Lumetra Engram

Persistent, explainable memory for your OpenClaw agent — store facts and recall them later via the hosted Engram MCP server (by Lumetra).

Engram Memory

You have access to Engram, a hosted memory service for AI agents. Engram lets you remember facts, decisions, and context across conversations using a hybrid retrieval engine (BM25 + vector + knowledge graph) and returns an explanation trace with every recall.

When to use

• Before answering anything that may rely on prior context: call query_memory first and ground your answer in the results.
• When the user shares a fact worth remembering (preferences, project details, decisions, deadlines): call store_memory to capture it.
• At the end of a useful conversation: capture stable takeaways with store_memory.

Tools

Tool	Description
store_memory(content, bucket?)	Save a fact. bucket defaults to "default".
query_memory(question, bucket?)	Hybrid retrieval + synthesized answer with citations.
list_memories(bucket, limit?)	List memories in a bucket, newest first (limit 1–100, default 20).
list_buckets()	Show all buckets in the tenant.
delete_memory(memory_id, bucket)	Delete one memory by ID.
clear_memories(bucket)	Delete every memory in a bucket (destructive!).

Style

• Store atomic, declarative facts, one concept per memory. Good: "User prefers dark mode." Bad: "The user mentioned they like dark mode, also they live in Seattle, also..."
• Use buckets to separate contexts: "work", "personal", "project-alpha". If no bucket fits, omit it and the default bucket is used.
• Quote citations from the explanation trace when the user asks "how do you know that?".

BYOK note

Engram is bring-your-own-key end-to-end — inference (embeddings, synthesis, graph extraction) runs through the user's OpenAI / Anthropic / Groq / Together / Fireworks key configured at https://lumetra.io/models. Without a provider key, every store_memory and query_memory returns HTTP 412. If you see that error, tell the user to visit the models page.