Deep Recall MCP Server

Your AI agent already thinks. We give it a memory.

Other memory systems intercept your conversations and run them through a separate LLM to decide what's worth remembering. That's like having a stranger take notes at your therapy session — they don't know what's significant to you.

Your agent IS an LLM. It already understands the conversation. Deep Recall gives it a memory layer with biological properties: memories that strengthen with use, fade when stale, catch their own contradictions, and self-organize into knowledge clusters. No extra LLM calls. No per-memory API costs. 41ms search.

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Install (30 seconds)

pip install deeprecall-mcp

Get your free API key (30 seconds)

Sign up at deeprecall.dev/signup or use the API:

curl -X POST https://api.deeprecall.dev/v1/signup \
  -H "Content-Type: application/json" \
  -d '{"name": "Your Name", "email": "you@example.com", "password": "your-password"}'

Save the api_key from the response — it's only shown once.

Configure (60 seconds)

Claude Code

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "deeprecall": {
      "command": "deeprecall-mcp",
      "env": {
        "DEEPRECALL_API_KEY": "ec_live_YOUR_KEY_HERE"
      }
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "deeprecall": {
      "command": "deeprecall-mcp",
      "env": {
        "DEEPRECALL_API_KEY": "ec_live_YOUR_KEY_HERE"
      }
    }
  }
}

Windsurf / Cline / Other MCP clients

Same JSON format in your MCP configuration file.

Done. Start using it.

Your AI now has memory tools. Try saying:

• "Remember that I prefer TypeScript over JavaScript"
• "What do you know about me?"
• "Search your memory for anything about our API architecture"
• "Check if any of your memories contradict each other"

How it works

Two tools. That's it.

Tool	What it does
deeprecall_search	Find memories. Hybrid keyword + semantic, salience-weighted.
deeprecall_remember	Store a memory. All biology runs automatically.

Your agent searches early, remembers what matters. Behind the scenes, every store automatically:

• Embeds for semantic search
• Builds graph edges to related memories
• Detects contradictions with existing knowledge
• Resolves temporal changes ("moved to NYC" auto-supersedes "lives in SF")
• Infers entity relationships from co-occurrence
• Consolidates episode clusters into durable facts
• Decays unused memories, strengthens recalled ones

No LLM calls. Pure biology in milliseconds. Two tools in your context window.

Why not Mem0 / Zep / Letta?

	Deep Recall	Mem0	Zep	Letta
Extra LLM calls	None	Required	Required	Required
Search latency	41ms	~200ms	~200ms	~300ms
Intelligent forgetting	ACT-R	No	No	No
Hebbian reinforcement	Yes	No	No	No
Contradiction detection	Yes	No	No	No
Emotional context	Yes	No	No	No
Agent decides what to store	Yes	No — LLM decides	No — LLM decides	Partial

Pricing

Plan	Price	Memories	Features
Free	$0/mo	10,000	All core features, 30 req/min
Builder	$19/mo	100,000	+ topology, 120 req/min
Pro	$49/mo	1,000,000	+ emotional search, priority support
Enterprise	$149/mo	10,000,000	+ dedicated support, 3,000 req/min

Links

• Website: deeprecall.dev
• Quick Start: deeprecall.dev/quickstart
• API Docs: api.deeprecall.dev/docs
• Dashboard: api.deeprecall.dev/dashboard
• npm SDK: @zappaidan/deeprecall

Support

Email: aidan@deeprecall.dev

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Built by Aidan Poole & Thomas.