智慧AI工作流 是 AI Skill Hub 本期精选Agent工作流之一。已获得 1.3k 颗 GitHub Star,综合评分 8.0 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
智慧AI工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
智慧AI工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 克隆仓库 git clone https://github.com/ThousandBirdsInc/chidori cd chidori # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 chidori --help # 基本运行 chidori [options] <input> # 详细使用说明请查阅文档 # https://github.com/ThousandBirdsInc/chidori
# chidori 配置说明 # 查看配置选项 chidori --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export CHIDORI_CONFIG="/path/to/config.yml"
export LITELLM_API_URL=http://localhost:4401/v1 export LITELLM_API_KEY=sk-litellm-master-key
```bash
./target/debug/chidori demo
See examples/:
agents/hello.ts — minimal agent, no LLMagents/summarizer.ts — LLM summary pipelineagents/streaming_progress.ts — labelled prompt progress streamsagents/webhook.ts — event-driven HTTP handleragents/tool_use.ts — tool call examplesdk_demo.py — Python SDK with checkpointing + replayprompts/analysis.jinja — shared prompt templatetools/web_search.ts — simple tool definitionlegacy-starlark/ — archived Starlark examples kept for migration referenceThe easiest way to explore Chidori is the interactive demo picker:
cargo build
./target/debug/chidori demo
chidori demo shows a numbered list of runnable examples, including demos that do not need an LLM provider and demos that exercise prompt tracing or streaming when provider environment variables are configured. Choose Hello agent for the fastest no-key path.
That demo runs a TypeScript agent, records a durable host-call log, and returns JSON. The direct command is:
./target/debug/chidori run examples/agents/hello.ts --input name=Colton
Expected output:
{
"greeting": "Hello, Colton!"
}
What this demonstrates:
- examples/agents/hello.ts exports agent(input, chidori). - The agent calls chidori.log(...), so the runtime records a host call. - The agent returns plain JSON, which is what CLI, server, and SDK users receive. - A checkpoint is written under examples/agents/.chidori/runs/<run_id>/ for trace/replay workflows.
You can inspect the most recent run:
RUN_ID=$(ls -t examples/agents/.chidori/runs | head -1)
./target/debug/chidori trace "$RUN_ID" --dir examples/agents
./target/debug/chidori snapshot "$RUN_ID" --dir examples/agents
This demo shows the session API pausing on chidori.input(...) and resuming from the persisted VM state.
Start the server:
./target/debug/chidori serve examples/agents/input_pause.ts --port 8080
In another terminal, create a session:
curl -s http://localhost:8080/sessions \
-H "Content-Type: application/json" \
-d '{"input":{"request":"ship the TypeScript runtime"}}'
The response will have "status":"paused", an "id", and "pending_prompt":"Approve this request?". Resume it with:
SESSION_ID=<paste id from the previous response>
curl -s http://localhost:8080/sessions/$SESSION_ID/resume \
-H "Content-Type: application/json" \
-d '{"response":"yes"}'
The completed response includes:
{
"output": {
"request": "ship the TypeScript runtime",
"approved": true
}
}
That flow is the core Chidori loop: TypeScript code runs until a durable host operation pauses, Chidori persists the run, and resume continues from the saved state.
cargo build ./target/debug/chidori run agents/summarizer.ts \ --input document="Rust is a systems programming language..." ```
chidori serve agents/my_agent.ts --port 8080
Exposes: - GET /health — health check - ANY /* — any request is passed to agent(event) as an event dict - POST /sessions — create a session and run the agent with given input - GET /sessions — list all sessions - GET /sessions/{id} — get session result - GET /sessions/{id}/checkpoint — get the call log and snapshot manifest metadata - GET /sessions/{id}/snapshot — inspect snapshot manifest metadata without raw VM bytes - POST /sessions/{id}/resume — resume a paused input() or approval session - POST /sessions/{id}/replay — replay from a session's checkpoint - POST /sessions/{id}/cancel — cancel a running or stored session - POST /sessions/stream — run a session with SSE call and prompt progress events
The Python SDK is a pure-stdlib HTTP client that talks to a running chidori serve instance. No pip install, no native bindings.
```python import sys sys.path.insert(0, "sdk/python")
from chidori import AgentClient, Checkpoint
client = AgentClient("http://localhost:8080")
高质量的AI工作流项目
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建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
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经综合评估,智慧AI工作流 在Agent工作流赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | chidori |
| 原始描述 | 开源AI工作流:A reactive runtime for building durable AI agents。⭐1.3k · C |
| Topics | AI工作流代理调试框架 |
| GitHub | https://github.com/ThousandBirdsInc/chidori |
| License | Apache-2.0 |
| 语言 | C |
收录时间:2026-05-28 · 更新时间:2026-05-28 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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