MCP 服务器

Deep Recall MCP Server

Provides AI agents with a memory layer that stores, retrieves, and manages memories with biological properties like forgetting, reinforcement, and contradiction detection, using two simple tools.

README

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.

Install (30 seconds)

pip install deeprecall-mcp

Get your free API key (30 seconds)

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