lumetra-engram

name	lumetra-engram
description	Persistent, explainable memory for your OpenClaw agent — store facts and recall them later via the hosted Engram MCP server (by Lumetra).
user-invocable	true
metadata	{"openclaw":{"emoji":"🧠","version":"0.1.1","homepage":"https://lumetra.io","repository":"https://github.com/lumetra-io/engram-openclaw-skill","license":"MIT","author":"Lumetra <[e-mail]>","keywords":["memory","mcp","engram","lumetra","context"],"requires":{"bins":["mcporter"],"env":["ENGRAM_API_KEY"]},"install":[{"id":"node","kind":"node","package":"mcporter","bins":["mcporter"],"label":"Install mcporter (node)"}]}}

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.

The Engram tools are surfaced through mcporter from the MCP server registered as engram-lumetra (or whatever name the operator chose during setup — see the one-time setup below). When in doubt, call mcporter list to see the available servers and tool selectors.

One-time setup (operator)

Before this skill can do anything, the operator must register the Engram MCP server with mcporter. Single command:

mcporter config add engram-lumetra https://mcp.lumetra.io/mcp/sse \
  --transport sse \
  --header "Authorization=Bearer $ENGRAM_API_KEY"

After that, mcporter list should show engram-lumetra with 6 tools and mcporter call engram-lumetra.list_buckets should return a JSON bucket list. If mcporter is missing, OpenClaw will offer to install it from the requirement declaration above.

The server is named engram-lumetra rather than just engram to avoid colliding with stale engram entries that mcporter may auto-import from ~/.cursor/mcp.json, ~/.codeium/windsurf/mcp_config.json, or similar editor configs.

When to use

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

Tools (invoke via mcporter call)

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

If the operator registered the server under a different name, substitute it for engram-lumetra. in every selector.

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 / DeepSeek 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.