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Deep Recall MCP Server

Deep Recall MCP Server

README.md
# 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](https://img.shields.io/pypi/v/deeprecall-mcp)](https://pypi.org/project/deeprecall-mcp/)

---

## Install (30 seconds)

```bash
pip install deeprecall-mcp
```

## Get your free API key (30 seconds)

Sign up at [deeprecall.dev/signup](https://deeprecall.dev/signup.html) or use the API:

```bash
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`:

```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:

```json
{
  "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](https://deeprecall.dev)
- Quick Start: [deeprecall.dev/quickstart](https://deeprecall.dev/quickstart.html)
- API Docs: [api.deeprecall.dev/docs](https://api.deeprecall.dev/docs)
- Dashboard: [api.deeprecall.dev/dashboard](https://api.deeprecall.dev/dashboard)
- npm SDK: [@zappaidan/deeprecall](https://www.npmjs.com/package/@zappaidan/deeprecall)

## Support

Email: aidan@deeprecall.dev

---

Built by [Aidan Poole](https://github.com/Relic-Studios) & Thomas.

TDQS

A3.8/5.0

Scored across 2 tools

Disambiguation5/5

The two tools are clearly distinct: one stores a memory, the other searches memories. There is no overlap or ambiguity in their purposes.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with the 'deeprecall_' prefix, using 'remember' and 'search' as clear action verbs.

Tool Count3/5

With only 2 tools, the server feels minimal. While it covers basic store and search, it lacks additional tools for management, making the count borderline for a memory system.

Completeness2/5

The server lacks common operations like delete, update, or list memories. Although it claims automatic resolution, agents may need explicit lifecycle tools, leaving significant gaps.

Maintenance

ActivityInactive
ResponsivenessNo recent community issues