Engram Mcp Server

Engram is ahosted MCP serverthat provides reliable, explainable memory for AI agents:

- Reliable memory: Agents remember conversations, facts, and decisions, with automatic knowledge graph extraction
- Explainable retrieval: Every answer cites the memories and graph edges that justified it
- Three-engine retrieval: BM25 + vector search + knowledge graph, fused and reranked
- Bring your own model: All LLM calls route through your provider — no inference markup
- Built-in controls: Organize memories into buckets, manage retention, and query with natural language

Some clients (Claude.ai web, ChatGPT) use OAuth instead of a pasted key — see those sections below.

MCP endpoint:https://mcp.lumetra.io/mcp/sse

claude mcp add-json engram '{"type":"sse","url":"https://mcp.lumetra.io/mcp/sse","headers":{"Authorization":"Bearer <your-api-key>"}}'

In Claude settings → Connectors →Add custom connector, paste:

You'll be redirected through Lumetra to authorize the connection. No API key required.

ChatGPT web (OAuth — Connector-capable plans)

In ChatGPT settings →Add custom MCP connector, paste:

~/.cursor/mcp.jsonor.cursor/mcp.json:

{ "mcpServers": { "engram": { "url": "https://mcp.lumetra.io/mcp/sse", "headers": { "Authorization": "Bearer <your-api-key>" } } } }

{ "mcpServers": { "engram": { "url": "https://mcp.lumetra.io/mcp/sse", "headers": { "Authorization": "Bearer <your-api-key>" } } } }

Windsurf accepts bothurlandserverUrlfor remote MCP servers. We useurlhere to match the other clients on this page.

{ "mcpServers": { "engram": { "url": "https://mcp.lumetra.io/mcp/sse", "headers": { "Authorization": "Bearer <your-api-key>" } } } }

openclaw skill add lumetra-engram # or clawhub install lumetra-engram

mkdir -p .openclaw/skills curl -fsSL https://codeload.github.com/lumetra-io/engram-openclaw-skill/tar.gz/refs/heads/main \ | tar -xz --strip-components=2 -C .openclaw/skills engram-openclaw-skill-main/skills/engram export ENGRAM_API_KEY="eng_live_..."

Your MCP client will now have access to Engram memory tools.

Once connected, your agent has these memory tools:

Multi-bucket query fusion (passing several buckets in one call) is available on the REST/v1/queryendpoint and in the official SDKs. The MCPquery_memorytool currently accepts a single bucket per call.

Add this to your agent's system prompt to encourage effective memory usage:

You have Engram Memory. Use it proactively to improve continuity and personalization. Tools: - store_memory(content, bucket?) - Store a fact or piece of information - query_memory(question, bucket?) - Search memories using natural language - list_memories(bucket, limit?) - List memories in a bucket, newest first - list_buckets() - List available memory buckets - delete_memory(memory_id, bucket) - Delete a specific memory - clear_memories(bucket) - Clear all memories in a bucket (destructive!) Policy: - Query-first: before answering anything that may rely on prior context, call query_memory. Ground your answers in the results. - Proactive storing: capture stable preferences, profile facts, project details, decisions, and outcomes. Keep each fact concise (1-2 sentences). - Use buckets: organize memories by project or context (e.g., "work", "personal", "project-alpha"). Style for stored content: short, declarative, atomic facts. Examples: - "User prefers dark mode." - "User timezone is US/Eastern." - "Project Alpha deadline is 2026-10-15."

Engram also provides a REST API for programmatic access from any HTTP client (Vercel AI SDK, LangChain, LlamaIndex, Mastra, CrewAI, AutoGen, n8n, your own scripts).

Authentication:Include your API key in the Authorization header:

curl -X POST https://api.lumetra.io/v1/buckets/default/memories \ -H "Authorization: Bearer $API_KEY" \ -H "Content-Type: application/json" \ -d '{"content": "Alice works at TechCorp"}'

- 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

- This README with setup instructions for popular MCP clients
- server.json— MCP server manifest following the official schema

Theserver.jsonfile 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 athttps://mcp.lumetra.io(MCP) andhttps://api.lumetra.io(REST) — there's no local installation required.

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