Engram: Open-source memory layer for Claude Code and MCP clients

What Engram is
Engram is an intelligent memory layer built using Claude Code that functions as an MCP (Model Context Protocol) server compatible with any MCP client. It's free and open-source under the AGPL license.
How it differs from Claude's native auto memory
Anthropic recently launched auto memory for Claude Code, which validates the need for persistent memory in agents. However, Engram offers several key differences:
- No cap: Stores unlimited memories with semantic vector search (only retrieves relevant content)
- 96% fewer tokens: Uses approximately 800 tokens per query versus 5K+ for loading an entire markdown file
- Higher accuracy: Achieves 80% accuracy on the LOCOMO benchmark (standard memory benchmark from Snap Research) versus 29% for flat-file approaches
- Cross-project: One vault across all projects, not siloed per repository
- Intelligence layer: Includes automatic extraction, consolidation, contradiction detection, and bi-temporal recall
- Client compatibility: Works with any MCP client, not locked to Claude Code
Claude's native system writes markdown files with a 200-line cap (approximately 5K tokens loaded every session) and operates per-project without semantic search.
Technical implementation
Behind the scenes, Engram uses:
- SQLite + sqlite-vec for vector search
- Knowledge graph for entity relationships
- LLM-powered consolidation that produces insights rather than just storage
Core functions
Your agent interacts with Engram through these primary calls:
engram_remember: Store memoriesengram_recall: Retrieve memories
Additional MCP tools
engram_ask: Question answeringengram_consolidate: Merge related memoriesengram_audit: Cross-reference external contentengram_briefing: Session start context
Getting started
Installation takes about 2 minutes:
npm install -g engram-sdk
engram initThis auto-detects Claude Code and Cursor, writes the MCP configuration, and creates your vault.
Availability
Engram is listed in the official MCP Registry as io.github.tstockham96/engram.
📖 Read the full source: r/ClaudeAI
👀 See Also

Dual-model architecture reduces token consumption by half for long conversations
A developer built a dual-model system where a small 'subconscious' model compresses conversation history in the background, allowing the main model to work with a curated ~35K context instead of 120K tokens of raw history. This architecture cuts token consumption roughly in half for sustained project work.

Hermes Agent v0.6.0 offers improved local model support with per-model tool call parsers
Hermes Agent v0.6.0 from Nous Research provides per-model tool call parsers that handle tool calling properly on 30B class models, supports Ollama, vLLM, and sglang out of the box, and includes six terminal backends including Modal and Daytona for serverless deployment.

Eden AI: European API Hub for AI Models – Pivots as OpenRouter Alternative
Eden AI offers a single unified API to access 500+ AI models (LLMs, vision, OCR, speech) with smart routing, fallback mechanisms, and region control. Positioned as a European alternative to OpenRouter.

ClawPort: Open Source Orchestration for AI Agent Workflows with Self-Healing Cron
ClawPort is an open source orchestration layer for AI agent workflows that auto-configures cron pipelines, self-heals on failures, and lets you test agents directly before they run on schedule.