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engram — AI coding memory

Install · Usage · MCP Server · How It Works · Integration Guide · Contributing

CI License Node Tests Zero LLM cost Zero native deps

Your AI coding assistant forgets everything. We fixed that.

engram gives AI coding tools persistent memory. One command scans your codebase, builds a knowledge graph, and makes every session start where the last one left off.

Zero LLM cost. Zero cloud. Works with Claude Code, Cursor, Codex, aider, and any MCP client.

npx engram init
🔍 Scanning codebase...
🌳 AST extraction complete (42ms, 0 tokens used)
   60 nodes, 96 edges from 14 files (2,155 lines)

📊 Token savings: 11x fewer tokens vs relevant files
   Full corpus: ~15,445 tokens | Graph query: ~285 tokens

✅ Ready. Your AI now has persistent memory.

Why

Every AI coding session starts from zero. Claude Code re-reads your files. Cursor reindexes. Copilot has no memory. CLAUDE.md is a sticky note you write by hand.

engram fixes this with five things no other tool combines:

  1. Persistent knowledge graph — survives across sessions, stored in .engram/graph.db
  2. Learns from every session — decisions, patterns, mistakes are extracted and remembered
  3. Universal protocol — MCP server + CLI + auto-generates CLAUDE.md, .cursorrules, AGENTS.md
  4. Skill-aware (v0.2) — indexes your ~/.claude/skills/ directory into the graph so queries return code and the skill to apply
  5. Regret buffer (v0.2) — surfaces past mistakes at the top of query results so your AI stops re-making the same wrong turns

Install

npx engramx init

Or install globally:

npm install -g engramx
engram init

Requires Node.js 20+. Zero native dependencies. No build tools needed.

Usage

engram init [path]              # Scan codebase, build knowledge graph
engram init --with-skills       # Also index ~/.claude/skills/ (v0.2)
engram query "how does auth"    # Query the graph (BFS, token-budgeted)
engram query "auth" --dfs       # DFS traversal (trace specific paths)
engram gods                     # Show most connected entities
engram stats                    # Node/edge counts, token savings
engram bench                    # Token reduction benchmark
engram path "auth" "database"   # Shortest path between concepts
engram learn "chose JWT..."     # Teach a decision or pattern
engram mistakes                 # List known mistakes (v0.2)
engram gen                      # Generate CLAUDE.md section from graph
engram gen --task bug-fix       # Task-aware view (v0.2: general|bug-fix|feature|refactor)
engram hooks install            # Auto-rebuild on git commit

How It Works

engram runs three miners on your codebase. None of them use an LLM.

AST Miner — Extracts code structure (classes, functions, imports, exports, call patterns) using pattern matching across 10 languages: TypeScript, JavaScript, Python, Go, Rust, Java, C, C++, Ruby, PHP. Zero tokens, deterministic, cached.

Git Miner — Reads git log for co-change patterns (files that change together), hot files (most frequently modified), and authorship. Creates INFERRED edges between structurally coupled files.

Session Miner — Scans CLAUDE.md, .cursorrules, AGENTS.md, and .engram/sessions/ for decisions, patterns, and mistakes your team has documented. Stores these as queryable graph nodes.

Results are stored in a local SQLite database (.engram/graph.db) and queryable via CLI, MCP server, or programmatic API.

MCP Server

Connect engram to Claude Code, Windsurf, or any MCP client:

{
  "mcpServers": {
    "engram": {
      "command": "npx",
      "args": ["-y", "engramx", "serve", "/path/to/your/project"]
    }
  }
}

Or if installed globally:

{
  "mcpServers": {
    "engram": {
      "command": "engram-serve",
      "args": ["/path/to/your/project"]
    }
  }
}

MCP Tools (6 total):

  • query_graph — Search the knowledge graph with natural language
  • god_nodes — Core abstractions (most connected entities)
  • graph_stats — Node/edge counts, confidence breakdown
  • shortest_path — Trace connections between two concepts
  • benchmark — Token reduction measurement
  • list_mistakes — Known failure modes from past sessions (v0.2)

Shell Wrapper (for Bash-based agents)

If your agent stack runs shell commands instead of JSON-RPC MCP, use the reference wrapper at scripts/mcp-engram. One command handles all projects via -p <path> — no per-project MCP server needed.

cp scripts/mcp-engram ~/bin/mcp-engram && chmod +x ~/bin/mcp-engram
mcp-engram query "how does auth work" -p ~/myrepo

See docs/INTEGRATION.md for multi-machine setups, rule-file integration, and gotchas.

Auto-Generated AI Instructions

After building a graph, run:

engram gen                    # Auto-detect CLAUDE.md / .cursorrules / AGENTS.md
engram gen --target claude    # Write to CLAUDE.md
engram gen --target cursor    # Write to .cursorrules
engram gen --target agents    # Write to AGENTS.md

This writes a structured codebase summary — god nodes, file structure, key dependencies, decisions — so your AI assistant navigates by structure instead of grepping.

Task-Aware Views (v0.2)

engram gen --task <name> emits different content based on what you're about to do. The four preset views are defined in src/autogen.ts as a data table — no branching logic — so you can add your own task modes without touching the renderer.

engram gen --task general     # default — balanced mix of sections
engram gen --task bug-fix     # emphasizes hot files + past mistakes
engram gen --task feature     # emphasizes architecture + decisions
engram gen --task refactor    # emphasizes god nodes + dependency graph

Each view picks a different set of sections with different limits. For example, bug-fix omits ## Decisions and ## Key dependencies entirely (they'd just be noise when you're chasing a regression) and leads with 🔥 Hot files and ⚠️ Past mistakes.

How engram Compares

engram Mem0 Graphify aider repo-map CLAUDE.md
Code structure AST extraction (10 langs) No Yes (tree-sitter) Yes (tree-sitter) No
Persistent memory SQLite graph, survives sessions Yes (vector + graph) Static snapshot Per-session only Manual text file
Session learning Mines decisions, patterns, mistakes Generic facts No No You write it by hand
Universal MCP + CLI + auto-gen API only Claude Code only aider only Claude Code only
LLM cost $0 $0 (self-host) / paid cloud Tokens for docs/images Per-session $0
Code-specific Built for codebases Generic AI memory Yes Yes No
Temporal Git history mining No No No No

The gap nobody fills: Code-structural understanding + persistent cross-session learning + temporal awareness + works with every AI tool. engram is the first to combine all four.

Confidence System

Every relationship in the graph is tagged:

Tag Meaning Score
EXTRACTED Found directly in source code (import, function definition) 1.0
INFERRED Reasoned from patterns (git co-changes, session decisions) 0.4-0.9
AMBIGUOUS Uncertain, flagged for review 0.1-0.3

You always know what was found vs guessed.

Token Savings

engram reports two honest baselines:

  • vs relevant files — compared to reading only the files that match your query. This is the fair comparison. Typical: 3-11x fewer tokens.
  • vs full corpus — compared to sending your entire codebase. This is the worst case you're avoiding. Typical: 30-400x fewer tokens.

Both are reported transparently. No inflated claims.

Git Hooks

Auto-rebuild the graph on every commit:

engram hooks install     # Install post-commit + post-checkout hooks
engram hooks status      # Check installation
engram hooks uninstall   # Remove hooks

Code changes trigger an instant AST rebuild (no LLM, <50ms). The graph stays fresh without manual re-runs.

Programmatic API

import { init, query, godNodes, stats } from "engram";

// Build the graph
const result = await init("./my-project");
console.log(`${result.nodes} nodes, ${result.edges} edges`);

// Query it
const answer = await query("./my-project", "how does auth work");
console.log(answer.text);

// Get god nodes
const gods = await godNodes("./my-project");
for (const g of gods) {
  console.log(`${g.label} — ${g.degree} connections`);
}

Architecture

src/
├── cli.ts                 CLI entry point
├── core.ts                API surface (init, query, stats, learn)
├── serve.ts               MCP server (5 tools, JSON-RPC stdio)
├── hooks.ts               Git hook install/uninstall
├── autogen.ts             CLAUDE.md / .cursorrules generation
├── graph/
│   ├── schema.ts          Types: nodes, edges, confidence
│   ├── store.ts           SQLite persistence (sql.js, zero native deps)
│   └── query.ts           BFS/DFS traversal, shortest path
├── miners/
│   ├── ast-miner.ts       Code structure extraction (10 languages)
│   ├── git-miner.ts       Change patterns from git history
│   └── session-miner.ts   Decisions/patterns from AI session docs
└── intelligence/
    └── token-tracker.ts   Cumulative token savings measurement

Supported Languages

TypeScript, JavaScript, Python, Go, Rust, Java, C, C++, Ruby, PHP.

Roadmap

v0.2 (current) — shipped April 2026

  • ✅ Skills miner — index ~/.claude/skills/ into the graph
  • ✅ Adaptive gen — task-aware views (--task general|bug-fix|feature|refactor)
  • ✅ Regret buffer — surface past mistakes at the top of query results
  • ✅ list_mistakes MCP tool
  • ✅ Atomic init lockfile
  • ✅ Marker-safe writeToFile + surrogate-safe truncation

v0.3

  • Tree-sitter WASM (20+ languages with full call-graph precision)
  • Cross-project graph (query patterns across all your projects)
  • Temporal graph (commit-snapshot deltas — "what changed in auth this week?")
  • Token enforcement PreToolUse hook for Claude Code

v0.4+

  • LLM-free semantic search (locality-sensitive hashing over n-grams)
  • Graph-as-IR experimental spike
  • Team memory sync (paid tier)

Privacy

Everything runs locally. No data leaves your machine. No telemetry. No cloud. The only network call is npm install.

License

Apache 2.0

Contributing

Issues and PRs welcome. Run engram init on a real codebase and share what it got right and wrong.

About

AI coding memory that learns from every session — persistent, structural, universal

Topics

typescript ai mcp ast knowledge-graph developer-tools cursor claude-code token-optimization coding-memory

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