Engram Memory MCP

# Engram MCP Give your AI agents a memory they can trust. Engram lets your AI remember past conversations, facts, and decisions, so it feels more like a real teammate. This repository contains configuration templates for connecting MCP clients to [Engram](https://lumetra.io), a hosted memory service for AI agents. ## What is Engram? Engram is a **hosted MCP server** that provides reliable memory for AI agents: - **Reliable memory**: Agents remember conversations, facts, and decisions — and can show why results were chosen - **Easy setup**: Connect via MCP in minutes. Works with Claude Code, Windsurf, Cursor, and other MCP clients - **Built-in controls**: Manage retention and cleanup with simple tools — no extra plumbing required **Free during public beta** • No credit card required ## Quick Setup ### 1. Get your API key Sign up at [lumetra.io](https://lumetra.io) to get your API key. ### 2. Add to your MCP client **Claude Code:** ```bash claude mcp add-json engram '{"type":"http","url":"https://engram.lumetra.io","headers":{"X-API-Key":"<your-api-key>"}}' ``` **Windsurf** (`~/.codeium/windsurf/mcp_config.json`): ```json { "mcpServers": { "engram": { "serverUrl": "https://engram.lumetra.io", "headers": { "X-API-Key": "<your-api-key>" } } } } ``` **Cursor** (`~/.cursor/mcp.json` or `.cursor/mcp.json`): ```json { "mcpServers": { "engram": { "url": "https://engram.lumetra.io", "headers": { "X-API-Key": "<your-api-key>" } } } } ``` ### 3. Restart your client Your MCP client will now have access to Engram memory tools. ## Available Tools Once connected, your agent will have access to these memory tools: - `store_memory(content)` — Store a single memory - `store_memories(contents[])` — Store multiple memories at once - `search_memories(query, max_candidates?, hints?)` — Search stored memories - `memory_index(page?, limit?)` — Browse all memories - `delete_by_event(event_id, dry_run?)` — Delete specific memories - `explain_retrieval(retrieval_id, verbosity?)` — Understand why results were chosen ## Recommended Agent Prompt Add this to your agent's system prompt to encourage effective memory usage: ``` You have Engram Memory. Use it aggressively to improve continuity and personalization. Tools: - store_memory(content) - store_memories(contents[]) - search_memories(query, max_candidates?, hints?) - memory_index(page?, limit?) - delete_by_event(event_id, dry_run?) - explain_retrieval(retrieval_id, verbosity?) Policy: - Retrieval-first: before answering anything that may rely on prior context, call search_memories (use max_candidates 20–80 for broad queries). Ground answers in results. - Aggressive storing: capture stable preferences, profile facts, recurring tasks, decisions, and outcomes. Keep each item ≤1–2 sentences. Batch at end of turn with store_memories; use store_memory for single critical facts. - Cleanup: when info changes, find and delete the old event (memory_index or search_memories → delete_by_event), then store the corrected fact. Style for stored content: short, declarative, atomic facts. Examples: - "User prefers dark mode." - "User timezone is US/Eastern." - "Project Alpha deadline is 2025-10-15." If asked how results were chosen, call explain_retrieval with the retrieval_id returned by search_memories. ``` ## Use Cases Teams use Engram for: - **Support with prior context**: Carry forward last ticket, environment, plan, and promised follow‑ups - **Code reviews with context**: Store ADRs, owner notes, brittle areas, and post‑mortems as memories - **Shared metric definitions**: Keep definitions, approved joins, and SQL snippets in one place - **On‑brand content, consistently**: Centralize voice and approved claims for writers ## About This Repository This repository contains: - This README with setup instructions for popular MCP clients - `server.json` - MCP server manifest following the official schema The `server.json` file uses the official MCP server schema and can be used by MCP clients that support remote server discovery. For manual configuration, use the client-specific examples above. The actual Engram service runs at `https://engram.lumetra.io` — there's no local installation required. ## Support - **Product site**: [lumetra.io](https://lumetra.io) - **Documentation**: See setup instructions above - **Status**: Free public beta (no credit card required)