Claude Engram

Persistent memory and session intelligence for Claude Code. Hooks into the session lifecycle to auto-track mistakes, decisions, and context — then mines your full session history so past work resurfaces exactly when it's relevant.

Zero manual effort. Works with any MCP-compatible client.

What It Does

Everything below is automatic (hooks) unless marked as a tool:

• Tracks every edit, error, test result, and session event; captures decisions straight from your prompts ("let's use X")
• Injects the 3 most relevant memories before each file edit; warns before you repeat a past mistake
• Error deja-vu: a failure matching a known recurring error gets the past fix injected at failure time
• Verifies imports and shows blast radius before edits, and orients before reads — all from a per-project code index (AST, no LLM)
• Lists the project's known-good test commands at session start
• Survives compaction: checkpoint before, re-inject after; deliberate checkpoints live in a durable per-project ring
• Mines your full history in the background (and live, mid-session): decisions, mistakes, recurring struggles — searchable across everything you've ever discussed, scoped to the right sub-project
• Stays honest: failing TDD runs aren't logged as mistakes, edit loops get flagged, subagents are tracked without wasting their context
• Tools (on demand): memory, session_mine, work, context checkpoints, deps_map, impact_analyze, scout_search — all annotated read-only/idempotent where true. /engram loads the full reference.

How to Use It Effectively

From the author — mostly it just works in the background. The few things worth doing on purpose:

• Pull /engram when you want Claude to actively reach for the tools (background tracking happens either way).
• Half-remember something from weeks ago? Ask Claude to mine the sessions for it — it searches everything, not just what's in context.
• Something it should never forget → save it as a rule. Per-project rules stay local; rules at your workspace root cascade to every project under it.
• Before compacting, it auto-checkpoints — but a manual checkpoint with what you're doing and what's left resumes far cleaner. Deliberate saves always beat automatic ones.
• On return, ask what you said you'd do this session (session_mine(commitments)) — a quick, best-effort reorient from the live transcript.

The less you poke at it, the better it works. Work in progress — issues welcome.

How It Works

Claude Code
    |
    +-- Hooks (remind.py)          <- intercept every tool call (1-2s budget)
    +-- Session mining (mining/)   <- background + live-tick intelligence
    +-- MCP server (server.py)     <- on-demand tools
    +-- Scorer daemon              <- warm encoder + hook dispatch, cpu-resident;
                                      bulk embeddings in a transient GPU worker

Benchmarks

Retrieval (recall@k): LongMemEval 0.966 R@5 / 0.982 R@10 (500 questions), ConvoMem 0.960 (250 items), LoCoMo 0.649 R@10 (~2k questions); ~43ms/query, 112ms cross-session over 7,310 chunks.

Product behavior: integration suites green — decision capture (97.8% precision), error auto-capture (100% recall), compaction survival (6/6), multi-project isolation (11/11), edit-loop detection (12/12), session mining (64/64), Obsidian-vault compat (25/25).

Full tables and reproduction commands: library-book.

Compatibility

Platform	What Works	Auto-Capture
Claude Code (CLI, desktop, VS Code, JetBrains)	Everything	Full — hooks + session mining
Cursor / Windsurf / Continue.dev / Zed / any MCP client	MCP tools	No hooks
Obsidian vaults	Full (with CLAUDE.md at root)	Full with Claude Code

Install

git clone https://github.com/20alexl/claude-engram.git
cd claude-engram
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

pip install -e .                # Core
pip install -e ".[semantic]"    # + embedding model for vector search and semantic scoring

python install.py               # Hooks, MCP server, /engram skill, migrations

Per-Project Setup

python install.py --setup /path/to/your/project

Or copy .mcp.json to your project root. That's the only per-project file — hooks and the /engram skill are global. (The CLAUDE.md in this repo documents engram for people working on engram; your projects don't need it.)

Updating

cd claude-engram
git pull
pip install -e ".[semantic]"    # Reinstall if dependencies changed
python install.py               # Re-run to update hooks and /engram skill

Hooks pick up code changes immediately (editable install); reconnect the MCP server (/mcp) to reload it. Data migrations run automatically and are forward-only, idempotent, and downgrade-safe.

Mid-Project Adoption

Install normally. On first session, engram detects your existing Claude Code history and mines it in the background — decisions, mistakes, and patterns from every past conversation.

Configuration

All optional. Deep detail on each lives in the library-book.

Variable	Default	Description
CLAUDE_ENGRAM_MODEL	gemma3:12b	Ollama model — only scout_search, memory(consolidate), session_mine(reflect) use it
CLAUDE_ENGRAM_EMBED_MODEL	BAAI/bge-base-en-v1.5	Embedding model (~1.1GB scorer RAM). all-MiniLM-L6-v2 for a ~90MB setup at lower accuracy
CLAUDE_ENGRAM_EMBED_DIM	model native	Matryoshka truncation dim. Stores are signature-stamped — model changes rebuild them automatically
CLAUDE_ENGRAM_DEVICE	smart	Unset: daemon stays on cpu, bulk jobs use a transient GPU worker (full VRAM release). cuda/cpu forces one device
CLAUDE_ENGRAM_GPU_BULK_MIN	512	Job size (texts) that routes to the GPU worker
CLAUDE_ENGRAM_LIVE_MINE	300	Live mining tick interval (seconds); 0 disables
CLAUDE_ENGRAM_ARCHIVE_DAYS	14	Days until inactive memories archive
CLAUDE_ENGRAM_SCORER_TIMEOUT	1800	Scorer daemon idle timeout (seconds)
CLAUDE_ENGRAM_DIR	~/.claude_engram	Storage location (also the test-isolation seam)
CLAUDE_ENGRAM_SESSION_RETENTION_DAYS	0 (keep all)	Prune session-search shards older than N days
CLAUDE_ENGRAM_LAST_FILE_PATH	unset	Mirror last-read file path to this file (statusline integration)
CLAUDE_ENGRAM_HOOK_DEBUG	unset	1 prints a stderr breadcrumb per hook

~/.claude_engram/config.json additionally accepts embed_model, embed_dim, and lessons_globs (opt-in lessons bridge: globs of curated markdown whose dated entries sync as protected memories).

Reindexing

If search quality degrades or after a big update:

python scripts/reindex.py "/path/to/your/workspace" --force            # rebuild search index
python scripts/reindex.py "/path/to/your/workspace" --force --extract  # also re-extract decisions/mistakes

Or via MCP: session_mine(operation="reindex", mode="bootstrap")

Documentation

Library Book — design, internals, full usage guide, API reference, gotchas, changelog.

/engram — quick tool reference (installed by install.py).

License

MIT