `create_agent()` + `_build_middlewares()`, same as `make_lead_agent`
+- Agent created lazily via `create_agent()` + `build_middlewares()`, same as `make_lead_agent`
 - Supports `checkpointer` parameter for state persistence across turns
 - `reset_agent()` forces agent recreation (e.g. after memory or skill changes)
 - See [docs/STREAMING.md](docs/STREAMING.md) for the full design: why Gateway and DeerFlowClient are parallel paths, LangGraph's `stream_mode` semantics, the per-id dedup invariants, and regression testing strategy

backend/Dockerfile

@@ -64,7 +64,7 @@ FROM builder AS dev
 # Install Docker CLI (for DooD: allows starting sandbox containers via host Docker socket)
 COPY --from=docker:cli /usr/local/bin/docker /usr/local/bin/docker
 
-EXPOSE 8001 2024
+EXPOSE 8001
 
 CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
 
@@ -94,8 +94,8 @@ WORKDIR /app
 # Copy backend with pre-built virtualenv from builder
 COPY --from=builder /app/backend ./backend
 
-# Expose ports (gateway: 8001, langgraph: 2024)
+# Expose Gateway API port.
-EXPOSE 8001 2024
+EXPOSE 8001
 
 # Default command (can be overridden in docker-compose)
 CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run --no-sync uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]

backend/app/channels/commands.py

@@ -18,3 +18,10 @@ KNOWN_CHANNEL_COMMANDS: frozenset[str] = frozenset(
         "/help",
     }
 )
+
+
+def is_known_channel_command(text: str) -> bool:
+    """Return whether text starts with a registered channel control command."""
+    if not text.startswith("/"):
+        return False
+    return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS

backend/app/channels/dingtalk.py

@@ -14,7 +14,7 @@ from typing import Any
 import httpx
 
 from app.channels.base import Channel
-from app.channels.commands import KNOWN_CHANNEL_COMMANDS
+from app.channels.commands import is_known_channel_command
 from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 
 logger = logging.getLogger(__name__)
@@ -59,9 +59,7 @@ def _normalize_allowed_users(allowed_users: Any) -> set[str]:
 
 
 def _is_dingtalk_command(text: str) -> bool:
-    if not text.startswith("/"):
+    return is_known_channel_command(text)
-        return False
-    return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
 
 
 def _extract_text_from_rich_text(rich_text_list: list) -> str:

backend/app/channels/discord.py

@@ -10,6 +10,7 @@ from pathlib import Path
 from typing import Any
 
 from app.channels.base import Channel
+from app.channels.commands import is_known_channel_command
 from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 
 logger = logging.getLogger(__name__)
@@ -300,7 +301,7 @@ class DiscordChannel(Channel):
 
             # If this is a known active thread, process normally
             if thread_id in self._active_thread_ids:
-                msg_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
+                msg_type = InboundMessageType.COMMAND if is_known_channel_command(text) else InboundMessageType.CHAT
                 inbound = self._make_inbound(
                     chat_id=chat_id,
                     user_id=str(message.author.id),
@@ -407,7 +408,7 @@ class DiscordChannel(Channel):
             chat_id = channel_id
             typing_target = message.channel  # Type into the channel
 
-        msg_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
+        msg_type = InboundMessageType.COMMAND if is_known_channel_command(text) else InboundMessageType.CHAT
         inbound = self._make_inbound(
             chat_id=chat_id,
             user_id=str(message.author.id),

backend/app/channels/feishu.py

@@ -7,22 +7,30 @@ import json
 import logging
 import re
 import threading
+import time
 from typing import Any, Literal
 
 from app.channels.base import Channel
-from app.channels.commands import KNOWN_CHANNEL_COMMANDS
+from app.channels.commands import is_known_channel_command
-from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
+from app.channels.message_bus import (
+    PENDING_CLARIFICATION_METADATA_KEY,
+    RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY,
+    InboundMessage,
+    InboundMessageType,
+    MessageBus,
+    OutboundMessage,
+    ResolvedAttachment,
+)
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox_provider import get_sandbox_provider
 
 logger = logging.getLogger(__name__)
+PENDING_CLARIFICATION_TTL_SECONDS = 30 * 60
 
 
 def _is_feishu_command(text: str) -> bool:
-    if not text.startswith("/"):
+    return is_known_channel_command(text)
-        return False
-    return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
 
 
 class FeishuChannel(Channel):
@@ -56,6 +64,7 @@ class FeishuChannel(Channel):
         self._background_tasks: set[asyncio.Task] = set()
         self._running_card_ids: dict[str, str] = {}
         self._running_card_tasks: dict[str, asyncio.Task] = {}
+        self._pending_clarifications: dict[tuple[str, str], list[dict[str, Any]]] = {}
         self._CreateFileRequest = None
         self._CreateFileRequestBody = None
         self._CreateImageRequest = None
@@ -63,6 +72,16 @@ class FeishuChannel(Channel):
         self._GetMessageResourceRequest = None
         self._thread_lock = threading.Lock()
 
+    @staticmethod
+    def _non_empty_str(value: Any) -> str | None:
+        if isinstance(value, str) and value.strip():
+            return value.strip()
+        return None
+
+    @staticmethod
+    def _pending_key(chat_id: str, user_id: str) -> tuple[str, str]:
+        return (chat_id, user_id)
+
     @property
     def supports_streaming(self) -> bool:
         return True
@@ -531,18 +550,25 @@ class FeishuChannel(Channel):
                         "[Feishu] failed to patch running card %s, falling back to final reply",
                         running_card_id,
                     )
-                    await self._reply_card(source_message_id, msg.text)
+                    fallback_card_id = await self._reply_card(source_message_id, msg.text)
+                    self._remember_thread_mapping(msg, source_message_id, fallback_card_id)
+                    self._remember_pending_clarification(msg, fallback_card_id)
                 else:
+                    self._remember_thread_mapping(msg, source_message_id, running_card_id)
+                    self._remember_pending_clarification(msg, running_card_id)
                     logger.info("[Feishu] running card updated: source=%s card=%s", source_message_id, running_card_id)
             elif msg.is_final:
-                await self._reply_card(source_message_id, msg.text)
+                final_card_id = await self._reply_card(source_message_id, msg.text)
+                self._remember_thread_mapping(msg, source_message_id, final_card_id)
+                self._remember_pending_clarification(msg, final_card_id)
             elif awaited_running_card_task:
                 logger.warning(
                     "[Feishu] running card task finished without message_id for source=%s, skipping duplicate non-final creation",
                     source_message_id,
                 )
             else:
-                await self._ensure_running_card(source_message_id, msg.text)
+                created_card_id = await self._ensure_running_card(source_message_id, msg.text)
+                self._remember_thread_mapping(msg, source_message_id, created_card_id)
 
             if msg.is_final:
                 self._running_card_ids.pop(source_message_id, None)
@@ -553,6 +579,129 @@ class FeishuChannel(Channel):
 
     # -- internal ----------------------------------------------------------
 
+    def _remember_thread_mapping(self, msg: OutboundMessage, *topic_ids: str | None) -> None:
+        store = self.config.get("channel_store")
+        if store is None or not msg.thread_id:
+            return
+
+        metadata_topic_ids = [
+            msg.metadata.get("message_id"),
+            msg.metadata.get("root_id"),
+            msg.metadata.get("parent_id"),
+            msg.metadata.get("thread_id"),
+            msg.metadata.get("topic_id"),
+        ]
+        user_id = ""
+        raw_user_id = msg.metadata.get("user_id")
+        if isinstance(raw_user_id, str):
+            user_id = raw_user_id
+
+        seen: set[str] = set()
+        for topic_id in [*topic_ids, *metadata_topic_ids]:
+            topic_id = self._non_empty_str(topic_id)
+            if not topic_id or topic_id in seen:
+                continue
+            seen.add(topic_id)
+            try:
+                store.set_thread_id(
+                    self.name,
+                    msg.chat_id,
+                    msg.thread_id,
+                    topic_id=topic_id,
+                    user_id=user_id,
+                )
+            except Exception:
+                logger.exception("[Feishu] failed to remember thread mapping for topic_id=%s", topic_id)
+
+    def _remember_pending_clarification(self, msg: OutboundMessage, card_message_id: str | None) -> None:
+        if not msg.is_final or msg.metadata.get(PENDING_CLARIFICATION_METADATA_KEY) is not True:
+            return
+
+        user_id = self._non_empty_str(msg.metadata.get("user_id"))
+        topic_id = self._non_empty_str(msg.metadata.get("topic_id"))
+        source_message_id = self._non_empty_str(msg.thread_ts) or self._non_empty_str(msg.metadata.get("message_id"))
+        if not (user_id and topic_id and msg.thread_id and source_message_id and card_message_id):
+            return
+
+        key = self._pending_key(msg.chat_id, user_id)
+        pending = {
+            "thread_id": msg.thread_id,
+            "topic_id": topic_id,
+            "source_message_id": source_message_id,
+            "card_message_id": card_message_id,
+            "created_at": time.time(),
+        }
+        with self._thread_lock:
+            # Plain-message clarification continuity is a short-lived in-memory
+            # hint; explicit Feishu replies are still covered by persisted
+            # message-id mappings.
+            self._pending_clarifications.setdefault(key, []).append(pending)
+        logger.info(
+            "[Feishu] pending clarification remembered: chat_id=%s user_id=%s topic_id=%s thread_id=%s",
+            msg.chat_id,
+            user_id,
+            topic_id,
+            msg.thread_id,
+        )
+
+    def _consume_pending_clarification(self, chat_id: str, user_id: str) -> dict[str, Any] | None:
+        key = self._pending_key(chat_id, user_id)
+        with self._thread_lock:
+            pending_items = self._pending_clarifications.get(key)
+            if not pending_items:
+                return None
+
+            now = time.time()
+            while pending_items:
+                pending = pending_items.pop(0)
+                created_at = pending.get("created_at")
+                if isinstance(created_at, (int, float)) and now - created_at <= PENDING_CLARIFICATION_TTL_SECONDS:
+                    if pending_items:
+                        self._pending_clarifications[key] = pending_items
+                    else:
+                        self._pending_clarifications.pop(key, None)
+                    return pending
+                logger.info("[Feishu] pending clarification expired: chat_id=%s user_id=%s", chat_id, user_id)
+
+            self._pending_clarifications.pop(key, None)
+            return None
+
+    def _ensure_pending_thread_mapping(self, chat_id: str, user_id: str, pending: dict[str, Any]) -> None:
+        store = self.config.get("channel_store")
+        topic_id = self._non_empty_str(pending.get("topic_id"))
+        thread_id = self._non_empty_str(pending.get("thread_id"))
+        if store is None or not topic_id or not thread_id:
+            return
+        try:
+            store.set_thread_id(self.name, chat_id, thread_id, topic_id=topic_id, user_id=user_id)
+        except Exception:
+            logger.exception("[Feishu] failed to restore pending clarification mapping for topic_id=%s", topic_id)
+
+    def _resolve_topic_id(
+        self,
+        chat_id: str,
+        msg_id: str,
+        *,
+        root_id: str | None,
+        parent_id: str | None,
+        thread_id: str | None,
+    ) -> tuple[str, bool]:
+        store = self.config.get("channel_store")
+        candidates = [root_id, parent_id, thread_id]
+
+        if store is not None:
+            for candidate in candidates:
+                candidate = self._non_empty_str(candidate)
+                if not candidate:
+                    continue
+                try:
+                    if store.get_thread_id(self.name, chat_id, topic_id=candidate):
+                        return candidate, True
+                except Exception:
+                    logger.exception("[Feishu] failed to resolve stored topic mapping for topic_id=%s", candidate)
+
+        return root_id or msg_id, False
+
     @staticmethod
     def _log_future_error(fut, name: str, msg_id: str) -> None:
         """Callback for run_coroutine_threadsafe futures to surface errors."""
@@ -593,7 +742,9 @@ class FeishuChannel(Channel):
 
             # root_id is set when the message is a reply within a Feishu thread.
             # Use it as topic_id so all replies share the same DeerFlow thread.
-            root_id = getattr(message, "root_id", None) or None
+            root_id = self._non_empty_str(getattr(message, "root_id", None))
+            parent_id = self._non_empty_str(getattr(message, "parent_id", None))
+            feishu_thread_id = self._non_empty_str(getattr(message, "thread_id", None))
 
             # Parse message content
             content = json.loads(message.content)
@@ -654,10 +805,12 @@ class FeishuChannel(Channel):
             text = text.strip()
 
             logger.info(
-                "[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, sender=%s, text=%r",
+                "[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, parent_id=%s, thread_id=%s, sender=%s, text=%r",
                 chat_id,
                 msg_id,
                 root_id,
+                parent_id,
+                feishu_thread_id,
                 sender_id,
                 text[:100] if text else "",
             )
@@ -673,8 +826,24 @@ class FeishuChannel(Channel):
             else:
                 msg_type = InboundMessageType.CHAT
 
-            # topic_id: use root_id for replies (same topic), msg_id for new messages (new topic)
+            # Prefer any platform message id that already maps to a DeerFlow
-            topic_id = root_id or msg_id
+            # thread. This keeps replies to bot clarification cards in the
+            # original conversation even when Feishu reports the card as root.
+            topic_id, resolved_from_stored_mapping = self._resolve_topic_id(
+                chat_id,
+                msg_id,
+                root_id=root_id,
+                parent_id=parent_id,
+                thread_id=feishu_thread_id,
+            )
+            resolved_from_pending = False
+            if msg_type == InboundMessageType.CHAT and not resolved_from_stored_mapping:
+                pending = self._consume_pending_clarification(chat_id, sender_id)
+                pending_topic_id = self._non_empty_str(pending.get("topic_id")) if pending else None
+                if pending_topic_id:
+                    topic_id = pending_topic_id
+                    self._ensure_pending_thread_mapping(chat_id, sender_id, pending)
+                    resolved_from_pending = True
 
             inbound = self._make_inbound(
                 chat_id=chat_id,
@@ -683,7 +852,15 @@ class FeishuChannel(Channel):
                 msg_type=msg_type,
                 thread_ts=msg_id,
                 files=files_list,
-                metadata={"message_id": msg_id, "root_id": root_id},
+                metadata={
+                    "message_id": msg_id,
+                    "root_id": root_id,
+                    "parent_id": parent_id,
+                    "thread_id": feishu_thread_id,
+                    "topic_id": topic_id,
+                    "user_id": sender_id,
+                    RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY: resolved_from_pending,
+                },
             )
             inbound.topic_id = topic_id
 

backend/app/channels/manager.py

@@ -8,6 +8,7 @@ import mimetypes
 import re
 import time
 from collections.abc import Awaitable, Callable, Mapping
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 
@@ -15,11 +16,24 @@ import httpx
 from langgraph_sdk.errors import ConflictError
 
 from app.channels.commands import KNOWN_CHANNEL_COMMANDS
-from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
+from app.channels.message_bus import (
+    PENDING_CLARIFICATION_METADATA_KEY,
+    InboundMessage,
+    InboundMessageType,
+    MessageBus,
+    OutboundMessage,
+    ResolvedAttachment,
+)
 from app.channels.store import ChannelStore
 from app.gateway.csrf_middleware import CSRF_COOKIE_NAME, CSRF_HEADER_NAME, generate_csrf_token
 from app.gateway.internal_auth import create_internal_auth_headers
+from deerflow.config.agents_config import load_agent_config
+from deerflow.config.paths import make_safe_user_id
 from deerflow.runtime.user_context import get_effective_user_id
+from deerflow.skills.slash import parse_slash_skill_reference
+from deerflow.skills.storage import get_or_new_skill_storage
+from deerflow.skills.storage.skill_storage import SkillStorage
+from deerflow.utils.messages import ORIGINAL_USER_CONTENT_KEY
 
 logger = logging.getLogger(__name__)
 
@@ -116,6 +130,16 @@ class InvalidChannelSessionConfigError(ValueError):
     """Raised when IM channel session overrides contain invalid agent config."""
 
 
+class SlashSkillCommandResolutionError(RuntimeError):
+    """Raised when IM slash-skill command resolution cannot complete safely."""
+
+
+@dataclass(frozen=True, slots=True)
+class _SlashSkillCommandResolution:
+    route_to_chat: bool = False
+    failure_message: str | None = None
+
+
 def _is_thread_busy_error(exc: BaseException | None) -> bool:
     if exc is None:
         return False
@@ -173,6 +197,8 @@ def _extract_response_text(result: dict | list) -> str:
 
         # Stop at the last human message — anything before it is a previous turn
         if msg_type == "human":
+            if _is_hidden_human_control_message(msg):
+                continue
             break
 
         # Check for tool messages from ask_clarification (interrupt case)
@@ -200,6 +226,54 @@ def _extract_response_text(result: dict | list) -> str:
     return ""
 
 
+def _messages_from_result(result: dict | list) -> list[Any]:
+    if isinstance(result, list):
+        return result
+    if isinstance(result, dict):
+        messages = result.get("messages", [])
+        if isinstance(messages, list):
+            return messages
+    return []
+
+
+def _current_turn_messages(result: dict | list) -> list[dict[str, Any]]:
+    messages = _messages_from_result(result)
+    current_turn: list[dict[str, Any]] = []
+    for msg in reversed(messages):
+        if not isinstance(msg, dict):
+            continue
+        if msg.get("type") == "human":
+            break
+        current_turn.append(msg)
+    current_turn.reverse()
+    return current_turn
+
+
+def _has_current_turn_clarification(result: dict | list) -> bool:
+    """Return True only when the current turn's final result is clarification."""
+    for msg in reversed(_current_turn_messages(result)):
+        msg_type = msg.get("type")
+        if msg_type == "tool":
+            return msg.get("name") == "ask_clarification"
+        if msg_type == "ai":
+            content = msg.get("content")
+            if isinstance(content, str):
+                if content:
+                    return False
+            elif content:
+                return False
+            if msg.get("tool_calls"):
+                return False
+    return False
+
+
+def _response_metadata(base_metadata: dict[str, Any], *, pending_clarification: bool = False) -> dict[str, Any]:
+    metadata = _slim_metadata(base_metadata)
+    if pending_clarification:
+        metadata[PENDING_CLARIFICATION_METADATA_KEY] = True
+    return metadata
+
+
 def _extract_text_content(content: Any) -> str:
     """Extract text from a streaming payload content field."""
     if isinstance(content, str):
@@ -313,6 +387,8 @@ def _extract_artifacts(result: dict | list) -> list[str]:
             continue
         # Stop at the last human message — anything before it is a previous turn
         if msg.get("type") == "human":
+            if _is_hidden_human_control_message(msg):
+                continue
             break
         # Look for AI messages with present_files tool calls
         if msg.get("type") == "ai":
@@ -325,6 +401,18 @@ def _extract_artifacts(result: dict | list) -> list[str]:
     return artifacts
 
 
+def _is_hidden_human_control_message(msg: Mapping[str, Any]) -> bool:
+    """Return whether a human message is an internal control message hidden from UI."""
+    if msg.get("type") != "human":
+        return False
+
+    additional_kwargs = msg.get("additional_kwargs")
+    if not isinstance(additional_kwargs, Mapping):
+        return False
+
+    return additional_kwargs.get("hide_from_ui") is True
+
+
 def _format_artifact_text(artifacts: list[str]) -> str:
     """Format artifact paths into a human-readable text block listing filenames."""
     import posixpath
@@ -338,6 +426,46 @@ def _format_artifact_text(artifacts: list[str]) -> str:
 _OUTPUTS_VIRTUAL_PREFIX = "/mnt/user-data/outputs/"
 
 
+def _unknown_command_reply(command: str | None = None) -> str:
+    available = " | ".join(sorted(KNOWN_CHANNEL_COMMANDS))
+    if command:
+        return f"Unknown command: /{command}. Available commands: {available}"
+    return f"Unknown command. Available commands: {available}"
+
+
+def _human_input_message(content: str, *, original_content: str | None = None) -> dict[str, Any]:
+    message: dict[str, Any] = {"role": "human", "content": content}
+    if original_content is not None and original_content != content:
+        message["additional_kwargs"] = {ORIGINAL_USER_CONTENT_KEY: original_content}
+    return message
+
+
+def _resolve_slash_skill_command(
+    text: str,
+    available_skills: set[str] | None = None,
+    storage: SkillStorage | Callable[[], SkillStorage] | None = None,
+) -> _SlashSkillCommandResolution | None:
+    reference = parse_slash_skill_reference(text)
+    if reference is None:
+        return None
+    try:
+        resolved_storage = storage() if callable(storage) else storage or get_or_new_skill_storage()
+        skills = resolved_storage.load_skills(enabled_only=False)
+
+        skill = next((candidate for candidate in skills if candidate.name == reference.name), None)
+        if skill is None:
+            return None
+        if not skill.enabled:
+            return _SlashSkillCommandResolution(failure_message=f"Skill `/{reference.name}` is installed but disabled. Enable it before using slash activation.")
+        if available_skills is not None and reference.name not in available_skills:
+            return _SlashSkillCommandResolution(failure_message=f"Skill `/{reference.name}` is not available for this agent.")
+
+        return _SlashSkillCommandResolution(route_to_chat=True)
+    except Exception as exc:
+        logger.exception("[Manager] failed to resolve slash skill command")
+        raise SlashSkillCommandResolutionError("Failed to resolve slash skill command. Please check the skill configuration.") from exc
+
+
 def _resolve_attachments(thread_id: str, artifacts: list[str]) -> list[ResolvedAttachment]:
     """Resolve virtual artifact paths to host filesystem paths with metadata.
 
@@ -552,6 +680,7 @@ class ChannelManager:
         self._default_session = _as_dict(default_session)
         self._channel_sessions = dict(channel_sessions or {})
         self._client = None  # lazy init — langgraph_sdk async client
+        self._skill_storage: SkillStorage | None = None
         self._csrf_token = generate_csrf_token()
         self._semaphore: asyncio.Semaphore | None = None
         self._running = False
@@ -599,12 +728,20 @@ class ChannelManager:
         configurable["checkpoint_ns"] = ""
         configurable["thread_id"] = thread_id
 
+        # ``user_id`` drives user-scoped filesystem buckets that only accept
+        # ``[A-Za-z0-9_-]``, so normalize the channel id and keep the raw value
+        # under ``channel_user_id`` for platform-facing lookups.
+        run_context_identity: dict[str, Any] = {"thread_id": thread_id}
+        if msg.user_id:
+            run_context_identity["user_id"] = make_safe_user_id(msg.user_id)
+            run_context_identity["channel_user_id"] = msg.user_id
+
         run_context = _merge_dicts(
             DEFAULT_RUN_CONTEXT,
             self._default_session.get("context"),
             channel_layer.get("context"),
             user_layer.get("context"),
-            {"thread_id": thread_id},
+            run_context_identity,
         )
 
         # Custom agents are implemented as lead_agent + agent_name context.
@@ -616,6 +753,21 @@ class ChannelManager:
 
         return assistant_id, run_config, run_context
 
+    def _resolve_available_skill_names(self, msg: InboundMessage) -> set[str] | None:
+        thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id) or ""
+        _, _, run_context = self._resolve_run_params(msg, thread_id)
+        if run_context.get("is_bootstrap"):
+            return {"bootstrap"}
+
+        agent_name = run_context.get("agent_name")
+        if not isinstance(agent_name, str) or not agent_name.strip():
+            return None
+
+        agent_config = load_agent_config(_normalize_custom_agent_name(agent_name))
+        if agent_config and agent_config.skills is not None:
+            return set(agent_config.skills)
+        return None
+
     # -- LangGraph SDK client (lazy) ----------------------------------------
 
     def _get_client(self):
@@ -633,6 +785,11 @@ class ChannelManager:
             )
         return self._client
 
+    def _get_skill_storage(self) -> SkillStorage:
+        if self._skill_storage is None:
+            self._skill_storage = get_or_new_skill_storage()
+        return self._skill_storage
+
     # -- lifecycle ---------------------------------------------------------
 
     async def start(self) -> None:
@@ -702,6 +859,14 @@ class ChannelManager:
                     exc,
                 )
                 await self._send_error(msg, str(exc))
+            except SlashSkillCommandResolutionError as exc:
+                logger.warning(
+                    "Slash skill command resolution failed for %s (chat=%s): %s",
+                    msg.channel_name,
+                    msg.chat_id,
+                    exc,
+                )
+                await self._send_error(msg, str(exc))
             except Exception:
                 logger.exception(
                     "Error handling message from %s (chat=%s)",
@@ -756,9 +921,11 @@ class ChannelManager:
         if extra_context:
             run_context.update(extra_context)
 
+        original_text = msg.text
         uploaded = await _ingest_inbound_files(thread_id, msg)
         if uploaded:
             msg.text = f"{_format_uploaded_files_block(uploaded)}\n\n{msg.text}".strip()
+        human_message = _human_input_message(msg.text, original_content=original_text)
 
         if self._channel_supports_streaming(msg.channel_name):
             await self._handle_streaming_chat(
@@ -768,6 +935,7 @@ class ChannelManager:
                 assistant_id,
                 run_config,
                 run_context,
+                human_message,
             )
             return
 
@@ -776,7 +944,7 @@ class ChannelManager:
             result = await client.runs.wait(
                 thread_id,
                 assistant_id,
-                input={"messages": [{"role": "human", "content": msg.text}]},
+                input={"messages": [human_message]},
                 config=run_config,
                 context=run_context,
                 multitask_strategy="reject",
@@ -790,6 +958,7 @@ class ChannelManager:
                 raise
 
         response_text = _extract_response_text(result)
+        pending_clarification = _has_current_turn_clarification(result)
         artifacts = _extract_artifacts(result)
 
         logger.info(
@@ -815,7 +984,7 @@ class ChannelManager:
             artifacts=artifacts,
             attachments=attachments,
             thread_ts=msg.thread_ts,
-            metadata=_slim_metadata(msg.metadata),
+            metadata=_response_metadata(msg.metadata, pending_clarification=pending_clarification),
         )
         logger.info("[Manager] publishing outbound message to bus: channel=%s, chat_id=%s", msg.channel_name, msg.chat_id)
         await self.bus.publish_outbound(outbound)
@@ -828,6 +997,7 @@ class ChannelManager:
         assistant_id: str,
         run_config: dict[str, Any],
         run_context: dict[str, Any],
+        human_message: dict[str, Any],
     ) -> None:
         logger.info("[Manager] invoking runs.stream(thread_id=%s, text=%r)", thread_id, msg.text[:100])
 
@@ -843,7 +1013,7 @@ class ChannelManager:
             async for chunk in client.runs.stream(
                 thread_id,
                 assistant_id,
-                input={"messages": [{"role": "human", "content": msg.text}]},
+                input={"messages": [human_message]},
                 config=run_config,
                 context=run_context,
                 stream_mode=["messages-tuple", "values"],
@@ -877,7 +1047,7 @@ class ChannelManager:
                         text=latest_text,
                         is_final=False,
                         thread_ts=msg.thread_ts,
-                        metadata=_slim_metadata(msg.metadata),
+                        metadata=_response_metadata(msg.metadata),
                     )
                 )
                 last_published_text = latest_text
@@ -891,6 +1061,7 @@ class ChannelManager:
         finally:
             result = last_values if last_values is not None else {"messages": [{"type": "ai", "content": latest_text}]}
             response_text = _extract_response_text(result)
+            pending_clarification = _has_current_turn_clarification(result)
             artifacts = _extract_artifacts(result)
             response_text, attachments = _prepare_artifact_delivery(thread_id, response_text, artifacts)
 
@@ -922,18 +1093,27 @@ class ChannelManager:
                     attachments=attachments,
                     is_final=True,
                     thread_ts=msg.thread_ts,
-                    metadata=_slim_metadata(msg.metadata),
+                    metadata=_response_metadata(msg.metadata, pending_clarification=pending_clarification),
                 )
             )
 
     # -- command handling --------------------------------------------------
 
     async def _handle_command(self, msg: InboundMessage) -> None:
-        text = msg.text.strip()
+        raw_text = msg.text
+        text = raw_text.strip()
         parts = text.split(maxsplit=1)
-        command = parts[0].lower().lstrip("/")
+        reply: str | None = None
+        if not parts:
+            command = None
+            reply = _unknown_command_reply()
+        else:
+            command = parts[0].lower().removeprefix("/")
 
-        if command == "bootstrap":
+        if reply is None and not raw_text.startswith("/"):
+            reply = _unknown_command_reply(command)
+
+        if reply is None and command == "bootstrap":
             from dataclasses import replace as _dc_replace
 
             chat_text = parts[1] if len(parts) > 1 else "Initialize workspace"
@@ -941,7 +1121,7 @@ class ChannelManager:
             await self._handle_chat(chat_msg, extra_context={"is_bootstrap": True})
             return
 
-        if command == "new":
+        if reply is None and command == "new":
             # Create a new thread through Gateway
             client = self._get_client()
             thread = await client.threads.create()
@@ -954,14 +1134,14 @@ class ChannelManager:
                 user_id=msg.user_id,
             )
             reply = "New conversation started."
-        elif command == "status":
+        elif reply is None and command == "status":
             thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id)
             reply = f"Active thread: {thread_id}" if thread_id else "No active conversation."
-        elif command == "models":
+        elif reply is None and command == "models":
             reply = await self._fetch_gateway("/api/models", "models")
-        elif command == "memory":
+        elif reply is None and command == "memory":
             reply = await self._fetch_gateway("/api/memory", "memory")
-        elif command == "help":
+        elif reply is None and command == "help":
             reply = (
                 "Available commands:\n"
                 "/bootstrap — Start a bootstrap session (enables agent setup)\n"
@@ -969,16 +1149,32 @@ class ChannelManager:
                 "/status — Show current thread info\n"
                 "/models — List available models\n"
                 "/memory — Show memory status\n"
+                "/<skill-name> <task> — Activate an enabled skill for one turn\n"
                 "/help — Show this help"
             )
-        else:
+        elif reply is None:
-            available = " | ".join(sorted(KNOWN_CHANNEL_COMMANDS))
+            slash_resolution = await asyncio.to_thread(
-            reply = f"Unknown command: /{command}. Available commands: {available}"
+                lambda: _resolve_slash_skill_command(
+                    raw_text,
+                    self._resolve_available_skill_names(msg),
+                    self._get_skill_storage,
+                )
+            )
+            if slash_resolution and slash_resolution.failure_message:
+                reply = slash_resolution.failure_message
+            elif slash_resolution and slash_resolution.route_to_chat:
+                from dataclasses import replace as _dc_replace
+
+                chat_msg = _dc_replace(msg, msg_type=InboundMessageType.CHAT)
+                await self._handle_chat(chat_msg)
+                return
+            else:
+                reply = _unknown_command_reply(command)
 
         outbound = OutboundMessage(
             channel_name=msg.channel_name,
             chat_id=msg.chat_id,
-            thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
+            thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id) or "",
             text=reply,
             thread_ts=msg.thread_ts,
             metadata=_slim_metadata(msg.metadata),
@@ -1016,7 +1212,7 @@ class ChannelManager:
         outbound = OutboundMessage(
             channel_name=msg.channel_name,
             chat_id=msg.chat_id,
-            thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
+            thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id) or "",
             text=error_text,
             thread_ts=msg.thread_ts,
             metadata=_slim_metadata(msg.metadata),

backend/app/channels/message_bus.py

@@ -13,6 +13,9 @@ from typing import Any
 
 logger = logging.getLogger(__name__)
 
+PENDING_CLARIFICATION_METADATA_KEY = "pending_clarification"
+RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY = "resolved_from_pending_clarification"
+
 
 # ---------------------------------------------------------------------------
 # Message types

backend/app/channels/slack.py

@@ -9,6 +9,7 @@ from typing import Any
 from markdown_to_mrkdwn import SlackMarkdownConverter
 
 from app.channels.base import Channel
+from app.channels.commands import is_known_channel_command
 from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 
 logger = logging.getLogger(__name__)
@@ -32,6 +33,20 @@ def _normalize_allowed_users(allowed_users: Any) -> set[str]:
     return {str(user_id) for user_id in values if str(user_id)}
 
 
+def _strip_leading_slack_bot_mention(text: str, bot_user_id: str | None) -> str:
+    if not bot_user_id:
+        return text
+    if not text.startswith("<@"):
+        return text
+    end = text.find(">")
+    if end <= 2:
+        return text
+    mentioned_user_id = text[2:end].split("|", 1)[0].lstrip("!")
+    if mentioned_user_id != bot_user_id:
+        return text
+    return text[end + 1 :].lstrip()
+
+
 class SlackChannel(Channel):
     """Slack IM channel using Socket Mode (WebSocket, no public IP).
 
@@ -49,6 +64,8 @@ class SlackChannel(Channel):
         self._web_client = None
         self._loop: asyncio.AbstractEventLoop | None = None
         self._allowed_users = _normalize_allowed_users(config.get("allowed_users", []))
+        configured_bot_user_id = config.get("bot_user_id")
+        self._bot_user_id = str(configured_bot_user_id).lstrip("@") if configured_bot_user_id else None
 
     async def start(self) -> None:
         if self._running:
@@ -72,6 +89,17 @@ class SlackChannel(Channel):
             return
 
         self._web_client = WebClient(token=bot_token)
+        if self._bot_user_id is None:
+            try:
+                auth_info = await asyncio.to_thread(self._web_client.auth_test)
+                user_id = auth_info.get("user_id") if isinstance(auth_info, dict) else None
+                if user_id is None:
+                    auth_get = getattr(auth_info, "get", None)
+                    user_id = auth_get("user_id") if callable(auth_get) else None
+                if isinstance(user_id, str) and user_id:
+                    self._bot_user_id = user_id
+            except Exception:
+                logger.warning("[Slack] failed to resolve bot user id; app mention text may include the bot mention", exc_info=True)
         self._socket_client = SocketModeClient(
             app_token=app_token,
             web_client=self._web_client,
@@ -210,6 +238,12 @@ class SlackChannel(Channel):
             if event_type != "events_api":
                 return
 
+            if self._bot_user_id is None:
+                authorization = next((item for item in req.payload.get("authorizations", []) if isinstance(item, dict)), None)
+                user_id = authorization.get("user_id") if authorization else None
+                if isinstance(user_id, str) and user_id:
+                    self._bot_user_id = user_id
+
             event = req.payload.get("event", {})
             etype = event.get("type", "")
 
@@ -233,13 +267,15 @@ class SlackChannel(Channel):
             return
 
         text = event.get("text", "").strip()
+        if event.get("type") == "app_mention":
+            text = _strip_leading_slack_bot_mention(text, self._bot_user_id)
         if not text:
             return
 
         channel_id = event.get("channel", "")
         thread_ts = event.get("thread_ts") or event.get("ts", "")
 
-        if text.startswith("/"):
+        if is_known_channel_command(text):
             msg_type = InboundMessageType.COMMAND
         else:
             msg_type = InboundMessageType.CHAT

backend/app/channels/telegram.py

@@ -60,12 +60,17 @@ class TelegramChannel(Channel):
 
         # Command handlers
         app.add_handler(CommandHandler("start", self._cmd_start))
+        app.add_handler(CommandHandler("bootstrap", self._cmd_generic))
         app.add_handler(CommandHandler("new", self._cmd_generic))
         app.add_handler(CommandHandler("status", self._cmd_generic))
         app.add_handler(CommandHandler("models", self._cmd_generic))
         app.add_handler(CommandHandler("memory", self._cmd_generic))
         app.add_handler(CommandHandler("help", self._cmd_generic))
 
+        # Slash skill commands are dynamic and cannot all be pre-registered
+        # with Telegram, so route unknown slash commands through chat handling.
+        app.add_handler(MessageHandler(filters.TEXT & filters.COMMAND, self._on_text))
+
         # General message handler
         app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, self._on_text))
 
@@ -228,6 +233,33 @@ class TelegramChannel(Channel):
             return True
         return user_id in self._allowed_users
 
+    def _get_bot_username(self, context) -> str | None:
+        bot = getattr(context, "bot", None)
+        username = getattr(bot, "username", None)
+        if not username and self._application is not None:
+            username = getattr(getattr(self._application, "bot", None), "username", None)
+        return str(username) if username else None
+
+    @staticmethod
+    def _strip_bot_username_from_leading_command(text: str, bot_username: str | None) -> str:
+        username = (bot_username or "").lstrip("@").lower()
+        if not username or not text.startswith("/"):
+            return text
+
+        parts = text.split(maxsplit=1)
+        command_token = parts[0]
+        if "@" not in command_token:
+            return text
+
+        command_name, addressed_username = command_token[1:].rsplit("@", 1)
+        if not command_name or addressed_username.lower() != username:
+            return text
+
+        normalized = f"/{command_name}"
+        if len(parts) > 1:
+            normalized = f"{normalized} {parts[1]}"
+        return normalized
+
     async def _cmd_start(self, update, context) -> None:
         """Handle /start command."""
         if not self._check_user(update.effective_user.id):
@@ -243,7 +275,7 @@ class TelegramChannel(Channel):
         if not self._check_user(update.effective_user.id):
             return
 
-        text = update.message.text
+        text = self._strip_bot_username_from_leading_command(update.message.text.strip(), self._get_bot_username(context))
         chat_id = str(update.effective_chat.id)
         user_id = str(update.effective_user.id)
         msg_id = str(update.message.message_id)
@@ -279,7 +311,7 @@ class TelegramChannel(Channel):
         if not self._check_user(update.effective_user.id):
             return
 
-        text = update.message.text.strip()
+        text = self._strip_bot_username_from_leading_command(update.message.text.strip(), self._get_bot_username(context))
         if not text:
             return
 

backend/app/channels/wechat.py

@@ -22,6 +22,7 @@ from cryptography.hazmat.primitives import padding
 from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
 
 from app.channels.base import Channel
+from app.channels.commands import is_known_channel_command
 from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 
 logger = logging.getLogger(__name__)
@@ -620,7 +621,7 @@ class WechatChannel(Channel):
             chat_id=chat_id,
             user_id=chat_id,
             text=text,
-            msg_type=InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT,
+            msg_type=InboundMessageType.COMMAND if is_known_channel_command(text) else InboundMessageType.CHAT,
             thread_ts=thread_ts,
             files=files,
             metadata={

backend/app/channels/wecom.py

@@ -8,6 +8,7 @@ from collections.abc import Awaitable, Callable
 from typing import Any, cast
 
 from app.channels.base import Channel
+from app.channels.commands import is_known_channel_command
 from app.channels.message_bus import (
     InboundMessageType,
     MessageBus,
@@ -270,7 +271,7 @@ class WeComChannel(Channel):
 
         user_id = (body.get("from") or {}).get("userid")
 
-        inbound_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
+        inbound_type = InboundMessageType.COMMAND if is_known_channel_command(text) else InboundMessageType.CHAT
         inbound = self._make_inbound(
             chat_id=user_id,  # keep user's conversation in memory
             user_id=user_id,

backend/app/gateway/app.py

@@ -179,6 +179,25 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
     config = get_gateway_config()
     logger.info(f"Starting API Gateway on {config.host}:{config.port}")
 
+    # Pre-warm tiktoken encoding cache so the first memory-injection request
+    # never blocks on the BPE data download (which hits an OpenAI/Azure URL
+    # that may be unreachable in restricted networks — see issue #3402).
+    try:
+        from deerflow.agents.memory.prompt import warm_tiktoken_cache
+
+        warmed = await asyncio.wait_for(
+            asyncio.to_thread(warm_tiktoken_cache),
+            timeout=5,
+        )
+        if warmed:
+            logger.info("tiktoken encoding cache warmed successfully")
+        else:
+            logger.warning("tiktoken encoding cache warm-up failed; token counting will use character-based fallback")
+    except TimeoutError:
+        logger.warning("tiktoken encoding cache warm-up timed out; token counting will use character-based fallback")
+    except Exception:
+        logger.warning("tiktoken warm-up skipped", exc_info=True)
+
     # Initialize LangGraph runtime components (StreamBridge, RunManager, checkpointer, store)
     async with langgraph_runtime(app, startup_config):
         logger.info("LangGraph runtime initialised")

backend/app/gateway/deps.py

@@ -17,6 +17,7 @@ Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
 
 from __future__ import annotations
 
+import asyncio
 import logging
 from collections.abc import AsyncGenerator, Callable
 from contextlib import AsyncExitStack, asynccontextmanager
@@ -33,6 +34,43 @@ from deerflow.runtime.runs.store.base import RunStore
 
 logger = logging.getLogger(__name__)
 
+# Upper bound (seconds) for draining in-flight runs during shutdown, before the
+# AsyncExitStack tears down the checkpointer (and its connection pool). Kept
+# local to avoid an app -> deps -> app import cycle. This is a *separate* budget
+# from ``app.gateway.app._SHUTDOWN_HOOK_TIMEOUT_SECONDS`` (currently also 5.0s,
+# which bounds channel-service stop): the two govern independent teardown steps
+# and may diverge, but both count toward the lifespan shutdown window — revisit
+# them together if their sum must stay within the server's graceful-shutdown
+# timeout.
+_RUN_DRAIN_TIMEOUT_SECONDS = 5.0
+
+
+async def _drain_inflight_runs(run_manager: RunManager) -> None:
+    """Drain in-flight runs before the checkpointer is torn down (issue #3373).
+
+    Shields the (internally-bounded) drain so that even if the lifespan
+    coroutine is itself cancelled mid-shutdown — a second SIGINT or the server's
+    graceful-shutdown timeout, i.e. the same signal storm behind #3373 — the
+    checkpointer pool is not closed while run tasks are still writing
+    checkpoints. On such a cancellation we let the already-running drain finish
+    (it is bounded by ``RunManager.shutdown``'s own timeout) and then propagate
+    the cancellation.
+    """
+    drain = asyncio.create_task(run_manager.shutdown(timeout=_RUN_DRAIN_TIMEOUT_SECONDS))
+    try:
+        await asyncio.shield(drain)
+    except asyncio.CancelledError:
+        # Re-shield so this second wait does not abandon the in-flight drain;
+        # it is bounded, so this cannot hang. Then re-raise to honour shutdown.
+        try:
+            await asyncio.shield(drain)
+        except Exception:
+            logger.exception("In-flight run drain failed after shutdown cancellation")
+        raise
+    except Exception:
+        logger.exception("Failed to drain in-flight runs during shutdown")
+
+
 if TYPE_CHECKING:
     from app.gateway.auth.local_provider import LocalAuthProvider
     from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
@@ -81,6 +119,16 @@ def get_config() -> AppConfig:
     split-brain where the worker / lead-agent thread saw a stale startup
     snapshot.
 
+    Hot-reload boundary: fields backed by startup-time singletons
+    (engines, sandbox provider, IM channels, logging handler) require a
+    process restart to change at runtime. The authoritative list lives in
+    :mod:`deerflow.config.reload_boundary` and is mirrored by the
+    standardised ``"startup-only:"`` prefix on the matching
+    ``Field(description=...)`` in :class:`AppConfig` — IDE hover on those
+    fields will surface the boundary inline. See
+    ``backend/CLAUDE.md`` "Config Hot-Reload Boundary" for the operator
+    summary.
+
     Any failure to materialise the config (missing file, permission denied,
     YAML parse error, validation error) is reported as 503 — semantically
     "the gateway cannot serve requests without a usable configuration" — and
@@ -177,6 +225,14 @@ async def langgraph_runtime(app: FastAPI, startup_config: AppConfig) -> AsyncGen
         try:
             yield
         finally:
+            # Drain in-flight run tasks BEFORE the AsyncExitStack tears down the
+            # checkpointer (and its connection pool). A run still mid-graph would
+            # otherwise leak into asyncio.run() shutdown, where langgraph's
+            # _checkpointer_put_after_previous aput races the closed pool and
+            # raises PoolClosed (issue #3373).
+            run_manager = getattr(app.state, "run_manager", None)
+            if run_manager is not None:
+                await _drain_inflight_runs(run_manager)
             await close_engine()
 
 

backend/app/gateway/internal_auth.py

@@ -10,6 +10,7 @@ from deerflow.runtime.user_context import DEFAULT_USER_ID
 
 INTERNAL_AUTH_HEADER_NAME = "X-DeerFlow-Internal-Token"
 INTERNAL_AUTH_ENV_VAR = "DEER_FLOW_INTERNAL_AUTH_TOKEN"
+INTERNAL_SYSTEM_ROLE = "internal"
 
 
 def _load_internal_auth_token() -> str:
@@ -34,4 +35,4 @@ def is_valid_internal_auth_token(token: str | None) -> bool:
 
 def get_internal_user():
     """Return the synthetic user used for trusted internal channel calls."""
-    return SimpleNamespace(id=DEFAULT_USER_ID, system_role="internal")
+    return SimpleNamespace(id=DEFAULT_USER_ID, system_role=INTERNAL_SYSTEM_ROLE)

backend/app/gateway/pagination.py

@@ -0,0 +1,15 @@
+"""Shared pagination helpers for gateway routers."""
+
+from __future__ import annotations
+
+
+def trim_run_message_page(rows: list[dict], *, limit: int, after_seq: int | None) -> tuple[list[dict], bool]:
+    """Trim a ``limit + 1`` run-message page while preserving page boundaries."""
+    has_more = len(rows) > limit
+    if not has_more:
+        return rows, False
+
+    if after_seq is not None:
+        return rows[:limit], True
+
+    return rows[-limit:], True

backend/app/gateway/routers/agents.py

@@ -1,5 +1,6 @@
 """CRUD API for custom agents."""
 
+import asyncio
 import logging
 import re
 import shutil
@@ -213,48 +214,61 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
     user_id = get_effective_user_id()
     paths = get_paths()
 
-    agent_dir = paths.user_agent_dir(user_id, normalized_name)
+    def _create_agent() -> AgentResponse | None:
-    legacy_dir = paths.agent_dir(normalized_name)
+        # Worker thread: base-dir resolution, existence checks, directory/file
+        # creation, read-back, and failure cleanup are all blocking filesystem
+        # IO that must stay off the event loop.
+        agent_dir = paths.user_agent_dir(user_id, normalized_name)
+        legacy_dir = paths.agent_dir(normalized_name)
 
-    if agent_dir.exists() or legacy_dir.exists():
+        if legacy_dir.exists():
-        raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")
+            return None  # signals 409 to the caller
+
+        try:
+            try:
+                agent_dir.mkdir(parents=True, exist_ok=False)
+            except FileExistsError:
+                return None  # signals 409 to the caller
+            # Write config.yaml
+            config_data: dict = {"name": normalized_name}
+            if request.description:
+                config_data["description"] = request.description
+            if request.model is not None:
+                config_data["model"] = request.model
+            if request.tool_groups is not None:
+                config_data["tool_groups"] = request.tool_groups
+            if request.skills is not None:
+                config_data["skills"] = request.skills
+
+            config_file = agent_dir / "config.yaml"
+            with open(config_file, "w", encoding="utf-8") as f:
+                yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
+
+            # Write SOUL.md
+            soul_file = agent_dir / "SOUL.md"
+            soul_file.write_text(request.soul, encoding="utf-8")
+
+            logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
+
+            agent_cfg = load_agent_config(normalized_name, user_id=user_id)
+            return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
+        except Exception:
+            # Clean up partial state on failure before surfacing the error.
+            if agent_dir.exists():
+                shutil.rmtree(agent_dir)
+            raise
 
     try:
-        agent_dir.mkdir(parents=True, exist_ok=True)
+        response = await asyncio.to_thread(_create_agent)
-
-        # Write config.yaml
-        config_data: dict = {"name": normalized_name}
-        if request.description:
-            config_data["description"] = request.description
-        if request.model is not None:
-            config_data["model"] = request.model
-        if request.tool_groups is not None:
-            config_data["tool_groups"] = request.tool_groups
-        if request.skills is not None:
-            config_data["skills"] = request.skills
-
-        config_file = agent_dir / "config.yaml"
-        with open(config_file, "w", encoding="utf-8") as f:
-            yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
-
-        # Write SOUL.md
-        soul_file = agent_dir / "SOUL.md"
-        soul_file.write_text(request.soul, encoding="utf-8")
-
-        logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
-
-        agent_cfg = load_agent_config(normalized_name, user_id=user_id)
-        return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
-
-    except HTTPException:
-        raise
     except Exception as e:
-        # Clean up on failure
-        if agent_dir.exists():
-            shutil.rmtree(agent_dir)
         logger.error(f"Failed to create agent '{request.name}': {e}", exc_info=True)
         raise HTTPException(status_code=500, detail=f"Failed to create agent: {str(e)}")
 
+    if response is None:
+        raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")
+
+    return response
+
 
 @router.put(
     "/agents/{name}",
@@ -428,19 +442,30 @@ async def delete_agent(name: str) -> None:
     name = _normalize_agent_name(name)
     user_id = get_effective_user_id()
     paths = get_paths()
-    agent_dir = paths.user_agent_dir(user_id, name)
 
-    if not agent_dir.exists():
+    def _remove_agent_dir() -> tuple[str, str]:
-        if paths.agent_dir(name).exists():
+        # Runs in a worker thread: resolving the base dir, probing the directory
-            raise HTTPException(
+        # (`exists`), and removing it (`rmtree`) are all blocking filesystem IO
-                status_code=409,
+        # that must stay off the event loop.
-                detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before deleting."),
+        agent_dir = paths.user_agent_dir(user_id, name)
-            )
+        if not agent_dir.exists():
-        raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
+            outcome = "legacy" if paths.agent_dir(name).exists() else "missing"
+            return outcome, str(agent_dir)
+        shutil.rmtree(agent_dir)
+        return "deleted", str(agent_dir)
 
     try:
-        shutil.rmtree(agent_dir)
+        outcome, agent_dir = await asyncio.to_thread(_remove_agent_dir)
-        logger.info(f"Deleted agent '{name}' from {agent_dir}")
     except Exception as e:
         logger.error(f"Failed to delete agent '{name}': {e}", exc_info=True)
         raise HTTPException(status_code=500, detail=f"Failed to delete agent: {str(e)}")
+
+    if outcome == "legacy":
+        raise HTTPException(
+            status_code=409,
+            detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before deleting."),
+        )
+    if outcome == "missing":
+        raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
+
+    logger.info(f"Deleted agent '{name}' from {agent_dir}")

backend/app/gateway/routers/mcp.py

@@ -1,9 +1,10 @@
 import json
 import logging
+import os
 from pathlib import Path
 from typing import Literal
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, HTTPException, Request, status
 from pydantic import BaseModel, Field
 
 from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
@@ -12,6 +13,11 @@ logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api", tags=["mcp"])
 
 
+_MCP_STDIO_COMMAND_ALLOWLIST_ENV = "DEER_FLOW_MCP_STDIO_COMMAND_ALLOWLIST"
+_DEFAULT_MCP_STDIO_COMMAND_ALLOWLIST = frozenset({"npx", "uvx"})
+_SHELL_METACHARS = frozenset(";|&`$<>\n\r")
+
+
 class McpOAuthConfigResponse(BaseModel):
     """OAuth configuration for an MCP server."""
 
@@ -66,6 +72,78 @@ class McpConfigUpdateRequest(BaseModel):
 _MASKED_VALUE = "***"
 
 
+async def _require_admin_user(request: Request) -> None:
+    """Require the authenticated caller to be an admin user.
+
+    ``AuthMiddleware`` normally stamps ``request.state.user`` before the
+    request reaches this router. Falling back to the strict dependency keeps
+    this route safe even in tests or alternative ASGI compositions that mount
+    the router without the global middleware.
+    """
+    user = getattr(request.state, "user", None)
+    if user is None:
+        from app.gateway.deps import get_current_user_from_request
+
+        user = await get_current_user_from_request(request)
+
+    if getattr(user, "system_role", None) != "admin":
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Admin privileges required to manage MCP configuration.",
+        )
+
+
+def _allowed_stdio_commands() -> set[str]:
+    """Return executable names allowed for API-managed stdio MCP servers."""
+    raw = os.environ.get(_MCP_STDIO_COMMAND_ALLOWLIST_ENV)
+    base = set(_DEFAULT_MCP_STDIO_COMMAND_ALLOWLIST)
+    if raw is None:
+        return base
+    extra = {item.strip() for item in raw.split(",") if item.strip()}
+    return base | extra
+
+
+def _stdio_command_name(command: str | None, *, server_name: str) -> str:
+    """Normalize and validate a stdio command field from the API boundary."""
+    if command is None or not command.strip():
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=f"MCP server '{server_name}' with stdio transport requires a command.",
+        )
+
+    stripped = command.strip()
+    has_path_separator = "/" in stripped or "\\" in stripped
+    if stripped != command or has_path_separator or any(ch.isspace() for ch in stripped) or any(ch in stripped for ch in _SHELL_METACHARS):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=(f"MCP server '{server_name}' command must be a single executable name; put parameters in args instead."),
+        )
+
+    return stripped
+
+
+def _validate_mcp_update_request(request: McpConfigUpdateRequest) -> None:
+    """Validate API-submitted MCP config before it is persisted.
+
+    Local config files can still express arbitrary advanced setups, but the
+    HTTP API is an untrusted boundary. Restricting stdio commands here reduces
+    the blast radius of a compromised authenticated browser session.
+    """
+    allowed_commands = _allowed_stdio_commands()
+    for name, server in request.mcp_servers.items():
+        transport_type = (server.type or "stdio").lower()
+        if transport_type != "stdio":
+            continue
+
+        command_name = _stdio_command_name(server.command, server_name=name)
+        if command_name not in allowed_commands:
+            allowed = ", ".join(sorted(allowed_commands)) or "<none>"
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=(f"MCP server '{name}' uses disallowed stdio command '{command_name}'. Allowed commands: {allowed}. Configure {_MCP_STDIO_COMMAND_ALLOWLIST_ENV} to extend this list."),
+            )
+
+
 def _mask_server_config(server: McpServerConfigResponse) -> McpServerConfigResponse:
     """Return a copy of server config with sensitive fields masked.
 
@@ -162,7 +240,7 @@ def _merge_preserving_secrets(
     summary="Get MCP Configuration",
     description="Retrieve the current Model Context Protocol (MCP) server configurations.",
 )
-async def get_mcp_configuration() -> McpConfigResponse:
+async def get_mcp_configuration(request: Request) -> McpConfigResponse:
     """Get the current MCP configuration.
 
     Returns:
@@ -183,6 +261,8 @@ async def get_mcp_configuration() -> McpConfigResponse:
         }
         ```
     """
+    await _require_admin_user(request)
+
     config = get_extensions_config()
 
     servers = {name: _mask_server_config(McpServerConfigResponse(**server.model_dump())) for name, server in config.mcp_servers.items()}
@@ -195,7 +275,7 @@ async def get_mcp_configuration() -> McpConfigResponse:
     summary="Update MCP Configuration",
     description="Update Model Context Protocol (MCP) server configurations and save to file.",
 )
-async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
+async def update_mcp_configuration(request: Request, body: McpConfigUpdateRequest) -> McpConfigResponse:
     """Update the MCP configuration.
 
     This will:
@@ -228,6 +308,9 @@ async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfig
         ```
     """
     try:
+        await _require_admin_user(request)
+        _validate_mcp_update_request(body)
+
         # Get the current config path (or determine where to save it)
         config_path = ExtensionsConfig.resolve_config_path()
 
@@ -255,7 +338,7 @@ async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfig
 
         # Merge incoming server configs with raw on-disk secrets
         merged_servers: dict[str, McpServerConfigResponse] = {}
-        for name, incoming in request.mcp_servers.items():
+        for name, incoming in body.mcp_servers.items():
             raw_server = raw_servers.get(name)
             if raw_server is not None:
                 merged_servers[name] = _merge_preserving_secrets(
@@ -276,14 +359,15 @@ async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfig
 
         logger.info(f"MCP configuration updated and saved to: {config_path}")
 
-        # NOTE: No need to reload/reset cache here - LangGraph Server (separate process)
+        # Reload the Gateway configuration and update the global cache. The
-        # will detect config file changes via mtime and reinitialize MCP tools automatically
+        # agent runtime lives in Gateway, so this keeps API reads and tool
-
+        # execution aligned after extensions_config.json changes.
-        # Reload the configuration and update the global cache
         reloaded_config = reload_extensions_config()
         servers = {name: _mask_server_config(McpServerConfigResponse(**server.model_dump())) for name, server in reloaded_config.mcp_servers.items()}
         return McpConfigResponse(mcp_servers=servers)
 
+    except HTTPException:
+        raise
     except Exception as e:
         logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)
         raise HTTPException(status_code=500, detail=f"Failed to update MCP configuration: {str(e)}")

backend/app/gateway/routers/runs.py

@@ -7,7 +7,6 @@ is reused so that conversation history is preserved across calls.
 
 from __future__ import annotations
 
-import asyncio
 import logging
 import uuid
 
@@ -16,8 +15,9 @@ from fastapi.responses import StreamingResponse
 
 from app.gateway.authz import require_permission
 from app.gateway.deps import get_checkpointer, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
+from app.gateway.pagination import trim_run_message_page
 from app.gateway.routers.thread_runs import RunCreateRequest
-from app.gateway.services import sse_consumer, start_run
+from app.gateway.services import sse_consumer, start_run, wait_for_run_completion
 from deerflow.runtime import serialize_channel_values
 
 logger = logging.getLogger(__name__)
@@ -66,24 +66,25 @@ async def stateless_wait(body: RunCreateRequest, request: Request) -> dict:
     Otherwise a new temporary thread is created.
     """
     thread_id = _resolve_thread_id(body)
+    bridge = get_stream_bridge(request)
+    run_mgr = get_run_manager(request)
     record = await start_run(body, thread_id, request)
 
+    completed = True
     if record.task is not None:
-        try:
+        completed = await wait_for_run_completion(bridge, record, request, run_mgr)
-            await record.task
-        except asyncio.CancelledError:
-            pass
 
-    checkpointer = get_checkpointer(request)
+    if completed:
-    config = {"configurable": {"thread_id": thread_id}}
+        checkpointer = get_checkpointer(request)
-    try:
+        config = {"configurable": {"thread_id": thread_id}}
-        checkpoint_tuple = await checkpointer.aget_tuple(config)
+        try:
-        if checkpoint_tuple is not None:
+            checkpoint_tuple = await checkpointer.aget_tuple(config)
-            checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
+            if checkpoint_tuple is not None:
-            channel_values = checkpoint.get("channel_values", {})
+                checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
-            return serialize_channel_values(channel_values)
+                channel_values = checkpoint.get("channel_values", {})
-    except Exception:
+                return serialize_channel_values(channel_values)
-        logger.exception("Failed to fetch final state for run %s", record.run_id)
+        except Exception:
+            logger.exception("Failed to fetch final state for run %s", record.run_id)
 
     return {"status": record.status.value, "error": record.error}
 
@@ -129,8 +130,7 @@ async def run_messages(
         before_seq=before_seq,
         after_seq=after_seq,
     )
-    has_more = len(rows) > limit
+    data, has_more = trim_run_message_page(rows, limit=limit, after_seq=after_seq)
-    data = rows[:limit] if has_more else rows
     return {"data": data, "has_more": has_more}
 
 

backend/app/gateway/routers/suggestions.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import re
 
 from fastapi import APIRouter, Depends, Request
 from langchain_core.messages import HumanMessage, SystemMessage
@@ -30,6 +31,31 @@ class SuggestionsResponse(BaseModel):
     suggestions: list[str] = Field(default_factory=list, description="Suggested follow-up questions")
 
 
+# Matches a complete <think>...</think> block (case-insensitive, spans newlines).
+_THINK_BLOCK_RE = re.compile(r"<think\b[^>]*>.*?</think\s*>", re.IGNORECASE | re.DOTALL)
+# Matches a dangling, unclosed <think> (model truncated at max_tokens mid-thought).
+_OPEN_THINK_RE = re.compile(r"<think\b[^>]*>", re.IGNORECASE)
+
+
+def _strip_think_blocks(text: str) -> str:
+    """Remove reasoning-model ``<think>...</think>`` blocks from the response.
+
+    Reasoning models such as MiniMax-M3 inline their chain-of-thought into the
+    message ``content`` wrapped in ``<think>...</think>`` (``reasoning_split``
+    defaults to false), rather than exposing a separate ``reasoning_content``
+    field. The thinking text frequently contains ``[`` / ``]`` characters, which
+    corrupted the downstream ``find('[')`` / ``rfind(']')`` JSON extraction and
+    produced empty suggestions. We strip the reasoning before parsing so only
+    the actual answer remains.
+    """
+    text = _THINK_BLOCK_RE.sub("", text)
+    # Drop any unclosed <think> (and everything after it) left by truncation.
+    open_match = _OPEN_THINK_RE.search(text)
+    if open_match:
+        text = text[: open_match.start()]
+    return text.strip()
+
+
 def _strip_markdown_code_fence(text: str) -> str:
     stripped = text.strip()
     if not stripped.startswith("```"):
@@ -41,7 +67,8 @@ def _strip_markdown_code_fence(text: str) -> str:
 
 
 def _parse_json_string_list(text: str) -> list[str] | None:
-    candidate = _strip_markdown_code_fence(text)
+    candidate = _strip_think_blocks(text)
+    candidate = _strip_markdown_code_fence(candidate)
     start = candidate.find("[")
     end = candidate.rfind("]")
     if start == -1 or end == -1 or end <= start:

backend/app/gateway/routers/thread_runs.py

@@ -21,7 +21,8 @@ from pydantic import BaseModel, Field
 
 from app.gateway.authz import require_permission
 from app.gateway.deps import get_checkpointer, get_current_user, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
-from app.gateway.services import sse_consumer, start_run
+from app.gateway.pagination import trim_run_message_page
+from app.gateway.services import sse_consumer, start_run, wait_for_run_completion
 from deerflow.runtime import RunRecord, RunStatus, serialize_channel_values
 
 logger = logging.getLogger(__name__)
@@ -175,24 +176,25 @@ async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -
 @require_permission("runs", "create", owner_check=True, require_existing=True)
 async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) -> dict:
     """Create a run and block until it completes, returning the final state."""
+    bridge = get_stream_bridge(request)
+    run_mgr = get_run_manager(request)
     record = await start_run(body, thread_id, request)
 
+    completed = True
     if record.task is not None:
-        try:
+        completed = await wait_for_run_completion(bridge, record, request, run_mgr)
-            await record.task
-        except asyncio.CancelledError:
-            pass
 
-    checkpointer = get_checkpointer(request)
+    if completed:
-    config = {"configurable": {"thread_id": thread_id}}
+        checkpointer = get_checkpointer(request)
-    try:
+        config = {"configurable": {"thread_id": thread_id}}
-        checkpoint_tuple = await checkpointer.aget_tuple(config)
+        try:
-        if checkpoint_tuple is not None:
+            checkpoint_tuple = await checkpointer.aget_tuple(config)
-            checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
+            if checkpoint_tuple is not None:
-            channel_values = checkpoint.get("channel_values", {})
+                checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
-            return serialize_channel_values(channel_values)
+                channel_values = checkpoint.get("channel_values", {})
-    except Exception:
+                return serialize_channel_values(channel_values)
-        logger.exception("Failed to fetch final state for run %s", record.run_id)
+        except Exception:
+            logger.exception("Failed to fetch final state for run %s", record.run_id)
 
     return {"status": record.status.value, "error": record.error}
 
@@ -277,7 +279,12 @@ async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingRe
     )
 
 
-@router.api_route("/{thread_id}/runs/{run_id}/stream", methods=["GET", "POST"], response_model=None)
+# Register GET and POST as separate routes so each method gets a unique OpenAPI
+# operationId. ``api_route(methods=["GET", "POST"])`` shares one route registration
+# across both methods, which makes FastAPI emit the same ``operationId`` twice and
+# warn about a duplicate operation id during OpenAPI generation.
+@router.get("/{thread_id}/runs/{run_id}/stream", response_model=None)
+@router.post("/{thread_id}/runs/{run_id}/stream", response_model=None)
 @require_permission("runs", "read", owner_check=True)
 async def stream_existing_run(
     thread_id: str,
@@ -396,8 +403,7 @@ async def list_run_messages(
         before_seq=before_seq,
         after_seq=after_seq,
     )
-    has_more = len(rows) > limit
+    data, has_more = trim_run_message_page(rows, limit=limit, after_seq=after_seq)
-    data = rows[:limit] if has_more else rows
     return {"data": data, "has_more": has_more}
 
 

backend/app/gateway/routers/threads.py

@@ -17,7 +17,7 @@ import uuid
 from typing import Any
 
 from fastapi import APIRouter, HTTPException, Request
-from langgraph.checkpoint.base import empty_checkpoint
+from langgraph.checkpoint.base import empty_checkpoint, uuid6
 from pydantic import BaseModel, Field, field_validator
 
 from app.gateway.authz import require_permission
@@ -536,9 +536,21 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
         metadata["step"] = metadata.get("step", 0) + 1
         metadata["writes"] = {body.as_node: body.values}
 
+    # Assign a new checkpoint ID so aput performs an INSERT rather than an
+    # in-place REPLACE of the existing row.  Use uuid6 (time-ordered) rather
+    # than uuid4 (random) so the new ID is always lexicographically greater
+    # than the previous one — LangGraph's checkpointers determine the "latest"
+    # checkpoint by max(checkpoint_ids) string order, matching the uuid6 epoch.
+    checkpoint["id"] = str(uuid6())
+
     # aput requires checkpoint_ns in the config — use the same config used for the
-    # read (which always includes checkpoint_ns="").  Do NOT include checkpoint_id
+    # read (which always includes checkpoint_ns=""). The fresh checkpoint ID is
-    # so that aput generates a fresh checkpoint ID for the new snapshot.
+    # assigned above via checkpoint["id"]; keep checkpoint_id out of the config so
+    # the write is keyed by the new checkpoint payload rather than the prior read.
+    # All supported savers (InMemorySaver, AsyncSqliteSaver, AsyncPostgresSaver)
+    # persist and echo back checkpoint["id"] verbatim — none mint their own — so
+    # the new_config below carries the uuid6 we assigned here. (Regression-locked
+    # by test_update_thread_state_inserts_new_checkpoint_each_call.)
     write_config: dict[str, Any] = {
         "configurable": {
             "thread_id": thread_id,
@@ -557,7 +569,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
 
     # Sync title changes through the ThreadMetaStore abstraction so /threads/search
     # reflects them immediately in both sqlite and memory backends.
-    if body.values and "title" in body.values:
+    if thread_store and body.values and "title" in body.values:
         new_title = body.values["title"]
         if new_title:  # Skip empty strings and None
             try:

backend/app/gateway/routers/uploads.py

@@ -39,15 +39,39 @@ DEFAULT_MAX_FILE_SIZE = 50 * 1024 * 1024
 DEFAULT_MAX_TOTAL_SIZE = 100 * 1024 * 1024
 
 
+class UploadedFileInfo(BaseModel):
+    """Uploaded file metadata exposed by upload and list APIs."""
+
+    filename: str
+    size: int
+    path: str
+    virtual_path: str
+    artifact_url: str
+    extension: str | None = None
+    modified: float | None = None
+    original_filename: str | None = None
+    markdown_file: str | None = None
+    markdown_path: str | None = None
+    markdown_virtual_path: str | None = None
+    markdown_artifact_url: str | None = None
+
+
 class UploadResponse(BaseModel):
     """Response model for file upload."""
 
     success: bool
-    files: list[dict[str, str]]
+    files: list[UploadedFileInfo]
     message: str
     skipped_files: list[str] = Field(default_factory=list)
 
 
+class UploadListResponse(BaseModel):
+    """Response model for uploaded file listing."""
+
+    files: list[UploadedFileInfo]
+    count: int
+
+
 class UploadLimits(BaseModel):
     """Application-level upload limits exposed to clients."""
 
@@ -256,7 +280,7 @@ async def upload_files(
 
             file_info = {
                 "filename": safe_filename,
-                "size": str(file_size),
+                "size": file_size,
                 "path": str(sandbox_uploads / safe_filename),
                 "virtual_path": virtual_path,
                 "artifact_url": upload_artifact_url(thread_id, safe_filename),
@@ -333,9 +357,9 @@ async def get_upload_limits(
     return _get_upload_limits(config)
 
 
-@router.get("/list", response_model=dict)
+@router.get("/list", response_model=UploadListResponse)
 @require_permission("threads", "read", owner_check=True)
-async def list_uploaded_files(thread_id: str, request: Request) -> dict:
+async def list_uploaded_files(thread_id: str, request: Request) -> UploadListResponse:
     """List all files in a thread's uploads directory."""
     try:
         uploads_dir = get_uploads_dir(thread_id)
@@ -349,7 +373,7 @@ async def list_uploaded_files(thread_id: str, request: Request) -> dict:
     for f in result["files"]:
         f["path"] = str(sandbox_uploads / f["filename"])
 
-    return result
+    return UploadListResponse(**result)
 
 
 @router.delete("/{filename}")

backend/app/gateway/services.py

@@ -19,6 +19,7 @@ from langchain_core.messages import BaseMessage
 from langchain_core.messages.utils import convert_to_messages
 
 from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
+from app.gateway.internal_auth import INTERNAL_SYSTEM_ROLE
 from app.gateway.utils import sanitize_log_param
 from deerflow.config.app_config import get_app_config
 from deerflow.runtime import (
@@ -140,7 +141,14 @@ def merge_run_context_overrides(config: dict[str, Any], context: Mapping[str, An
     """Merge whitelisted keys from ``body.context`` into both ``config['configurable']``
     and ``config['context']`` so they are visible to legacy configurable readers and
     to LangGraph ``ToolRuntime.context`` consumers (e.g. the ``setup_agent`` tool —
-    see issue #2677)."""
+    see issue #2677).
+
+    ``user_id`` is intentionally propagated into ``config['context']`` in addition to
+    the whitelisted keys, so non-web callers (e.g. IM channels) that supply identity in
+    ``body.context`` keep it on ``ToolRuntime.context``. It is merged with
+    ``setdefault`` so a server-authenticated id stamped by
+    :func:`inject_authenticated_user_context` always wins over the client-supplied one.
+    """
     if not context:
         return
     configurable = config.setdefault("configurable", {})
@@ -151,6 +159,8 @@ def merge_run_context_overrides(config: dict[str, Any], context: Mapping[str, An
                 configurable.setdefault(key, context[key])
             if isinstance(runtime_context, dict):
                 runtime_context.setdefault(key, context[key])
+    if "user_id" in context and isinstance(runtime_context, dict):
+        runtime_context.setdefault("user_id", context["user_id"])
 
 
 def inject_authenticated_user_context(config: dict[str, Any], request: Request) -> None:
@@ -166,6 +176,9 @@ def inject_authenticated_user_context(config: dict[str, Any], request: Request)
     if user_id is None:
         return
 
+    if getattr(user, "system_role", None) == INTERNAL_SYSTEM_ROLE:
+        return
+
     runtime_context = config.setdefault("context", {})
     if isinstance(runtime_context, dict):
         runtime_context["user_id"] = str(user_id)
@@ -402,3 +415,51 @@ async def sse_consumer(
         if record.status in (RunStatus.pending, RunStatus.running):
             if record.on_disconnect == DisconnectMode.cancel:
                 await run_mgr.cancel(record.run_id)
+
+
+async def wait_for_run_completion(
+    bridge: StreamBridge,
+    record: RunRecord,
+    request: Request,
+    run_mgr: RunManager,
+) -> bool:
+    """Block until the run publishes ``END_SENTINEL``, honouring on_disconnect.
+
+    The non-streaming ``/wait`` endpoints used to ``await record.task``
+    directly with no disconnect handling.  When the client (or an
+    intermediate HTTP proxy) timed out during a long tool call such as
+    ``pip install``, the handler would swallow ``CancelledError`` and
+    serialize whatever checkpoint happened to exist — masking a half-finished
+    run as a normal completion (issue #3265).
+
+    This helper consumes the same bridge that ``sse_consumer`` does so the
+    wait path shares its disconnect semantics: each wake-up polls
+    ``request.is_disconnected()``; on a real disconnect it cancels the
+    background run when ``record.on_disconnect`` is ``cancel``.  The bridge's
+    heartbeat sentinels guarantee at least one wake-up per
+    ``heartbeat_interval`` even when the agent emits no events for a while.
+
+    Returns:
+        ``True`` when ``END_SENTINEL`` was observed (run reached a terminal
+        state), ``False`` when the loop exited because the client
+        disconnected.  Callers must skip checkpoint serialization on
+        ``False`` so a partial checkpoint is not returned as a normal
+        response.
+    """
+    completed = False
+    try:
+        async for entry in bridge.subscribe(record.run_id):
+            # END_SENTINEL means the run reached a terminal state; honour it
+            # even if the client just disconnected so the caller still serializes
+            # the real final checkpoint.
+            if entry is END_SENTINEL:
+                completed = True
+                return True
+            if await request.is_disconnected():
+                break
+            # Heartbeats and regular events: keep waiting for END_SENTINEL.
+        return completed
+    finally:
+        if not completed and record.status in (RunStatus.pending, RunStatus.running):
+            if record.on_disconnect == DisconnectMode.cancel:
+                await run_mgr.cancel(record.run_id)

backend/docs/API.md

@@ -228,10 +228,13 @@ Get current MCP server configurations.
 GET /api/mcp/config
 ```
 
+Requires an authenticated admin session. Sensitive env/header/OAuth secret
+values are masked in the response.
+
 **Response:**
 ```json
 {
-  "mcpServers": {
+  "mcp_servers": {
     "github": {
       "enabled": true,
       "type": "stdio",
@@ -255,10 +258,15 @@ PUT /api/mcp/config
 Content-Type: application/json
 ```
 
+Requires an authenticated admin session. API-managed `stdio` MCP servers may
+only use allowed executable names for `command` (default: `npx`, `uvx`). Set
+`DEER_FLOW_MCP_STDIO_COMMAND_ALLOWLIST` to a comma-separated list when a
+deployment needs additional trusted launchers.
+
 **Request Body:**
 ```json
 {
-  "mcpServers": {
+  "mcp_servers": {
     "github": {
       "enabled": true,
       "type": "stdio",
@@ -276,8 +284,18 @@ Content-Type: application/json
 **Response:**
 ```json
 {
-  "success": true,
+  "mcp_servers": {
-  "message": "MCP configuration updated"
+    "github": {
+      "enabled": true,
+      "type": "stdio",
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-github"],
+      "env": {
+        "GITHUB_TOKEN": "***"
+      },
+      "description": "GitHub operations"
+    }
+  }
 }
 ```
 

backend/docs/AUTH_TEST_DOCKER_GAP.md

@@ -29,7 +29,7 @@ All other test plan sections were executed against either:
 | TC-DOCKER-03 | Per-worker rate limiter divergence | Confirms in-process `_login_attempts` dict doesn't share state across `gunicorn` workers (4 by default in the compose file); known limitation, documented | needs multi-worker container |
 | TC-DOCKER-04 | IM channels use internal Gateway auth | Verify Feishu/Slack/Telegram dispatchers attach the process-local internal auth header plus CSRF cookie/header when calling Gateway-compatible LangGraph APIs | needs `docker logs` |
 | TC-DOCKER-05 | Reset credentials surfacing | `reset_admin` writes a 0600 credential file in `DEER_FLOW_HOME` instead of logging plaintext. The file-based behavior is validated by non-Docker reset tests, so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
-| TC-DOCKER-06 | Gateway-mode Docker deploy | `./scripts/deploy.sh --gateway` produces a 3-container topology (no `langgraph` container); same auth flow as standard mode | needs `docker compose --profile gateway` |
+| TC-DOCKER-06 | Docker deploy uses Gateway embedded runtime | `./scripts/deploy.sh` produces a Gateway + frontend + nginx topology (no `langgraph` container); same auth flow as local `make dev` | needs `docker compose up` |
 
 ## Coverage already provided by non-Docker tests
 
@@ -43,7 +43,7 @@ the test cases that ran on sg_dev or local:
 | TC-DOCKER-03 (per-worker rate limit) | TC-GW-04 + TC-REENT-09 (single-worker rate limit + 5min expiry). The cross-worker divergence is an architectural property of the in-memory dict; no auth code path differs |
 | TC-DOCKER-04 (IM channels use internal auth) | Code-level: `app/channels/manager.py` creates the `langgraph_sdk` client with `create_internal_auth_headers()` plus CSRF cookie/header, so channel workers do not rely on browser cookies |
 | TC-DOCKER-05 (credential surfacing) | `reset_admin` writes `.deer-flow/admin_initial_credentials.txt` with mode 0600 and logs only the path — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
-| TC-DOCKER-06 (gateway-mode container) | Section 七 7.2 covered by TC-GW-01..05 + Section 二 (gateway-mode auth flow on sg_dev) — same Gateway code, container is just a packaging change |
+| TC-DOCKER-06 (Gateway embedded runtime container) | Section 七 7.2 covered by TC-GW-01..05 + Section 二 (Gateway auth flow on sg_dev) — same Gateway code, container is just a packaging change |
 
 ## Reproduction steps when Docker becomes available
 

backend/docs/AUTH_TEST_PLAN.md

@@ -4,10 +4,12 @@
 
 | 模式 | 启动命令 | Auth 层 | 端口 |
 |------|---------|---------|------|
-| 标准模式 | `make dev` | Gateway AuthMiddleware + LangGraph auth | 2026 (nginx) |
+| 标准模式 | `make dev` | Gateway AuthMiddleware（全量） | 2026 (nginx) |
-| Gateway 模式 | `make dev-pro` | Gateway AuthMiddleware（全量） | 2026 (nginx) |
 | 直连 Gateway | `cd backend && make gateway` | Gateway AuthMiddleware | 8001 |
-| 直连 LangGraph | `cd backend && make dev` | LangGraph auth | 2024 |
+| 直连 LangGraph 兼容性 | 手动运行 LangGraph 工具链时使用 | LangGraph auth | 2024 |
+
+`make dev`、Docker dev 和生产部署默认都运行 Gateway embedded runtime。
+`app.gateway.langgraph_auth` 仅用于保留的直连 LangGraph 工具链 / Studio 兼容性测试，不是标准服务启动路径。
 
 每种模式下都需执行以下测试。
 
@@ -21,10 +23,8 @@
 # 清除已有数据
 rm -f backend/.deer-flow/data/deerflow.db
 
-# 选择模式启动
+# 启动标准模式（Gateway embedded runtime）
-make dev          # 标准模式
+make dev
-# 或
-make dev-pro      # Gateway 模式
 ```
 
 **验证点：**
@@ -57,7 +57,7 @@ make dev
 
 ## 二、接口流程测试
 
-> 以下用 `BASE=http://localhost:2026` 为例。标准模式和 Gateway 模式都用此地址。
+> 以下用 `BASE=http://localhost:2026` 为例。标准模式经 nginx 暴露此地址。
 > 直连测试替换为对应端口。
 >
 > **CSRF token 提取**：多处用到从 cookie jar 提取 CSRF token，统一使用：
@@ -211,20 +211,18 @@ curl -s -X POST $BASE/api/threads/search \
 
 **预期：** 返回 0 或仅包含 user2 自己的 thread
 
-### 2.3 标准模式 LangGraph Server 隔离
+### 2.3 LangGraph-compatible Gateway 路由隔离
 
-> 仅在标准模式下测试。Gateway 模式不跑 LangGraph Server。
+#### TC-API-10: LangGraph-compatible 端点需要 cookie
-
-#### TC-API-10: LangGraph 端点需要 cookie
 
 ```bash
-# 不带 cookie 访问 LangGraph 接口
+# 不带 cookie 访问 LangGraph-compatible 接口
 curl -s -w "%{http_code}" $BASE/api/langgraph/threads
 ```
 
 **预期：** 401
 
-#### TC-API-11: LangGraph 带 cookie 可访问
+#### TC-API-11: LangGraph-compatible 路由带 cookie 可访问
 
 ```bash
 curl -s $BASE/api/langgraph/threads -b user1.txt | jq length
@@ -232,10 +230,10 @@ curl -s $BASE/api/langgraph/threads -b user1.txt | jq length
 
 **预期：** 200，返回 user1 的 thread 列表
 
-#### TC-API-12: LangGraph 隔离 — 用户只看到自己的
+#### TC-API-12: LangGraph-compatible 路由隔离 — 用户只看到自己的
 
 ```bash
-# user2 查 LangGraph threads
+# user2 查 threads
 curl -s $BASE/api/langgraph/threads -b user2.txt | jq length
 ```
 
@@ -1234,21 +1232,11 @@ P2=$(awk -F': ' '/^password:/ {print $2}' /tmp/deerflow-reset-p2.txt)
 ## 七、模式差异测试
 
 > 以下用 `GW=http://localhost:8001` 表示直连 Gateway，`BASE=http://localhost:2026` 表示经 nginx。
-> Gateway 模式启动命令：`make dev-pro`（或 `./scripts/serve.sh --dev --gateway`）。
+> 标准启动命令：`make dev`（或 `./scripts/serve.sh --dev`）。
 
-### 7.1 标准模式独有
+### 7.1 标准启动模式
 
-> 启动命令：`make dev`（或 `./scripts/serve.sh --dev`）
+#### TC-MODE-01: Gateway AuthMiddleware 的 token_version 检查
-
-#### TC-MODE-01: LangGraph Server 独立运行，需 cookie
-
-```bash
-# 无 cookie 访问 LangGraph
-curl -s -w "%{http_code}" -o /dev/null $BASE/api/langgraph/threads/search
-# 预期: 403（LangGraph auth handler 拒绝）
-```
-
-#### TC-MODE-02: LangGraph auth 的 token_version 检查
 
 ```bash
 # 登录拿 cookie
@@ -1261,9 +1249,9 @@ curl -s -X POST $BASE/api/v1/auth/change-password \
   -b cookies.txt -H "Content-Type: application/json" -H "X-CSRF-Token: $CSRF" \
   -d '{"current_password":"正确密码","new_password":"NewPass1!"}' -c new_cookies.txt
 
-# 用旧 cookie 访问 LangGraph
+# 用旧 cookie 访问 LangGraph-compatible 路由
 curl -s -w "%{http_code}" $BASE/api/langgraph/threads/search -b cookies.txt
-# 预期: 403（token_version 不匹配）
+# 预期: 401（token_version 不匹配）
 
 # 用新 cookie 访问
 CSRF2=$(grep csrf_token new_cookies.txt | awk '{print $NF}')
@@ -1272,7 +1260,7 @@ curl -s -w "%{http_code}" -X POST $BASE/api/langgraph/threads/search \
 # 预期: 200
 ```
 
-#### TC-MODE-03: LangGraph auth 的 owner filter 隔离
+#### TC-MODE-02: Gateway owner filter 隔离
 
 ```bash
 # user1 创建 thread
@@ -1297,18 +1285,9 @@ print('OK: user2 sees', len(threads), 'threads, none belong to user1')
 "
 ```
 
-### 7.2 Gateway 模式独有
+#### TC-MODE-03: 所有请求经 AuthMiddleware
-
-> 启动命令：`make dev-pro`（或 `./scripts/serve.sh --dev --gateway`）
-> 无 LangGraph Server 进程，agent runtime 嵌入 Gateway。
-
-#### TC-MODE-04: 所有请求经 AuthMiddleware
 
 ```bash
-# 确认 LangGraph Server 未运行
-curl -s -w "%{http_code}" -o /dev/null http://localhost:2024/ok
-# 预期: 000（连接被拒）
-
 # Gateway API 受保护
 curl -s -w "%{http_code}" -o /dev/null $BASE/api/models
 # 预期: 401
@@ -1319,7 +1298,7 @@ curl -s -w "%{http_code}" -o /dev/null -X POST $BASE/api/langgraph/threads/searc
 # 预期: 401
 ```
 
-#### TC-MODE-05: Gateway 模式下完整 auth 流程
+#### TC-MODE-04: 标准模式下完整 auth 流程
 
 ```bash
 # 登录
@@ -1334,7 +1313,7 @@ curl -s -X POST $BASE/api/langgraph/threads \
   -d '{"metadata":{}}' | python3 -c "import sys,json; print(json.load(sys.stdin)['thread_id'])"
 # 预期: 返回 thread_id
 
-# CSRF 保护（Gateway 模式下 CSRFMiddleware 直接覆盖所有路由）
+# CSRF 保护（CSRFMiddleware 覆盖所有 Gateway 路由）
 curl -s -w "%{http_code}" -o /dev/null -X POST $BASE/api/langgraph/threads \
   -b cookies.txt -H "Content-Type: application/json" -d '{"metadata":{}}'
 # 预期: 403（CSRF token missing）
@@ -1433,7 +1412,7 @@ done
 
 ### 7.4 Docker 部署
 
-> 启动命令：`./scripts/deploy.sh`（标准）或 `./scripts/deploy.sh --gateway`（Gateway 模式）
+> 启动命令：`./scripts/deploy.sh`
 > Docker Compose 文件：`docker/docker-compose.yaml`
 >
 > 前置条件：
@@ -1542,16 +1521,16 @@ docker logs deer-flow-gateway 2>&1 | grep -iE "Password: .{15,}" && echo "FAIL:
 - 容器日志输出**路径**（不是密码本身），符合 CodeQL `py/clear-text-logging-sensitive-data` 规则
 - `grep "Password:"` 在日志中**应当无匹配**（旧行为已废弃，simplify pass 移除了日志泄露路径）
 
-#### TC-DOCKER-06: Gateway 模式 Docker 部署
+#### TC-DOCKER-06: Docker 部署
 
 ```bash
-# Gateway 模式：无 langgraph 容器
+# 标准 Docker 模式：runtime 嵌入 gateway 容器
-./scripts/deploy.sh --gateway
+./scripts/deploy.sh
 sleep 15
 
-# 确认 langgraph 容器不存在
+# 确认 gateway 容器存在
-docker ps --filter name=deer-flow-langgraph --format '{{.Names}}' | wc -l
+docker ps --filter name=deer-flow-gateway --format '{{.Names}}'
-# 预期: 0
+# 预期: deer-flow-gateway
 
 # auth 流程正常：未登录受保护接口返回 401
 curl -s -w "%{http_code}" -o /dev/null $BASE/api/models

backend/docs/AUTH_UPGRADE.md

@@ -124,8 +124,8 @@ python -c "import secrets; print(secrets.token_urlsafe(32))"
 
 ## 兼容性
 
-- **标准模式**（`make dev`）：完全兼容；无 admin 时访问 `/setup` 初始化
+- **本地开发**（`make dev`）：Gateway embedded runtime 完全兼容；无 admin 时访问 `/setup` 初始化
-- **Gateway 模式**（`make dev-pro`）：完全兼容
+- **Gateway embedded runtime**：标准脚本、Docker dev 和生产部署均通过 Gateway 提供认证与 LangGraph-compatible API
 - **Docker 部署**：完全兼容，`.deer-flow/data/deerflow.db` 需持久化卷挂载
 - **IM 渠道**（Feishu/Slack/Telegram）：通过 Gateway 内部认证通信，使用 `default` 用户桶
 - **DeerFlowClient**（嵌入式）：不经过 HTTP，不受认证影响

backend/docs/BLOCKING_IO_DETECTION.md

@@ -0,0 +1,154 @@
+# Blocking IO detection usage and maintenance
+
+This document describes how to use and maintain DeerFlow backend blocking-IO
+detection for async event-loop safety.
+
+The goal is narrow: find and prevent synchronous IO from blocking backend
+async event-loop paths. Static and runtime detection are complementary, but
+they have different jobs.
+
+## Static detector
+
+The static detector is the discovery tool. It scans backend source code and
+reports candidate blocking-IO call sites that may need human review.
+
+Run it from the repository root:
+
+```bash
+make detect-blocking-io
+```
+
+Or from `backend/`:
+
+```bash
+make detect-blocking-io
+```
+
+The report is written to:
+
+```text
+.deer-flow/blocking-io-findings.json
+```
+
+Use this output for review and triage. A static finding is a candidate, not
+proof that production blocks the event loop at runtime. The current static
+rules are intentionally broad; prefer triaging existing output before adding
+new static rules.
+
+Add a static rule only when review finds a recurring high-risk blocking
+pattern that is invisible to the current detector.
+
+## Runtime detector
+
+The runtime detector is the CI regression guard. It uses Blockbuster to fail a
+focused test when code under `app.*` or `deerflow.*` performs blocking IO on
+the asyncio event-loop thread.
+
+Run it from `backend/`:
+
+```bash
+make test-blocking-io
+```
+
+The runtime gate starts from confirmed production bugs and protects those
+paths from regressing. It does not prove that the entire backend is free of
+blocking IO; it only covers the production paths exercised by
+`backend/tests/blocking_io/`.
+
+## Maintenance workflow
+
+Use the static detector to find candidates, then use review to decide which
+async production paths are worth protecting in CI.
+
+The normal workflow is:
+
+1. Run the static detector to find backend blocking-IO candidates.
+2. Use human review to pick high-risk production async paths.
+3. Add or update a focused runtime anchor in `backend/tests/blocking_io/`.
+4. Let CI prevent that path from regressing.
+
+Runtime detection has two maintenance paths.
+
+### Add a runtime rule
+
+Add a runtime rule when Blockbuster's default rules do not cover a generic
+blocking primitive used by production code.
+
+Rules belong in:
+
+```text
+backend/tests/support/detectors/blocking_io_runtime.py
+```
+
+Add them to `_PROJECT_BLOCKING_RULES`, not directly inside individual tests.
+Keeping rules centralized makes it clear which extra primitives DeerFlow
+expects Blockbuster to catch.
+
+Example shape:
+
+```python
+import subprocess
+
+from blockbuster import BlockBusterFunction
+
+_PROJECT_BLOCKING_RULES = (
+    (
+        "subprocess.Popen.__init__",
+        BlockBusterFunction(
+            subprocess.Popen,
+            "__init__",
+            scanned_modules=["app", "deerflow"],
+        ),
+    ),
+)
+```
+
+Do not add a runtime rule just because a business path is not tested. A rule
+only expands what Blockbuster can intercept after code runs.
+
+### Add a runtime anchor
+
+Add a runtime anchor when a high-risk async production path should be protected
+by CI but no existing `backend/tests/blocking_io/` test executes it.
+
+Anchors belong in:
+
+```text
+backend/tests/blocking_io/
+```
+
+A good anchor should:
+
+- Call the real production async entry point.
+- Avoid bypassing the blocking surface with test-only `asyncio.to_thread`
+  wrappers.
+- Use real local filesystem inputs when the bug shape is filesystem IO.
+- Mock only the external dependency boundary, such as a network service or
+  third-party saver class.
+- Fail if a future change moves the blocking operation back onto the event
+  loop.
+
+Avoid testing only the low-level helper unless that helper is the production
+async entry point. The runtime gate is most useful when it protects the caller
+that production actually executes.
+
+## Current runtime coverage
+
+The runtime anchors protect confirmed blocking-IO bug shapes:
+
+- SQLite checkpointer setup, including path resolution and parent-directory
+  creation.
+- Subagent skill metadata loading through `SubagentExecutor._load_skills()`.
+- `JsonlRunEventStore` async API (`put` / `list_*` / `delete_*`): the JSONL
+  run-event backend offloads its synchronous file IO via `asyncio.to_thread`
+  (fix #3084); this anchor drives the real async API under the gate so any
+  blocking IO reintroduced on the loop fails, not only removal of one
+  `to_thread` call.
+- `UploadsMiddleware.before_agent` uploads-directory scan: a sync-only middleware
+  hook runs on the event loop under async graph execution, so the scan is
+  offloaded via `abefore_agent` + `run_in_executor`.
+- Gate health checks: Blockbuster catches unoffloaded calls, opt-out works, and
+  patches are restored after exceptions.
+
+As static detection and review identify more high-risk async paths, add new
+runtime anchors incrementally.

backend/docs/CONFIGURATION.md

@@ -36,6 +36,7 @@ models:
 - OpenAI (`langchain_openai:ChatOpenAI`)
 - Anthropic (`langchain_anthropic:ChatAnthropic`)
 - DeepSeek (`langchain_deepseek:ChatDeepSeek`)
+- Xiaomi MiMo (`deerflow.models.patched_mimo:PatchedChatMiMo`)
 - Claude Code OAuth (`deerflow.models.claude_provider:ClaudeChatModel`)
 - Codex CLI (`deerflow.models.openai_codex_provider:CodexChatModel`)
 - Any LangChain-compatible provider
@@ -94,25 +95,35 @@ models:
         thinking:
           type: enabled
 
-  - name: minimax-m2.5
+  - name: minimax-m3
-    display_name: MiniMax M2.5
+    display_name: MiniMax M3
     use: langchain_openai:ChatOpenAI
-    model: MiniMax-M2.5
+    model: MiniMax-M3
     api_key: $MINIMAX_API_KEY
     base_url: https://api.minimax.io/v1
     max_tokens: 4096
     temperature: 1.0  # MiniMax requires temperature in (0.0, 1.0]
     supports_vision: true
 
-  - name: minimax-m2.5-highspeed
+  - name: minimax-m2.7
-    display_name: MiniMax M2.5 Highspeed
+    display_name: MiniMax M2.7
     use: langchain_openai:ChatOpenAI
-    model: MiniMax-M2.5-highspeed
+    model: MiniMax-M2.7
     api_key: $MINIMAX_API_KEY
     base_url: https://api.minimax.io/v1
     max_tokens: 4096
     temperature: 1.0  # MiniMax requires temperature in (0.0, 1.0]
-    supports_vision: true
+    supports_vision: false  # M2.7 is text-only; M3 supports vision
+
+  - name: minimax-m2.7-highspeed
+    display_name: MiniMax M2.7 Highspeed
+    use: langchain_openai:ChatOpenAI
+    model: MiniMax-M2.7-highspeed
+    api_key: $MINIMAX_API_KEY
+    base_url: https://api.minimax.io/v1
+    max_tokens: 4096
+    temperature: 1.0  # MiniMax requires temperature in (0.0, 1.0]
+    supports_vision: false  # M2.7 is text-only; M3 supports vision
   - name: openrouter-gemini-2.5-flash
     display_name: Gemini 2.5 Flash (OpenRouter)
     use: langchain_openai:ChatOpenAI
@@ -166,6 +177,37 @@ models:
 
 For Gemini accessed **without** thinking (e.g. via OpenRouter where thinking is not activated), the plain `langchain_openai:ChatOpenAI` with `supports_thinking: false` is sufficient and no patch is needed.
 
+**MiMo with thinking via OpenAI-compatible API**:
+
+MiMo returns `reasoning_content` on assistant messages in thinking mode. In multi-turn agent conversations with tool calls, subsequent requests must preserve that historical `reasoning_content` on assistant messages or the MiMo API can return HTTP 400. Standard `langchain_openai:ChatOpenAI` drops this provider-specific field, so use `deerflow.models.patched_mimo:PatchedChatMiMo`:
+
+For pay-as-you-go API keys (`sk-...`), use `https://api.xiaomimimo.com/v1`. For Token Plan keys (`tp-...`), use the regional Token Plan Base URL shown in the MiMo console, such as `https://token-plan-cn.xiaomimimo.com/v1`. MiMo documents these key types as separate and non-interchangeable.
+
+`PatchedChatMiMo` is model-id agnostic. Use it for every MiMo thinking model entry you configure, including model entries referenced by `subagents.*.model` overrides (for example `mimo-v2.5-pro`, `mimo-v2.5`, `mimo-v2-pro`, `mimo-v2-omni`, or `mimo-v2-flash`).
+
+```yaml
+models:
+  - name: mimo-v2.5-pro
+    display_name: MiMo V2.5 Pro
+    use: deerflow.models.patched_mimo:PatchedChatMiMo
+    model: mimo-v2.5-pro
+    api_key: $MIMO_API_KEY
+    base_url: https://api.xiaomimimo.com/v1
+    max_tokens: 8192
+    supports_thinking: true
+    supports_vision: false
+    when_thinking_enabled:
+      extra_body:
+        thinking:
+          type: enabled
+    when_thinking_disabled:
+      extra_body:
+        thinking:
+          type: disabled
+```
+
+`PatchedChatMiMo` preserves MiMo's `choices[].message.reasoning_content`, streaming `delta.reasoning_content`, and request-history assistant `reasoning_content` fields. It does not reuse the DeepSeek provider.
+
 ### Tool Groups
 
 Organize tools into logical groups:
@@ -319,6 +361,7 @@ models:
 - `OPENAI_API_KEY` - OpenAI API key
 - `ANTHROPIC_API_KEY` - Anthropic API key
 - `DEEPSEEK_API_KEY` - DeepSeek API key
+- `MIMO_API_KEY` - Xiaomi MiMo API key
 - `NOVITA_API_KEY` - Novita API key (OpenAI-compatible endpoint)
 - `TAVILY_API_KEY` - Tavily search API key
 - `DEER_FLOW_PROJECT_ROOT` - Project root for relative runtime paths

backend/docs/README.md

@@ -19,6 +19,7 @@ This directory contains detailed documentation for the DeerFlow backend.
 | [STREAMING.md](STREAMING.md) | Token-level streaming design: Gateway vs DeerFlowClient paths, `stream_mode` semantics, per-id dedup |
 | [FILE_UPLOAD.md](FILE_UPLOAD.md) | File upload functionality |
 | [PATH_EXAMPLES.md](PATH_EXAMPLES.md) | Path types and usage examples |
+| [SANDBOX_MEMORY_PROFILING.md](SANDBOX_MEMORY_PROFILING.md) | Sandbox memory baseline and runtime comparison guide |
 | [summarization.md](summarization.md) | Context summarization feature |
 | [plan_mode_usage.md](plan_mode_usage.md) | Plan mode with TodoList |
 | [AUTO_TITLE_GENERATION.md](AUTO_TITLE_GENERATION.md) | Automatic title generation |

backend/docs/REPLAY_E2E.md

@@ -0,0 +1,120 @@
+# Record/Replay E2E — front-back contract verification
+
+Deterministic, **key-free** end-to-end checks that a backend change can't
+silently break the frontend (and vice-versa). Two complementary layers, fed by a
+single recording.
+
+## Why
+
+The mock-based frontend e2e hand-writes the backend's JSON/SSE, so a backend
+schema or SSE change passes green ("fake green"). These layers replay a recorded
+**real** run against the **real** backend (and, for Layer 2, the real frontend),
+so contract drift turns the build red instead.
+
+## The two layers
+
+- **Layer 1 — backend golden** (`tests/test_replay_golden.py`): replays a fixture
+  through the real FastAPI gateway with `ReplayChatModel` and asserts the streamed
+  SSE event sequence equals a committed golden. Fast, no browser. Guards protocol
+  *shape*.
+- **Layer 2 — full-stack render** (`frontend/tests/e2e-real-backend/`): real
+  Next.js + real gateway (replay model) + Chromium; asserts the replayed
+  auto-title and a follow-up suggestion render in the browser. Guards semantic
+  *render*. (Complementary to Layer 1 — neither subsumes the other.)
+
+Layer 2 also hosts **cross-stack contract scenarios** — the dangerous class
+where a backend change silently breaks a frontend assumption and *both sides'
+unit tests stay green*. See below.
+
+## Cross-stack scenario: multi-run render order (`multi-run-order.spec.ts`)
+
+Regression guard for issue **#3352** (after context compression, refreshing a
+thread rendered history out of order). Root cause was a front-back desync:
+backend `RunManager.list_by_thread` returns runs **newest-first** (PR #2932),
+while the frontend (`core/threads/hooks.ts`) iterated runs and **prepended** each
+loaded page — inverting chronological order once the checkpoint no longer held
+the older messages. The backend ordering test was green throughout, and the
+frontend regression unit test hardcodes "backend returns newest-first" in a mock,
+so only a *real frontend against a real backend* catches the desync.
+
+This scenario does **not** record a conversation. It uses a **test-only seeder**
+(`tests/seed_runs_router.py`, mounted on the replay gateway only when
+`DEERFLOW_ENABLE_TEST_SEED=1`) to stand up a thread with ≥2 runs and per-run
+message events — and deliberately **no checkpoint**, which is the #3352
+precondition: it forces the frontend's per-run reload path to be the sole source
+of truth so the ordering bug becomes observable. The seeder writes through the
+gateway's own run/event stores using the request's auth context, so the real
+`list_by_thread` → `/runs/{id}/messages` → prepend path runs live. Reverting the
+#3354 frontend fix turns this spec red.
+
+## How replay works
+
+`tests/replay_provider.py::ReplayChatModel` returns recorded assistant turns keyed
+by a **normalized hash of the model caller + conversation**. The conversation is
+human / ai / tool messages — role, text, tool-call name+args; with
+`<system-reminder>`, dates, UUIDs, tmp paths stripped. The caller is the stable
+source of the model call (`lead_agent`, `middleware:title`, `suggest_agent`,
+`subagent:*`, etc.). A miss raises loudly rather than passing silently.
+
+**The system prompt is excluded from the match key.** The lead-agent system
+prompt is a living, frequently-edited implementation detail — its wording changes
+across PRs (e.g. #3195 added a "File Editing Workflow" section). Hashing it would
+make every fixture go stale and red-fail unrelated PRs the moment anyone edits the
+prompt. The conversation flow (user input → tool calls → results → answer) is the
+stable contract that identifies a recorded turn. The caller still stays in the
+key so two different model users with identical conversation text do not compete
+for the same replay bucket. (This mirrors how open-design's mock picker keys on
+the user prompt, not the system internals.) Combined with pinning skills +
+extensions empty and disabling memory/summarization
+(`tests/_replay_fixture.py::build_config_yaml`), a fixture replays the same across
+machines, days, prompt edits, and CI. Replaying needs **no API key**.
+
+A swallowed hash-miss keeps the SSE *event shapes* identical (the gateway wraps it
+into a normal assistant error message), so the Layer-1 golden can't catch a miss
+by shape alone — it inspects `replay_provider.replay_misses()` and fails loud
+instead. Layer-2 already fails on a miss (the recorded turns never render).
+
+## Record a new scenario (needs a real key — dev machine only)
+
+Recording drives the **real frontend** so captured inputs match exactly what the
+browser sends; fixtures contain no API key.
+
+```bash
+# 1. drive the real frontend against a real-model gateway, capturing model calls
+OPENAI_API_KEY=... OPENAI_API_BASE=<openai-compatible-endpoint>/v1 \
+  DEERFLOW_RECORD_OUT=/tmp/rec/turns.jsonl RECORD_MODEL=<model> \
+  bash -c 'cd frontend && pnpm exec playwright test -c playwright.record.config.ts'
+
+# 2. stitch the capture into a fixture
+cd backend && uv run python scripts/build_fixture_from_jsonl.py \
+  --jsonl /tmp/rec/turns.jsonl --meta /tmp/rec/turns.jsonl.meta.json \
+  --out tests/fixtures/replay/<scenario>.<mode>.json --model <model>
+
+# 3. regenerate the committed golden
+DEERFLOW_WRITE_GOLDEN=1 PYTHONPATH=. uv run pytest tests/test_replay_golden.py
+```
+
+## Run (no key)
+
+```bash
+cd backend  && PYTHONPATH=. uv run pytest tests/test_replay_golden.py          # Layer 1
+cd frontend && pnpm exec playwright test -c playwright.real-backend.config.ts  # Layer 2
+```
+
+## CI
+
+`.github/workflows/replay-e2e.yml` runs both layers on changes to **either** side
+of the contract (`frontend/**`, `backend/app/gateway/**`,
+`backend/packages/harness/**`, fixtures). DOM assertions are the gate; the rendered
+screenshot + Playwright HTML report are uploaded as a CI artifact.
+
+## Known limitations
+
+- Visual regression baselines are OS-specific, so they are a **local dev gate
+  only** (gitignored); CI uploads the render as an artifact for human review
+  instead of hard-asserting a cross-OS baseline.
+- Fixtures are coupled to the recording-time prompt; if new
+  environment-dependent content enters the system prompt, extend the
+  normalization in `replay_provider.py` (or pin it in `build_config_yaml`).
+- Re-record a scenario if the agent graph changes how many model calls it makes
+  — the replay raises loudly on a hash miss pointing at the divergence.

backend/docs/SANDBOX_MEMORY_PROFILING.md

@@ -0,0 +1,81 @@
+# Sandbox Memory Profiling
+
+This guide records a repeatable baseline before changing the sandbox runtime.
+Issue #3213 reports per-sandbox memory near 1 GiB in Kubernetes. Before adding
+or recommending a new provider, capture the current AIO sandbox baseline and
+compare candidates with the same DeerFlow workload.
+
+## What to Measure
+
+Measure at least these samples:
+
+1. Empty sandbox after it becomes ready.
+2. After a simple bash command.
+3. After a Python task that imports common packages.
+4. After a Node task when Node-based workloads are expected.
+5. After generating files under `/mnt/user-data/outputs`.
+6. After release and warm reuse.
+7. At the target concurrency level, for example 10, 50, or 100 sandboxes.
+
+`kubectl top` reports Kubernetes/container working set memory. Treat it as a
+capacity signal, not exclusive RSS/PSS. Pod-level memory includes every
+container in the Pod and may include cache charged to the cgroup. If a result
+looks surprising, inspect the sandbox processes and cgroup metrics on the node
+before drawing conclusions.
+
+## Capture a Snapshot
+
+Run this from the repository root:
+
+```bash
+python scripts/sandbox_memory_profile.py \
+  --namespace deer-flow \
+  --selector app=deer-flow-sandbox \
+  --sample empty \
+  --include-processes \
+  --format markdown
+```
+
+Use a descriptive `--sample` value for each phase:
+
+```bash
+python scripts/sandbox_memory_profile.py --sample after-bash --format json
+python scripts/sandbox_memory_profile.py --sample after-python --format json
+python scripts/sandbox_memory_profile.py --sample after-artifact --format json
+```
+
+`--include-processes` runs `kubectl exec ... ps` in each sandbox Pod and adds
+the highest-RSS processes to the report. This helps distinguish Pod-level cgroup
+memory from process RSS. The two numbers will not match exactly because cgroup
+memory can include cache and other kernel-accounted memory.
+
+Save the raw JSON when comparing backends so totals, pod names, images,
+requests, limits, and timestamps can be audited later.
+
+## Candidate Runtime Matrix
+
+For AIO, CubeSandbox, OpenSandbox, gVisor, Kata, or another candidate, compare
+the same workload and record:
+
+| Area | Required Evidence |
+| --- | --- |
+| Capacity | Pod or instance count, total memory, average memory, max memory |
+| Startup | Ready latency at 1, 10, 50, and 100 concurrent sandboxes |
+| Commands | Bash output, timeout behavior, failure shape |
+| Files | `read_file`, `write_file`, binary `update_file`, `list_dir`, `glob`, `grep` |
+| Uploads | Files uploaded by the gateway are visible inside the sandbox |
+| Artifacts | Files written to `/mnt/user-data/outputs` are readable by the backend artifact API |
+| Paths | `/mnt/user-data/workspace`, `/mnt/user-data/uploads`, `/mnt/user-data/outputs`, `/mnt/acp-workspace`, and skills paths keep their expected semantics |
+| Isolation | Different users and threads cannot read each other's data |
+| Cleanup | Release, idle timeout, process restart, and orphan cleanup free resources |
+| Operations | Deployment prerequisites, privileged components, networking, storage, and upgrade path |
+
+## PR Guidance
+
+Do not claim that a new provider fixes high-concurrency memory usage until the
+same DeerFlow workload has been measured on both the current AIO sandbox and the
+candidate backend.
+
+For an experimental provider PR, prefer `Related to #3213` unless the PR also
+includes reproducible DeerFlow workload data that demonstrates the target memory
+reduction and preserves uploads, outputs, artifacts, and isolation behavior.

backend/docs/TODO.md

@@ -26,7 +26,7 @@
   - Replace sync `requests` with `httpx.AsyncClient` in community tools (tavily, jina_ai, firecrawl, infoquest, image_search)
   - [x] Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
   - Consider `asyncio.to_thread()` wrapper for remaining blocking file I/O
-  - For production: use `langgraph up` (multi-worker) instead of `langgraph dev` (single-worker)
+  - For production: tune Gateway worker/runtime settings for long-running agent workloads
 
 ## Resolved Issues
 

backend/docs/plan_mode_usage.md

@@ -127,8 +127,8 @@ complex_agent = create_agent_for_task("high")
 ## How It Works
 
 1. When `make_lead_agent(config)` is called, it extracts `is_plan_mode` from `config.configurable`
-2. The config is passed to `_build_middlewares(config)`
+2. The config is passed to `build_middlewares(config)`
-3. `_build_middlewares()` reads `is_plan_mode` and calls `_create_todo_list_middleware(is_plan_mode)`
+3. `build_middlewares()` reads `is_plan_mode` and calls `_create_todo_list_middleware(is_plan_mode)`
 4. If `is_plan_mode=True`, a `TodoListMiddleware` instance is created and added to the middleware chain
 5. The middleware automatically adds a `write_todos` tool to the agent's toolset
 6. The agent can use this tool to manage tasks during execution
@@ -141,7 +141,7 @@ make_lead_agent(config)
   │
   ├─> Extracts: is_plan_mode = config.configurable.get("is_plan_mode", False)
   │
-  └─> _build_middlewares(config)
+  └─> build_middlewares(config)
         │
         ├─> ThreadDataMiddleware
         ├─> SandboxMiddleware
@@ -156,7 +156,7 @@ make_lead_agent(config)
 ### Agent Module
 - **Location**: `packages/harness/deerflow/agents/lead_agent/agent.py`
 - **Function**: `_create_todo_list_middleware(is_plan_mode: bool)` - Creates TodoListMiddleware if plan mode is enabled
-- **Function**: `_build_middlewares(config: RunnableConfig)` - Builds middleware chain based on runtime config
+- **Function**: `build_middlewares(config: RunnableConfig)` - Builds middleware chain based on runtime config
 - **Function**: `make_lead_agent(config: RunnableConfig)` - Creates agent with appropriate middlewares
 
 ### Runtime Configuration

backend/packages/harness/deerflow/agents/lead_agent/agent.py

@@ -18,6 +18,8 @@ middleware, and the async path inside ``TitleMiddleware``. Any new in-graph
 ``create_chat_model`` call must add to this list and pass the flag.
 """
 
+from __future__ import annotations
+
 import logging
 
 from langchain.agents import create_agent
@@ -47,6 +49,8 @@ from deerflow.tracing import build_tracing_callbacks
 
 logger = logging.getLogger(__name__)
 
+_BOOTSTRAP_SKILL_NAMES = {"bootstrap"}
+
 
 def _get_runtime_config(config: RunnableConfig) -> dict:
     """Merge legacy configurable options with LangGraph runtime context."""
@@ -263,20 +267,31 @@ Being proactive with task management demonstrates thoroughness and ensures all r
 # ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
 # ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
 # ClarificationMiddleware should be last to intercept clarification requests after model calls
-def _build_middlewares(
+def build_middlewares(
     config: RunnableConfig,
     model_name: str | None,
     agent_name: str | None = None,
     custom_middlewares: list[AgentMiddleware] | None = None,
     *,
+    available_skills: set[str] | None = None,
     app_config: AppConfig | None = None,
+    deferred_setup=None,
 ):
-    """Build middleware chain based on runtime configuration.
+    """Build the lead-agent middleware chain based on runtime configuration.
+
+    Public entry point for the lead agent's full middleware composition. Used by
+    ``make_lead_agent`` and by the embedded ``DeerFlowClient`` (a lead-agent variant
+    that needs the identical chain). Keep this name stable: it is imported across a
+    module boundary, so renames/signature changes ripple into ``client.py``.
 
     Args:
         config: Runtime configuration containing configurable options like is_plan_mode.
+        model_name: Resolved runtime model name; gates vision-only middleware.
         agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
         custom_middlewares: Optional list of custom middlewares to inject into the chain.
+        app_config: Explicit AppConfig; falls back to ``get_app_config()`` when omitted.
+        deferred_setup: Optional deferred-MCP-tool setup that attaches
+            ``DeferredToolFilterMiddleware`` when ``tool_search`` is enabled.
 
     Returns:
         List of middleware instances.
@@ -290,6 +305,13 @@ def _build_middlewares(
 
     middlewares.append(DynamicContextMiddleware(agent_name=agent_name, app_config=resolved_app_config))
 
+    # Deterministically load a full SKILL.md when the user starts the turn with
+    # /skill-name. This keeps the base system prompt metadata-only while giving
+    # explicit user activation priority over model-side relevance guessing.
+    from deerflow.agents.middlewares.skill_activation_middleware import SkillActivationMiddleware
+
+    middlewares.append(SkillActivationMiddleware(available_skills=available_skills, app_config=resolved_app_config))
+
     # Add summarization middleware if enabled
     summarization_middleware = _create_summarization_middleware(app_config=resolved_app_config)
     if summarization_middleware is not None:
@@ -318,11 +340,13 @@ def _build_middlewares(
     if model_config is not None and model_config.supports_vision:
         middlewares.append(ViewImageMiddleware())
 
-    # Add DeferredToolFilterMiddleware to hide deferred tool schemas from model binding
+    # Hide deferred tool schemas from model binding until tool_search promotes them.
-    if resolved_app_config.tool_search.enabled:
+    # The deferred set + catalog hash come from the build-time setup (assembled
+    # after tool-policy filtering); promotion is read from graph state.
+    if deferred_setup is not None and deferred_setup.deferred_names:
         from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
 
-        middlewares.append(DeferredToolFilterMiddleware())
+        middlewares.append(DeferredToolFilterMiddleware(deferred_setup.deferred_names, deferred_setup.catalog_hash))
 
     # Add SubagentLimitMiddleware to truncate excess parallel task calls
     subagent_enabled = cfg.get("subagent_enabled", False)
@@ -355,7 +379,7 @@ def _build_middlewares(
 
 def _available_skill_names(agent_config, is_bootstrap: bool) -> set[str] | None:
     if is_bootstrap:
-        return {"bootstrap"}
+        return set(_BOOTSTRAP_SKILL_NAMES)
     if agent_config and agent_config.skills is not None:
         return set(agent_config.skills)
     return None
@@ -386,6 +410,7 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
     # Lazy import to avoid circular dependency
     from deerflow.tools import get_available_tools
     from deerflow.tools.builtins import setup_agent, update_agent
+    from deerflow.tools.builtins.tool_search import assemble_deferred_tools
 
     cfg = _get_runtime_config(config)
     resolved_app_config = app_config
@@ -460,16 +485,27 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
 
     if is_bootstrap:
         # Special bootstrap agent with minimal prompt for initial custom agent creation flow
-        tools = get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent]
+        # Keep the bootstrap skill set intentionally narrow so agent creation
+        # remains deterministic before the custom agent's own config exists.
+        raw_tools = get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent]
+        filtered = filter_tools_by_skill_allowed_tools(raw_tools, skills_for_tool_policy)
+        final_tools, setup = assemble_deferred_tools(filtered, enabled=resolved_app_config.tool_search.enabled)
         return create_agent(
             model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=resolved_app_config, attach_tracing=False),
-            tools=filter_tools_by_skill_allowed_tools(tools, skills_for_tool_policy),
+            tools=final_tools,
-            middleware=_build_middlewares(config, model_name=model_name, app_config=resolved_app_config),
+            middleware=build_middlewares(
+                config,
+                model_name=model_name,
+                available_skills=set(_BOOTSTRAP_SKILL_NAMES),
+                app_config=resolved_app_config,
+                deferred_setup=setup,
+            ),
             system_prompt=apply_prompt_template(
                 subagent_enabled=subagent_enabled,
                 max_concurrent_subagents=max_concurrent_subagents,
-                available_skills=set(["bootstrap"]),
+                available_skills=set(_BOOTSTRAP_SKILL_NAMES),
                 app_config=resolved_app_config,
+                deferred_names=setup.deferred_names,
             ),
             state_schema=ThreadState,
         )
@@ -478,17 +514,27 @@ def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
     # The default agent (no agent_name) does not see this tool.
     extra_tools = [update_agent] if agent_name else []
     # Default lead agent (unchanged behavior)
-    tools = get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=resolved_app_config)
+    raw_tools = get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=resolved_app_config)
+    filtered = filter_tools_by_skill_allowed_tools(raw_tools + extra_tools, skills_for_tool_policy)
+    final_tools, setup = assemble_deferred_tools(filtered, enabled=resolved_app_config.tool_search.enabled)
     return create_agent(
         model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=resolved_app_config, attach_tracing=False),
-        tools=filter_tools_by_skill_allowed_tools(tools + extra_tools, skills_for_tool_policy),
+        tools=final_tools,
-        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name, app_config=resolved_app_config),
+        middleware=build_middlewares(
+            config,
+            model_name=model_name,
+            agent_name=agent_name,
+            available_skills=available_skills,
+            app_config=resolved_app_config,
+            deferred_setup=setup,
+        ),
         system_prompt=apply_prompt_template(
             subagent_enabled=subagent_enabled,
             max_concurrent_subagents=max_concurrent_subagents,
             agent_name=agent_name,
-            available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None,
+            available_skills=available_skills,
             app_config=resolved_app_config,
+            deferred_names=setup.deferred_names,
         ),
         state_schema=ThreadState,
     )

backend/packages/harness/deerflow/agents/lead_agent/prompt.py

@@ -10,6 +10,7 @@ from deerflow.config.agents_config import load_agent_soul
 from deerflow.skills.storage import get_or_new_skill_storage
 from deerflow.skills.types import Skill, SkillCategory
 from deerflow.subagents import get_available_subagent_names
+from deerflow.tools.builtins.tool_search import get_deferred_tools_prompt_section
 
 if TYPE_CHECKING:
     from deerflow.config.app_config import AppConfig
@@ -542,6 +543,14 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
 {subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
 - Progressive Loading: Load resources incrementally as referenced in skills
 - Output Files: Final deliverables must be in `/mnt/user-data/outputs`
+- File Editing Workflow: When revising an existing file, prefer
+  `str_replace` over `write_file` — it sends only the diff and avoids
+  re-emitting the whole file (mirrors Claude Code's Edit and Codex's
+  apply_patch). When writing long new content from scratch, split it
+  into sections: the first `write_file` call creates the file, then use
+  `write_file` with append=True to extend it section by section. This
+  keeps each tool call small and avoids mid-stream chunk-gap timeouts
+  on oversized single-shot writes. (See issue #3189.)  
 - Clarity: Be direct and helpful, avoid unnecessary meta-commentary
 - Including Images and Mermaid: Images and Mermaid diagrams are always welcomed in the Markdown format, and you're encouraged to use `![Image Description](image_path)\n\n` or "```mermaid" to display images in response or Markdown files
 - Multi-task: Better utilize parallel tool calling to call multiple tools at one time for better performance
@@ -616,6 +625,11 @@ You have access to skills that provide optimized workflows for specific tasks. E
 4. Load referenced resources only when needed during execution
 5. Follow the skill's instructions precisely
 
+**Explicit Slash Skill Activation:**
+- If the user starts a request with `/<skill-name>`, that skill was explicitly requested for the current turn.
+- Follow the activated skill before choosing a general workflow.
+- The runtime injects the activated skill content for explicit slash activations; do not call `read_file` for that SKILL.md again unless the injected skill references supporting resources you need.
+
 **Skills are located at:** {container_base_path}
 {skill_evolution_section}
 {skills_list}
@@ -678,42 +692,13 @@ SOUL.md or config.yaml — those write into a temporary sandbox/tool workspace a
 Rules:
 - Always pass the FULL replacement text for `soul` (no patch semantics). Start from your current SOUL above and apply the user's edits.
 - Only pass the fields that should change. Omit the others to preserve them.
+- Never pass literal strings like `"null"`, `"none"`, or `"undefined"` for unchanged fields.
 - Pass `skills=[]` to disable all skills, or omit `skills` to keep the existing whitelist.
 - After `update_agent` returns successfully, tell the user the change is persisted and will take effect on the next turn.
 </self_update>
 """
 
 
-def get_deferred_tools_prompt_section(*, app_config: AppConfig | None = None) -> str:
-    """Generate <available-deferred-tools> block for the system prompt.
-
-    Lists only deferred tool names so the agent knows what exists
-    and can use tool_search to load them.
-    Returns empty string when tool_search is disabled or no tools are deferred.
-    """
-    from deerflow.tools.builtins.tool_search import get_deferred_registry
-
-    if app_config is None:
-        try:
-            from deerflow.config import get_app_config
-
-            config = get_app_config()
-        except Exception:
-            return ""
-    else:
-        config = app_config
-
-    if not config.tool_search.enabled:
-        return ""
-
-    registry = get_deferred_registry()
-    if not registry:
-        return ""
-
-    names = "\n".join(e.name for e in registry.entries)
-    return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
-
-
 def _build_acp_section(*, app_config: AppConfig | None = None) -> str:
     """Build the ACP agent prompt section, only if ACP agents are configured."""
     if app_config is None:
@@ -772,6 +757,7 @@ def apply_prompt_template(
     agent_name: str | None = None,
     available_skills: set[str] | None = None,
     app_config: AppConfig | None = None,
+    deferred_names: frozenset[str] = frozenset(),
 ) -> str:
     # Include subagent section only if enabled (from runtime parameter)
     n = max_concurrent_subagents
@@ -799,7 +785,7 @@ def apply_prompt_template(
     skills_section = get_skills_prompt_section(available_skills, app_config=app_config)
 
     # Get deferred tools section (tool_search)
-    deferred_tools_section = get_deferred_tools_prompt_section(app_config=app_config)
+    deferred_tools_section = get_deferred_tools_prompt_section(deferred_names=deferred_names)
 
     # Build ACP agent section only if ACP agents are configured
     acp_section = _build_acp_section(app_config=app_config)

backend/packages/harness/deerflow/agents/memory/prompt.py

@@ -1,9 +1,14 @@
 """Prompt templates for memory update and injection."""
 
+from __future__ import annotations
+
+import logging
 import math
 import re
 from typing import Any
 
+logger = logging.getLogger(__name__)
+
 try:
     import tiktoken
 
@@ -160,6 +165,39 @@ Rules:
 Return ONLY valid JSON."""
 
 
+# Module-level tiktoken encoding cache.  Populated lazily on first use;
+# subsequent calls are a dict lookup (no network I/O).  Pre-warming at
+# startup via :func:`warm_tiktoken_cache` avoids blocking a request on the
+# (potentially slow) first ``get_encoding`` call.
+_tiktoken_encoding_cache: dict[str, tiktoken.Encoding] = {}
+
+
+def _get_tiktoken_encoding(encoding_name: str = "cl100k_base") -> tiktoken.Encoding | None:
+    """Return a cached tiktoken encoding, or ``None`` on failure / unavailability.
+
+    On the very first call for a given *encoding_name*, tiktoken may need to
+    download the BPE data from ``openaipublic.blob.core.windows.net``.  In
+    network-restricted environments (e.g. deployments behind the GFW) this
+    download can block for tens of minutes before the OS TCP timeout kicks in.
+    The caller must therefore be prepared for this to block and should run it
+    off the event loop (e.g. via ``asyncio.to_thread``).
+    """
+    if not TIKTOKEN_AVAILABLE:
+        return None
+
+    cached = _tiktoken_encoding_cache.get(encoding_name)
+    if cached is not None:
+        return cached
+
+    try:
+        encoding = tiktoken.get_encoding(encoding_name)
+        _tiktoken_encoding_cache[encoding_name] = encoding
+        return encoding
+    except Exception:
+        logger.warning("Failed to load tiktoken encoding %r; falling back to char-based estimation", encoding_name, exc_info=True)
+        return None
+
+
 def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
     """Count tokens in text using tiktoken.
 
@@ -170,18 +208,30 @@ def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
     Returns:
         The number of tokens in the text.
     """
-    if not TIKTOKEN_AVAILABLE:
+    encoding = _get_tiktoken_encoding(encoding_name)
+    if encoding is None:
         # Fallback to character-based estimation if tiktoken is not available
+        # or the encoding failed to load.
         return len(text) // 4
 
     try:
-        encoding = tiktoken.get_encoding(encoding_name)
         return len(encoding.encode(text))
     except Exception:
         # Fallback to character-based estimation on error
         return len(text) // 4
 
 
+def warm_tiktoken_cache() -> bool:
+    """Pre-warm the tiktoken encoding cache.
+
+    Call at startup (off the event loop) so the first request never blocks
+    on the BPE download.  Returns ``True`` if the encoding was loaded
+    successfully (or was already cached), ``False`` if tiktoken is
+    unavailable or the download failed.
+    """
+    return _get_tiktoken_encoding("cl100k_base") is not None
+
+
 def _coerce_confidence(value: Any, default: float = 0.0) -> float:
     """Coerce a confidence-like value to a bounded float in [0, 1].
 

backend/packages/harness/deerflow/agents/memory/updater.py

@@ -227,6 +227,110 @@ def _extract_text(content: Any) -> str:
     return str(content)
 
 
+_REQUIRED_MEMORY_UPDATE_TOP_LEVEL_KEYS = frozenset({"user", "history", "newFacts", "factsToRemove"})
+
+
+def _normalize_memory_update_fact(fact: Any) -> dict[str, Any] | None:
+    """Normalize a single fact entry from a model-produced memory update."""
+    if not isinstance(fact, dict):
+        return None
+
+    raw_content = fact.get("content")
+    if not isinstance(raw_content, str):
+        return None
+    content = raw_content.strip()
+    if not content:
+        return None
+
+    raw_category = fact.get("category")
+    category = raw_category.strip() if isinstance(raw_category, str) and raw_category.strip() else "context"
+
+    raw_confidence = fact.get("confidence", 0.5)
+    if isinstance(raw_confidence, bool):
+        return None
+    if isinstance(raw_confidence, str):
+        raw_confidence = raw_confidence.strip()
+        if not raw_confidence:
+            return None
+        try:
+            raw_confidence = float(raw_confidence)
+        except ValueError:
+            return None
+    elif isinstance(raw_confidence, (int, float)):
+        raw_confidence = float(raw_confidence)
+    else:
+        return None
+
+    if not math.isfinite(raw_confidence):
+        return None
+
+    normalized_fact = {
+        "content": content,
+        "category": category,
+        "confidence": raw_confidence,
+    }
+    source_error = fact.get("sourceError")
+    if isinstance(source_error, str):
+        normalized_source_error = source_error.strip()
+        if normalized_source_error:
+            normalized_fact["sourceError"] = normalized_source_error
+
+    return normalized_fact
+
+
+def _normalize_memory_update_data(update_data: dict[str, Any]) -> dict[str, Any]:
+    """Coerce parsed memory update data into the shape consumed by _apply_updates."""
+    user = update_data.get("user")
+    history = update_data.get("history")
+    new_facts = update_data.get("newFacts")
+    facts_to_remove = update_data.get("factsToRemove")
+    normalized_facts_to_remove = [fact_id for fact_id in facts_to_remove if isinstance(fact_id, str)] if isinstance(facts_to_remove, list) else []
+    normalized_new_facts = []
+    dropped_new_fact = not isinstance(new_facts, list)
+    if isinstance(new_facts, list):
+        for fact in new_facts:
+            normalized_fact = _normalize_memory_update_fact(fact)
+            if normalized_fact is not None:
+                normalized_new_facts.append(normalized_fact)
+            else:
+                dropped_new_fact = True
+
+    if normalized_facts_to_remove and dropped_new_fact:
+        raise json.JSONDecodeError(
+            "Unsafe partial memory update: factsToRemove with malformed newFacts",
+            json.dumps(update_data, ensure_ascii=False),
+            0,
+        )
+
+    return {
+        "user": user if isinstance(user, dict) else {},
+        "history": history if isinstance(history, dict) else {},
+        "newFacts": normalized_new_facts,
+        "factsToRemove": normalized_facts_to_remove,
+    }
+
+
+def _parse_memory_update_response(response_content: Any) -> dict[str, Any]:
+    """Parse the first valid memory-update JSON object from an LLM response.
+
+    Some providers may wrap JSON in thinking traces, prose, or markdown fences
+    even when prompted to return JSON only. This parser accepts safely
+    extractable JSON objects but does not repair truncated or malformed JSON.
+    """
+    response_text = _extract_text(response_content).strip()
+    decoder = json.JSONDecoder()
+
+    for match in re.finditer(r"\{", response_text):
+        try:
+            parsed, _end = decoder.raw_decode(response_text[match.start() :])
+        except json.JSONDecodeError:
+            continue
+        if isinstance(parsed, dict) and _REQUIRED_MEMORY_UPDATE_TOP_LEVEL_KEYS.issubset(parsed):
+            return _normalize_memory_update_data(parsed)
+
+    raise json.JSONDecodeError("No valid memory update JSON object found", response_text, 0)
+
+
 # Matches sentences that describe a file-upload *event* rather than general
 # file-related work.  Deliberately narrow to avoid removing legitimate facts
 # such as "User works with CSV files" or "prefers PDF export".
@@ -353,13 +457,7 @@ class MemoryUpdater:
         user_id: str | None = None,
     ) -> bool:
         """Parse the model response, apply updates, and persist memory."""
-        response_text = _extract_text(response_content).strip()
+        update_data = _parse_memory_update_response(response_content)
-
-        if response_text.startswith("```"):
-            lines = response_text.split("\n")
-            response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
-
-        update_data = json.loads(response_text)
         # Deep-copy before in-place mutation so a subsequent save() failure
         # cannot corrupt the still-cached original object reference.
         updated_memory = self._apply_updates(copy.deepcopy(current_memory), update_data, thread_id)

backend/packages/harness/deerflow/agents/middlewares/dangling_tool_call_middleware.py

@@ -26,6 +26,11 @@ from langchain_core.messages import ToolMessage
 
 logger = logging.getLogger(__name__)
 
+# Workaround for issue #2894: malformed write_file calls can carry huge Markdown
+# payloads in invalid tool-call args. Keep recovery error details short so the
+# synthetic ToolMessage does not echo large or malformed content back to the model.
+_MAX_RECOVERY_ERROR_DETAIL_LEN = 500
+
 
 class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
     """Inserts placeholder ToolMessages for dangling tool calls before model invocation.
@@ -98,9 +103,25 @@ class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
     @staticmethod
     def _synthetic_tool_message_content(tool_call: dict) -> str:
         if tool_call.get("invalid"):
+            name = tool_call.get("name")
             error = tool_call.get("error")
-            if isinstance(error, str) and error:
+            error_text = error[:_MAX_RECOVERY_ERROR_DETAIL_LEN] if isinstance(error, str) and error else ""
-                return f"[Tool call could not be executed because its arguments were invalid: {error}]"
+            # Workaround for issue #2894: malformed write_file calls can carry huge Markdown
+            # payloads in invalid tool-call args. Keep recovery guidance actionable without
+            # echoing large or malformed content back to the model.
+            if name == "write_file":
+                details = f" Parser error: {error_text}" if error_text else ""
+                return (
+                    "[write_file failed before execution: the tool-call arguments were not valid JSON, "
+                    "so no file was written. This often happens when the model tries to write a very "
+                    "large Markdown file in a single tool call, especially when `content` contains "
+                    "unescaped quotes, inline JSON, backslashes, or code fences. Do not retry the same "
+                    "large `write_file` payload for this artifact; provide the report/content directly "
+                    "as normal assistant text in your next response. If a file write is still needed "
+                    f"later, split the file into smaller sections instead of one large payload.{details}]"
+                )
+            if error_text:
+                return f"[Tool call could not be executed because its arguments were invalid: {error_text}]"
             return "[Tool call could not be executed because its arguments were invalid.]"
         return "[Tool call was interrupted and did not return a result.]"
 

backend/packages/harness/deerflow/agents/middlewares/deferred_tool_filter_middleware.py

@@ -1,12 +1,15 @@
 """Middleware to filter deferred tool schemas from model binding.
 
-When tool_search is enabled, MCP tools are registered in the DeferredToolRegistry
+When tool_search is enabled, MCP tools are still passed to ToolNode for
-and passed to ToolNode for execution, but their schemas should NOT be sent to the
+execution, but their schemas must NOT be sent to the LLM via bind_tools until
-LLM via bind_tools (that's the whole point of deferral — saving context tokens).
+the model has discovered them via tool_search. This middleware removes the
+still-deferred tools from request.tools before model binding, and blocks tool
+calls to tools that have not been promoted yet.
 
-This middleware intercepts wrap_model_call and removes deferred tools from
+The deferred name set and the catalog hash are injected at construction time
-request.tools so that model.bind_tools only receives active tool schemas.
+(no ContextVar). Promotion state is read from graph state (``state["promoted"]``),
-The agent discovers deferred tools at runtime via the tool_search tool.
+scoped by catalog hash so a stale persisted promotion cannot expose a renamed
+or drifted tool.
 """
 
 import logging
@@ -24,47 +27,49 @@ logger = logging.getLogger(__name__)
 
 
 class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
-    """Remove deferred tools from request.tools before model binding.
+    """Hide deferred tool schemas from the bound model until promoted.
 
     ToolNode still holds all tools (including deferred) for execution routing,
-    but the LLM only sees active tool schemas — deferred tools are discoverable
+    but the LLM only sees active tool schemas plus tools that have already been
-    via tool_search at runtime.
+    promoted (recorded in ``state["promoted"]`` under the current catalog hash).
     """
 
+    def __init__(self, deferred_names: frozenset[str], catalog_hash: str | None):
+        super().__init__()
+        self._deferred = deferred_names
+        self._catalog_hash = catalog_hash
+
+    def _promoted(self, state) -> set[str]:
+        promoted = (state or {}).get("promoted")
+        if promoted and promoted.get("catalog_hash") == self._catalog_hash:
+            return set(promoted.get("names") or [])
+        return set()
+
+    def _hidden(self, state) -> set[str]:
+        return set(self._deferred) - self._promoted(state)
+
     def _filter_tools(self, request: ModelRequest) -> ModelRequest:
-        from deerflow.tools.builtins.tool_search import get_deferred_registry
+        if not self._deferred:
-
-        registry = get_deferred_registry()
-        if not registry:
             return request
-
+        hide = self._hidden(request.state)
-        deferred_names = registry.deferred_names
+        if not hide:
-        active_tools = [t for t in request.tools if getattr(t, "name", None) not in deferred_names]
+            return request
-
+        active = [t for t in request.tools if getattr(t, "name", None) not in hide]
-        if len(active_tools) < len(request.tools):
+        if len(active) < len(request.tools):
-            logger.debug(f"Filtered {len(request.tools) - len(active_tools)} deferred tool schema(s) from model binding")
+            logger.debug("Filtered %d deferred tool schema(s) from model binding", len(request.tools) - len(active))
-
+        return request.override(tools=active)
-        return request.override(tools=active_tools)
 
     def _blocked_tool_message(self, request: ToolCallRequest) -> ToolMessage | None:
-        from deerflow.tools.builtins.tool_search import get_deferred_registry
+        if not self._deferred:
-
-        registry = get_deferred_registry()
-        if not registry:
             return None
-
+        name = str(request.tool_call.get("name") or "")
-        tool_name = str(request.tool_call.get("name") or "")
+        if not name or name not in self._hidden(request.state):
-        if not tool_name:
             return None
-
-        if not registry.contains(tool_name):
-            return None
-
         tool_call_id = str(request.tool_call.get("id") or "missing_tool_call_id")
         return ToolMessage(
-            content=(f"Error: Tool '{tool_name}' is deferred and has not been promoted yet. Call tool_search first to expose and promote this tool's schema, then retry."),
+            content=(f"Error: Tool '{name}' is deferred and has not been promoted yet. Call tool_search first to expose and promote this tool's schema, then retry."),
             tool_call_id=tool_call_id,
-            name=tool_name,
+            name=name,
             status="error",
         )
 

backend/packages/harness/deerflow/agents/middlewares/dynamic_context_middleware.py

@@ -28,6 +28,7 @@ Date-update format:
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import re
 import uuid
@@ -43,6 +44,12 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
+# Upper bound (seconds) for a single _inject() offload.  If the warm-up at
+# gateway startup failed silently, the first request may still hit a cold
+# tiktoken BPE download that blocks until the OS TCP timeout (~26 min).
+# This cap ensures the request degrades gracefully instead of hanging.
+_INJECT_TIMEOUT_SECONDS = 5.0
+
 _DATE_RE = re.compile(r"<current_date>([^<]+)</current_date>")
 _DYNAMIC_CONTEXT_REMINDER_KEY = "dynamic_context_reminder"
 _SUMMARY_MESSAGE_NAME = "summary"
@@ -201,4 +208,25 @@ class DynamicContextMiddleware(AgentMiddleware):
 
     @override
     async def abefore_agent(self, state, runtime: Runtime) -> dict | None:
-        return self._inject(state)
+        # _inject() performs synchronous file I/O (memory JSON loading) and
+        # potentially blocking network calls (tiktoken encoding download on
+        # first use).  Offload to a thread so the event loop is never blocked
+        # — a blocking call here starves all concurrent HTTP handlers (auth,
+        # SSE heartbeats, etc.).  See issue #3402.
+        #
+        # Bounded timeout: if startup warm-up failed silently (e.g. network
+        # blip during deploy), the first request's cold tiktoken download can
+        # block for tens of minutes (OS TCP timeout).  Time-box injection so
+        # the request degrades gracefully (no memory context) rather than
+        # hanging.
+        try:
+            return await asyncio.wait_for(
+                asyncio.to_thread(self._inject, state),
+                timeout=_INJECT_TIMEOUT_SECONDS,
+            )
+        except TimeoutError:
+            logger.warning(
+                "DynamicContextMiddleware: injection timed out (%.1fs); skipping memory/date injection for this turn",
+                _INJECT_TIMEOUT_SECONDS,
+            )
+            return None

backend/packages/harness/deerflow/agents/middlewares/llm_error_handling_middleware.py

@@ -62,6 +62,41 @@ _AUTH_PATTERNS = (
     "未授权",
 )
 
+# Per-exception retry budget overrides.
+#
+# Some transient errors are retriable in principle but expensive to retry at
+# the default budget. StreamChunkTimeoutError in particular fires after the
+# upstream provider has already stalled for `stream_chunk_timeout` seconds
+# (typically 120-240s); a full 3-attempt loop can therefore stack 6-12 minutes
+# of dead air before surfacing the failure to the user. We keep exactly one
+# retry (cheap reconnect that catches genuine transient TCP blips) and then
+# fail fast — the same buffered payload is overwhelmingly likely to fail
+# again at the upstream provider for the same reason.
+#
+# Keys are exception class *names* (not classes) so we don't introduce
+# import-time coupling on optional dependencies like langchain-openai. The
+# value is the absolute max attempt count, NOT additional retries — so a
+# value of 2 means "1 first attempt + 1 retry" (the CR-requested
+# "keep one retry" behavior).
+_RETRY_BUDGET_OVERRIDES: dict[str, int] = {
+    "StreamChunkTimeoutError": 2,
+}
+
+# Exception class names that indicate the upstream stream-chunk watchdog
+# fired because the model stalled mid-flight. These deserve a more specific
+# user-facing message than the generic "temporarily unavailable" copy,
+# because the typical root cause is a long tool-call serialization stalling
+# the upstream stream — and the most actionable advice we can give the user
+# is "ask for a shorter / split output" rather than "wait and retry".
+# Generic connection drops (httpx RemoteProtocolError / ReadError) are
+# intentionally excluded: they routinely fire on transient network blips
+# with normal payloads, where the "split the work" guidance is misleading.
+_STREAM_DROP_EXCEPTIONS: frozenset[str] = frozenset(
+    {
+        "StreamChunkTimeoutError",
+    }
+)
+
 
 class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
     """Retry transient LLM errors and surface graceful assistant messages."""
@@ -83,6 +118,18 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         self._circuit_state = "closed"
         self._circuit_probe_in_flight = False
 
+    def _max_attempts_for(self, exc: BaseException) -> int:
+        """Return the effective max attempt count for this exception.
+
+        Falls back to `self.retry_max_attempts` unless the exception class name
+        appears in the per-exception override table.
+        """
+        override = _RETRY_BUDGET_OVERRIDES.get(type(exc).__name__)
+        if override is None:
+            return self.retry_max_attempts
+
+        return min(override, self.retry_max_attempts)
+
     def _check_circuit(self) -> bool:
         """Returns True if circuit is OPEN (fast fail), False otherwise."""
         with self._circuit_lock:
@@ -153,6 +200,7 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
             "InternalServerError",
             "ReadError",  # httpx.ReadError: connection dropped mid-stream
             "RemoteProtocolError",  # httpx: server closed connection unexpectedly
+            "StreamChunkTimeoutError",  # langchain-openai: chunk gap exceeded stream_chunk_timeout
         }:
             return True, "transient"
         if status_code in _RETRIABLE_STATUS_CODES:
@@ -177,6 +225,24 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
     def _build_circuit_breaker_message(self) -> str:
         return "The configured LLM provider is currently unavailable due to continuous failures. Circuit breaker is engaged to protect the system. Please wait a moment before trying again."
 
+    def _build_error_fallback_message(
+        self,
+        content: str,
+        *,
+        error_type: str,
+        reason: str,
+        detail: str,
+    ) -> AIMessage:
+        return AIMessage(
+            content=content,
+            additional_kwargs={
+                "deerflow_error_fallback": True,
+                "error_type": error_type,
+                "error_reason": reason,
+                "error_detail": detail,
+            },
+        )
+
     def _build_user_message(self, exc: BaseException, reason: str) -> str:
         detail = _extract_error_detail(exc)
         if reason == "quota":
@@ -184,9 +250,31 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         if reason == "auth":
             return "The configured LLM provider rejected the request because authentication or access is invalid. Please check the provider credentials and try again."
         if reason in {"busy", "transient"}:
+            # Stream-drop failures (chunk-gap timeout, peer-closed connection,
+            # raw read error) almost always point at a single oversized
+            # tool-call payload — the model spent so long serializing JSON
+            # arguments that the upstream provider buffered and the stream
+            # gap exceeded `stream_chunk_timeout`. Surfacing this distinct
+            # cause lets the user split or shorten their next request
+            # instead of helplessly retrying the same prompt.
+            if type(exc).__name__ in _STREAM_DROP_EXCEPTIONS:
+                return (
+                    "The model's streaming response was interrupted before it could "
+                    "finish. This usually happens when a single response or tool call "
+                    "is very large — please ask the assistant to split the work into "
+                    "smaller steps, or shorten the requested output, and try again."
+                )
             return "The configured LLM provider is temporarily unavailable after multiple retries. Please wait a moment and continue the conversation."
         return f"LLM request failed: {detail}"
 
+    def _build_user_fallback_message(self, exc: BaseException, reason: str) -> AIMessage:
+        return self._build_error_fallback_message(
+            self._build_user_message(exc, reason),
+            error_type=type(exc).__name__,
+            reason=reason,
+            detail=_extract_error_detail(exc),
+        )
+
     def _emit_retry_event(self, attempt: int, wait_ms: int, reason: str) -> None:
         try:
             from langgraph.config import get_stream_writer
@@ -212,7 +300,12 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         handler: Callable[[ModelRequest], ModelResponse],
     ) -> ModelCallResult:
         if self._check_circuit():
-            return AIMessage(content=self._build_circuit_breaker_message())
+            return self._build_error_fallback_message(
+                self._build_circuit_breaker_message(),
+                error_type="CircuitBreakerOpen",
+                reason="circuit_open",
+                detail="LLM circuit breaker is open",
+            )
 
         attempt = 1
         while True:
@@ -228,7 +321,8 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                 raise
             except Exception as exc:
                 retriable, reason = self._classify_error(exc)
-                if retriable and attempt < self.retry_max_attempts:
+                max_attempts = self._max_attempts_for(exc)
+                if retriable and attempt < max_attempts:
                     wait_ms = self._build_retry_delay_ms(attempt, exc)
                     logger.warning(
                         "Transient LLM error on attempt %d/%d; retrying in %dms: %s",
@@ -249,7 +343,7 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                 )
                 if retriable:
                     self._record_failure()
-                return AIMessage(content=self._build_user_message(exc, reason))
+                return self._build_user_fallback_message(exc, reason)
 
     @override
     async def awrap_model_call(
@@ -258,7 +352,12 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
     ) -> ModelCallResult:
         if self._check_circuit():
-            return AIMessage(content=self._build_circuit_breaker_message())
+            return self._build_error_fallback_message(
+                self._build_circuit_breaker_message(),
+                error_type="CircuitBreakerOpen",
+                reason="circuit_open",
+                detail="LLM circuit breaker is open",
+            )
 
         attempt = 1
         while True:
@@ -274,7 +373,8 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                 raise
             except Exception as exc:
                 retriable, reason = self._classify_error(exc)
-                if retriable and attempt < self.retry_max_attempts:
+                max_attempts = self._max_attempts_for(exc)
+                if retriable and attempt < max_attempts:
                     wait_ms = self._build_retry_delay_ms(attempt, exc)
                     logger.warning(
                         "Transient LLM error on attempt %d/%d; retrying in %dms: %s",
@@ -295,7 +395,7 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
                 )
                 if retriable:
                     self._record_failure()
-                return AIMessage(content=self._build_user_message(exc, reason))
+                return self._build_user_fallback_message(exc, reason)
 
 
 def _matches_any(detail: str, patterns: tuple[str, ...]) -> bool:

backend/packages/harness/deerflow/agents/middlewares/skill_activation_middleware.py

@@ -0,0 +1,289 @@
+"""Middleware for explicit slash skill activation."""
+
+from __future__ import annotations
+
+import asyncio
+import hashlib
+import html
+import logging
+import uuid
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, override
+
+from langchain.agents.middleware import AgentMiddleware
+from langchain.agents.middleware.types import ModelRequest, ModelResponse
+from langchain_core.messages import AIMessage, HumanMessage
+
+from deerflow.skills.slash import parse_slash_skill_reference, resolve_slash_skill
+from deerflow.skills.storage import get_or_new_skill_storage
+from deerflow.skills.storage.skill_storage import SkillStorage
+from deerflow.skills.types import SKILL_MD_FILE
+from deerflow.utils.messages import get_original_user_content_text
+
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
+logger = logging.getLogger(__name__)
+
+_SLASH_SKILL_ACTIVATION_KEY = "slash_skill_activation"
+_SLASH_SKILL_ACTIVATION_TARGET_ID_KEY = "slash_skill_activation_target_id"
+_SUMMARY_MESSAGE_NAME = "summary"
+
+
+@dataclass(frozen=True, slots=True)
+class _Activation:
+    skill_name: str
+    category: str
+    container_file_path: str
+    skill_content: str
+    content_hash: str
+    remaining_text: str
+
+
+@dataclass(frozen=True, slots=True)
+class _ActivationResolution:
+    activation: _Activation | None = None
+    failure_message: str | None = None
+
+
+def is_slash_skill_activation_reminder(message: object) -> bool:
+    """Return whether a message is hidden slash-skill activation context."""
+    return isinstance(message, HumanMessage) and bool(message.additional_kwargs.get(_SLASH_SKILL_ACTIVATION_KEY))
+
+
+def _is_user_activation_target(message: object) -> bool:
+    if not isinstance(message, HumanMessage):
+        return False
+    if message.name == _SUMMARY_MESSAGE_NAME:
+        return False
+    if message.additional_kwargs.get("hide_from_ui"):
+        return False
+    return True
+
+
+class SkillActivationMiddleware(AgentMiddleware):
+    """Inject full SKILL.md content when the user explicitly types /skill-name."""
+
+    def __init__(
+        self,
+        *,
+        available_skills: set[str] | None = None,
+        app_config: AppConfig | None = None,
+    ) -> None:
+        super().__init__()
+        self._available_skills = set(available_skills) if available_skills is not None else None
+        self._app_config = app_config
+
+    def _storage(self) -> SkillStorage:
+        if self._app_config is not None:
+            return get_or_new_skill_storage(app_config=self._app_config)
+        return get_or_new_skill_storage()
+
+    @staticmethod
+    def _read_skill_content(skill_file: Path, skills_root: Path) -> str:
+        if skill_file.name != SKILL_MD_FILE:
+            raise ValueError(f"Expected {SKILL_MD_FILE}, got {skill_file.name}")
+        resolved_root = skills_root.resolve()
+        resolved_file = skill_file.resolve()
+        try:
+            resolved_file.relative_to(resolved_root)
+        except ValueError as exc:
+            raise ValueError("Resolved skill file must stay within the configured skills root.") from exc
+        if not resolved_file.is_file():
+            raise FileNotFoundError(resolved_file)
+        return resolved_file.read_text(encoding="utf-8")
+
+    def _resolve_activation(self, text: str) -> _ActivationResolution | None:
+        reference = parse_slash_skill_reference(text)
+        if reference is None:
+            return None
+
+        storage = self._storage()
+        skills = storage.load_skills(enabled_only=False)
+        skill = next((candidate for candidate in skills if candidate.name == reference.name), None)
+        if skill is None:
+            return _ActivationResolution(failure_message=f"Skill `/{reference.name}` is not installed.")
+        if not skill.enabled:
+            return _ActivationResolution(failure_message=f"Skill `/{reference.name}` is installed but disabled. Enable it before using slash activation.")
+        if self._available_skills is not None and reference.name not in self._available_skills:
+            return _ActivationResolution(failure_message=f"Skill `/{reference.name}` is not available for this agent.")
+
+        resolved = resolve_slash_skill(
+            text,
+            skills,
+            available_skills=self._available_skills,
+            container_base_path=storage.get_container_root(),
+        )
+        if resolved is None:
+            return _ActivationResolution(failure_message=f"Skill `/{reference.name}` could not be resolved.")
+
+        try:
+            skill_content = self._read_skill_content(resolved.skill.skill_file, storage.get_skills_root_path())
+        except (OSError, ValueError):
+            logger.exception("Failed to read slash-activated skill %s", resolved.skill.name)
+            return _ActivationResolution(failure_message=f"Skill `/{reference.name}` could not be loaded safely. Please check the skill installation.")
+
+        content_hash = hashlib.sha256(skill_content.encode("utf-8")).hexdigest()
+        return _ActivationResolution(
+            activation=_Activation(
+                skill_name=resolved.skill.name,
+                category=str(resolved.skill.category),
+                container_file_path=resolved.container_file_path,
+                skill_content=skill_content,
+                content_hash=content_hash,
+                remaining_text=resolved.remaining_text,
+            )
+        )
+
+    @staticmethod
+    def _build_activation_reminder(activation: _Activation) -> str:
+        user_request = activation.remaining_text or ("No additional task text was provided after the slash skill command. Ask the user what they want to do with this skill if the next step is unclear.")
+        escaped_user_request = html.escape(user_request, quote=False)
+        escaped_skill_content = html.escape(activation.skill_content, quote=False)
+        escaped_skill_name = html.escape(activation.skill_name, quote=True)
+        escaped_category = html.escape(activation.category, quote=True)
+        escaped_path = html.escape(activation.container_file_path, quote=True)
+        escaped_content_hash = html.escape(activation.content_hash, quote=True)
+        return f"""<slash_skill_activation>
+The user explicitly activated the `{activation.skill_name}` skill for this turn.
+Treat the task text as:
+<user_request>
+{escaped_user_request}
+</user_request>
+
+Follow this skill before choosing a general workflow. Load supporting resources from the same skill directory only when needed.
+
+<skill name="{escaped_skill_name}" category="{escaped_category}" path="{escaped_path}" sha256="{escaped_content_hash}">
+<skill_content encoding="xml-escaped">
+{escaped_skill_content}
+</skill_content>
+</skill>
+</slash_skill_activation>"""
+
+    @staticmethod
+    def _has_existing_activation_for_target(messages: list, target_index: int, target: HumanMessage) -> bool:
+        if target_index <= 0:
+            return False
+
+        if target.id:
+            for previous in messages[:target_index]:
+                if not is_slash_skill_activation_reminder(previous):
+                    continue
+                target_id = previous.additional_kwargs.get(_SLASH_SKILL_ACTIVATION_TARGET_ID_KEY)
+                if target_id == target.id or previous.id == f"{target.id}__slash_activation":
+                    return True
+
+        previous = messages[target_index - 1]
+        return is_slash_skill_activation_reminder(previous)
+
+    def _find_activation_target(self, messages: list) -> tuple[int, HumanMessage, _ActivationResolution] | None:
+        if not messages:
+            return None
+
+        target_index = next((idx for idx in range(len(messages) - 1, -1, -1) if _is_user_activation_target(messages[idx])), None)
+        if target_index is None:
+            return None
+
+        target = messages[target_index]
+        if target is None:
+            return None
+        if self._has_existing_activation_for_target(messages, target_index, target):
+            return None
+
+        content = get_original_user_content_text(target.content, target.additional_kwargs)
+        resolution = self._resolve_activation(content)
+        if resolution is None:
+            return None
+        return target_index, target, resolution
+
+    @staticmethod
+    def _record_activation(request: ModelRequest, activation: _Activation, *, hook: str) -> None:
+        runtime = getattr(request, "runtime", None)
+        context = getattr(runtime, "context", None)
+        journal = context.get("__run_journal") if isinstance(context, dict) else None
+        if journal is None:
+            return
+        try:
+            journal.record_middleware(
+                "skill_activation",
+                name="SkillActivationMiddleware",
+                hook=hook,
+                action="activate",
+                changes={
+                    "skill_name": activation.skill_name,
+                    "category": activation.category,
+                    "path": activation.container_file_path,
+                    "content_hash": activation.content_hash,
+                },
+            )
+        except Exception:
+            logger.debug("Failed to record slash skill activation audit event", exc_info=True)
+
+    def _prepare_model_request(self, request: ModelRequest, *, hook: str) -> ModelRequest | AIMessage | None:
+        target_and_resolution = self._find_activation_target(list(request.messages))
+        if target_and_resolution is None:
+            return None
+
+        target_index, target, resolution = target_and_resolution
+        if resolution.failure_message:
+            return AIMessage(content=resolution.failure_message)
+
+        activation = resolution.activation
+        if activation is None:
+            return None
+
+        logger.info(
+            "SkillActivationMiddleware: activating slash skill %s category=%s path=%s hash=%s",
+            activation.skill_name,
+            activation.category,
+            activation.container_file_path,
+            activation.content_hash,
+        )
+        self._record_activation(request, activation, hook=hook)
+        activation_msg = self._make_activation_message(target, self._build_activation_reminder(activation))
+        messages = list(request.messages)
+        messages.insert(target_index, activation_msg)
+        return request.override(messages=messages)
+
+    @staticmethod
+    def _make_activation_message(target: HumanMessage, activation_content: str) -> HumanMessage:
+        stable_id = target.id or str(uuid.uuid4())
+        additional_kwargs = {
+            "hide_from_ui": True,
+            _SLASH_SKILL_ACTIVATION_KEY: True,
+        }
+        if target.id:
+            additional_kwargs[_SLASH_SKILL_ACTIVATION_TARGET_ID_KEY] = target.id
+        return HumanMessage(
+            content=activation_content,
+            id=f"{stable_id}__slash_activation",
+            additional_kwargs=additional_kwargs,
+        )
+
+    @override
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelResponse | AIMessage:
+        prepared = self._prepare_model_request(request, hook="wrap_model_call")
+        if prepared is None:
+            return handler(request)
+        if isinstance(prepared, AIMessage):
+            return prepared
+        return handler(prepared)
+
+    @override
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelResponse | AIMessage:
+        prepared = await asyncio.to_thread(self._prepare_model_request, request, hook="awrap_model_call")
+        if prepared is None:
+            return await handler(request)
+        if isinstance(prepared, AIMessage):
+            return prepared
+        return await handler(prepared)

backend/packages/harness/deerflow/agents/middlewares/summarization_middleware.py

@@ -9,8 +9,9 @@ from typing import Any, Protocol, override, runtime_checkable
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import SummarizationMiddleware
-from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage, ToolMessage
+from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage, ToolMessage, get_buffer_string
 from langgraph.config import get_config
+from langgraph.constants import TAG_NOSTREAM
 from langgraph.graph.message import REMOVE_ALL_MESSAGES
 from langgraph.runtime import Runtime
 
@@ -116,6 +117,74 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
         self._preserve_recent_skill_count = max(0, preserve_recent_skill_count)
         self._preserve_recent_skill_tokens = max(0, preserve_recent_skill_tokens)
         self._preserve_recent_skill_tokens_per_skill = max(0, preserve_recent_skill_tokens_per_skill)
+        # The summary LLM call runs inside a LangGraph middleware hook, so its token
+        # stream would otherwise be captured by the messages-tuple stream callback and
+        # broadcast to the frontend as a phantom AI message. Tag a dedicated model copy
+        # with TAG_NOSTREAM so the streaming handler skips it.
+        # Keep self.model untagged so the parent's profile / ls_params inspection still works.
+        #
+        # Preserve any tags already bound on the model (e.g. "middleware:summarize" set in
+        # lead_agent/agent.py for RunJournal attribution): RunnableBinding.with_config does a
+        # shallow merge that would otherwise overwrite the existing tags list entirely.
+        existing_tags = list((getattr(self.model, "config", None) or {}).get("tags") or [])
+        merged_tags = [*existing_tags, TAG_NOSTREAM] if TAG_NOSTREAM not in existing_tags else existing_tags
+        self._summary_model = self.model.with_config(tags=merged_tags)
+
+    @override
+    def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        return self._summarize_with(messages_to_summarize)
+
+    @override
+    async def _acreate_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        return await self._asummarize_with(messages_to_summarize)
+
+    def _summarize_with(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Mirror the parent ``_create_summary`` but invoke the nostream-tagged model.
+
+        We do not swap ``self.model`` at the instance level: the agent/middleware is
+        cached and reused across concurrent runs, so a temporary swap would leak the
+        ``RunnableBinding`` to other coroutines during ``await`` and break parent logic
+        that inspects the raw model (``profile`` / ``_get_ls_params``).
+        """
+        if not messages_to_summarize:
+            return "No previous conversation history."
+        prompt = self._build_summary_prompt(messages_to_summarize)
+        if prompt is None:
+            return "Previous conversation was too long to summarize."
+        try:
+            response = self._summary_model.invoke(
+                prompt,
+                config={"metadata": {"lc_source": "summarization"}},
+            )
+            return response.text.strip()
+        except Exception as e:
+            return f"Error generating summary: {e!s}"
+
+    async def _asummarize_with(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Async counterpart of :meth:`_summarize_with` using the nostream model."""
+        if not messages_to_summarize:
+            return "No previous conversation history."
+        prompt = self._build_summary_prompt(messages_to_summarize)
+        if prompt is None:
+            return "Previous conversation was too long to summarize."
+        try:
+            response = await self._summary_model.ainvoke(
+                prompt,
+                config={"metadata": {"lc_source": "summarization"}},
+            )
+            return response.text.strip()
+        except Exception as e:
+            return f"Error generating summary: {e!s}"
+
+    def _build_summary_prompt(self, messages_to_summarize: list[AnyMessage]) -> str | None:
+        """Build the summary prompt, returning ``None`` when trimming leaves nothing."""
+        trimmed_messages = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed_messages:
+            return None
+        # Format messages to avoid token inflation from metadata when str() is called on
+        # message objects.
+        formatted_messages = get_buffer_string(trimmed_messages)
+        return self.summary_prompt.format(messages=formatted_messages).rstrip()
 
     def before_model(self, state: AgentState, runtime: Runtime) -> dict | None:
         return self._maybe_summarize(state, runtime)

backend/packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py

@@ -2,7 +2,7 @@
 
 import logging
 from collections.abc import Awaitable, Callable
-from typing import override
+from typing import TYPE_CHECKING, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
@@ -12,10 +12,48 @@ from langgraph.prebuilt.tool_node import ToolCallRequest
 from langgraph.types import Command
 
 from deerflow.config.app_config import AppConfig
+from deerflow.subagents.status_contract import (
+    extract_subagent_status,
+    make_subagent_additional_kwargs,
+)
+
+if TYPE_CHECKING:
+    from deerflow.tools.builtins.tool_search import DeferredToolSetup
 
 logger = logging.getLogger(__name__)
 
 _MISSING_TOOL_CALL_ID = "missing_tool_call_id"
+_TASK_TOOL_NAME = "task"
+
+
+def _stamp_task_subagent_status(message: ToolMessage, *, tool_name: str, error: str | None = None) -> ToolMessage:
+    """Centralised stamping of ``additional_kwargs.subagent_status``.
+
+    Bytedance/deer-flow issue #3146: the frontend now reads the subagent
+    status from a structured field instead of parsing the leading text of
+    the task tool's return string. That contract is enforced here, in the
+    one place every task tool result flows through, rather than at the 5
+    normal-return + 3 ``Error:`` pre-execution branches inside
+    ``task_tool.py``. Centralisation prevents the "added a new return
+    path, forgot the stamp" drift mode.
+
+    For non-``task`` tools this is a no-op so other tools' additional_kwargs
+    conventions are untouched.
+    """
+    if tool_name != _TASK_TOOL_NAME:
+        return message
+    content = message.content if isinstance(message.content, str) else ""
+    status = extract_subagent_status(content)
+    if status is None:
+        # Non-terminal streaming chunks or unrecognised shapes leave the
+        # field unset so the frontend can keep the card on its in-progress
+        # placeholder until a real terminal frame arrives.
+        return message
+    stamp = make_subagent_additional_kwargs(status, error=error)
+    existing = dict(message.additional_kwargs or {})
+    existing.update(stamp)
+    message.additional_kwargs = existing
+    return message
 
 
 class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
@@ -29,12 +67,31 @@ class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
             detail = detail[:497] + "..."
 
         content = f"Error: Tool '{tool_name}' failed with {exc.__class__.__name__}: {detail}. Continue with available context, or choose an alternative tool."
-        return ToolMessage(
+        message = ToolMessage(
             content=content,
             tool_call_id=tool_call_id,
             name=tool_name,
             status="error",
         )
+        # Stamp the structured subagent status on the wrapper too: the
+        # frontend would otherwise have to fall back to prefix-matching
+        # ``Error: Tool 'task' failed ...`` on the wire. The ``subagent_error``
+        # carries the same ``ExcClass: detail`` shape the wrapper string
+        # uses so debugging artifacts stay aligned.
+        structured_error = f"{exc.__class__.__name__}: {detail}"
+        return _stamp_task_subagent_status(message, tool_name=tool_name, error=structured_error)
+
+    @staticmethod
+    def _maybe_stamp(result: ToolMessage | Command, request: ToolCallRequest) -> ToolMessage | Command:
+        """Apply the subagent stamp to successful task tool returns.
+
+        ``Command`` results bypass the stamp — they encode LangGraph
+        control flow rather than user-facing tool output.
+        """
+        if not isinstance(result, ToolMessage):
+            return result
+        tool_name = str(request.tool_call.get("name") or "")
+        return _stamp_task_subagent_status(result, tool_name=tool_name)
 
     @override
     def wrap_tool_call(
@@ -43,13 +100,14 @@ class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         handler: Callable[[ToolCallRequest], ToolMessage | Command],
     ) -> ToolMessage | Command:
         try:
-            return handler(request)
+            result = handler(request)
         except GraphBubbleUp:
             # Preserve LangGraph control-flow signals (interrupt/pause/resume).
             raise
         except Exception as exc:
             logger.exception("Tool execution failed (sync): name=%s id=%s", request.tool_call.get("name"), request.tool_call.get("id"))
             return self._build_error_message(request, exc)
+        return self._maybe_stamp(result, request)
 
     @override
     async def awrap_tool_call(
@@ -58,13 +116,14 @@ class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
         handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
     ) -> ToolMessage | Command:
         try:
-            return await handler(request)
+            result = await handler(request)
         except GraphBubbleUp:
             # Preserve LangGraph control-flow signals (interrupt/pause/resume).
             raise
         except Exception as exc:
             logger.exception("Tool execution failed (async): name=%s id=%s", request.tool_call.get("name"), request.tool_call.get("id"))
             return self._build_error_message(request, exc)
+        return self._maybe_stamp(result, request)
 
 
 def _build_runtime_middlewares(
@@ -77,9 +136,11 @@ def _build_runtime_middlewares(
     """Build shared base middlewares for agent execution."""
     from deerflow.agents.middlewares.llm_error_handling_middleware import LLMErrorHandlingMiddleware
     from deerflow.agents.middlewares.thread_data_middleware import ThreadDataMiddleware
+    from deerflow.agents.middlewares.tool_output_budget_middleware import ToolOutputBudgetMiddleware
     from deerflow.sandbox.middleware import SandboxMiddleware
 
     middlewares: list[AgentMiddleware] = [
+        ToolOutputBudgetMiddleware.from_app_config(app_config),
         ThreadDataMiddleware(lazy_init=lazy_init),
         SandboxMiddleware(lazy_init=lazy_init),
     ]
@@ -87,7 +148,7 @@ def _build_runtime_middlewares(
     if include_uploads:
         from deerflow.agents.middlewares.uploads_middleware import UploadsMiddleware
 
-        middlewares.insert(1, UploadsMiddleware())
+        middlewares.insert(2, UploadsMiddleware())
 
     if include_dangling_tool_call_patch:
         from deerflow.agents.middlewares.dangling_tool_call_middleware import DanglingToolCallMiddleware
@@ -141,6 +202,7 @@ def build_subagent_runtime_middlewares(
     app_config: AppConfig | None = None,
     model_name: str | None = None,
     lazy_init: bool = True,
+    deferred_setup: "DeferredToolSetup | None" = None,
 ) -> list[AgentMiddleware]:
     """Middlewares shared by subagent runtime before subagent-only middlewares."""
     if app_config is None:
@@ -164,6 +226,16 @@ def build_subagent_runtime_middlewares(
 
         middlewares.append(ViewImageMiddleware())
 
+    # Hide deferred (MCP) tool schemas from the subagent's model binding until
+    # tool_search promotes them. This is the same wiring the lead agent gets. The deferred
+    # set + catalog hash come from the build-time setup (assembled after
+    # tool-policy filtering); promotion is read from graph state. Empty/None
+    # setup (deferral disabled or no MCP tool survived) is a pure no-op.
+    if deferred_setup is not None and deferred_setup.deferred_names:
+        from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
+
+        middlewares.append(DeferredToolFilterMiddleware(deferred_setup.deferred_names, deferred_setup.catalog_hash))
+
     # Same provider safety-termination guard the lead agent uses — subagents
     # are equally exposed to truncated tool_calls returned with
     # finish_reason=content_filter (and friends), and the bad call would then

backend/packages/harness/deerflow/agents/middlewares/tool_output_budget_middleware.py

@@ -0,0 +1,643 @@
+"""Middleware that enforces a per-result budget on tool outputs.
+
+Oversized tool results are persisted to disk and replaced with a compact
+preview containing a file reference.  When disk persistence is
+unavailable the middleware falls back to head+tail truncation so the
+model context is never blown by a single large tool return.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import shlex
+import uuid
+from collections.abc import Awaitable, Callable
+from dataclasses import replace as dc_replace
+from typing import TYPE_CHECKING, Any, override
+
+from langchain.agents import AgentState
+from langchain.agents.middleware import AgentMiddleware
+from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
+from langchain_core.messages import ToolMessage
+from langgraph.prebuilt.tool_node import ToolCallRequest
+from langgraph.types import Command
+
+from deerflow.config.tool_output_config import ToolOutputConfig
+from deerflow.sandbox.sandbox_provider import get_sandbox_provider
+
+if TYPE_CHECKING:
+    from deerflow.sandbox.sandbox import Sandbox
+
+logger = logging.getLogger(__name__)
+
+# Virtual outputs root inside the sandbox. Host-mounted sandboxes map this to
+# the thread outputs dir on the host; for non-mounted (remote) sandboxes the
+# same path is written directly into the sandbox filesystem so the model's
+# ``read_file`` tool can read it back (issue #3416).
+_VIRTUAL_OUTPUTS_BASE = "/mnt/user-data/outputs"
+
+
+def _default_config() -> ToolOutputConfig:
+    return ToolOutputConfig()
+
+
+# ---------------------------------------------------------------------------
+# Text helpers
+# ---------------------------------------------------------------------------
+
+
+def _message_text(content: Any) -> str | None:
+    """Extract a plain-text representation from a ToolMessage content field.
+
+    Returns ``None`` for non-string / multimodal content so the caller
+    can skip budget enforcement (images, structured blocks, etc.).
+    """
+    if isinstance(content, str):
+        return content
+    if content is None:
+        return None
+    if isinstance(content, list):
+        pieces: list[str] = []
+        for part in content:
+            if isinstance(part, str):
+                pieces.append(part)
+            elif isinstance(part, dict) and isinstance(part.get("text"), str):
+                pieces.append(part["text"])
+            else:
+                return None
+        return "\n".join(pieces) if pieces else None
+    return None
+
+
+def _snap_to_line_boundary(text: str, pos: int) -> int:
+    """Return *pos* or the nearest preceding newline+1, whichever is closer.
+
+    Used so that previews and truncations end on a complete line when
+    possible.  If no newline exists in the second half of ``text[:pos]``
+    the original *pos* is returned unchanged.
+    """
+    if pos <= 0 or pos >= len(text):
+        return pos
+    half = pos // 2
+    nl = text.rfind("\n", half, pos)
+    if nl >= 0:
+        return nl + 1
+    return pos
+
+
+# ---------------------------------------------------------------------------
+# Disk persistence
+# ---------------------------------------------------------------------------
+
+_EXT_MAP: dict[str, str] = {
+    "bash": "log",
+    "bash_tool": "log",
+    "web_fetch": "log",
+}
+
+
+def _sanitize_tool_name(name: str) -> str:
+    """Strip path separators and traversal components from a tool name."""
+    base = os.path.basename(name)
+    safe = base.replace("..", "").replace("/", "_").replace("\\", "_")
+    return safe or "unknown"
+
+
+def _build_externalized_filename(*, tool_name: str, tool_call_id: str) -> str:
+    """Build the on-disk filename for an externalized tool output.
+
+    Shared by the host-disk and sandbox externalization paths so both
+    produce the identical naming scheme.
+    """
+    safe_name = _sanitize_tool_name(tool_name)
+    ext = _EXT_MAP.get(tool_name, "txt")
+    short_id = uuid.uuid4().hex[:12]
+    return f"{safe_name}-{short_id}.{ext}"
+
+
+def _externalize(
+    content: str,
+    *,
+    tool_name: str,
+    tool_call_id: str,
+    outputs_path: str,
+    storage_subdir: str,
+) -> str | None:
+    """Write *content* to disk and return the virtual path, or ``None`` on failure."""
+    if os.path.isabs(storage_subdir) or ".." in storage_subdir:
+        return None
+    storage_dir = os.path.join(outputs_path, storage_subdir)
+    try:
+        os.makedirs(storage_dir, exist_ok=True)
+    except OSError:
+        return None
+
+    filename = _build_externalized_filename(tool_name=tool_name, tool_call_id=tool_call_id)
+    filepath = os.path.join(storage_dir, filename)
+
+    if not os.path.abspath(filepath).startswith(os.path.abspath(storage_dir)):
+        return None
+
+    try:
+        with open(filepath, "w", encoding="utf-8") as f:
+            f.write(content)
+    except OSError:
+        return None
+
+    return f"{_VIRTUAL_OUTPUTS_BASE}/{storage_subdir}/{filename}"
+
+
+def _externalize_to_sandbox(
+    content: str,
+    *,
+    tool_name: str,
+    tool_call_id: str,
+    storage_subdir: str,
+    sandbox: Sandbox,
+) -> str | None:
+    """Write *content* into the sandbox filesystem and return the virtual path.
+
+    Used when the sandbox does not use thread-data mounts (e.g. a remote AIO
+    sandbox): the host-side :func:`_externalize` virtual path would not exist
+    inside the sandbox, so the model's ``read_file`` tool could not read it
+    back (issue #3416). Returns the same virtual-path contract on success, or
+    ``None`` to signal the caller to fall back to inline truncation.
+    """
+    if os.path.isabs(storage_subdir) or ".." in storage_subdir:
+        return None
+    filename = _build_externalized_filename(tool_name=tool_name, tool_call_id=tool_call_id)
+    virtual_dir = f"{_VIRTUAL_OUTPUTS_BASE}/{storage_subdir}"
+    virtual_path = f"{virtual_dir}/{filename}"
+    try:
+        # AIO sandbox write_file does NOT create parent directories, so create
+        # them explicitly before writing. execute_command returns its stdout
+        # verbatim (including an "Error: ..." string on failure) rather than
+        # raising, so we cannot rely on exception propagation here.
+        sandbox.execute_command(f"mkdir -p {shlex.quote(virtual_dir)}")
+        sandbox.write_file(virtual_path, content)
+        # Validate the file landed: execute_command may have silently failed
+        # to create the directory, and write_file backends differ. Refuse to
+        # hand the model an unreadable read_file path.
+        check = sandbox.execute_command(f"test -s {shlex.quote(virtual_path)} && echo OK || echo MISSING")
+        if not isinstance(check, str) or check.strip() != "OK":
+            logger.warning(
+                "Sandbox externalize validation failed: path=%s, check=%r",
+                virtual_path,
+                check,
+            )
+            return None
+    except Exception:
+        logger.exception(
+            "Failed to externalize %s output to sandbox (call_id=%s)",
+            tool_name,
+            tool_call_id,
+        )
+        return None
+    return virtual_path
+
+
+# ---------------------------------------------------------------------------
+# Preview / fallback builders
+# ---------------------------------------------------------------------------
+
+
+def _build_preview(
+    content: str,
+    *,
+    tool_name: str,
+    virtual_path: str,
+    head_chars: int,
+    tail_chars: int,
+) -> str:
+    """Build a preview with a file reference for externalized output."""
+    total = len(content)
+    head_end = _snap_to_line_boundary(content, min(head_chars, total))
+    tail_start = max(head_end, total - tail_chars)
+    tail_start_snapped = _snap_to_line_boundary(content, tail_start)
+    if tail_start_snapped > head_end:
+        tail_start = tail_start_snapped
+
+    head = content[:head_end]
+    tail = content[tail_start:] if tail_start < total else ""
+
+    omitted = total - len(head) - len(tail)
+    ref = f"\n\n[Full {tool_name} output saved to {virtual_path} ({total} chars, ~{total // 4} tokens). Use read_file with start_line and end_line to access specific sections. {omitted} chars omitted from this preview.]\n\n"
+
+    parts = [head, ref]
+    if tail:
+        parts.append(tail)
+    return "".join(parts)
+
+
+def _build_fallback(
+    content: str,
+    *,
+    tool_name: str,
+    max_chars: int,
+    head_chars: int,
+    tail_chars: int,
+) -> str:
+    """Build a head+tail truncation when disk persistence is unavailable.
+
+    The returned string is guaranteed to be no longer than *max_chars*.
+    """
+    total = len(content)
+    if max_chars <= 0 or total <= max_chars:
+        return content
+
+    marker_template = "\n\n[... {n} chars omitted from {tn} output. Persistent storage unavailable. Consider narrowing the query or using more specific parameters.]\n\n"
+    marker_overhead = len(marker_template.format(n=total, tn=tool_name))
+
+    if marker_overhead >= max_chars:
+        return content[:max_chars]
+
+    budget = max_chars - marker_overhead
+    effective_head = min(head_chars, budget)
+    effective_tail = min(tail_chars, max(0, budget - effective_head))
+
+    head_end = _snap_to_line_boundary(content, min(effective_head, total))
+    tail_start = max(head_end, total - effective_tail)
+    tail_start_snapped = _snap_to_line_boundary(content, tail_start)
+    if tail_start_snapped > head_end:
+        tail_start = tail_start_snapped
+
+    head = content[:head_end]
+    tail = content[tail_start:] if tail_start < total else ""
+    omitted = total - len(head) - len(tail)
+
+    marker = marker_template.format(n=omitted, tn=tool_name)
+
+    parts = [head, marker]
+    if tail:
+        parts.append(tail)
+    return "".join(parts)
+
+
+# ---------------------------------------------------------------------------
+# Core budget logic
+# ---------------------------------------------------------------------------
+
+
+def _resolve_outputs_path(request: ToolCallRequest) -> str | None:
+    """Best-effort extraction of the thread outputs path."""
+    runtime = getattr(request, "runtime", None)
+    if runtime is None:
+        return None
+    state = getattr(runtime, "state", None)
+    if state is None:
+        return None
+    thread_data = state.get("thread_data")
+    if not isinstance(thread_data, dict):
+        return None
+    outputs_path = thread_data.get("outputs_path")
+    return outputs_path if isinstance(outputs_path, str) else None
+
+
+def _resolve_sandbox(request: ToolCallRequest) -> Sandbox | None:
+    """Resolve the active sandbox for the current tool call, or ``None``.
+
+    Reads the sandbox_id that ``SandboxMiddleware`` (and the sandbox tools
+    themselves) write into ``runtime.state["sandbox"]``. We intentionally do
+    NOT call ``provider.acquire`` here: acquiring a sandbox can trigger
+    blocking remote I/O, and this resolver runs on every tool call. Tools
+    that do not use a sandbox (``web_search``, MCP, ...) will return ``None``
+    here, which is fine -- the caller falls back to inline truncation.
+    """
+    runtime = getattr(request, "runtime", None)
+    state = getattr(runtime, "state", None)
+    if not isinstance(state, dict):
+        return None
+    sandbox_state = state.get("sandbox")
+    if not isinstance(sandbox_state, dict):
+        return None
+    sandbox_id = sandbox_state.get("sandbox_id")
+    if not sandbox_id:
+        return None
+    try:
+        return get_sandbox_provider().get(sandbox_id)
+    except Exception:
+        logger.exception("Failed to look up sandbox %s for tool-output externalization", sandbox_id)
+        return None
+
+
+def _budget_content(
+    content: str,
+    *,
+    tool_name: str,
+    tool_call_id: str,
+    outputs_path: str | None,
+    config: ToolOutputConfig,
+    sandbox: Sandbox | None = None,
+) -> str | None:
+    """Apply budget to *content*. Returns ``None`` if no change needed."""
+    threshold = config.tool_overrides.get(tool_name, config.externalize_min_chars)
+    if threshold <= 0 and config.fallback_max_chars <= 0:
+        return None
+    if len(content) <= threshold and len(content) <= config.fallback_max_chars:
+        return None
+
+    if threshold > 0 and len(content) > threshold:
+        virtual_path: str | None = None
+        # Decide persistence target based on what's available, without touching
+        # the sandbox provider unless a sandbox was actually resolved for this
+        # call. This keeps the legacy host-disk path provider-free, so callers
+        # without a configured sandbox (and CI environments without a
+        # config.yaml) continue to externalize to the host as before.
+        if sandbox is not None:
+            provider = None
+            try:
+                provider = get_sandbox_provider()
+            except Exception:
+                logger.exception("Failed to get sandbox provider for tool-output externalization; falling back to inline truncation")
+            if provider is not None and getattr(provider, "uses_thread_data_mounts", False):
+                # Host-mounted sandbox: host outputs path is bind-mounted into
+                # the sandbox at the same virtual path, so writing host-side is
+                # equivalent. Preserve the original behavior to avoid extra
+                # sandbox round-trips.
+                if outputs_path:
+                    virtual_path = _externalize(
+                        content,
+                        tool_name=tool_name,
+                        tool_call_id=tool_call_id,
+                        outputs_path=outputs_path,
+                        storage_subdir=config.storage_subdir,
+                    )
+            else:
+                virtual_path = _externalize_to_sandbox(
+                    content,
+                    tool_name=tool_name,
+                    tool_call_id=tool_call_id,
+                    storage_subdir=config.storage_subdir,
+                    sandbox=sandbox,
+                )
+        elif outputs_path:
+            # No sandbox in this call (legacy / non-sandbox tools): write to
+            # host outputs path directly, no provider needed.
+            virtual_path = _externalize(
+                content,
+                tool_name=tool_name,
+                tool_call_id=tool_call_id,
+                outputs_path=outputs_path,
+                storage_subdir=config.storage_subdir,
+            )
+        if virtual_path is not None:
+            logger.info(
+                "Externalized %s output (%d chars) to %s",
+                tool_name,
+                len(content),
+                virtual_path,
+            )
+            return _build_preview(
+                content,
+                tool_name=tool_name,
+                virtual_path=virtual_path,
+                head_chars=config.preview_head_chars,
+                tail_chars=config.preview_tail_chars,
+            )
+
+    if config.fallback_max_chars > 0 and len(content) > config.fallback_max_chars:
+        logger.warning(
+            "Fallback-truncating %s output: %d chars → %d max",
+            tool_name,
+            len(content),
+            config.fallback_max_chars,
+        )
+        return _build_fallback(
+            content,
+            tool_name=tool_name,
+            max_chars=config.fallback_max_chars,
+            head_chars=config.fallback_head_chars,
+            tail_chars=config.fallback_tail_chars,
+        )
+
+    return None
+
+
+# ---------------------------------------------------------------------------
+# Result patchers
+# ---------------------------------------------------------------------------
+
+
+def _patch_tool_message(
+    msg: ToolMessage,
+    config: ToolOutputConfig,
+    outputs_path: str | None,
+    sandbox: Sandbox | None = None,
+) -> ToolMessage:
+    """Apply budget to a single ToolMessage. Returns the original if unchanged."""
+    tool_name = msg.name or "unknown"
+    if tool_name in config.exempt_tools:
+        return msg
+
+    text = _message_text(msg.content)
+    if text is None:
+        return msg
+
+    replacement = _budget_content(
+        text,
+        tool_name=tool_name,
+        tool_call_id=msg.tool_call_id or "",
+        outputs_path=outputs_path,
+        config=config,
+        sandbox=sandbox,
+    )
+    if replacement is None:
+        return msg
+
+    update: dict[str, Any] = {"content": replacement}
+    if getattr(msg, "response_metadata", None):
+        update["response_metadata"] = dict(msg.response_metadata)
+    if getattr(msg, "additional_kwargs", None):
+        update["additional_kwargs"] = dict(msg.additional_kwargs)
+    return msg.model_copy(update=update)
+
+
+def _effective_trigger(tool_name: str, config: ToolOutputConfig) -> int:
+    """Smallest content length that could trigger budgeting for *tool_name*.
+
+    Mirrors the trigger conditions in :func:`_budget_content` (per-tool
+    externalize threshold OR global fallback), so the pre-scan never produces
+    a false negative. Returns ``-1`` when nothing could ever trigger.
+    """
+    candidates: list[int] = []
+    externalize = config.tool_overrides.get(tool_name, config.externalize_min_chars)
+    if externalize > 0:
+        candidates.append(externalize)
+    if config.fallback_max_chars > 0:
+        candidates.append(config.fallback_max_chars)
+    return min(candidates) if candidates else -1
+
+
+def _tool_message_over_budget(msg: ToolMessage, config: ToolOutputConfig) -> bool:
+    """Cheap, per-tool-aware check: is this ToolMessage non-exempt and over its trigger?"""
+    if (msg.name or "") in config.exempt_tools:
+        return False
+    trigger = _effective_trigger(msg.name or "", config)
+    if trigger < 0:
+        return False
+    text = _message_text(msg.content)
+    return text is not None and len(text) > trigger
+
+
+def _needs_budget(result: ToolMessage | Command, config: ToolOutputConfig) -> bool:
+    """Fast check whether *result* could need budgeting (avoids thread offload for small outputs)."""
+    if isinstance(result, ToolMessage):
+        return _tool_message_over_budget(result, config)
+    update = getattr(result, "update", None)
+    if isinstance(update, dict):
+        for msg in update.get("messages", []):
+            if isinstance(msg, ToolMessage) and _tool_message_over_budget(msg, config):
+                return True
+    return False
+
+
+def _patch_result(
+    result: ToolMessage | Command,
+    config: ToolOutputConfig,
+    outputs_path: str | None,
+    sandbox: Sandbox | None = None,
+) -> ToolMessage | Command:
+    """Apply budget to a tool call result (ToolMessage or Command)."""
+    if isinstance(result, ToolMessage):
+        return _patch_tool_message(result, config, outputs_path, sandbox)
+
+    update = getattr(result, "update", None)
+    if not isinstance(update, dict):
+        return result
+
+    messages = update.get("messages")
+    if not isinstance(messages, list):
+        return result
+
+    new_messages: list[Any] = []
+    changed = False
+    for msg in messages:
+        if isinstance(msg, ToolMessage):
+            patched = _patch_tool_message(msg, config, outputs_path, sandbox)
+            if patched is not msg:
+                changed = True
+            new_messages.append(patched)
+        else:
+            new_messages.append(msg)
+
+    if not changed:
+        return result
+
+    return dc_replace(result, update={**update, "messages": new_messages})
+
+
+def _patch_model_messages(messages: list[Any], config: ToolOutputConfig) -> list[Any] | None:
+    """Apply budget to historical ToolMessages in a model request. Returns ``None`` if unchanged.
+
+    A cheap pre-scan bails out before allocating a new list when no historical
+    ToolMessage exceeds the budget — the common case once every result has
+    already been budgeted at tool-call time, so a long history is not rebuilt
+    on every model call.
+
+    Historical messages do not get a ``sandbox`` argument: any oversized tool
+    message in history was already budgeted (and possibly externalized) at
+    tool-call time, so the only thing left for the history path to do is
+    inline fallback truncation, which needs no sandbox.
+    """
+    if not any(isinstance(msg, ToolMessage) and _tool_message_over_budget(msg, config) for msg in messages):
+        return None
+
+    updated: list[Any] = []
+    changed = False
+    for msg in messages:
+        if isinstance(msg, ToolMessage):
+            patched = _patch_tool_message(msg, config, outputs_path=None)
+            if patched is not msg:
+                changed = True
+            updated.append(patched)
+        else:
+            updated.append(msg)
+    return updated if changed else None
+
+
+# ---------------------------------------------------------------------------
+# Middleware class
+# ---------------------------------------------------------------------------
+
+
+class ToolOutputBudgetMiddleware(AgentMiddleware[AgentState]):
+    """Enforce per-result budget on tool outputs via externalization or truncation."""
+
+    def __init__(self, config: ToolOutputConfig | None = None) -> None:
+        super().__init__()
+        self._config = config if config is not None else _default_config()
+
+    @classmethod
+    def from_app_config(cls, app_config: Any) -> ToolOutputBudgetMiddleware:
+        tool_output = getattr(app_config, "tool_output", None)
+        if isinstance(tool_output, ToolOutputConfig):
+            return cls(config=tool_output)
+        return cls()
+
+    # -- tool call hooks ---------------------------------------------------
+
+    @override
+    def wrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], ToolMessage | Command],
+    ) -> ToolMessage | Command:
+        result = handler(request)
+        if not self._config.enabled:
+            return result
+        if not _needs_budget(result, self._config):
+            return result
+        outputs_path = _resolve_outputs_path(request)
+        sandbox = _resolve_sandbox(request)
+        return _patch_result(result, self._config, outputs_path, sandbox)
+
+    @override
+    async def awrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
+    ) -> ToolMessage | Command:
+        result = await handler(request)
+        if not self._config.enabled:
+            return result
+        if not _needs_budget(result, self._config):
+            return result
+        outputs_path = _resolve_outputs_path(request)
+        # _resolve_sandbox only touches runtime.state and the provider's
+        # in-memory sandbox registry, so it is safe to call on the event
+        # loop. The actual sandbox I/O (mkdir/write/test) happens inside
+        # _patch_result, which is offloaded to a worker thread below.
+        sandbox = _resolve_sandbox(request)
+        return await asyncio.to_thread(_patch_result, result, self._config, outputs_path, sandbox)
+
+    # -- model call hooks (historical message truncation) ------------------
+
+    @override
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelCallResult:
+        if self._config.enabled:
+            messages = getattr(request, "messages", None)
+            if isinstance(messages, list):
+                patched = _patch_model_messages(messages, self._config)
+                if patched is not None:
+                    request = request.override(messages=patched)
+        return handler(request)
+
+    @override
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelCallResult:
+        if self._config.enabled:
+            messages = getattr(request, "messages", None)
+            if isinstance(messages, list):
+                patched = _patch_model_messages(messages, self._config)
+                if patched is not None:
+                    request = request.override(messages=patched)
+        return await handler(request)

backend/packages/harness/deerflow/agents/middlewares/uploads_middleware.py

@@ -7,11 +7,13 @@ from typing import NotRequired, override
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
 from langchain_core.messages import HumanMessage
+from langchain_core.runnables import run_in_executor
 from langgraph.runtime import Runtime
 
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.utils.file_conversion import extract_outline
+from deerflow.utils.messages import ORIGINAL_USER_CONTENT_KEY, message_content_to_text
 
 logger = logging.getLogger(__name__)
 
@@ -264,6 +266,8 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
 
         # Extract original content - handle both string and list formats
         original_content = last_message.content
+        additional_kwargs = dict(last_message.additional_kwargs or {})
+        additional_kwargs.setdefault(ORIGINAL_USER_CONTENT_KEY, message_content_to_text(original_content))
         if isinstance(original_content, str):
             # Simple case: string content, just prepend files message
             updated_content = f"{files_message}\n\n{original_content}"
@@ -284,7 +288,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
             content=updated_content,
             id=last_message.id,
             name=last_message.name,
-            additional_kwargs=last_message.additional_kwargs,
+            additional_kwargs=additional_kwargs,
         )
 
         messages[last_message_index] = updated_message
@@ -293,3 +297,16 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
             "uploaded_files": new_files,
             "messages": messages,
         }
+
+    @override
+    async def abefore_agent(self, state: UploadsMiddlewareState, runtime: Runtime) -> dict | None:
+        """Async hook that offloads the synchronous uploads scan off the event loop.
+
+        ``before_agent`` performs blocking filesystem IO (directory enumeration,
+        ``stat``, reading sibling ``.md`` outlines). When the graph runs async,
+        langgraph would otherwise execute the sync hook directly on the event
+        loop, so it is dispatched to a worker thread via ``run_in_executor``.
+        ``run_in_executor`` copies the current context, so the ``user_id``
+        contextvar read by ``get_effective_user_id()`` is preserved.
+        """
+        return await run_in_executor(None, self.before_agent, state, runtime)

backend/packages/harness/deerflow/agents/middlewares/view_image_middleware.py

@@ -179,8 +179,10 @@ class ViewImageMiddleware(AgentMiddleware[ViewImageMiddlewareState]):
         # Create the image details message with text and image content
         image_content = self._create_image_details_message(state)
 
-        # Create a new human message with mixed content (text + images)
+        # Create a new human message with mixed content (text + images). This is
-        human_msg = HumanMessage(content=image_content)
+        # internal context for the model only, so hide it from the chat UI and IM
+        # channels (matches the other middleware-injected context messages).
+        human_msg = HumanMessage(content=image_content, additional_kwargs={"hide_from_ui": True})
 
         logger.debug("Injecting image details message with images before LLM call")
 

backend/packages/harness/deerflow/agents/thread_state.py

@@ -58,6 +58,32 @@ def merge_todos(existing: list | None, new: list | None) -> list | None:
     return new
 
 
+class PromotedTools(TypedDict):
+    catalog_hash: str
+    names: list[str]
+
+
+def merge_promoted(existing: PromotedTools | None, new: PromotedTools | None) -> PromotedTools | None:
+    """Reducer for deferred-tool promotions, scoped by catalog hash.
+
+    - new None/empty -> preserve existing (node didn't touch promotions).
+    - catalog_hash changed -> replace wholesale, dropping stale names (prevents a
+      persisted bare name from exposing a different tool after catalog drift).
+    - same catalog_hash -> union names, dedupe, preserve order.
+    """
+    if not new:
+        return existing
+    if existing is None or existing.get("catalog_hash") != new["catalog_hash"]:
+        return {
+            "catalog_hash": new["catalog_hash"],
+            "names": list(dict.fromkeys(new["names"])),
+        }
+    return {
+        "catalog_hash": existing["catalog_hash"],
+        "names": list(dict.fromkeys(existing["names"] + new["names"])),
+    }
+
+
 class ThreadState(AgentState):
     sandbox: NotRequired[SandboxState | None]
     thread_data: NotRequired[ThreadDataState | None]
@@ -66,3 +92,4 @@ class ThreadState(AgentState):
     todos: Annotated[list | None, merge_todos]
     uploaded_files: NotRequired[list[dict] | None]
     viewed_images: Annotated[dict[str, ViewedImageData], merge_viewed_images]  # image_path -> {base64, mime_type}
+    promoted: Annotated[PromotedTools | None, merge_promoted]

backend/packages/harness/deerflow/client.py

@@ -33,7 +33,7 @@ from langchain.agents.middleware import AgentMiddleware
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 from langchain_core.runnables import RunnableConfig
 
-from deerflow.agents.lead_agent.agent import _build_middlewares
+from deerflow.agents.lead_agent.agent import build_middlewares
 from deerflow.agents.lead_agent.prompt import apply_prompt_template
 from deerflow.agents.thread_state import ThreadState
 from deerflow.config.agents_config import AGENT_NAME_PATTERN
@@ -43,6 +43,7 @@ from deerflow.config.paths import get_paths
 from deerflow.models import create_chat_model
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.skills.storage import get_or_new_skill_storage
+from deerflow.tools.builtins.tool_search import assemble_deferred_tools
 from deerflow.tracing import build_tracing_callbacks, inject_langfuse_metadata
 from deerflow.uploads.manager import (
     claim_unique_filename,
@@ -237,19 +238,30 @@ class DeerFlowClient:
         subagent_enabled = cfg.get("subagent_enabled", False)
         max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
 
+        tools = self._get_tools(model_name=model_name, subagent_enabled=subagent_enabled)
+        final_tools, deferred_setup = assemble_deferred_tools(tools, enabled=self._app_config.tool_search.enabled)
         kwargs: dict[str, Any] = {
             # attach_tracing=False because ``stream()`` injects tracing
             # callbacks at the graph invocation root so a single embedded run
             # produces one trace with correct session_id / user_id propagation.
             # Attaching them again on the model would emit duplicate spans.
             "model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled, attach_tracing=False),
-            "tools": self._get_tools(model_name=model_name, subagent_enabled=subagent_enabled),
+            "tools": final_tools,
-            "middleware": _build_middlewares(config, model_name=model_name, agent_name=self._agent_name, custom_middlewares=self._middlewares),
+            "middleware": build_middlewares(
+                config,
+                model_name=model_name,
+                agent_name=self._agent_name,
+                available_skills=self._available_skills,
+                custom_middlewares=self._middlewares,
+                app_config=self._app_config,
+                deferred_setup=deferred_setup,
+            ),
             "system_prompt": apply_prompt_template(
                 subagent_enabled=subagent_enabled,
                 max_concurrent_subagents=max_concurrent_subagents,
                 agent_name=self._agent_name,
                 available_skills=self._available_skills,
+                deferred_names=deferred_setup.deferred_names,
             ),
             "state_schema": ThreadState,
         }
@@ -1206,7 +1218,7 @@ class DeerFlowClient:
 
                 info: dict[str, Any] = {
                     "filename": dest_name,
-                    "size": str(dest.stat().st_size),
+                    "size": dest.stat().st_size,
                     "path": str(dest),
                     "virtual_path": upload_virtual_path(dest_name),
                     "artifact_url": upload_artifact_url(thread_id, dest_name),

backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox.py

@@ -39,11 +39,63 @@ class AioSandbox(Sandbox):
         self._client = AioSandboxClient(base_url=base_url, timeout=600)
         self._home_dir = home_dir
         self._lock = threading.Lock()
+        self._closed = False
 
     @property
     def base_url(self) -> str:
         return self._base_url
 
+    def close(self) -> None:
+        """Best-effort close of the host-side HTTP client owned by this sandbox.
+
+        The agent_sandbox SDK is Fern-generated and exposes no ``close()`` /
+        ``__exit__``, so we reach the socket-owning ``httpx.Client`` explicitly
+        through its attribute chain::
+
+            Sandbox._client_wrapper        -> SyncClientWrapper
+                .httpx_client              -> Fern HttpClient (a wrapper, NOT httpx.Client)
+                    .httpx_client          -> httpx.Client     <- the real socket owner
+
+        Closing it releases pooled sockets so long-running provider lifecycles
+        do not accumulate unreclaimed host-side resources (#2872).
+
+        Resolution is most-specific-first with graceful degradation: if a future
+        SDK adds a top-level ``Sandbox.close()`` it is picked up automatically
+        without changing this code. Idempotent, thread-safe, and non-fatal:
+        failures during teardown are logged and swallowed so provider/backend
+        cleanup is never blocked.
+        """
+        with self._lock:
+            if self._closed:
+                return
+            self._closed = True
+            client = self._client
+            # Drop the reference under the lock for use-after-close safety: any
+            # later command on this instance fails loudly instead of reusing a
+            # half-closed client.
+            self._client = None
+
+        if client is None:
+            return
+
+        # Walk from the real httpx.Client up to the top-level client, picking the
+        # first object that actually exposes close().
+        wrapper = getattr(client, "_client_wrapper", None)
+        fern_http = getattr(wrapper, "httpx_client", None)
+        real_httpx = getattr(fern_http, "httpx_client", None)
+        target = next(
+            (c for c in (real_httpx, fern_http, client) if c is not None and hasattr(c, "close")),
+            None,
+        )
+        if target is None:
+            logger.debug("AioSandbox %s: no closable client found, nothing to release", self.id)
+            return
+
+        try:
+            target.close()
+        except Exception as e:
+            logger.warning(f"Error closing AioSandbox client for {self.id}: {e}")
+
     @property
     def home_dir(self) -> str:
         """Get the home directory inside the sandbox."""

backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox_provider.py

@@ -790,14 +790,20 @@ class AioSandboxProvider(SandboxProvider):
         thread on its next turn without a cold-start.  The container will only be
         stopped when the replicas limit forces eviction or during shutdown.
 
+        The host-side HTTP client owned by the cached ``AioSandbox`` instance is
+        closed before the instance is dropped (#2872). The warm-pool entry only
+        stores ``SandboxInfo``, so a fresh ``AioSandbox`` (and a fresh client)
+        is constructed if the container is later reclaimed.
+
         Args:
             sandbox_id: The ID of the sandbox to release.
         """
         info = None
+        sandbox = None
         thread_ids_to_remove: list[str] = []
 
         with self._lock:
-            self._sandboxes.pop(sandbox_id, None)
+            sandbox = self._sandboxes.pop(sandbox_id, None)
             info = self._sandbox_infos.pop(sandbox_id, None)
             thread_ids_to_remove = [tid for tid, sid in self._thread_sandboxes.items() if sid == sandbox_id]
             for tid in thread_ids_to_remove:
@@ -807,6 +813,15 @@ class AioSandboxProvider(SandboxProvider):
             if info and sandbox_id not in self._warm_pool:
                 self._warm_pool[sandbox_id] = (info, time.time())
 
+        if sandbox is not None:
+            # Defense-in-depth: close() already swallows its own errors; this
+            # guard only protects against a future close() that misbehaves, so
+            # host-side client cleanup can never block parking in the warm pool.
+            try:
+                sandbox.close()
+            except Exception as e:
+                logger.warning(f"Error closing sandbox {sandbox_id} during release: {e}")
+
         logger.info(f"Released sandbox {sandbox_id} to warm pool (container still running)")
 
     def destroy(self, sandbox_id: str) -> None:
@@ -815,14 +830,19 @@ class AioSandboxProvider(SandboxProvider):
         Unlike release(), this actually stops the container.  Use this for
         explicit cleanup, capacity-driven eviction, or shutdown.
 
+        The host-side HTTP client owned by the cached ``AioSandbox`` instance is
+        closed alongside backend/container destruction so no client/socket
+        resources leak (#2872).
+
         Args:
             sandbox_id: The ID of the sandbox to destroy.
         """
         info = None
+        sandbox = None
         thread_ids_to_remove: list[str] = []
 
         with self._lock:
-            self._sandboxes.pop(sandbox_id, None)
+            sandbox = self._sandboxes.pop(sandbox_id, None)
             info = self._sandbox_infos.pop(sandbox_id, None)
             thread_ids_to_remove = [tid for tid, sid in self._thread_sandboxes.items() if sid == sandbox_id]
             for tid in thread_ids_to_remove:
@@ -834,6 +854,15 @@ class AioSandboxProvider(SandboxProvider):
             else:
                 self._warm_pool.pop(sandbox_id, None)
 
+        if sandbox is not None:
+            # Defense-in-depth: close() already swallows its own errors; this
+            # guard only protects against a future close() that misbehaves, so
+            # host-side client cleanup can never block container destruction.
+            try:
+                sandbox.close()
+            except Exception as e:
+                logger.warning(f"Error closing sandbox {sandbox_id} during destroy: {e}")
+
         if info:
             self._backend.destroy(info)
             logger.info(f"Destroyed sandbox {sandbox_id}")

backend/packages/harness/deerflow/community/ddg_search/tools.py

@@ -11,12 +11,85 @@ from deerflow.config import get_app_config
 
 logger = logging.getLogger(__name__)
 
+DEFAULT_BACKEND = "auto"
+DEFAULT_REGION = "wt-wt"
+DEFAULT_SAFESEARCH = "moderate"
+DEFAULT_WIKIPEDIA_REGION = "us-en"
+
+WIKIPEDIA_BACKENDS = {"auto", "all", "wikipedia"}
+WIKIPEDIA_LANGUAGE_ALIASES = {
+    "jp": "ja",
+    "kr": "ko",
+    "tzh": "zh",
+    "wt": "en",
+}
+
+
+def _normalize_backend(backend: str | list[str] | tuple[str, ...] | None) -> str:
+    if backend is None:
+        return DEFAULT_BACKEND
+    if isinstance(backend, (list, tuple)):
+        return ",".join(str(part).strip() for part in backend if str(part).strip()) or DEFAULT_BACKEND
+    return str(backend).strip() or DEFAULT_BACKEND
+
+
+def _normalize_setting(value: str | None, default: str) -> str:
+    return str(value).strip() if value else default
+
+
+def _backend_includes_wikipedia(backend: str | list[str] | tuple[str, ...] | None) -> bool:
+    backend = _normalize_backend(backend)
+    return any(part.strip().lower() in WIKIPEDIA_BACKENDS for part in backend.split(","))
+
+
+def _contains_codepoint(query: str, ranges: tuple[tuple[int, int], ...]) -> bool:
+    return any(start <= ord(char) <= end for char in query for start, end in ranges)
+
+
+def _infer_wikipedia_region(query: str) -> str:
+    """Pick a valid Wikipedia language region when DDGS' worldwide region is used."""
+    if _contains_codepoint(query, ((0x3040, 0x30FF), (0x31F0, 0x31FF))):
+        return "jp-ja"
+    if _contains_codepoint(query, ((0xAC00, 0xD7AF), (0x1100, 0x11FF), (0x3130, 0x318F))):
+        return "kr-ko"
+    if _contains_codepoint(query, ((0x3400, 0x9FFF),)):
+        return "cn-zh"
+    if _contains_codepoint(query, ((0x0400, 0x04FF),)):
+        return "ru-ru"
+    if _contains_codepoint(query, ((0x0370, 0x03FF),)):
+        return "gr-el"
+    if _contains_codepoint(query, ((0x0590, 0x05FF),)):
+        return "il-he"
+    if _contains_codepoint(query, ((0x0600, 0x06FF),)):
+        return "xa-ar"
+    return DEFAULT_WIKIPEDIA_REGION
+
+
+def _resolve_ddgs_region(query: str, region: str | None, backend: str | list[str] | tuple[str, ...] | None) -> str:
+    """
+    DDGS' wikipedia engine treats the second part of region as a Wikipedia
+    subdomain. Its default worldwide region, wt-wt, becomes wt.wikipedia.org.
+    """
+    normalized_region = _normalize_setting(region, DEFAULT_REGION).lower()
+    if not _backend_includes_wikipedia(backend):
+        return normalized_region
+
+    if normalized_region == DEFAULT_REGION:
+        return _infer_wikipedia_region(query)
+
+    if "-" not in normalized_region:
+        return DEFAULT_WIKIPEDIA_REGION
+
+    country, language = normalized_region.split("-", 1)
+    return f"{country}-{WIKIPEDIA_LANGUAGE_ALIASES.get(language, language)}"
+
 
 def _search_text(
     query: str,
     max_results: int = 5,
-    region: str = "wt-wt",
+    region: str | None = DEFAULT_REGION,
-    safesearch: str = "moderate",
+    safesearch: str | None = DEFAULT_SAFESEARCH,
+    backend: str | list[str] | tuple[str, ...] | None = DEFAULT_BACKEND,
 ) -> list[dict]:
     """
     Execute text search using DuckDuckGo.
@@ -26,6 +99,7 @@ def _search_text(
         max_results: Maximum number of results
         region: Search region
         safesearch: Safe search level
+        backend: DDGS backend(s), e.g. "auto", "duckduckgo", or "duckduckgo,brave"
 
     Returns:
         List of search results
@@ -39,11 +113,15 @@ def _search_text(
     ddgs = DDGS(timeout=30)
 
     try:
+        backend = _normalize_backend(backend)
+        safesearch = _normalize_setting(safesearch, DEFAULT_SAFESEARCH)
+        effective_region = _resolve_ddgs_region(query, region, backend)
         results = ddgs.text(
             query,
-            region=region,
+            region=effective_region,
             safesearch=safesearch,
             max_results=max_results,
+            backend=backend,
         )
         return list(results) if results else []
 
@@ -64,14 +142,23 @@ def web_search_tool(
         max_results: Maximum number of results to return. Default is 5.
     """
     config = get_app_config().get_tool_config("web_search")
+    region = DEFAULT_REGION
+    safesearch = DEFAULT_SAFESEARCH
+    backend = DEFAULT_BACKEND
 
-    # Override max_results from config if set
+    if config is not None:
-    if config is not None and "max_results" in config.model_extra:
+        # Override tool call defaults from config if set.
         max_results = config.model_extra.get("max_results", max_results)
+        region = config.model_extra.get("region", region)
+        safesearch = config.model_extra.get("safesearch", safesearch)
+        backend = config.model_extra.get("backend", backend)
 
     results = _search_text(
         query=query,
         max_results=max_results,
+        region=region,
+        safesearch=safesearch,
+        backend=backend,
     )
 
     if not results:

backend/packages/harness/deerflow/community/jina_ai/jina_client.py

@@ -9,7 +9,7 @@ _api_key_warned = False
 
 
 class JinaClient:
-    async def crawl(self, url: str, return_format: str = "html", timeout: int = 10) -> str:
+    async def crawl(self, url: str, return_format: str = "html", timeout: int = 10, proxy: str | None = None, trust_env: bool = True) -> str:
         global _api_key_warned
         headers = {
             "Content-Type": "application/json",
@@ -23,7 +23,10 @@ class JinaClient:
             logger.warning("Jina API key is not set. Provide your own key to access a higher rate limit. See https://jina.ai/reader for more information.")
         data = {"url": url}
         try:
-            async with httpx.AsyncClient() as client:
+            client_kwargs: dict[str, object] = {"trust_env": trust_env}
+            if proxy:
+                client_kwargs["proxy"] = proxy
+            async with httpx.AsyncClient(**client_kwargs) as client:
                 response = await client.post("https://r.jina.ai/", headers=headers, json=data, timeout=timeout)
 
             if response.status_code != 200:

backend/packages/harness/deerflow/community/jina_ai/tools.py

@@ -9,6 +9,38 @@ from deerflow.utils.readability import ReadabilityExtractor
 readability_extractor = ReadabilityExtractor()
 
 
+def _coerce_bool(value: object, default: bool) -> bool:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        normalized = value.strip().lower()
+        if normalized in {"1", "true", "yes", "on"}:
+            return True
+        if normalized in {"0", "false", "no", "off"}:
+            return False
+    return default
+
+
+def _coerce_timeout(value: object, default: int) -> int:
+    if isinstance(value, bool):
+        return default
+    if isinstance(value, int):
+        return value
+    if isinstance(value, str):
+        try:
+            return int(value)
+        except ValueError:
+            return default
+    return default
+
+
+def _coerce_proxy(value: object) -> str | None:
+    if not isinstance(value, str):
+        return None
+    proxy = value.strip()
+    return proxy or None
+
+
 @tool("web_fetch", parse_docstring=True)
 async def web_fetch_tool(url: str) -> str:
     """Fetch the contents of a web page at a given URL.
@@ -22,10 +54,14 @@ async def web_fetch_tool(url: str) -> str:
     """
     jina_client = JinaClient()
     timeout = 10
+    proxy = None
+    trust_env = True
     config = get_app_config().get_tool_config("web_fetch")
-    if config is not None and "timeout" in config.model_extra:
+    if config is not None:
-        timeout = config.model_extra.get("timeout")
+        timeout = _coerce_timeout(config.model_extra.get("timeout"), timeout)
-    html_content = await jina_client.crawl(url, return_format="html", timeout=timeout)
+        proxy = _coerce_proxy(config.model_extra.get("proxy"))
+        trust_env = _coerce_bool(config.model_extra.get("trust_env"), trust_env)
+    html_content = await jina_client.crawl(url, return_format="html", timeout=timeout, proxy=proxy, trust_env=trust_env)
     if isinstance(html_content, str) and html_content.startswith("Error:"):
         return html_content
     article = await asyncio.to_thread(readability_extractor.extract_article, html_content)

backend/packages/harness/deerflow/config/app_config.py

@@ -7,7 +7,7 @@ from typing import Any, Self
 
 import yaml
 from dotenv import load_dotenv
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from deerflow.config.acp_config import ACPAgentConfig, load_acp_config_from_dict
 from deerflow.config.agents_api_config import AgentsApiConfig, load_agents_api_config_from_dict
@@ -18,6 +18,7 @@ from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_
 from deerflow.config.loop_detection_config import LoopDetectionConfig
 from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
 from deerflow.config.model_config import ModelConfig
+from deerflow.config.reload_boundary import format_field_description
 from deerflow.config.run_events_config import RunEventsConfig
 from deerflow.config.runtime_paths import existing_project_file
 from deerflow.config.safety_finish_reason_config import SafetyFinishReasonConfig
@@ -30,6 +31,7 @@ from deerflow.config.summarization_config import SummarizationConfig, load_summa
 from deerflow.config.title_config import TitleConfig, load_title_config_from_dict
 from deerflow.config.token_usage_config import TokenUsageConfig
 from deerflow.config.tool_config import ToolConfig, ToolGroupConfig
+from deerflow.config.tool_output_config import ToolOutputConfig
 from deerflow.config.tool_search_config import ToolSearchConfig, load_tool_search_config_from_dict
 
 load_dotenv()
@@ -84,15 +86,27 @@ def apply_logging_level(name: str | None) -> None:
 class AppConfig(BaseModel):
     """Config for the DeerFlow application"""
 
-    log_level: str = Field(default="info", description="Logging level for deerflow and app modules (debug/info/warning/error); third-party libraries are not affected")
+    log_level: str = Field(
+        default="info",
+        description=format_field_description(
+            "log_level",
+            field_doc="Logging level for deerflow and app modules (debug/info/warning/error); third-party libraries are not affected.",
+        ),
+    )
     token_usage: TokenUsageConfig = Field(default_factory=TokenUsageConfig, description="Token usage tracking configuration")
     models: list[ModelConfig] = Field(default_factory=list, description="Available models")
-    sandbox: SandboxConfig = Field(description="Sandbox configuration")
+    sandbox: SandboxConfig = Field(
+        description=format_field_description(
+            "sandbox",
+            field_doc="Sandbox provider configuration (local filesystem or Docker-based aio sandbox).",
+        ),
+    )
     tools: list[ToolConfig] = Field(default_factory=list, description="Available tools")
     tool_groups: list[ToolGroupConfig] = Field(default_factory=list, description="Available tool groups")
     skills: SkillsConfig = Field(default_factory=SkillsConfig, description="Skills configuration")
     skill_evolution: SkillEvolutionConfig = Field(default_factory=SkillEvolutionConfig, description="Agent-managed skill evolution configuration")
     extensions: ExtensionsConfig = Field(default_factory=ExtensionsConfig, description="Extensions configuration (MCP servers and skills state)")
+    tool_output: ToolOutputConfig = Field(default_factory=ToolOutputConfig, description="Tool output budget protection configuration")
     tool_search: ToolSearchConfig = Field(default_factory=ToolSearchConfig, description="Tool search / deferred loading configuration")
     title: TitleConfig = Field(default_factory=TitleConfig, description="Automatic title generation configuration")
     summarization: SummarizationConfig = Field(default_factory=SummarizationConfig, description="Conversation summarization configuration")
@@ -105,10 +119,49 @@ class AppConfig(BaseModel):
     loop_detection: LoopDetectionConfig = Field(default_factory=LoopDetectionConfig, description="Loop detection middleware configuration")
     safety_finish_reason: SafetyFinishReasonConfig = Field(default_factory=SafetyFinishReasonConfig, description="Provider safety-filter finish_reason interception middleware configuration")
     model_config = ConfigDict(extra="allow")
-    database: DatabaseConfig = Field(default_factory=DatabaseConfig, description="Unified database backend configuration")
+    database: DatabaseConfig = Field(
-    run_events: RunEventsConfig = Field(default_factory=RunEventsConfig, description="Run event storage configuration")
+        default_factory=DatabaseConfig,
-    checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
+        description=format_field_description(
-    stream_bridge: StreamBridgeConfig | None = Field(default=None, description="Stream bridge configuration")
+            "database",
+            field_doc="Unified database backend for run/feedback metadata (memory, sqlite, or postgres).",
+        ),
+    )
+    run_events: RunEventsConfig = Field(
+        default_factory=RunEventsConfig,
+        description=format_field_description(
+            "run_events",
+            field_doc="Run-event store backend (memory for dev, db for production queries, jsonl for lightweight single-node persistence).",
+        ),
+    )
+    checkpointer: CheckpointerConfig | None = Field(
+        default=None,
+        description=format_field_description(
+            "checkpointer",
+            field_doc="LangGraph state-persistence checkpointer configuration.",
+        ),
+    )
+    stream_bridge: StreamBridgeConfig | None = Field(
+        default=None,
+        description=format_field_description(
+            "stream_bridge",
+            field_doc="Stream bridge connecting agent workers to SSE endpoints.",
+        ),
+    )
+
+    @field_validator("models", "tools", "tool_groups", mode="before")
+    @classmethod
+    def _coerce_null_list_sections(cls, value: Any) -> Any:
+        """Treat a present-but-empty config section as an empty list.
+
+        Commenting out every entry under a top-level YAML key — e.g. ``models:``
+        with only comments beneath it, exactly as shipped in
+        ``config.example.yaml`` — makes PyYAML parse the value as ``None``.
+        Without this, the documented ``cp config.example.yaml config.yaml``
+        first-run flow crashes with an opaque ``Input should be a valid list``
+        pydantic error. Coercing ``None`` to ``[]`` keeps that flow working and
+        matches the field's own ``default_factory=list``.
+        """
+        return [] if value is None else value
 
     @classmethod
     def resolve_config_path(cls, config_path: str | None = None) -> Path:
@@ -171,6 +224,11 @@ class AppConfig(BaseModel):
         config_data["extensions"] = extensions_config.model_dump()
 
         result = cls.model_validate(config_data)
+        if not result.models:
+            logger.warning(
+                "No models are configured in %s. Add at least one entry under `models:` (see the commented examples in config.example.yaml) or run `make setup`.",
+                resolved_path,
+            )
         acp_agents = cls._validate_acp_agents(config_data.get("acp_agents", {}))
         cls._apply_singleton_configs(result, acp_agents)
         return result

backend/packages/harness/deerflow/config/checkpointer_config.py

@@ -41,6 +41,20 @@ def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
     _checkpointer_config = config
 
 
+def ensure_config_loaded() -> None:
+    """Lazily load app config when checkpointer config has not been initialized."""
+    from deerflow.config.app_config import _app_config, get_app_config
+
+    config = get_checkpointer_config()
+    if config is not None or _app_config is not None:
+        return
+
+    try:
+        get_app_config()
+    except FileNotFoundError:
+        pass
+
+
 def load_checkpointer_config_from_dict(config_dict: dict | None) -> None:
     """Load checkpointer configuration from a dictionary."""
     global _checkpointer_config

backend/packages/harness/deerflow/config/extensions_config.py

@@ -5,7 +5,7 @@ import os
 from pathlib import Path
 from typing import Any, Literal
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
 from deerflow.config.runtime_paths import existing_project_file
 
@@ -47,6 +47,24 @@ class McpServerConfig(BaseModel):
     description: str = Field(default="", description="Human-readable description of what this MCP server provides")
     model_config = ConfigDict(extra="allow")
 
+    @model_validator(mode="before")
+    @classmethod
+    def _accept_transport_alias(cls, data: Any) -> Any:
+        """Accept the MCP-spec ``transport`` field as an alias for ``type``.
+
+        The official MCP configuration schema uses ``transport`` to indicate
+        the transport mechanism (``stdio``/``sse``/``http``). Earlier versions
+        of this project only honored ``type``, which caused remote SSE/HTTP
+        servers configured with just ``transport`` to be incorrectly treated as
+        ``stdio`` (the default). This validator normalizes the two so either
+        spelling works, with ``type`` taking precedence when both are provided.
+        """
+        if isinstance(data, dict):
+            transport = data.get("transport")
+            if transport and not data.get("type"):
+                data = {**data, "type": transport}
+        return data
+
 
 class SkillStateConfig(BaseModel):
     """Configuration for a single skill's state."""

backend/packages/harness/deerflow/config/model_config.py

@@ -32,6 +32,16 @@ class ModelConfig(BaseModel):
         description="Extra settings to be passed to the model when thinking is disabled",
     )
     supports_vision: bool = Field(default_factory=lambda: False, description="Whether the model supports vision/image inputs")
+    stream_chunk_timeout: float | None = Field(
+        default=None,
+        description=(
+            "Maximum seconds to wait between successive streaming chunks before "
+            "langchain-openai raises StreamChunkTimeoutError. None means use the "
+            "factory default (240s for OpenAI-compatible clients). Tune higher for "
+            "reasoning models with long thinking pauses; lower for latency-sensitive "
+            "interactive endpoints. Has no effect on non-OpenAI-compatible providers."
+        ),
+    )
     thinking: dict | None = Field(
         default_factory=lambda: None,
         description=(

backend/packages/harness/deerflow/config/paths.py

@@ -1,3 +1,4 @@
+import hashlib
 import os
 import re
 import shutil
@@ -10,6 +11,8 @@ VIRTUAL_PATH_PREFIX = "/mnt/user-data"
 
 _SAFE_THREAD_ID_RE = re.compile(r"^[A-Za-z0-9_\-]+$")
 _SAFE_USER_ID_RE = re.compile(r"^[A-Za-z0-9_\-]+$")
+_UNSAFE_USER_ID_CHAR_RE = re.compile(r"[^A-Za-z0-9_\-]")
+_SAFE_USER_ID_DIGEST_HEX_LEN = 16
 
 
 def _default_local_base_dir() -> Path:
@@ -31,6 +34,23 @@ def _validate_user_id(user_id: str) -> str:
     return user_id
 
 
+def make_safe_user_id(raw: str) -> str:
+    """Normalize an external identity into the user-id charset (``[A-Za-z0-9_-]``).
+
+    IM channel ids (Feishu/Slack/Telegram) may contain characters that
+    :func:`_validate_user_id` rejects. Already-safe ids pass through unchanged;
+    lossy ones get a short digest suffix so two distinct inputs never share a
+    storage bucket.
+    """
+    if not raw:
+        raise ValueError("user_id must be a non-empty string.")
+    sanitized = _UNSAFE_USER_ID_CHAR_RE.sub("-", raw)
+    if sanitized == raw:
+        return raw
+    digest = hashlib.sha1(raw.encode("utf-8")).hexdigest()[:_SAFE_USER_ID_DIGEST_HEX_LEN]
+    return f"{sanitized}-{digest}"
+
+
 def _join_host_path(base: str, *parts: str) -> str:
     """Join host filesystem path segments while preserving native style.
 

backend/packages/harness/deerflow/config/reload_boundary.py

@@ -0,0 +1,104 @@
+"""Single source of truth for the config hot-reload boundary.
+
+Bytedance/deer-flow issue #3144: gateway request dependencies resolve
+``AppConfig`` through ``get_app_config()`` on every request, so per-run
+fields take effect on the next message without restarting the gateway.
+The fields listed in this module are the **infrastructure** subset that
+the gateway captures once at startup — engines, singletons, IM clients,
+the logging handler — and that therefore require a process restart to
+change at runtime.
+
+The registry covers two kinds of entries:
+
+- Top-level ``AppConfig`` fields (``database``, ``checkpointer``,
+  ``run_events``, ``stream_bridge``, ``sandbox``, ``log_level``). For
+  these, :func:`format_field_description` produces the standardised
+  ``"startup-only: ..."`` prefix that the matching Pydantic
+  ``Field(description=...)`` carries, so the boundary surfaces in IDE
+  hover next to the field itself.
+- Top-level ``config.yaml`` sections that are not part of the
+  ``AppConfig`` schema (``channels``). These cannot be standardised at
+  the schema level, so the registry is their only canonical location.
+
+Any future "needs restart" scanner — operator tooling, lint hooks, doc
+generators — should drive off this registry rather than re-parsing
+prose.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterator
+
+#: The standardised prefix every restart-required field description starts
+#: with. ``test_reload_boundary`` enforces both directions: registered
+#: fields must use this prefix in the schema, and any schema field using
+#: this prefix must be in the registry.
+STARTUP_ONLY_PREFIX = "startup-only:"
+
+
+#: Restart-required field paths mapped to the human-readable reason.
+#:
+#: The reason text is what surfaces in ``Field(description=...)``, so it
+#: must explain *what* code captures the snapshot — not just that the
+#: field is restart-required — so an operator changing the value knows
+#: which subsystem to restart.
+STARTUP_ONLY_FIELDS: dict[str, str] = {
+    "database": ("init_engine_from_config() runs once during langgraph_runtime() startup; the SQLAlchemy engine holds the connection pool and is not rebuilt on config.yaml edits."),
+    "checkpointer": ("make_checkpointer() binds the persistent checkpointer once at startup, including SQLite WAL / busy_timeout settings."),
+    "run_events": ("make_run_event_store() picks the memory- vs SQL-backed implementation at startup and is frozen onto app.state.run_events_config to stay paired with the underlying event store."),
+    "stream_bridge": ("make_stream_bridge() constructs the stream-bridge singleton once during startup."),
+    "sandbox": ("get_sandbox_provider() caches the provider singleton (``_default_sandbox_provider``); a different ``sandbox.use`` class path only takes effect on next process start."),
+    "log_level": (
+        "apply_logging_level() runs only during app.py startup; it sets the deerflow/app logger levels and may lower root handler thresholds so configured messages can propagate. A freshly reloaded AppConfig does not retrigger it."
+    ),
+    # Not part of the AppConfig Pydantic schema — channel credentials are
+    # consumed directly by ``start_channel_service()`` once at lifespan
+    # startup and the live channel clients are not rebuilt on
+    # config.yaml edits.
+    "channels": ("start_channel_service() is invoked once during startup; the live IM channel clients (Feishu, Slack, Telegram, DingTalk) are not rebuilt when channels.* changes."),
+}
+
+
+def iter_startup_only_field_paths() -> Iterator[str]:
+    """Yield every registered restart-required field path."""
+    return iter(STARTUP_ONLY_FIELDS)
+
+
+def is_startup_only_field(field_path: str) -> bool:
+    """Return ``True`` when *field_path* is registered as restart-required.
+
+    Accepts only top-level paths (``"database"``, ``"sandbox"`` etc.);
+    nested keys like ``"database.url"`` are not modelled here because the
+    boundary is per-section, not per-leaf.
+    """
+    return field_path in STARTUP_ONLY_FIELDS
+
+
+def format_field_description(field_path: str, *, field_doc: str | None = None) -> str:
+    """Build the standardised description for a registered field.
+
+    Used inside ``AppConfig`` ``Field(description=...)`` so the hover
+    text in IDEs matches the registry and the drift tests can pin one
+    side against the other.
+
+    Args:
+        field_path: A registered top-level field path (e.g. ``"log_level"``).
+        field_doc: Optional human-facing description for the field itself
+            (allowed values, semantics, etc.). When supplied, it is
+            appended after the ``startup-only:`` marker block separated by
+            a blank line so IDE hover shows both the restart-required
+            reason *and* the field's normal documentation. Composition
+            keeps the marker as the leading token machine-readable tooling
+            pivots on while restoring the prose that ``Field(description=)``
+            used to carry before the registry took over.
+
+    Raises:
+        KeyError: when *field_path* is not registered. This is deliberate
+            — silently returning a placeholder would let a typo bypass
+            the drift coverage.
+    """
+    reason = STARTUP_ONLY_FIELDS[field_path]
+    header = f"{STARTUP_ONLY_PREFIX} {reason}"
+    if field_doc is None:
+        return header
+    return f"{header}\n\n{field_doc.strip()}"

backend/packages/harness/deerflow/config/tool_output_config.py

@@ -0,0 +1,62 @@
+"""Configuration for tool output budget protection."""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+
+class ToolOutputConfig(BaseModel):
+    """Config section for tool-result output budget enforcement.
+
+    When a tool returns more than ``externalize_min_chars`` characters,
+    the full output is persisted to disk and replaced with a compact
+    preview + file reference.  If disk persistence is unavailable the
+    output falls back to head+tail truncation.
+    """
+
+    enabled: bool = Field(
+        default=True,
+        description="Enable the tool output budget middleware.",
+    )
+    externalize_min_chars: int = Field(
+        default=12_000,
+        ge=0,
+        description="Character threshold to trigger disk externalization. Outputs below this pass through unchanged. Set to 0 to disable externalization (fallback truncation still applies when output exceeds fallback_max_chars).",
+    )
+    preview_head_chars: int = Field(
+        default=2_000,
+        ge=0,
+        description="Characters to keep from the head of the output in the preview.",
+    )
+    preview_tail_chars: int = Field(
+        default=1_000,
+        ge=0,
+        description="Characters to keep from the tail of the output in the preview.",
+    )
+    fallback_max_chars: int = Field(
+        default=30_000,
+        ge=0,
+        description="Maximum characters when disk persistence is unavailable. 0 disables fallback truncation.",
+    )
+    fallback_head_chars: int = Field(
+        default=8_000,
+        ge=0,
+        description="Head characters for fallback truncation.",
+    )
+    fallback_tail_chars: int = Field(
+        default=3_000,
+        ge=0,
+        description="Tail characters for fallback truncation.",
+    )
+    storage_subdir: str = Field(
+        default=".tool-results",
+        description="Subdirectory under the thread outputs path for persisted tool results.",
+    )
+    exempt_tools: list[str] = Field(
+        default_factory=lambda: ["read_file", "read_file_tool"],
+        description="Tool names exempt from budget enforcement (prevents persist→read→persist loops).",
+    )
+    tool_overrides: dict[str, int] = Field(
+        default_factory=dict,
+        description="Per-tool externalize_min_chars overrides. Keys are tool names, values are char thresholds. Use 0 to disable externalization for a specific tool.",
+    )

backend/packages/harness/deerflow/mcp/__init__.py

@@ -1,6 +1,10 @@
 """MCP (Model Context Protocol) integration using langchain-mcp-adapters."""
 
-from .cache import get_cached_mcp_tools, initialize_mcp_tools, reset_mcp_tools_cache
+from .cache import (
+    get_cached_mcp_tools,
+    initialize_mcp_tools,
+    reset_mcp_tools_cache,
+)
 from .client import build_server_params, build_servers_config
 from .tools import get_mcp_tools
 

backend/packages/harness/deerflow/mcp/cache.py

@@ -87,8 +87,7 @@ def get_cached_mcp_tools() -> list[BaseTool]:
 
     Also checks if the config file has been modified since last initialization,
     and re-initializes if needed. This ensures that changes made through the
-    Gateway API (which runs in a separate process) are reflected in the
+    Gateway API are reflected in the Gateway-embedded LangGraph runtime.
-    LangGraph Server.
 
     Returns:
         List of cached MCP tools.
@@ -144,11 +143,20 @@ def reset_mcp_tools_cache() -> None:
 
     # Close persistent sessions – they will be recreated by the next
     # get_mcp_tools() call with the (possibly updated) connection config.
+    #
+    # close_all_sync() already picks the correct strategy per owning loop:
+    #   * sessions owned by the *current* running loop are only *signalled*
+    #     (their owner task runs __aexit__ once the loop regains control –
+    #     this is correct and leak-free, since the loop keeps the task alive),
+    #   * sessions on other threads' loops are torn down deterministically,
+    #   * idle/closed loops are handled or skipped.
+    # We deliberately do NOT try to synchronously wait for the current running
+    # loop to finish teardown here: that is a self-deadlock (the loop can only
+    # run the teardown after this synchronous call returns control to it).
     try:
         from deerflow.mcp.session_pool import get_session_pool
 
-        pool = get_session_pool()
+        get_session_pool().close_all_sync()
-        pool.close_all_sync()
     except Exception:
         logger.debug("Could not close MCP session pool on cache reset", exc_info=True)
 

backend/packages/harness/deerflow/mcp/session_pool.py

@@ -8,6 +8,27 @@ This module provides a session pool that maintains persistent MCP sessions,
 scoped by ``(server_name, scope_key)`` — typically scope_key is the thread_id —
 so that consecutive tool calls share the same session and server-side state.
 Sessions are evicted in LRU order when the pool reaches capacity.
+
+Lifecycle model (owner task)
+----------------------------
+An MCP ``ClientSession`` is implemented on top of an ``anyio`` task group, and
+anyio enforces that a cancel scope must be exited from the *same task* that
+entered it. Calling ``cm.__aexit__`` from any task other than the one that ran
+``cm.__aenter__`` raises::
+
+    RuntimeError: Attempted to exit cancel scope in a different task than it
+    was entered in
+
+The sync-tool path (``make_sync_tool_wrapper``) drives each call through a fresh
+``asyncio.run`` event loop, so a session entered while answering one call would
+otherwise be exited while answering another — from a different task — and crash
+(GitHub issue #3379).
+
+To make this impossible, every pooled session is owned by a dedicated
+``_run_session`` task. That task enters the context manager, hands the live
+session back to the caller, and then *waits* on a close event. All shutdown
+paths only ever **signal** that event; the owner task performs ``__aexit__``
+itself, guaranteeing enter and exit always happen in the same task.
 """
 
 from __future__ import annotations
@@ -27,18 +48,81 @@ class MCPSessionPool:
     """Manages persistent MCP sessions scoped by ``(server_name, scope_key)``."""
 
     MAX_SESSIONS = 256
-    SESSION_CLOSE_TIMEOUT = 5.0  # seconds to wait when closing a session via run_coroutine_threadsafe
+    SESSION_CLOSE_TIMEOUT = 5.0  # seconds to wait when closing a session on a foreign loop
 
     def __init__(self) -> None:
+        # Each entry: (session, owning_loop, owner_task, close_event).
         self._entries: OrderedDict[
             tuple[str, str],
-            tuple[ClientSession, asyncio.AbstractEventLoop],
+            tuple[
+                ClientSession,
+                asyncio.AbstractEventLoop,
+                asyncio.Task[Any],
+                asyncio.Event,
+            ],
         ] = OrderedDict()
-        self._context_managers: dict[tuple[str, str], Any] = {}
+        # In-flight creations, keyed by (server, scope). Lets concurrent callers
+        # on the same loop share a single creation instead of each spawning a
+        # duplicate session. Value: (loop, ready_future, owner_task, close_event).
+        self._inflight: dict[
+            tuple[str, str],
+            tuple[
+                asyncio.AbstractEventLoop,
+                asyncio.Future[ClientSession],
+                asyncio.Task[Any],
+                asyncio.Event,
+            ],
+        ] = {}
         # threading.Lock is not bound to any event loop, so it is safe to
         # acquire from both async paths and sync/worker-thread paths.
         self._lock = threading.Lock()
 
+    # ------------------------------------------------------------------
+    # Session owner task
+    # ------------------------------------------------------------------
+
+    async def _run_session(
+        self,
+        connection: dict[str, Any],
+        ready: asyncio.Future[ClientSession],
+        close_evt: asyncio.Event,
+    ) -> None:
+        """Own a single MCP session for its entire lifetime.
+
+        Enters the session context manager, initializes it, publishes the live
+        session via ``ready``, then blocks until ``close_evt`` is set. The
+        context manager is *always* exited from this task, satisfying anyio's
+        cancel-scope same-task requirement.
+        """
+        from langchain_mcp_adapters.sessions import create_session
+
+        cm = create_session(connection)
+        try:
+            session = await cm.__aenter__()
+        except BaseException as e:
+            # Never entered the cancel scope, so there is nothing to exit.
+            if not ready.done():
+                ready.set_exception(e)
+            return
+
+        # The context manager is now entered. From here on __aexit__ MUST run in
+        # this task — on init failure, on cancellation, or on the close signal —
+        # to satisfy anyio's same-task cancel-scope requirement and to avoid
+        # leaking the session/subprocess.
+        try:
+            await session.initialize()
+            if not ready.done():
+                ready.set_result(session)
+            await close_evt.wait()
+        except BaseException as e:
+            if not ready.done():
+                ready.set_exception(e)
+        finally:
+            try:
+                await cm.__aexit__(None, None, None)
+            except Exception:
+                logger.warning("Error closing MCP session", exc_info=True)
+
     async def get_session(
         self,
         server_name: str,
@@ -47,9 +131,9 @@ class MCPSessionPool:
     ) -> ClientSession:
         """Get or create a persistent MCP session.
 
-        If an existing session was created in a different event loop (e.g.
+        If an existing session was created in a different (or closed) event
-        the sync-wrapper path), it is closed and replaced with a fresh one
+        loop, it is evicted and replaced with a fresh one owned by a task on
-        in the current loop.
+        the current loop.
 
         Args:
             server_name: MCP server name.
@@ -63,44 +147,118 @@ class MCPSessionPool:
         current_loop = asyncio.get_running_loop()
 
         # Phase 1: inspect/mutate the registry under the thread lock (no awaits).
-        cms_to_close: list[tuple[tuple[str, str], Any]] = []
+        # Decide one of three outcomes atomically: return an existing session,
+        # join an in-flight creation, or become the creator for this key.
+        # Each item: (loop, owner_task, close_event, cancel). ``cancel`` is True
+        # for in-flight creations, whose owner may be blocked inside
+        # ``initialize()`` where close_evt cannot wake it — it must be cancelled.
+        evicted: list[tuple[asyncio.AbstractEventLoop, asyncio.Task[Any], asyncio.Event, bool]] = []
+        join: asyncio.Future[ClientSession] | None = None
+        ready: asyncio.Future[ClientSession] | None = None
+        close_evt: asyncio.Event | None = None
+        task: asyncio.Task[Any] | None = None
         with self._lock:
             if key in self._entries:
-                session, loop = self._entries[key]
+                session, loop, ent_task, ent_close = self._entries[key]
-                if loop is current_loop:
+                if loop is current_loop and not loop.is_closed():
                     self._entries.move_to_end(key)
                     return session
-                # Session belongs to a different event loop – evict it.
+                # Session belongs to a different/closed event loop – evict it.
-                cm = self._context_managers.pop(key, None)
                 self._entries.pop(key)
-                if cm is not None:
+                evicted.append((loop, ent_task, ent_close, False))
-                    cms_to_close.append((key, cm))
+
+            inflight = self._inflight.get(key)
+            if inflight is not None and inflight[0] is current_loop and not inflight[0].is_closed():
+                # Another caller on this loop is already creating the session;
+                # wait for the same result instead of building a duplicate.
+                join = inflight[1]
+            else:
+                if inflight is not None:
+                    # Stale in-flight creation owned by a different/closed loop.
+                    # Drop the record and tear its owner down; because that owner
+                    # may be blocked inside initialize() (where close_evt cannot
+                    # wake it), it must be cancelled. We then create a fresh
+                    # session here.
+                    self._inflight.pop(key)
+                    evicted.append((inflight[0], inflight[2], inflight[3], True))
+                # Become the creator: publish an in-flight record before any
+                # await so concurrent callers join us instead of racing.
+                ready = current_loop.create_future()
+                close_evt = asyncio.Event()
+                task = current_loop.create_task(self._run_session(connection, ready, close_evt))
+                self._inflight[key] = (current_loop, ready, task, close_evt)
 
             # Evict LRU entries when at capacity.
             while len(self._entries) >= self.MAX_SESSIONS:
-                oldest_key = next(iter(self._entries))
+                oldest_key, (_, loop, ent_task, ent_close) = next(iter(self._entries.items()))
-                cm = self._context_managers.pop(oldest_key, None)
                 self._entries.pop(oldest_key)
-                if cm is not None:
+                evicted.append((loop, ent_task, ent_close, False))
-                    cms_to_close.append((oldest_key, cm))
 
-        # Phase 2: async cleanup outside the lock so we never await while holding it.
+        # Phase 2: shut down evicted sessions/creations. Same-loop owners are
-        for close_key, cm in cms_to_close:
+        # awaited so they finish deterministically; foreign-loop owners are
+        # routed to their own loop. In every case the owner task — never this
+        # one — runs __aexit__. In-flight owners are cancelled (cancel=True) so a
+        # blocking initialize() cannot leave them hung.
+        for loop, ent_task, ent_close, cancel in evicted:
+            if loop is current_loop and not loop.is_closed():
+                await self._shutdown(ent_close, ent_task, cancel)
+            elif cancel:
+                await self._shutdown_entry(loop, ent_task, ent_close, cancel=True)
+            else:
+                self._signal_close(loop, ent_close)
+
+        # Phase 2b: a concurrent creation for this key is already in progress on
+        # this loop — share its result rather than create a duplicate session.
+        if join is not None:
+            return await asyncio.shield(join)
+
+        assert ready is not None and close_evt is not None and task is not None
+
+        # Phase 3: wait for our owner task to publish the initialized session.
+        try:
+            session = await asyncio.shield(ready)
+        except BaseException:
+            # Two distinct cases reach here:
+            #
+            # 1. The owner task failed (e.g. connect/initialize error) and
+            #    reported it via ready.set_exception(). It is *already* in its
+            #    finally block running cm.__aexit__ in its own task, so we must
+            #    NOT cancel it — doing so would interrupt that cleanup. We only
+            #    wait for it to finish unwinding.
+            # 2. This call itself was cancelled (CancelledError). Because of the
+            #    shield, `ready` is still pending and the owner task is alive and
+            #    blocked. We signal close and cancel it so it exits the cancel
+            #    scope in its own task, then wait for it to finish.
+            #
+            # The session is never registered yet, so nobody else can close it;
+            # waiting here guarantees we never leak a session or owner task.
+            owner_already_failed = ready.done() and not ready.cancelled() and ready.exception() is not None
+            if not owner_already_failed:
+                close_evt.set()
+                task.cancel()
             try:
-                await cm.__aexit__(None, None, None)
+                await asyncio.shield(task)
-            except Exception:
+            except BaseException:
-                logger.warning("Error closing MCP session %s", close_key, exc_info=True)
+                logger.debug("Owner task ended during get_session unwind", exc_info=True)
+            with self._lock:
+                if self._inflight.get(key) == (current_loop, ready, task, close_evt):
+                    self._inflight.pop(key)
+            raise
 
-        from langchain_mcp_adapters.sessions import create_session
+        # Phase 4: promote the in-flight creation to a registered entry — but
-
+        # only if our in-flight record is still the live one. A concurrent
-        cm = create_session(connection)
+        # close_* / close_all may have removed it while we were initializing; in
-        session = await cm.__aenter__()
+        # that case we must NOT resurrect the session into _entries. Instead we
-        await session.initialize()
+        # own the teardown: signal our owner task and wait for it to run
-
+        # __aexit__ in its own task, then surface the cancellation.
-        # Phase 3: register the new session under the lock.
         with self._lock:
-            self._entries[key] = (session, current_loop)
+            still_ours = self._inflight.get(key) == (current_loop, ready, task, close_evt)
-            self._context_managers[key] = cm
+            if still_ours:
+                self._inflight.pop(key)
+                self._entries[key] = (session, current_loop, task, close_evt)
+        if not still_ours:
+            await self._shutdown(close_evt, task)
+            raise asyncio.CancelledError("MCP session pool was closed while the session was being created")
         logger.info("Created persistent MCP session for %s/%s", server_name, scope_key)
         return session
 
@@ -108,70 +266,169 @@ class MCPSessionPool:
     # Cleanup helpers
     # ------------------------------------------------------------------
 
-    async def _close_cm(self, key: tuple[str, str], cm: Any) -> None:
+    @staticmethod
-        """Close a single context manager (must be called WITHOUT the lock)."""
+    def _signal_close(loop: asyncio.AbstractEventLoop, close_evt: asyncio.Event) -> None:
+        """Ask an owner task to shut down without waiting.
+
+        ``asyncio.Event.set`` is not thread-safe, so it is scheduled on the
+        owning loop. A closed loop means the owner task is already gone.
+        """
+        if loop.is_closed():
+            return
         try:
-            await cm.__aexit__(None, None, None)
+            loop.call_soon_threadsafe(close_evt.set)
-        except Exception:
+        except RuntimeError:
-            logger.warning("Error closing MCP session %s", key, exc_info=True)
+            # Loop was closed between the is_closed() check and now.
+            pass
+
+    async def _shutdown(
+        self,
+        close_evt: asyncio.Event,
+        task: asyncio.Task[Any],
+        cancel: bool = False,
+    ) -> None:
+        """Signal an owner task and wait for it to finish (runs on its loop).
+
+        ``cancel=True`` is used for in-flight creations: the owner task may be
+        blocked inside ``initialize()`` where ``close_evt`` cannot wake it, so it
+        must be cancelled. Its ``finally`` block still runs ``__aexit__`` in its
+        own task, satisfying anyio's same-task cancel-scope requirement.
+        """
+        close_evt.set()
+        if cancel:
+            task.cancel()
+        try:
+            await task
+        except (Exception, asyncio.CancelledError):
+            logger.debug("Owner task ended during shutdown", exc_info=True)
+
+    async def _shutdown_entry(
+        self,
+        loop: asyncio.AbstractEventLoop,
+        task: asyncio.Task[Any],
+        close_evt: asyncio.Event,
+        cancel: bool = False,
+    ) -> None:
+        """Shut down one entry, routing the close to its owning loop."""
+        if loop.is_closed():
+            return
+        current_loop = asyncio.get_running_loop()
+        if loop is current_loop:
+            await self._shutdown(close_evt, task, cancel)
+        elif loop.is_running():
+            future = asyncio.run_coroutine_threadsafe(self._shutdown(close_evt, task, cancel), loop)
+            try:
+                await asyncio.wrap_future(future)
+            except Exception:
+                logger.warning("Error closing MCP session on owning loop", exc_info=True)
+        else:
+            # Owning loop exists but is neither the current loop nor running.
+            # We are inside an async context here, so run_until_complete() would
+            # raise "Cannot run the event loop while another loop is running";
+            # and the loop may belong to another thread, where driving it from
+            # here is unsafe. This branch is not expected in practice — a
+            # session's owning loop is either the long-lived gateway loop (which
+            # is running) or a short-lived asyncio.run loop (which is closed and
+            # caught above). Fall back to a best-effort thread-safe signal so the
+            # owner task tears down if/when its loop runs again.
+            logger.warning("Owning loop for MCP session is idle; signalling close best-effort. Session may leak until the loop runs again.")
+            self._signal_close(loop, close_evt)
+            if cancel:
+                try:
+                    loop.call_soon_threadsafe(task.cancel)
+                except RuntimeError:
+                    pass
 
     async def close_scope(self, scope_key: str) -> None:
         """Close all sessions for a given scope (e.g. thread_id)."""
         with self._lock:
             keys = [k for k in self._entries if k[1] == scope_key]
-            cms = [(k, self._context_managers.pop(k, None)) for k in keys]
+            entries = [(self._entries.pop(k)) for k in keys]
-            for k in keys:
+            inflight_keys = [k for k in self._inflight if k[1] == scope_key]
-                self._entries.pop(k, None)
+            inflight = [self._inflight.pop(k) for k in inflight_keys]
-        for key, cm in cms:
+        for _session, loop, task, close_evt in entries:
-            if cm is not None:
+            await self._shutdown_entry(loop, task, close_evt)
-                await self._close_cm(key, cm)
+        for loop, _ready, task, close_evt in inflight:
+            await self._shutdown_entry(loop, task, close_evt, cancel=True)
 
     async def close_server(self, server_name: str) -> None:
         """Close all sessions for a given server."""
         with self._lock:
             keys = [k for k in self._entries if k[0] == server_name]
-            cms = [(k, self._context_managers.pop(k, None)) for k in keys]
+            entries = [(self._entries.pop(k)) for k in keys]
-            for k in keys:
+            inflight_keys = [k for k in self._inflight if k[0] == server_name]
-                self._entries.pop(k, None)
+            inflight = [self._inflight.pop(k) for k in inflight_keys]
-        for key, cm in cms:
+        for _session, loop, task, close_evt in entries:
-            if cm is not None:
+            await self._shutdown_entry(loop, task, close_evt)
-                await self._close_cm(key, cm)
+        for loop, _ready, task, close_evt in inflight:
+            await self._shutdown_entry(loop, task, close_evt, cancel=True)
 
     async def close_all(self) -> None:
         """Close every managed session."""
         with self._lock:
-            cms = list(self._context_managers.items())
+            entries = list(self._entries.values())
-            self._context_managers.clear()
             self._entries.clear()
-        for key, cm in cms:
+            inflight = list(self._inflight.values())
-            await self._close_cm(key, cm)
+            self._inflight.clear()
+        for _session, loop, task, close_evt in entries:
+            await self._shutdown_entry(loop, task, close_evt)
+        for loop, _ready, task, close_evt in inflight:
+            await self._shutdown_entry(loop, task, close_evt, cancel=True)
 
     def close_all_sync(self) -> None:
-        """Close all sessions using their owning event loops (synchronous).
+        """Close all sessions on their owning event loops (synchronous).
 
-        Each session is closed on the loop it was created in, avoiding
+        Each session is closed by its owner task on the loop it was created in,
-        cross-loop resource leaks.  Safe to call from any thread without an
+        avoiding cross-loop and cross-task errors. Safe to call from any thread
-        active event loop.
+        without an active event loop.
+
+        Closing semantics differ by where the owning loop runs:
+
+        * Owning loop is idle, or running on another thread — this call blocks
+          until teardown completes (or ``SESSION_CLOSE_TIMEOUT`` elapses).
+        * Owning loop is the one currently running on *this* thread — we cannot
+          block on it without deadlocking, so teardown is only *signalled* here
+          and completes asynchronously once control returns to that loop. The
+          caller must therefore keep that loop running afterwards; if it stops
+          the loop immediately, the owner task's ``__aexit__`` may not run. When
+          a deterministic close is required from inside a running loop, ``await
+          close_all()`` instead.
         """
         with self._lock:
-            entries = list(self._entries.items())
+            entries = list(self._entries.values())
-            cms = dict(self._context_managers)
             self._entries.clear()
-            self._context_managers.clear()
+            inflight = list(self._inflight.values())
+            self._inflight.clear()
 
-        for key, (_, loop) in entries:
+        # Entries are initialized (gentle close_evt path). In-flight creations
-            cm = cms.get(key)
+        # may be blocked mid-init, so they are cancelled to unblock teardown.
-            if cm is None or loop.is_closed():
+        owners = [(loop, task, close_evt, False) for _s, loop, task, close_evt in entries]
+        owners += [(loop, task, close_evt, True) for loop, _r, task, close_evt in inflight]
+        try:
+            current_running_loop = asyncio.get_running_loop()
+        except RuntimeError:
+            current_running_loop = None
+        for loop, task, close_evt, cancel in owners:
+            if loop.is_closed():
                 continue
             try:
-                if loop.is_running():
+                if loop is current_running_loop:
-                    # Schedule on the owning loop from this (different) thread.
+                    # We are executing inside this loop's thread, so synchronously
-                    future = asyncio.run_coroutine_threadsafe(cm.__aexit__(None, None, None), loop)
+                    # waiting on run_coroutine_threadsafe(...).result() would
+                    # deadlock until timeout. Signal the owner task directly and
+                    # let it finish once this synchronous call returns control to
+                    # the running loop.
+                    close_evt.set()
+                    if cancel:
+                        task.cancel()
+                elif loop.is_running():
+                    # Schedule the shutdown on the owning loop from this thread.
+                    future = asyncio.run_coroutine_threadsafe(self._shutdown(close_evt, task, cancel), loop)
                     future.result(timeout=self.SESSION_CLOSE_TIMEOUT)
                 else:
-                    loop.run_until_complete(cm.__aexit__(None, None, None))
+                    loop.run_until_complete(self._shutdown(close_evt, task, cancel))
             except Exception:
-                logger.debug("Error closing MCP session %s during sync close", key, exc_info=True)
+                logger.debug("Error closing MCP session during sync close", exc_info=True)
 
 
 # ------------------------------------------------------------------

backend/packages/harness/deerflow/mcp/tools.py

@@ -1,8 +1,9 @@
-"""Load MCP tools using langchain-mcp-adapters with persistent sessions."""
+"""Load MCP tools using langchain-mcp-adapters with stdio session pooling."""
 
 from __future__ import annotations
 
 import logging
+from collections.abc import Mapping
 from typing import Any
 
 from langchain_core.tools import BaseTool, StructuredTool
@@ -137,7 +138,15 @@ def _make_session_pool_tool(
             from langchain_mcp_adapters.interceptors import MCPToolCallRequest
 
             async def base_handler(request: MCPToolCallRequest) -> Any:
-                return await session.call_tool(request.name, request.args)
+                # Preserve interceptor-injected headers for stdio MCP calls by
+                # forwarding them through MCP call meta.
+                call_kwargs: dict[str, Any] = {}
+                if request.headers:
+                    if isinstance(request.headers, Mapping):
+                        call_kwargs["meta"] = {"headers": dict(request.headers)}
+                    else:
+                        logger.warning("Ignoring MCP interceptor headers with unsupported type: %s", type(request.headers).__name__)
+                return await session.call_tool(request.name, request.args, **call_kwargs)
 
             handler = base_handler
             for interceptor in reversed(tool_interceptors):
@@ -173,8 +182,10 @@ def _make_session_pool_tool(
 async def get_mcp_tools() -> list[BaseTool]:
     """Get all tools from enabled MCP servers.
 
-    Tools are wrapped with persistent-session logic so that consecutive
+    Tools using stdio transport are wrapped with persistent-session logic so
-    calls within the same thread reuse the same MCP session.
+    consecutive calls within the same thread reuse the same MCP session.
+    HTTP/SSE tools are returned unwrapped to avoid cross-task TaskGroup
+    cleanup errors.
 
     Returns:
         List of LangChain tools from all enabled MCP servers.
@@ -251,6 +262,9 @@ async def get_mcp_tools() -> list[BaseTool]:
         logger.info(f"Successfully loaded {len(tools)} tool(s) from MCP servers")
 
         # Wrap each tool with persistent-session logic.
+        # Only pool stdio sessions. HTTP/SSE transports use anyio TaskGroups
+        # internally which cannot be closed from a different async task, so
+        # pooling them causes RuntimeError on cleanup (see #3203).
         wrapped_tools: list[BaseTool] = []
         for tool in tools:
             tool_server: str | None = None
@@ -260,7 +274,11 @@ async def get_mcp_tools() -> list[BaseTool]:
                     break
 
             if tool_server is not None:
-                wrapped_tools.append(_make_session_pool_tool(tool, tool_server, servers_config[tool_server], tool_interceptors))
+                transport = servers_config[tool_server].get("transport", "stdio")
+                if transport == "stdio":
+                    wrapped_tools.append(_make_session_pool_tool(tool, tool_server, servers_config[tool_server], tool_interceptors))
+                else:
+                    wrapped_tools.append(tool)
             else:
                 wrapped_tools.append(tool)
 

backend/packages/harness/deerflow/models/assistant_payload_replay.py

@@ -0,0 +1,124 @@
+"""Helpers for replaying provider-specific assistant message fields.
+
+Several provider adapters need to preserve fields that LangChain stores on the
+original ``AIMessage`` but drops when serializing request payloads. This module
+keeps the assistant-message matching logic shared while letting each provider
+decide which fields to restore.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Callable, Sequence
+from typing import Any
+
+from langchain_core.messages import AIMessage, BaseMessage
+
+AssistantPayloadRestorer = Callable[[dict[str, Any], AIMessage], None]
+
+
+def restore_assistant_payloads(
+    payload_messages: Sequence[dict[str, Any]],
+    original_messages: Sequence[BaseMessage],
+    restore: AssistantPayloadRestorer,
+) -> None:
+    """Restore provider-specific fields onto serialized assistant payloads."""
+    if len(payload_messages) == len(original_messages):
+        for payload_msg, orig_msg in zip(payload_messages, original_messages):
+            if payload_msg.get("role") == "assistant" and isinstance(orig_msg, AIMessage):
+                restore(payload_msg, orig_msg)
+        return
+
+    ai_messages = [m for m in original_messages if isinstance(m, AIMessage)]
+    assistant_payloads = [m for m in payload_messages if m.get("role") == "assistant"]
+    used_ai_indexes: set[int] = set()
+
+    for ordinal, payload_msg in enumerate(assistant_payloads):
+        ai_msg = _match_ai_message(payload_msg, ai_messages, used_ai_indexes, ordinal)
+        if ai_msg is not None:
+            restore(payload_msg, ai_msg)
+
+
+def restore_additional_kwargs_field(payload_msg: dict[str, Any], orig_msg: AIMessage, field_name: str) -> None:
+    """Copy a provider-specific ``additional_kwargs`` field onto a payload message."""
+    value = orig_msg.additional_kwargs.get(field_name)
+    if value is not None:
+        payload_msg[field_name] = value
+
+
+def restore_reasoning_content(payload_msg: dict[str, Any], orig_msg: AIMessage) -> None:
+    """Copy provider reasoning content onto a serialized assistant payload."""
+    restore_additional_kwargs_field(payload_msg, orig_msg, "reasoning_content")
+
+
+def _match_ai_message(
+    payload_msg: dict[str, Any],
+    ai_messages: Sequence[AIMessage],
+    used_ai_indexes: set[int],
+    fallback_ordinal: int,
+) -> AIMessage | None:
+    payload_key = _assistant_signature(payload_msg)
+    if payload_key is not None:
+        matches = [index for index, ai_msg in enumerate(ai_messages) if index not in used_ai_indexes and _ai_signature(ai_msg) == payload_key]
+        if len(matches) == 1:
+            used_ai_indexes.add(matches[0])
+            return ai_messages[matches[0]]
+
+    fallback_index = _next_unused_index_at_or_after(len(ai_messages), used_ai_indexes, fallback_ordinal)
+    if fallback_index is not None:
+        used_ai_indexes.add(fallback_index)
+        return ai_messages[fallback_index]
+
+    return None
+
+
+def _next_unused_index_at_or_after(count: int, used_ai_indexes: set[int], start: int) -> int | None:
+    """Return the next unused AI index at or after ``start``.
+
+    Scanning forward from the payload's ordinal preserves the positional bias of
+    the previous behaviour while still recovering when serialization drops or
+    reorders messages so the exact ordinal index is already taken. It does not
+    wrap to earlier indexes because those messages may be represented by payload
+    entries that were already dropped.
+    """
+    if count == 0 or start >= count:
+        return None
+    for index in range(start, count):
+        if index not in used_ai_indexes:
+            return index
+    return None
+
+
+def _assistant_signature(payload_msg: dict[str, Any]) -> tuple[str, str] | None:
+    return _signature(
+        payload_msg.get("content"),
+        _tool_call_ids(payload_msg.get("tool_calls") or []),
+    )
+
+
+def _ai_signature(message: AIMessage) -> tuple[str, str] | None:
+    tool_calls = message.tool_calls or message.additional_kwargs.get("tool_calls") or []
+    return _signature(message.content, _tool_call_ids(tool_calls))
+
+
+def _signature(content: Any, tool_call_ids: tuple[str, ...]) -> tuple[str, str] | None:
+    if content in (None, "") and not tool_call_ids:
+        return None
+    return (_stable_repr(content), "|".join(tool_call_ids))
+
+
+def _stable_repr(value: Any) -> str:
+    try:
+        return json.dumps(value, sort_keys=True, ensure_ascii=False)
+    except TypeError:
+        return repr(value)
+
+
+def _tool_call_ids(tool_calls: Sequence[Any]) -> tuple[str, ...]:
+    ids: list[str] = []
+    for tool_call in tool_calls:
+        if isinstance(tool_call, dict):
+            call_id = tool_call.get("id")
+            if isinstance(call_id, str) and call_id:
+                ids.append(call_id)
+    return tuple(ids)

backend/packages/harness/deerflow/models/factory.py

@@ -47,6 +47,38 @@ def _enable_stream_usage_by_default(model_use_path: str, model_settings_from_con
         model_settings_from_config["stream_usage"] = True
 
 
+# Default chunk-gap budget for OpenAI-compatible streaming responses.
+#
+# langchain-openai raises ``StreamChunkTimeoutError`` after this many seconds
+# without receiving a chunk. Its own default is 60s, which is too aggressive for
+# reasoning models (DeepSeek-R1, Doubao-thinking, GPT-5) whose first chunk can
+# legitimately take 90~150s. We default to 240s so the streaming layer rarely
+# trips on long thinking pauses; the LLMErrorHandlingMiddleware still retries
+# (budget=2) if a real stall happens. Users can override per-model in config.yaml.
+_DEFAULT_STREAM_CHUNK_TIMEOUT_SECONDS: float = 240.0
+
+
+def _apply_stream_chunk_timeout_default(model_use_path: str, model_settings_from_config: dict) -> None:
+    """Inject a generous ``stream_chunk_timeout`` for OpenAI-compatible clients.
+
+    The ``stream_chunk_timeout`` kwarg is specific to ``langchain_openai:ChatOpenAI``
+    and is rejected by other providers' constructors as an unexpected keyword
+    argument. Behaviour:
+
+    * OpenAI-compatible path: an explicit value in ``config.yaml`` is preserved.
+      An explicit ``null`` is dropped upstream by ``model_dump(exclude_none=True)``
+      and therefore treated as "unset", so the default is injected.
+    * Non-OpenAI path: drop the key so it is never forwarded to an incompatible
+      constructor (which would raise ``TypeError: unexpected keyword argument``).
+    """
+    if model_use_path != "langchain_openai:ChatOpenAI":
+        model_settings_from_config.pop("stream_chunk_timeout", None)
+        return
+    if "stream_chunk_timeout" in model_settings_from_config:
+        return
+    model_settings_from_config["stream_chunk_timeout"] = _DEFAULT_STREAM_CHUNK_TIMEOUT_SECONDS
+
+
 def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *, app_config: AppConfig | None = None, attach_tracing: bool = True, **kwargs) -> BaseChatModel:
     """Create a chat model instance from the config.
 
@@ -128,6 +160,7 @@ def create_chat_model(name: str | None = None, thinking_enabled: bool = False, *
         model_settings_from_config.pop("reasoning_effort", None)
 
     _enable_stream_usage_by_default(model_config.use, model_settings_from_config)
+    _apply_stream_chunk_timeout_default(model_config.use, model_settings_from_config)
 
     # For Codex Responses API models: map thinking mode to reasoning_effort
     from deerflow.models.openai_codex_provider import CodexChatModel

backend/packages/harness/deerflow/models/patched_deepseek.py

@@ -10,9 +10,10 @@ on all assistant messages when thinking mode is enabled.
 from typing import Any
 
 from langchain_core.language_models import LanguageModelInput
-from langchain_core.messages import AIMessage
 from langchain_deepseek import ChatDeepSeek
 
+from deerflow.models.assistant_payload_replay import restore_assistant_payloads, restore_reasoning_content
+
 
 class PatchedChatDeepSeek(ChatDeepSeek):
     """ChatDeepSeek with proper reasoning_content preservation.
@@ -49,25 +50,10 @@ class PatchedChatDeepSeek(ChatDeepSeek):
         # Call parent to get the base payload
         payload = super()._get_request_payload(input_, stop=stop, **kwargs)
 
-        # Match payload messages with original messages to restore reasoning_content
+        restore_assistant_payloads(
-        payload_messages = payload.get("messages", [])
+            payload.get("messages", []),
-
+            original_messages,
-        # The payload messages and original messages should be in the same order
+            restore_reasoning_content,
-        # Iterate through both and match by position
+        )
-        if len(payload_messages) == len(original_messages):
-            for payload_msg, orig_msg in zip(payload_messages, original_messages):
-                if payload_msg.get("role") == "assistant" and isinstance(orig_msg, AIMessage):
-                    reasoning_content = orig_msg.additional_kwargs.get("reasoning_content")
-                    if reasoning_content is not None:
-                        payload_msg["reasoning_content"] = reasoning_content
-        else:
-            # Fallback: match by counting assistant messages
-            ai_messages = [m for m in original_messages if isinstance(m, AIMessage)]
-            assistant_payloads = [(i, m) for i, m in enumerate(payload_messages) if m.get("role") == "assistant"]
-
-            for (idx, payload_msg), ai_msg in zip(assistant_payloads, ai_messages):
-                reasoning_content = ai_msg.additional_kwargs.get("reasoning_content")
-                if reasoning_content is not None:
-                    payload_messages[idx]["reasoning_content"] = reasoning_content
 
         return payload

backend/packages/harness/deerflow/models/patched_mimo.py

@@ -0,0 +1,140 @@
+"""Patched ChatOpenAI adapter for Xiaomi MiMo reasoning_content replay.
+
+MiMo's OpenAI-compatible API returns ``reasoning_content`` in thinking mode and
+requires that value to be replayed on historical assistant messages in
+multi-turn agent conversations. Standard ``langchain_openai.ChatOpenAI`` drops
+that provider-specific field, which can cause HTTP 400 errors once tool calls
+enter the conversation history.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any
+
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_openai import ChatOpenAI
+
+from deerflow.models.assistant_payload_replay import restore_assistant_payloads, restore_reasoning_content
+
+_MISSING = object()
+
+
+def _extract_reasoning_content(value: Any) -> str | object:
+    """Return reasoning_content from a dict/Pydantic object, preserving empty strings."""
+    if isinstance(value, Mapping):
+        if "reasoning_content" in value and value["reasoning_content"] is not None:
+            return value["reasoning_content"]
+        return _MISSING
+
+    reasoning = getattr(value, "reasoning_content", _MISSING)
+    if reasoning is not _MISSING and reasoning is not None:
+        return reasoning
+
+    model_extra = getattr(value, "model_extra", None)
+    if isinstance(model_extra, Mapping) and "reasoning_content" in model_extra and model_extra["reasoning_content"] is not None:
+        return model_extra["reasoning_content"]
+
+    return _MISSING
+
+
+def _with_reasoning_content(message: AIMessage | AIMessageChunk, reasoning: str) -> AIMessage | AIMessageChunk:
+    additional_kwargs = dict(message.additional_kwargs)
+    if additional_kwargs.get("reasoning_content") != reasoning:
+        additional_kwargs["reasoning_content"] = reasoning
+    return message.model_copy(update={"additional_kwargs": additional_kwargs})
+
+
+def _get_typed_choice_message(response: Any, index: int) -> Any:
+    choices = getattr(response, "choices", None)
+    if choices is None:
+        return None
+    try:
+        return choices[index].message
+    except (AttributeError, IndexError, TypeError):
+        return None
+
+
+class PatchedChatMiMo(ChatOpenAI):
+    """ChatOpenAI with ``reasoning_content`` preservation for MiMo thinking mode."""
+
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return True
+
+    @property
+    def lc_secrets(self) -> dict[str, str]:
+        return {"api_key": "MIMO_API_KEY", "openai_api_key": "MIMO_API_KEY"}
+
+    def _get_request_payload(
+        self,
+        input_: LanguageModelInput,
+        *,
+        stop: list[str] | None = None,
+        **kwargs: Any,
+    ) -> dict:
+        original_messages = self._convert_input(input_).to_messages()
+        payload = super()._get_request_payload(input_, stop=stop, **kwargs)
+        restore_assistant_payloads(
+            payload.get("messages", []),
+            original_messages,
+            restore_reasoning_content,
+        )
+
+        return payload
+
+    def _convert_chunk_to_generation_chunk(
+        self,
+        chunk: dict,
+        default_chunk_class: type,
+        base_generation_info: dict | None,
+    ) -> ChatGenerationChunk | None:
+        generation_chunk = super()._convert_chunk_to_generation_chunk(
+            chunk,
+            default_chunk_class,
+            base_generation_info,
+        )
+        if generation_chunk is None:
+            return None
+
+        choices = chunk.get("choices", [])
+        if choices:
+            delta = choices[0].get("delta") or {}
+            reasoning = _extract_reasoning_content(delta)
+            if reasoning is not _MISSING and isinstance(generation_chunk.message, AIMessageChunk):
+                generation_chunk = ChatGenerationChunk(
+                    message=_with_reasoning_content(generation_chunk.message, reasoning),
+                    generation_info=generation_chunk.generation_info,
+                )
+
+        return generation_chunk
+
+    def _create_chat_result(
+        self,
+        response: dict | Any,
+        generation_info: dict | None = None,
+    ) -> ChatResult:
+        result = super()._create_chat_result(response, generation_info)
+        response_dict = response if isinstance(response, dict) else response.model_dump()
+        choices = response_dict.get("choices", [])
+
+        patched_generations: list[ChatGeneration] | None = None
+        for index, generation in enumerate(result.generations):
+            choice = choices[index] if index < len(choices) else {}
+            choice_message = choice.get("message", {}) if isinstance(choice, Mapping) else {}
+            reasoning = _extract_reasoning_content(choice_message)
+            if reasoning is _MISSING and not isinstance(response, dict):
+                reasoning = _extract_reasoning_content(_get_typed_choice_message(response, index))
+
+            message = generation.message
+            if reasoning is not _MISSING and isinstance(message, AIMessage):
+                if patched_generations is None:
+                    patched_generations = list(result.generations)
+                patched_generations[index] = ChatGeneration(
+                    message=_with_reasoning_content(message, reasoning),
+                    generation_info=generation.generation_info,
+                )
+
+        return ChatResult(generations=patched_generations or result.generations, llm_output=result.llm_output)

backend/packages/harness/deerflow/models/patched_minimax.py

@@ -114,8 +114,27 @@ class PatchedChatMiniMax(ChatOpenAI):
             }
         else:
             payload["extra_body"] = {"reasoning_split": True}
+        self._strip_user_message_names(payload)
         return payload
 
+    @staticmethod
+    def _strip_user_message_names(payload: dict) -> None:
+        """Drop the per-message ``name`` field from user-role messages.
+
+        DeerFlow middlewares tag user messages with internal provenance names
+        (``user-input``, ``summary``, ``loop_warning``, ...). ``langchain_openai``
+        serializes those into the OpenAI-compatible request, but MiniMax requires
+        every user-role ``name`` to be identical and otherwise rejects the request
+        with ``invalid params, user name must be consistent (2013)``. MiniMax does
+        not use the per-message author name, so strip it.
+        """
+        messages = payload.get("messages")
+        if not isinstance(messages, list):
+            return
+        for message in messages:
+            if isinstance(message, dict) and message.get("role") == "user":
+                message.pop("name", None)
+
     def _convert_chunk_to_generation_chunk(
         self,
         chunk: dict,

backend/packages/harness/deerflow/models/patched_openai.py

@@ -27,6 +27,8 @@ from langchain_core.language_models import LanguageModelInput
 from langchain_core.messages import AIMessage
 from langchain_openai import ChatOpenAI
 
+from deerflow.models.assistant_payload_replay import restore_assistant_payloads
+
 
 class PatchedChatOpenAI(ChatOpenAI):
     """ChatOpenAI with ``thought_signature`` preservation for Gemini thinking via OpenAI gateway.
@@ -75,18 +77,7 @@ class PatchedChatOpenAI(ChatOpenAI):
         # Obtain the base payload from the parent implementation.
         payload = super()._get_request_payload(input_, stop=stop, **kwargs)
 
-        payload_messages = payload.get("messages", [])
+        restore_assistant_payloads(payload.get("messages", []), original_messages, _restore_tool_call_signatures)
-
-        if len(payload_messages) == len(original_messages):
-            for payload_msg, orig_msg in zip(payload_messages, original_messages):
-                if payload_msg.get("role") == "assistant" and isinstance(orig_msg, AIMessage):
-                    _restore_tool_call_signatures(payload_msg, orig_msg)
-        else:
-            # Fallback: match assistant-role entries positionally against AIMessages.
-            ai_messages = [m for m in original_messages if isinstance(m, AIMessage)]
-            assistant_payloads = [(i, m) for i, m in enumerate(payload_messages) if m.get("role") == "assistant"]
-            for (_, payload_msg), ai_msg in zip(assistant_payloads, ai_messages):
-                _restore_tool_call_signatures(payload_msg, ai_msg)
 
         return payload
 

backend/packages/harness/deerflow/models/patched_stepfun.py

@@ -0,0 +1,175 @@
+"""Patched ChatOpenAI adapter for StepFun reasoning models.
+
+StepFun returns ``reasoning`` (or ``reasoning_content`` with deepseek-style) in
+both streaming deltas and non-streaming responses. Standard ``ChatOpenAI``
+ignores these non-standard fields, so reasoning content is silently dropped.
+This adapter captures reasoning from all response paths and replays it on
+historical assistant messages for multi-turn tool-call conversations.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any
+
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.messages import AIMessage, AIMessageChunk
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_openai import ChatOpenAI
+
+from deerflow.models.assistant_payload_replay import (
+    restore_assistant_payloads,
+    restore_reasoning_content,
+)
+
+_MISSING = object()
+
+
+def _extract_reasoning(value: Any) -> str | object:
+    """Return reasoning content from a dict/Pydantic object.
+
+    StepFun may return reasoning via ``reasoning`` (default) or
+    ``reasoning_content`` (deepseek-style). Check both fields.
+    """
+    if isinstance(value, Mapping):
+        # Check reasoning_content first (deepseek-style), then reasoning (default)
+        for field in ("reasoning_content", "reasoning"):
+            if field in value and value[field] is not None:
+                return value[field]
+        return _MISSING
+
+    # Pydantic / SDK object attributes
+    for field in ("reasoning_content", "reasoning"):
+        attr = getattr(value, field, _MISSING)
+        if attr is not _MISSING and attr is not None:
+            return attr
+
+    # Some SDK versions store extra fields in model_extra
+    model_extra = getattr(value, "model_extra", None)
+    if isinstance(model_extra, Mapping):
+        for field in ("reasoning_content", "reasoning"):
+            if field in model_extra and model_extra[field] is not None:
+                return model_extra[field]
+
+    return _MISSING
+
+
+def _with_reasoning_content(message: AIMessage | AIMessageChunk, reasoning: str) -> AIMessage | AIMessageChunk:
+    """Return a copy of *message* with reasoning_content stored in additional_kwargs."""
+    additional_kwargs = dict(message.additional_kwargs)
+    if additional_kwargs.get("reasoning_content") != reasoning:
+        additional_kwargs["reasoning_content"] = reasoning
+    return message.model_copy(update={"additional_kwargs": additional_kwargs})
+
+
+def _get_typed_choice_message(response: Any, index: int) -> Any:
+    """Extract the SDK-typed choice message at *index*, if available."""
+    choices = getattr(response, "choices", None)
+    if choices is None:
+        return None
+    try:
+        return choices[index].message
+    except (AttributeError, IndexError, TypeError):
+        return None
+
+
+class PatchedChatStepFun(ChatOpenAI):
+    """ChatOpenAI with full reasoning support for StepFun models.
+
+    Captures ``reasoning`` / ``reasoning_content`` from both streaming and
+    non-streaming responses and replays it on historical assistant messages in
+    multi-turn tool-call conversations.
+    """
+
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        return True
+
+    @property
+    def lc_secrets(self) -> dict[str, str]:
+        return {"api_key": "STEPFUN_API_KEY", "openai_api_key": "STEPFUN_API_KEY"}
+
+    # --- Request payload replay ---
+
+    def _get_request_payload(
+        self,
+        input_: LanguageModelInput,
+        *,
+        stop: list[str] | None = None,
+        **kwargs: Any,
+    ) -> dict:
+        """Restore ``reasoning_content`` on historical assistant messages."""
+        original_messages = self._convert_input(input_).to_messages()
+        payload = super()._get_request_payload(input_, stop=stop, **kwargs)
+
+        restore_assistant_payloads(
+            payload.get("messages", []),
+            original_messages,
+            restore_reasoning_content,
+        )
+
+        return payload
+
+    # --- Streaming reasoning capture ---
+
+    def _convert_chunk_to_generation_chunk(
+        self,
+        chunk: dict,
+        default_chunk_class: type,
+        base_generation_info: dict | None,
+    ) -> ChatGenerationChunk | None:
+        """Capture ``reasoning`` / ``reasoning_content`` from streaming deltas."""
+        generation_chunk = super()._convert_chunk_to_generation_chunk(
+            chunk,
+            default_chunk_class,
+            base_generation_info,
+        )
+        if generation_chunk is None:
+            return None
+
+        choices = chunk.get("choices", [])
+        if choices:
+            delta = choices[0].get("delta") or {}
+            reasoning = _extract_reasoning(delta)
+            if reasoning is not _MISSING and isinstance(generation_chunk.message, AIMessageChunk):
+                generation_chunk = ChatGenerationChunk(
+                    message=_with_reasoning_content(generation_chunk.message, reasoning),
+                    generation_info=generation_chunk.generation_info,
+                )
+
+        return generation_chunk
+
+    # --- Non-streaming reasoning capture ---
+
+    def _create_chat_result(
+        self,
+        response: dict | Any,
+        generation_info: dict | None = None,
+    ) -> ChatResult:
+        """Extract ``reasoning`` / ``reasoning_content`` from non-streaming responses."""
+        result = super()._create_chat_result(response, generation_info)
+        response_dict = response if isinstance(response, dict) else response.model_dump()
+        choices = response_dict.get("choices", [])
+
+        patched_generations: list[ChatGeneration] | None = None
+        for index, generation in enumerate(result.generations):
+            choice = choices[index] if index < len(choices) else {}
+            choice_message = choice.get("message", {}) if isinstance(choice, Mapping) else {}
+            reasoning = _extract_reasoning(choice_message)
+
+            if reasoning is _MISSING and not isinstance(response, dict):
+                reasoning = _extract_reasoning(_get_typed_choice_message(response, index))
+
+            message = generation.message
+            if reasoning is not _MISSING and isinstance(message, AIMessage):
+                if patched_generations is None:
+                    patched_generations = list(result.generations)
+                patched_generations[index] = ChatGeneration(
+                    message=_with_reasoning_content(message, reasoning),
+                    generation_info=generation.generation_info,
+                )
+
+        return ChatResult(
+            generations=patched_generations or result.generations,
+            llm_output=result.llm_output,
+        )

backend/packages/harness/deerflow/runtime/checkpointer/async_provider.py

@@ -47,6 +47,41 @@ def _prepare_database_sqlite_checkpointer_path(db_config) -> str:
     return conn_str
 
 
+def _build_postgres_pool(conn_string: str):
+    """Build an AsyncConnectionPool with TCP keepalive and connection checking."""
+    from psycopg.rows import dict_row
+    from psycopg_pool import AsyncConnectionPool
+
+    return AsyncConnectionPool(
+        conn_string,
+        kwargs={
+            "autocommit": True,
+            "prepare_threshold": 0,
+            "row_factory": dict_row,
+            "keepalives": 1,
+            "keepalives_idle": 60,
+            "keepalives_interval": 10,
+            "keepalives_count": 6,
+        },
+        check=AsyncConnectionPool.check_connection,
+    )
+
+
+def _ensure_postgres_imports():
+    """Import and return (AsyncPostgresSaver, AsyncConnectionPool), raising ImportError on failure."""
+    try:
+        from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
+    except ImportError as exc:
+        raise ImportError(POSTGRES_INSTALL) from exc
+
+    try:
+        from psycopg_pool import AsyncConnectionPool
+    except ImportError as exc:
+        raise ImportError(POSTGRES_INSTALL) from exc
+
+    return AsyncPostgresSaver, AsyncConnectionPool
+
+
 # ---------------------------------------------------------------------------
 # Async factory
 # ---------------------------------------------------------------------------
@@ -74,15 +109,13 @@ async def _async_checkpointer(config) -> AsyncIterator[Checkpointer]:
         return
 
     if config.type == "postgres":
-        try:
-            from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
-        except ImportError as exc:
-            raise ImportError(POSTGRES_INSTALL) from exc
-
         if not config.connection_string:
             raise ValueError(POSTGRES_CONN_REQUIRED)
 
-        async with AsyncPostgresSaver.from_conn_string(config.connection_string) as saver:
+        AsyncPostgresSaver, _ = _ensure_postgres_imports()
+        pool = _build_postgres_pool(config.connection_string)
+        async with pool:
+            saver = AsyncPostgresSaver(conn=pool)
             await saver.setup()
             yield saver
         return
@@ -117,15 +150,13 @@ async def _async_checkpointer_from_database(db_config) -> AsyncIterator[Checkpoi
         return
 
     if db_config.backend == "postgres":
-        try:
-            from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
-        except ImportError as exc:
-            raise ImportError(POSTGRES_INSTALL) from exc
-
         if not db_config.postgres_url:
             raise ValueError("database.postgres_url is required for the postgres backend")
 
-        async with AsyncPostgresSaver.from_conn_string(db_config.postgres_url) as saver:
+        AsyncPostgresSaver, _ = _ensure_postgres_imports()
+        pool = _build_postgres_pool(db_config.postgres_url)
+        async with pool:
+            saver = AsyncPostgresSaver(conn=pool)
             await saver.setup()
             yield saver
         return

backend/packages/harness/deerflow/runtime/checkpointer/provider.py

@@ -21,12 +21,13 @@ from __future__ import annotations
 
 import contextlib
 import logging
+import threading
 from collections.abc import Iterator
 
 from langgraph.types import Checkpointer
 
 from deerflow.config.app_config import get_app_config
-from deerflow.config.checkpointer_config import CheckpointerConfig
+from deerflow.config.checkpointer_config import CheckpointerConfig, ensure_config_loaded
 from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
 
 logger = logging.getLogger(__name__)
@@ -100,6 +101,7 @@ def _sync_checkpointer_cm(config: CheckpointerConfig) -> Iterator[Checkpointer]:
 
 _checkpointer: Checkpointer | None = None
 _checkpointer_ctx = None  # open context manager keeping the connection alive
+_checkpointer_lock = threading.Lock()
 
 
 def get_checkpointer() -> Checkpointer:
@@ -116,34 +118,29 @@ def get_checkpointer() -> Checkpointer:
     if _checkpointer is not None:
         return _checkpointer
 
-    # Ensure app config is loaded before checking checkpointer config
+    # Config loading can reset both persistence singletons. Keep it outside
-    # This prevents returning InMemorySaver when config.yaml actually has a checkpointer section
+    # this provider lock to avoid cross-provider lock-order inversion.
-    # but hasn't been loaded yet
+    ensure_config_loaded()
-    from deerflow.config.app_config import _app_config
-    from deerflow.config.checkpointer_config import get_checkpointer_config
 
-    config = get_checkpointer_config()
+    with _checkpointer_lock:
+        if _checkpointer is not None:
+            return _checkpointer
+
+        from deerflow.config.checkpointer_config import get_checkpointer_config
 
-    if config is None and _app_config is None:
-        # Only load app config lazily when neither the app config nor an explicit
-        # checkpointer config has been initialized yet. This keeps tests that
-        # intentionally set the global checkpointer config isolated from any
-        # ambient config.yaml on disk.
-        try:
-            get_app_config()
-        except FileNotFoundError:
-            # In test environments without config.yaml, this is expected.
-            pass
         config = get_checkpointer_config()
-    if config is None:
-        from langgraph.checkpoint.memory import InMemorySaver
 
-        logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
+        if config is None:
-        _checkpointer = InMemorySaver()
+            from langgraph.checkpoint.memory import InMemorySaver
-        return _checkpointer
 
-    _checkpointer_ctx = _sync_checkpointer_cm(config)
+            logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
-    _checkpointer = _checkpointer_ctx.__enter__()
+            _checkpointer = InMemorySaver()
+            return _checkpointer
+
+        checkpointer_ctx = _sync_checkpointer_cm(config)
+        checkpointer = checkpointer_ctx.__enter__()
+        _checkpointer_ctx = checkpointer_ctx
+        _checkpointer = checkpointer
 
     return _checkpointer
 
@@ -155,13 +152,14 @@ def reset_checkpointer() -> None:
     Useful in tests or after a configuration change.
     """
     global _checkpointer, _checkpointer_ctx
-    if _checkpointer_ctx is not None:
+    with _checkpointer_lock:
-        try:
+        if _checkpointer_ctx is not None:
-            _checkpointer_ctx.__exit__(None, None, None)
+            try:
-        except Exception:
+                _checkpointer_ctx.__exit__(None, None, None)
-            logger.warning("Error during checkpointer cleanup", exc_info=True)
+            except Exception:
-        _checkpointer_ctx = None
+                logger.warning("Error during checkpointer cleanup", exc_info=True)
-    _checkpointer = None
+            _checkpointer_ctx = None
+        _checkpointer = None
 
 
 # ---------------------------------------------------------------------------

backend/packages/harness/deerflow/runtime/events/store/db.py

@@ -144,10 +144,13 @@ class DbRunEventStore(RunEventStore):
     async def put_batch(self, events):
         if not events:
             return []
+        thread_ids = {e["thread_id"] for e in events}
+        if len(thread_ids) > 1:
+            raise ValueError(f"put_batch requires all events to belong to the same thread; got {thread_ids!r}")
         user_id = self._user_id_from_context()
         async with self._sf() as session:
             async with session.begin():
-                # Get max seq for the thread (assume all events in batch belong to same thread).
+                # All events belong to the same thread (validated above).
                 thread_id = events[0]["thread_id"]
                 max_seq = await self._max_seq_for_thread(session, thread_id)
                 seq = max_seq or 0

backend/packages/harness/deerflow/runtime/events/store/jsonl.py

@@ -6,6 +6,15 @@ Each run's events are stored in a single file:
 All categories (message, trace, lifecycle) are in the same file.
 This backend is suitable for lightweight single-node deployments.
 
+**Single-process guarantee**: the in-memory seq counter is process-local.
+Multi-process deployments sharing the same directory will produce duplicate
+or non-monotonic seq values. Use ``DbRunEventStore`` for multi-process or
+high-concurrency deployments.
+
+File I/O is offloaded to a thread pool via ``asyncio.to_thread`` so the
+event loop is never blocked. Per-thread ``asyncio.Lock`` objects serialise
+writes within a single process to prevent interleaved JSONL lines.
+
 Known trade-off: ``list_messages()`` must scan all run files for a
 thread since messages from multiple runs need unified seq ordering.
 ``list_events()`` reads only one file -- the fast path.
@@ -13,6 +22,7 @@ thread since messages from multiple runs need unified seq ordering.
 
 from __future__ import annotations
 
+import asyncio
 import json
 import logging
 import re
@@ -30,6 +40,11 @@ class JsonlRunEventStore(RunEventStore):
     def __init__(self, base_dir: str | Path | None = None):
         self._base_dir = Path(base_dir) if base_dir else Path(".deer-flow")
         self._seq_counters: dict[str, int] = {}  # thread_id -> current max seq
+        # Per-thread asyncio.Lock — serialises concurrent writes within one process.
+        self._write_locks: dict[str, asyncio.Lock] = {}
+
+    def _get_write_lock(self, thread_id: str) -> asyncio.Lock:
+        return self._write_locks.setdefault(thread_id, asyncio.Lock())
 
     @staticmethod
     def _validate_id(value: str, label: str) -> str:
@@ -50,10 +65,8 @@ class JsonlRunEventStore(RunEventStore):
         self._seq_counters[thread_id] = self._seq_counters.get(thread_id, 0) + 1
         return self._seq_counters[thread_id]
 
-    def _ensure_seq_loaded(self, thread_id: str) -> None:
+    def _compute_max_seq(self, thread_id: str) -> int:
-        """Load max seq from existing files if not yet cached."""
+        """Scan all run files for a thread and return the current max seq (blocking I/O)."""
-        if thread_id in self._seq_counters:
-            return
         max_seq = 0
         thread_dir = self._thread_dir(thread_id)
         if thread_dir.exists():
@@ -64,7 +77,13 @@ class JsonlRunEventStore(RunEventStore):
                         max_seq = max(max_seq, record.get("seq", 0))
                     except json.JSONDecodeError:
                         logger.debug("Skipping malformed JSONL line in %s", f)
-                        continue
+        return max_seq
+
+    async def _ensure_seq_loaded(self, thread_id: str) -> None:
+        """Load max seq from existing files into the in-memory counter (non-blocking)."""
+        if thread_id in self._seq_counters:
+            return
+        max_seq = await asyncio.to_thread(self._compute_max_seq, thread_id)
         self._seq_counters[thread_id] = max_seq
 
     def _write_record(self, record: dict) -> None:
@@ -74,7 +93,7 @@ class JsonlRunEventStore(RunEventStore):
             f.write(json.dumps(record, default=str, ensure_ascii=False) + "\n")
 
     def _read_thread_events(self, thread_id: str) -> list[dict]:
-        """Read all events for a thread, sorted by seq."""
+        """Read all events for a thread, sorted by seq (blocking I/O)."""
         events = []
         thread_dir = self._thread_dir(thread_id)
         if not thread_dir.exists():
@@ -87,12 +106,11 @@ class JsonlRunEventStore(RunEventStore):
                     events.append(json.loads(line))
                 except json.JSONDecodeError:
                     logger.debug("Skipping malformed JSONL line in %s", f)
-                    continue
         events.sort(key=lambda e: e.get("seq", 0))
         return events
 
     def _read_run_events(self, thread_id: str, run_id: str) -> list[dict]:
-        """Read events for a specific run file."""
+        """Read events for a specific run file (blocking I/O)."""
         path = self._run_file(thread_id, run_id)
         if not path.exists():
             return []
@@ -104,25 +122,36 @@ class JsonlRunEventStore(RunEventStore):
                 events.append(json.loads(line))
             except json.JSONDecodeError:
                 logger.debug("Skipping malformed JSONL line in %s", path)
-                continue
         events.sort(key=lambda e: e.get("seq", 0))
         return events
 
+    def _delete_thread_files(self, thread_id: str) -> None:
+        thread_dir = self._thread_dir(thread_id)
+        if thread_dir.exists():
+            for f in thread_dir.glob("*.jsonl"):
+                f.unlink()
+
+    def _delete_run_file(self, thread_id: str, run_id: str) -> None:
+        path = self._run_file(thread_id, run_id)
+        if path.exists():
+            path.unlink()
+
     async def put(self, *, thread_id, run_id, event_type, category, content="", metadata=None, created_at=None):
-        self._ensure_seq_loaded(thread_id)
+        async with self._get_write_lock(thread_id):
-        seq = self._next_seq(thread_id)
+            await self._ensure_seq_loaded(thread_id)
-        record = {
+            seq = self._next_seq(thread_id)
-            "thread_id": thread_id,
+            record = {
-            "run_id": run_id,
+                "thread_id": thread_id,
-            "event_type": event_type,
+                "run_id": run_id,
-            "category": category,
+                "event_type": event_type,
-            "content": content,
+                "category": category,
-            "metadata": metadata or {},
+                "content": content,
-            "seq": seq,
+                "metadata": metadata or {},
-            "created_at": created_at or datetime.now(UTC).isoformat(),
+                "seq": seq,
-        }
+                "created_at": created_at or datetime.now(UTC).isoformat(),
-        self._write_record(record)
+            }
-        return record
+            await asyncio.to_thread(self._write_record, record)
+            return record
 
     async def put_batch(self, events):
         if not events:
@@ -134,7 +163,7 @@ class JsonlRunEventStore(RunEventStore):
         return results
 
     async def list_messages(self, thread_id, *, limit=50, before_seq=None, after_seq=None):
-        all_events = self._read_thread_events(thread_id)
+        all_events = await asyncio.to_thread(self._read_thread_events, thread_id)
         messages = [e for e in all_events if e.get("category") == "message"]
 
         if before_seq is not None:
@@ -147,13 +176,13 @@ class JsonlRunEventStore(RunEventStore):
             return messages[-limit:]
 
     async def list_events(self, thread_id, run_id, *, event_types=None, limit=500):
-        events = self._read_run_events(thread_id, run_id)
+        events = await asyncio.to_thread(self._read_run_events, thread_id, run_id)
         if event_types is not None:
             events = [e for e in events if e.get("event_type") in event_types]
         return events[:limit]
 
     async def list_messages_by_run(self, thread_id, run_id, *, limit=50, before_seq=None, after_seq=None):
-        events = self._read_run_events(thread_id, run_id)
+        events = await asyncio.to_thread(self._read_run_events, thread_id, run_id)
         filtered = [e for e in events if e.get("category") == "message"]
         if before_seq is not None:
             filtered = [e for e in filtered if e.get("seq", 0) < before_seq]
@@ -165,23 +194,25 @@ class JsonlRunEventStore(RunEventStore):
             return filtered[-limit:] if len(filtered) > limit else filtered
 
     async def count_messages(self, thread_id):
-        all_events = self._read_thread_events(thread_id)
+        all_events = await asyncio.to_thread(self._read_thread_events, thread_id)
         return sum(1 for e in all_events if e.get("category") == "message")
 
     async def delete_by_thread(self, thread_id):
-        all_events = self._read_thread_events(thread_id)
+        async with self._get_write_lock(thread_id):
-        count = len(all_events)
+            all_events = await asyncio.to_thread(self._read_thread_events, thread_id)
-        thread_dir = self._thread_dir(thread_id)
+            count = len(all_events)
-        if thread_dir.exists():
+            await asyncio.to_thread(self._delete_thread_files, thread_id)
-            for f in thread_dir.glob("*.jsonl"):
+            self._seq_counters.pop(thread_id, None)
-                f.unlink()
+            # Pop the lock inside the held scope to minimise the window where a new caller
-        self._seq_counters.pop(thread_id, None)
+            # could obtain a fresh lock while a waiting coroutine still holds the old one.
-        return count
+            # Note: coroutines that already acquired a reference to this lock before the
+            # delete will still proceed after we release — this is an accepted narrow race.
+            self._write_locks.pop(thread_id, None)
+            return count
 
     async def delete_by_run(self, thread_id, run_id):
-        events = self._read_run_events(thread_id, run_id)
+        async with self._get_write_lock(thread_id):
-        count = len(events)
+            events = await asyncio.to_thread(self._read_run_events, thread_id, run_id)
-        path = self._run_file(thread_id, run_id)
+            count = len(events)
-        if path.exists():
+            await asyncio.to_thread(self._delete_run_file, thread_id, run_id)
-            path.unlink()
+            return count
-        return count