mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-22 07:56:48 +00:00
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).
89 lines
1.7 KiB
Plaintext
89 lines
1.7 KiB
Plaintext
---
|
||
title: 创建你的第一个 Harness
|
||
description: 本教程介绍如何以编程方式使用 DeerFlow Harness Python SDK——直接在你的 Python 代码中导入和使用 DeerFlow，而不是通过 Web 界面。
|
||
---
|
||
|
||
# 创建你的第一个 Harness
|
||
|
||
本教程介绍如何以编程方式使用 DeerFlow Harness Python SDK——直接在你的 Python 代码中导入和使用 DeerFlow，而不是通过 Web 界面。
|
||
|
||
## 前置条件
|
||
|
||
- Python 3.12+
|
||
- 已安装 `uv`
|
||
- 已克隆 DeerFlow 仓库
|
||
|
||
## 安装
|
||
|
||
```bash
|
||
cd deer-flow/backend
|
||
uv sync
|
||
```
|
||
|
||
## 创建配置
|
||
|
||
创建一个最小的 `config.yaml`：
|
||
|
||
```yaml
|
||
config_version: 6
|
||
|
||
models:
|
||
- name: gpt-4o
|
||
use: langchain_openai:ChatOpenAI
|
||
model: gpt-4o
|
||
api_key: $OPENAI_API_KEY
|
||
|
||
sandbox:
|
||
use: deerflow.sandbox.local:LocalSandboxProvider
|
||
|
||
tools:
|
||
- use: deerflow.community.ddg_search.tools:web_search_tool
|
||
- use: deerflow.sandbox.tools:read_file_tool
|
||
- use: deerflow.sandbox.tools:write_file_tool
|
||
```
|
||
|
||
## 编写代码
|
||
|
||
创建 `my_agent.py`：
|
||
|
||
```python
|
||
import asyncio
|
||
import os
|
||
from deerflow.client import DeerFlowClient
|
||
from deerflow.config import load_config
|
||
|
||
# 设置 API Key
|
||
os.environ["OPENAI_API_KEY"] = "sk-..."
|
||
|
||
# 加载配置
|
||
load_config()
|
||
|
||
client = DeerFlowClient()
|
||
|
||
async def main():
|
||
async for event in client.astream(
|
||
thread_id="my-first-thread",
|
||
message="用 Python 写一个斐波那契数列函数，包含文档字符串",
|
||
config={
|
||
"configurable": {
|
||
"model_name": "gpt-4o",
|
||
}
|
||
},
|
||
):
|
||
print(event)
|
||
|
||
asyncio.run(main())
|
||
```
|
||
|
||
## 运行
|
||
|
||
```bash
|
||
cd backend
|
||
uv run python my_agent.py
|
||
```
|
||
|
||
## 下一步
|
||
|
||
- [使用工具和技能](/docs/tutorials/use-tools-and-skills)
|
||
- [快速上手](/docs/harness/quick-start)
|