Engram v1.0.0: Persistent Memory for Local LLMs via Knowledge Graph

What Engram Does

Engram solves the problem of LLMs forgetting everything between sessions by providing persistent memory via a knowledge graph. Unlike vector databases that only find similar text, Engram understands relationships and can reason over them.

Core Features

• Knowledge graph with typed entities, relationships, and properties
• Hybrid search combining BM25 + vector similarity using Ollama/OpenAI embeddings or local ONNX
• Confidence lifecycle where facts strengthen with confirmation, weaken with time, and correct on contradiction
• Inference engine with forward/backward chaining that derives new facts from rules
• Built-in MCP server that works with Claude Code, Cursor, and Windsurf out of the box
• HTTP REST API with 25+ endpoints on port 3030
• Built-in web UI for graph exploration, search, and natural language queries
• Peer-to-peer mesh sync between instances with ed25519 authentication
• CORS enabled for any frontend integration

Technical Details

The entire system runs as an 8.3 MB binary with zero external dependencies. All data lives in a single .brain file that can be copied to back up or moved to migrate. No cloud, Docker, Python, or external database is required.

MCP Integration

MCP configuration is simple:

{
  "mcpServers": {
    "engram": {
      "command": "engram",
      "args": ["mcp", "/path/to/knowledge.brain"]
    }
  }
}

The MCP server exposes these tools: engram_store, engram_relate, engram_query, engram_search, engram_prove, and engram_explain.

Quick Start Commands

engram create my.brain
engram store "PostgreSQL" my.brain
engram serve my.brain

After running engram serve, the web UI is available at http://localhost:3030.

Availability

Engram is free for personal, research, and education use, with a commercial license available. The source and releases are on GitHub.