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docs: align runtime docs with gateway mode (#2868)
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
This commit is contained in:
Eilen Shin
+20
-11
@@ -561,12 +561,13 @@ location /api/ {
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---
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## WebSocket Support
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## Streaming Support
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The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
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Gateway's LangGraph-compatible API streams run events with Server-Sent Events (SSE):
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```
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ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
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```http
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POST /api/langgraph/threads/{thread_id}/runs/stream
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Accept: text/event-stream
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```
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---
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@@ -602,13 +603,21 @@ const response = await fetch('/api/models');
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const data = await response.json();
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console.log(data.models);
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// Using EventSource for streaming
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const eventSource = new EventSource(
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`/api/langgraph/threads/${threadId}/runs/stream`
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);
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eventSource.onmessage = (event) => {
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console.log(JSON.parse(event.data));
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};
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// Create a run and stream SSE events
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const streamResponse = await fetch(`/api/langgraph/threads/${threadId}/runs/stream`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Accept: "text/event-stream",
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},
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body: JSON.stringify({
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input: { messages: [{ role: "user", content: "Hello" }] },
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stream_mode: ["values", "messages-tuple", "custom"],
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}),
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});
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const reader = streamResponse.body?.getReader();
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// Decode and parse SSE frames from reader in your client code.
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```
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### cURL Examples
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@@ -20,24 +20,22 @@ This document provides a comprehensive overview of the DeerFlow backend architec
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│ └────────────────────────────────────────────────────────────────────┘ │
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└─────────────────────────────────┬────────────────────────────────────────┘
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│
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┌───────────────────────┼───────────────────────┐
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│ │ │
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▼ ▼ ▼
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┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
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│ Embedded Runtime │ │ Gateway API │ │ Frontend │
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│ (inside Gateway) │ │ (Port 8001) │ │ (Port 3000) │
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│ │ │ │ │ │
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│ - Agent Runtime │ │ - Models API │ │ - Next.js App │
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│ - Thread Mgmt │ │ - MCP Config │ │ - React UI │
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│ - SSE Streaming │ │ - Skills Mgmt │ │ - Chat Interface │
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│ - Checkpointing │ │ - File Uploads │ │ │
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│ │ │ - Thread Cleanup │ │ │
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│ │ │ - Artifacts │ │ │
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└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
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│ │
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│ ┌─────────────────┘
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│ │
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▼ ▼
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┌───────────────────────┴───────────────────────┐
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│ │
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▼ ▼
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┌─────────────────────────────────────────────┐ ┌─────────────────────┐
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│ Gateway API │ │ Frontend │
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│ (Port 8001) │ │ (Port 3000) │
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│ │ │ │
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│ - LangGraph-compatible runs/threads API │ │ - Next.js App │
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│ - Embedded Agent Runtime │ │ - React UI │
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│ - SSE Streaming │ │ - Chat Interface │
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│ - Checkpointing │ │ │
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│ - Models, MCP, Skills, Uploads, Artifacts │ │ │
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│ - Thread Cleanup │ │ │
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└─────────────────────────────────────────────┘ └─────────────────────┘
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│
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▼
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┌──────────────────────────────────────────────────────────────────────────┐
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│ Shared Configuration │
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│ ┌─────────────────────────┐ ┌────────────────────────────────────────┐ │
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@@ -52,9 +50,9 @@ This document provides a comprehensive overview of the DeerFlow backend architec
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## Component Details
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### Embedded LangGraph Runtime
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### Gateway Embedded Agent Runtime
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The LangGraph-compatible runtime runs inside the Gateway process and is built on LangGraph for robust multi-agent workflow orchestration.
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The agent runtime is embedded in the FastAPI Gateway and built on LangGraph for robust multi-agent workflow orchestration. Nginx rewrites `/api/langgraph/*` to Gateway's native `/api/*` routes, so the public API remains compatible with LangGraph SDK clients without running a separate LangGraph server.
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**Entry Point**: `packages/harness/deerflow/agents/lead_agent/agent.py:make_lead_agent`
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@@ -65,7 +63,7 @@ The LangGraph-compatible runtime runs inside the Gateway process and is built on
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- Tool execution orchestration
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- SSE streaming for real-time responses
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**Configuration**: `langgraph.json`
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**Graph registry**: `langgraph.json` remains available for tooling and Studio compatibility.
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```json
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{
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@@ -84,6 +82,7 @@ FastAPI application providing REST endpoints plus the public LangGraph-compatibl
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**Routers**:
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- `models.py` - `/api/models` - Model listing and details
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- `thread_runs.py` / `runs.py` - `/api/threads/{id}/runs`, `/api/runs/*` - LangGraph-compatible runs and streaming
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- `mcp.py` - `/api/mcp` - MCP server configuration
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- `skills.py` - `/api/skills` - Skills management
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- `uploads.py` - `/api/threads/{id}/uploads` - File upload
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@@ -91,7 +90,7 @@ FastAPI application providing REST endpoints plus the public LangGraph-compatibl
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- `artifacts.py` - `/api/threads/{id}/artifacts` - Artifact serving
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- `suggestions.py` - `/api/threads/{id}/suggestions` - Follow-up suggestion generation
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The web conversation delete flow is now split across both backend surfaces: LangGraph handles `DELETE /api/langgraph/threads/{thread_id}` for thread state, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
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The web conversation delete flow first deletes Gateway-managed thread state through the LangGraph-compatible route, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
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### Agent Architecture
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@@ -354,9 +353,9 @@ SKILL.md Format:
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{"input": {"messages": [{"role": "user", "content": "Hello"}]}}
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2. Nginx → Gateway API (8001)
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Routes `/api/langgraph/*` to the Gateway's LangGraph-compatible runtime
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`/api/langgraph/*` is rewritten to Gateway's LangGraph-compatible `/api/*` routes
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3. Embedded LangGraph runtime
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3. Gateway embedded runtime
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a. Load/create thread state
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b. Execute middleware chain:
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- ThreadDataMiddleware: Set up paths
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@@ -412,7 +411,7 @@ SKILL.md Format:
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### Thread Cleanup Flow
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```
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1. Client deletes conversation via LangGraph
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1. Client deletes conversation via the LangGraph-compatible Gateway route
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DELETE /api/langgraph/threads/{thread_id}
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2. Web UI follows up with Gateway cleanup
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