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

@johnnywuj81

Repository

Basic information

Category

Memory & Knowledge

License

MIT

Author

johnnywuj81

About 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 config

Add this server to your MCP-compatible client using the configuration below.

{
  "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 tools

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Overview

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.

Frequently asked questions

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.

Comments

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