Labsco
MCP SERVER

Memory in three kinds — facts, events, and workflows that rewrite themselves when you report a failure, recording which assumption turned out to be false.

Agent Memory
Summary
It remembers why the workflow changed, not just that it did.

Every memory tool stores what happened. The interesting move here is the procedure record: version three of "deploy" arrives carrying the assumption that turned out false — the build container had enough memory — and a precondition that travels with it. An agent picking it up does not have to re-derive the two mistakes that produced it. The rest is a competent hosted memory service; this is the part that is hard to copy.

What it is

A hosted memory service reachable over MCP, plus Python and JavaScript SDKs and a CLI. What separates it from a fact store is the third memory type: procedures carry a version, and reporting a failure produces a new version with the violated assumption and a derived precondition attached, so an agent loading the latest one knows what to check first.

What you get
  • 30 tools for memory management over MCP, from Claude Desktop, Cursor, Codex, Windsurf, Cline or a hosted Claude agent
  • Semantic memory for facts and preferences, episodic memory for events and decisions with their outcomes, and procedural memory for workflows
  • Procedures that evolve: report an outcome and a new version is produced carrying `violated_assumption`, `preconditions`, and running `success_count` / `fail_count`
  • Automatic failure detection — describing a failed deploy in conversation links the episode to the procedure and evolves it without an explicit feedback call
  • A synthesised answer with citations that link each claim back to the fact it came from, rather than a raw list of matches
  • A cognitive profile generated from all memories in one call, ready to paste into a system prompt
  • Retrieval across 23 languages: ask in Russian, Chinese, Spanish or Japanese and get an answer in the same language, cited back to facts stored in their original one
  • Multi-user isolation on a single API key, so each `user_id` sees only its own memories and profile
  • Claude Code hooks that load the profile at session start, recall on every prompt and save after responses — surviving `/clear` and auto-compaction
  • History import from ChatGPT exports, Obsidian vaults or plain markdown, with secrets redacted on your machine before upload
Requirements

A Mengram API key, in the `om-` form, free from mengram.io. `server.json` publishes a streamable-http remote at `https://mengram.io/mcp` that takes your key as a bearer token in an `Authorization` header; the local path is `pip install mengram-ai` (2.30.0) and running the `mengram` command with `server` and `--cloud`, with `MENGRAM_API_KEY` set. `mengram try` runs entirely on your machine with no account if you want to see what it would store first. Self-hosting against Ollama is supported but the extraction prompt is around 4,000 tokens, so the project asks for a model of 8B parameters or more with an 8K or larger context window. Licensed Apache 2.0.

Setup effort

One command plus a key — pip install mengram-ai, then supply credentials