Deep Recall MCP Server

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.