mirror of
https://github.com/bytedance/deer-flow.git
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greatmengqi
3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
65 lines
1.9 KiB
Plaintext
65 lines
1.9 KiB
Plaintext
---
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title: Use Tools and Skills
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description: This tutorial shows you how to configure and use tools and skills in DeerFlow to give the agent access to web search, file operations, and domain-specific capabilities.
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---
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import { Callout } from "nextra/components";
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# Use Tools and Skills
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This tutorial shows you how to configure and use tools and skills in DeerFlow to give the agent access to web search, file operations, and domain-specific capabilities.
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## Configuring tools
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Add tools to `config.yaml`:
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```yaml
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tools:
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# Web search
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- use: deerflow.community.ddg_search.tools:web_search_tool
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# Web content fetching
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- use: deerflow.community.jina_ai.tools:web_fetch_tool
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# Sandbox file operations
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- use: deerflow.sandbox.tools:ls_tool
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- use: deerflow.sandbox.tools:read_file_tool
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- use: deerflow.sandbox.tools:write_file_tool
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- use: deerflow.sandbox.tools:bash_tool
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```
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## Enabling skills
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Enable skills through the DeerFlow app's extensions panel, or edit `extensions_config.json` directly.
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**Via the app UI:**
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1. Open the DeerFlow app
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2. Click the Extensions/Skills icon in the sidebar
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3. Find `deep-research` and toggle it on
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## Using a skill for research
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With the `deep-research` skill enabled, select it in the conversation input, then send a research request:
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```
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Do a deep research on the latest advances in quantum computing, focusing on practical applications.
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```
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The agent will run a multi-step research workflow including web search, information synthesis, and report generation.
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## Using the data analysis skill
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Enable `data-analysis`, then upload a CSV or data file and ask the agent to analyze it:
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```
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Analyze this CSV file and identify the top trends.
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```
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The agent will use the sandbox tools to read the file, run analysis, and produce charts.
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## Next steps
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- [Work with Memory](/docs/tutorials/work-with-memory)
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- [Tools Reference](/docs/harness/tools)
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- [Skills Reference](/docs/harness/skills)
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