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fubenshuo/traceclaw

12PythonAudience · developerComplexity · 4/5Setup · hard

TLDR

A Python framework for building AI assistants that log every step they take, use a two-step safety check before running tools, maintain persistent memory across sessions, and can receive and reply to messages in Feishu group chats.

Mindmap

mindmap
  root((TraceClaw))
    Core Feature
      Transparent AI steps
      Structured action logs
    Safety
      Two-step tool review
      80 percent fewer mistakes
    Memory
      Long-term user profile
      Short-term conversation DB
    Scheduling
      Background heartbeat
      Recurring tasks
    Integrations
      Feishu messaging
      OpenAI-compatible APIs
      Anthropic
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Code map

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Things people build with this

USE CASE 1

Build an AI assistant that logs every tool call and decision so you can audit exactly what it did and why.

USE CASE 2

Connect an AI agent to a Feishu group chat so team members can interact with it from a phone without a separate interface.

USE CASE 3

Schedule daily or weekly recurring AI tasks that survive a restart because they are saved to a local file.

Tech stack

Python

Getting it running

Difficulty · hard Time to first run · 1h+

Requires configuring an AI provider API key, a local database, and optionally a Feishu app token, documentation is primarily in Chinese.

No license information was mentioned in the explanation.

In plain English

TraceClaw is a Python framework for building AI assistants that show their work. The central idea is that AI systems often behave like black boxes, making decisions and taking actions without explanation. TraceClaw tries to fix that by logging every step the AI takes in a structured format, so you can see exactly what inputs it received, what tools it called, and what results came back. The safety design uses a two-step process before any tool actually runs. First the AI reads the tool's documentation, then it decides whether to proceed with that specific tool or pick a different one. The README claims this reduces the rate of critical mistakes by about 80% compared to single-step tool execution. Memory works in two layers. A long-term profile file stores facts about the user that persist across sessions, such as preferences or job context. A short-term layer in a local database stores summaries of recent conversations and trims old context automatically to avoid running out of token space. A background heartbeat process checks a task queue every second and fires scheduled reminders or actions at set times, including daily, weekly, and monthly repeating tasks. These tasks survive a restart because they are saved to a local file. The framework can connect to Feishu, a business messaging platform popular in China (also known as Lark outside China), so the AI can receive messages from a Feishu group and reply directly into that conversation. This makes it possible to interact with the agent from a phone without running a separate interface. TraceClaw supports skills from two other projects (OpenClaw and Claude Code) without requiring rewriting, and it works with several AI providers including OpenAI-compatible APIs, Anthropic, and several Chinese cloud AI services. The primary documentation and interface text is in Chinese, though the code structure follows standard Python conventions.

Copy-paste prompts

Prompt 1
Using TraceClaw, build an AI assistant that connects to my Feishu group and answers questions about our project status by reading a local Markdown file.
Prompt 2
Set up TraceClaw with Anthropic as the AI provider and configure a daily reminder task that runs every morning at 9am and survives restarts.
Prompt 3
How does TraceClaw's two-step tool-use safety check work? Show me an example trace log from a session where the AI decided to switch tools after reading documentation.
Prompt 4
Add a long-term memory entry to TraceClaw that stores my name and preferred coding language so the agent personalizes replies across sessions.
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