Without a memory server, every Claude / Copilot / Cursor conversation starts cold. You re-explain the codebase, the conventions, the decisions, the gotchas — every time. Recall fixes that.

Start here: what Recall does for one developer, one AI

Install it once, point your MCP client at it, and your AI now:

• Remembers across sessions — "what did we decide about the auth flow last week?" returns the actual decision, not a hallucination
• Indexes your code and docs — index_file + recall = local semantic RAG over your repo
• Cites where the answer came from — answer returns text plus the chunks it pulled from
• Builds project knowledge — every checkpoint, reflect, and anti_pattern becomes searchable later
• Survives restarts — append-only artifacts on disk, vector store rebuildable from them

One pip install, one config block, done. No API key. No external service. No per-token bill. MIT license. This is what 95% of users will ever use Recall for.

How is this different from mem0 / Letta / Zep?

Recall does the same job they do — persistent memory across AI sessions, semantic recall, "remember what the user said last week." The difference is where and how:

If you're happy paying a hosted memory provider per token, those are great products and you don't need Recall. If you'd rather your AI's memory live on your laptop or your own server, free and offline, that's what Recall is for.

Scaling up: coordination when you run more than one agent

The same install that gives one developer a personal AI memory also works as a shared brain when more than one agent talks to it. Two

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