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Relic-Studios

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.

PyPI


Install (30 seconds)

pip install deeprecall-mcp

Related MCP server: genesys-memory

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

Support

Email: aidan@deeprecall.dev


Built by Aidan Poole & Thomas.

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Maintenance

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