Rekindle

Rekindle

Your AI forgets everything between sessions. Rekindle fixes that. For Claude Code users who lose time re-explaining project context every session.

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<div align="center">

Rekindle

npm tests license

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.

</div>


Rekindle init demo

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

v0.2.0 — orientation domain layer, end_session tool, typed continuity records. Release notes

Quick Start

npx rekindle init

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

<details open> <summary><strong>Claude Code</strong></summary>

Add to ~/.claude.json:

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

</details>

<details> <summary><strong>Claude Desktop</strong></summary>

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

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

</details>

<details> <summary><strong>Cursor</strong></summary>

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

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

</details>

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:

Metric Value
Sessions analyzed 43
Clean boots (all context loaded) 33%
High-signal failures (5+ gaps) 26%
Total retrieval failures 173

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?)
  +-- Detect gaps (what am I missing?)
  +-- Calculate orientation score (how oriented am I?)
  --> "Carrying forward: [context loaded, gaps identified, score: 80/100]"

Healthy output:

## Orientation Score
100/100

+20  Identity document loaded
+20  Recent checkpoint exists
+20  Session transcript found
+20  Recent memories exist (last 7 days)
+10  Relationship/preference memories populated
+10  Project-scoped memories found

Sparse output (flags what's missing):

## Gaps Detected
- [critical] identity_missing: No identity document found
- [warning] checkpoint_missing: No recent checkpoint

## Orientation Score
20/100

 ✗  Identity document loaded (20pts)
 ✗  Recent checkpoint exists (20pts)
 ✗  Session transcript found (20pts)
+20  Recent memories exist (last 7 days)
 ✗  Relationship/preference memories populated (10pts)
 ✗  Project-scoped memories found (10pts)

Capture: close the loop at session end

end_session stores structured continuity records — not just a summary:

Field What it captures
checkpoint Where we left off (required)
decisions What was decided and why
open_loops Unresolved tasks or questions
constraints Boundaries that must not be violated
relational_delta What changed in the working relationship
next_session_focus Where to resume next session
preferences New user preferences learned
warnings Things next session should watch for

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

Between sessions: search and manage

Tool Description
store_memory Store with content, category, importance (1-10), and project scope
search_memory Full-text search with BM25 ranking, boosted by importance
list_memories Browse memories, newest first. Filter by category or project
delete_memory Delete by ID
update_memory Update content, category, or importance

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

v0.2.0 Highlights

  • 7 MCP tools — added end_session for structured session close
  • Orientation domain layerboot_report is now a thin wrapper; all logic in OrientationService, GapDetector, Scorer
  • Typed continuity records — memories carry type, source, session_id instead of content prefixes
  • Orientation scoring — 100-point additive checklist across 6 criteria
  • Structured gaps{ code, severity, message } with 8 gap codes
  • 64 automated tests — unit, integration, and performance

Install from Source

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

<details> <summary><strong>Session Hooks</strong></summary>

Two optional Python hooks for Claude Code (stdlib only, zero external dependencies):

extract-session.py (Stop hook): Extracts a Markdown transcript from the session JSONL when a session ends.

pre-compact-capture.py (PreCompact hook): Saves the last 80 messages before context compaction.

{
  "hooks": {
    "Stop": [{
      "type": "command",
      "command": "python3 /path/to/rekindle/hooks/extract-session.py"
    }]
  }
}
Variable Default Description
REKINDLE_TRANSCRIPT_DIR .rekindle/transcripts/ Where transcripts are saved
REKINDLE_SESSIONS_DIR Auto-detected Claude Code sessions directory
REKINDLE_HUMAN_NAME Human Name for human messages
REKINDLE_AI_NAME Assistant Name for AI messages
REKINDLE_TIMEZONE UTC Timezone for timestamps

</details>

<details> <summary><strong>Privacy and Security</strong></summary>

  • 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.
  • Transcript capture is optional. The hooks are not installed by default.
  • 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.

</details>

Compatibility

Client Transport Status
Claude Code (macOS) stdio Tested
Claude Code (Linux/WSL2) stdio Tested
Claude Code (Windows) stdio Tested
Claude Desktop stdio Compatible (same MCP config format)
Cursor stdio Compatible (same MCP config format)
Any MCP stdio client stdio Compatible

<details> <summary><strong>Architecture</strong></summary>

rekindle/
  src/
    index.ts          MCP server entry point
    server.ts         Server setup, tool registration
    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
    tools/
      boot-report.ts  Thin wrapper over OrientationService
      end-session.ts  Structured session close
      store.ts search.ts list.ts delete.ts update.ts
    init/
      cli.ts scaffold.ts templates/
  hooks/
    extract-session.py
    pre-compact-capture.py

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.

</details>

Tests

npm test

64 tests: storage CRUD + FTS5 ranking, orientation domain (gap detection, scoring, service, rendering), MCP integration (all 7 tools), and performance (1000-memory search under 100ms).

Roadmap

v0.3: "It thinks in networks" — Spreading activation, semantic search via embeddings, open loops in boot reports, gap analysis tooling, eval harness.

License

MIT

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