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TokenKnows — distill AI coding sessions into weekly reports, ADR…

@johnnywuj81

代码仓库

基本信息

分类

记忆与知识

许可证

MIT

作者

johnnywuj81

关于 TokenKnows — distill AI coding sessions into weekly reports, ADRs and a knowledge graph MCP Server

Distill AI coding sessions (Claude Code / Codex / Cursor) into weekly reports, ADRs, incident reviews and a knowledge graph — local-first, evidence-linked.

TokenKnows — distill AI coding sessions into weekly reports, ADRs and a knowledge graph MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "tokenknows": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        ".venv",
        "&&",
        ".venv/bin/pip",
        "install",
        "-e",
        ".[dev]"
      ]
    }
  }
}

TokenKnows — distill AI coding sessions into weekly reports, ADRs and a knowledge graph MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is TokenKnows?

TokenKnows automatically captures AI coding sessions (from tools like Claude Code, Codex, and Cursor) and distills them into structured knowledge assets such as weekly reports, ADRs, and a knowledge graph. It is for developers who want to preserve decisions and insights from AI pair-programming sessions.

How to use TokenKnows?

Install the TokenKnows backend (FastAPI) and web UI, then add the MCP plugin for your AI tool (Claude Code, Codex, Cursor, or VS Code). The plugin provides MCP tools like submit_session_events and distill_document, plus slash commands like /tokenknows:weekly. Run the backend with uvicorn app.main:app and frontend with npm run dev.

Key features of TokenKnows

  • Captures AI coding sessions from Claude Code, Codex, Cursor, and more.
  • Distills into seven asset types including weekly reports, ADRs, and incident reviews.
  • Every paragraph traces back to original PR, conversation, or commit.
  • Local-first with three-layer LLM egress gate for privacy.
  • Can run fully offline with Ollama and zero cloud keys.
  • All collectors run locally without webhooks or tunnels.

Use cases of TokenKnows

  • Automatically generate weekly reports from AI coding sessions.
  • Create Architecture Decision Records (ADRs) from design discussions.
  • Produce incident reviews with evidence from PRs and commits.
  • Build a knowledge graph of entities from multiple sessions.
  • Reusable agent skills (SKILL.md) for future automation.

常见问题

1

Which AI tools does TokenKnows support?

It captures sessions from Claude Code, Codex, Cursor, VS Code, GitHub (PRs/commits/issues), and local documents.

2

Does TokenKnows require cloud LLM keys?

No. It can run fully locally with Ollama; optional cloud providers (Anthropic, OpenAI, MiniMax) require all three egress switches to be on.

3

How are citations ranked in documents?

Citations are ranked by `cosine × trust × recency`, requiring at least two distinct sources. Each paragraph links back to the original evidence.

4

Is TokenKnows local-first?

Yes. Zero egress by default; a three-layer LLM egress gate (instance, project, task) controls any cloud calls, with full audit logging.

5

What platforms are supported?

macOS provides a full experience with auto-starting collectors. Linux runs backend, frontend, and collectors manually. Windows is untested; WSL2 is recommended.

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