| 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 <hi@lumetra.io>","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.
