Deep Recall MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Deep Recall MCP ServerRemember that I prefer Python over Java"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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-mcpRelated 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 |
| Find memories. Hybrid keyword + semantic, salience-weighted. |
| 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
Built by Aidan Poole & Thomas.
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