Rekindle

For Claude Code users who lose time re-explaining project context every session.

npx rekindle init

Your AI forgets everything between sessions. Rekindle fixes that.

Rekindle is an MCP continuity engine that solves session orientation, not just storage. Orient at session start, capture at session end, survive mid-session compaction. All local, all SQLite, zero API keys.

v0.3.0 — "Survive the Long Middle" — PreCompact capture system, open loops, review tracking. Release notes

Quick Start

npx rekindle init

This creates .rekindle/ in your project with a SQLite database, identity template, captures directory, and transcript directory. Then add the MCP server config for your client:

Add to ~/.claude.json:

{
  "mcpServers": {
    "rekindle": {
      "command": "npx",
      "args": ["-y", "rekindle"]
    }
  }
}

Enable PreCompact protection (captures context before mid-session compaction):

npx rekindle setup-hooks

Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "rekindle": {
      "command": "npx",
      "args": ["-y", "rekindle"]
    }
  }
}

Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "rekindle": {
      "command": "npx",
      "args": ["-y", "rekindle"]
    }
  }
}

Then fill in .rekindle/identity.md and paste the boot instructions into your project's CLAUDE.md.

Session 1 stores. Session 2 remembers. Session 10 anticipates.

The Problem (43 Sessions of Data)

Over 43 sessions, we measured what an AI assistant failed to load at session start:

Existing memory tools (Mem0, Letta, Zep) optimize for retrieval accuracy: can the AI find what it stored? That's necessary but not sufficient. None of them address whether the AI loaded the right context for this session, or whether it can detect what it missed.

Rekindle solves session orientation: loading identity, recent context, memory health, and missing-context warnings before the assistant starts work.

See docs/gap-analysis.md for the full research dataset.

What It Does

Boot: orient at session start

boot_report runs an orientation pipeline before any work begins:

boot_report
  +-- Read identity document (who am I working with?)
  +-- Scan memory stats (what do I know?)
  +-- Find latest checkpoint (where did we leave off?)
  +-- Read last transcript (what actually happened?)
  +-- Surface open loops (what needs follow-up?)
  +-- Surface PreCompact captures (what survived compaction?)
  +-- Detect gaps (what am I missing?)
  +-- Calculate orientation score (how oriented am I?)
  --> "Carrying forward: [context loaded, gaps identified, score: 80/100]"

Survive the Long Middle: PreCompact capture (v0.3)

Mid-session compaction destroys reasoning chains, failed approaches, relational texture, and tone. The PreCompact hook fires automatically before compaction and saves what would otherwise be lost:

PreCompact hook fires
  +-- Parse JSONL transcript (last N messages)
  +-- Write raw Markdown capture (.rekindle/captures/)
  +-- Write structured JSON snapshot (decisions, open loops, files)
  +-- Update manifest for cheap listing
  --> boot_report surfaces captures on next session start
  --> end_session warns if captures exist but weren't reviewed

Three read modes control token cost:

• summary — one paragraph, cheap
• structured — decisions/loops/warnings, moderate
• raw — full transcript excerpt, expensive (only when needed)

Capture: close the loop at session end

end_session stores structured continuity records — not just a summary:

All records stored with type, source, and session_id metadata. Next boot_report loads the checkpoint automatically.

Between sessions: search and manage

Categories: preference lesson context relationship general

Why not just CLAUDE.md?

A static file is passive. Your AI reads it, but it can't search it, rank it, track what's been retrieved, or tell you what's missing. Rekindle adds:

• Search — full-text with importance-weighted ranking
• Structure — category and project scoping across memories
• Orientation — proactive context loading at boot, not just on-demand retrieval
• Gap detection — flags missing identity, empty categories, stale data
• Scoring — transparent checklist so you know how oriented the AI is
• Session capture — structured close with checkpoints, decisions, and open loops
• Compaction survival — PreCompact captures preserve what summaries flatten

v0.3.0 Highlights

• 10 MCP tools — added list_captures, read_capture, capture_now
• PreCompact capture system — automatic context preservation before mid-session compaction
• Open loops in boot_report — surfaces unresolved tasks from prior sessions
• Review tracking — captures marked reviewed after read_capture; end_session warns if unreviewed
• Hook setup — npx rekindle setup-hooks configures Claude Code PreCompact hook
• Auto-discovery — capture_now discovers session transcript without manual paths
• 101 automated tests — unit, integration, capture manager, hook setup

CLI Commands

Install from Source

git clone https://github.com/Skitchy/rekindle.git
cd rekindle
npm install
npm run build
node dist/init/cli.js init

The setup-hooks command writes this to .claude/settings.local.json:

{
  "hooks": {
    "PreCompact": [
      {
        "matcher": "auto",
        "hooks": [
          {
            "type": "command",
            "command": "npx rekindle precompact-capture",
            "timeout": 60
          }
        ]
      },
      {
        "matcher": "manual",
        "hooks": [
          {
            "type": "command",
            "command": "npx rekindle precompact-capture",
            "timeout": 60
          }
        ]
      }
    ]
  }
}

The hook receives session context on stdin (session_id, transcript_path, cwd, hook_event_name) and writes captures to .rekindle/captures/.

• All data is local. Nothing is sent to external servers.
• No network calls. The MCP server communicates via stdio. No HTTP, no telemetry, no analytics.
• Transcripts contain conversation text. Do not enable transcript capture if your sessions contain secrets or credentials.
• Hook installation is opt-in. setup-hooks must be run explicitly (not auto-installed by init).
• SQLite database is a regular file. Not encrypted. Use OS-level disk encryption if needed.
• .rekindle/ is gitignored. The init command handles this automatically.
• boot_report reads local files. Paths are not sandboxed. Only use with MCP clients and prompts you trust.

Compatibility

rekindle/
  src/
    index.ts          MCP server entry point
    server.ts         Server setup, tool registration (10 tools)
    storage/
      sqlite.ts       SQLite + FTS5, schema migration, sessions
    orientation/
      types.ts        OrientationResult, Gap, ScoreItem
      GapDetector.ts  Structural gap detection (8 codes)
      Scorer.ts       Orientation scoring (6 criteria, 100pts)
      OrientationService.ts   Orchestrator
      OrientationRenderer.ts  Markdown + JSON output
    captures/
      types.ts        CaptureEntry, StructuredSnapshot, HookInput
      CaptureManager.ts   Parse, capture, list, read, review tracking
      discover-transcript.ts  Auto-discover session transcripts
      precompact-capture.ts   CLI hook entry point
      capture-now.ts          Manual capture CLI
    tools/
      boot-report.ts  Orientation + open loops + capture awareness
      end-session.ts  Structured session close + capture warning
      list-captures.ts  List PreCompact captures
      read-capture.ts   Read captures in 3 modes
      capture-now.ts    Model-triggered manual capture
      store.ts search.ts list.ts delete.ts update.ts
    init/
      cli.ts scaffold.ts setup-hooks.ts templates/

Storage: SQLite + FTS5 via better-sqlite3. BM25 ranking boosted by importance. Typed records with type, source, session_id.

Transport: stdio (standard MCP). Works with Claude Code out of the box.

Tests

npm test

101 tests: storage CRUD + FTS5 ranking, orientation domain (gap detection, scoring, service, rendering), capture manager (parsing, limits, review tracking, formatting), hook setup (schema, idempotency), and MCP integration (all 10 tools).

Roadmap

v0.4: "It thinks in networks" — Spreading activation, semantic search via embeddings, gap analysis tooling, eval harness.

License

MIT