central-intelligence

Central Intelligence

Agents forget. CI remembers.

Persistent memory for AI agents. Store, recall, and share information across sessions. Works with Claude Code, Cursor, LangChain, CrewAI, and any agent that supports MCP.

CI never rewrites your memories. Facts are extracted for search, but your content is always returned verbatim. No junk memories, no hallucinated rewrites, no data loss.

 


  

Quick Start (30 seconds)

# One command — gets API key + auto-configures your AI tools
npx central-intelligence-local signup

# Done. Your agent now has persistent memory.
# Restart Claude Code / Cursor / Windsurf to activate.

Or run locally with no cloud:

npm i -g central-intelligence-local && ci dashboard
# Installs and opens the dashboard at localhost:3141

When to Use Central Intelligence

Heuristic: If you would write it in a note to your future self, store it in Central Intelligence.

| Scenario | What to do | |----------|-----------| | Starting a new session, need context from before | recall or context | | Discovered something important (architecture, preferences, fixes) | remember | | Multiple agents working on the same project | share with user/org scope | | You keep re-learning the same things each session | remember once, recall forever | | Handing off a task to another agent or session | remember key decisions, next agent calls context | | User tells you the same preferences repeatedly | remember them, check with recall next time |

Don't store: secrets, passwords, API keys, PII, large binary files, or ephemeral scratch data.

The Problem

Every AI agent session starts from zero. Your agent learns your preferences, understands your codebase, figures out your architecture — then the session ends and it forgets everything. Next session? Same questions. Same mistakes. Same context-building from scratch.

Central Intelligence fixes this.

What It Does

Five MCP tools give your agent a long-term memory:

| Tool | Description | Example | |------|-------------|---------| | remember | Store information for later | "User prefers TypeScript and deploys to Fly.io" | | recall | Semantic search across past memories | "What does the user prefer?" | | context | Auto-load relevant memories for the current task | "Working on the auth system refactor" | | forget | Delete outdated or incorrect memories | forget("memory_abc123") | | share | Make memories available to other agents | scope: "agent" → "org" |

Benchmarks

LifeBench (2026) — Long-Term Multi-Source Memory

CI scores 52.2% on LifeBench, the hardest published memory benchmark (2,003 questions across 10 users, 51K real-world events including messages, calendar, health records, notes, and calls).

| Overall | Info Extraction | Multi-hop | Temporal | Nondeclarative | |---------|-----------------|-----------|----------|----------------| | 52.2% | 47.2% | 52.9% | 46.4% | 64.1% |

Answer model: gpt-5.4-mini. Judge: gpt-4.1-mini. Evaluation harness: lifebench-eval.

LongMemEval (ICLR 2025) — Conversational Memory

CI scores 75.0% on LongMemEval, testing conversational memory across 500 questions spanning single-session recall, multi-session reasoning, temporal reasoning, knowledge updates, and preference tracking.

| Overall | Single-session | Multi-session | Temporal | Preference | |---------|----------------|---------------|----------|------------| | 75.0% | 91.9% | 66.2% | 69.9% | 76.7% |

Answer model: gpt-5.4-mini. Judge: gpt-4o. Evaluation harness: lifebench-eval.

Agent Memory Benchmark (AMB) — Infrastructure Testing

Test CI against other providers using the open-source Agent Memory Benchmark:

npx agent-memory-benchmark --provider central-intelligence --api-key $CI_API_KEY

Note: AMB is maintained by the same author as Central Intelligence. Run it yourself and verify the results. PRs with new provider adapters are welcome.

Roadmap

Advanced retrieval — fact extraction, entity graph, multi-hop reasoning, temporal inference, explainability traces — is prototyped in the codebase and coming to Enterprise. Architecture details: v1.0.0 prototype release. Commercial availability: pricing.

Cross-Tool Memory

CI Local reads config files from 5 AI coding platforms and makes them searchable alongside your stored memories:

| Platform | Config file | How it's parsed | |----------|------------|-----------------| | Claude Code | CLAUDE.md | Section-based (## headings) | | Cursor | .cursor/rules | Paragraph-based | | Windsurf | .windsurf/rules | Paragraph-based | | Codex | codex.md | Section-based | | GitHub Copilot | .github/copilot-instructions.md | Section-based |

Memories stored via Claude Code are discoverable when using Cursor, and vice versa. Your AI memory works everywhere, not just in one tool.

Recall responses now include source (which tool the memory came from), freshness_score (how recent), and duplicate_group (near-duplicate detection across tools).

How It Works

Agent (Claude, Cursor, Windsurf, Copilot, Codex)
    ↓ MCP protocol
Central Intelligence MCP Server (local, thin client)
    ↓
SQLite + vector embeddings + config file parsing
    ↓
Hybrid search: vector + FTS5 + fuzzy + temporal decay
    ↓
Central Intelligence API (hosted)
    ↓
PostgreSQL + pgvector + fact decomposition + entity graph
    ↓
4-way retrieval: vector + BM25 + graph traversal + temporal
    ↓
Local ONNX cross-encoder reranker (zero API cost)

Every memory is decomposed into structured facts with entities, temporal info, and causal relations. Recall runs a dual-path architecture: both fact-based 4-way search (vector, BM25, graph traversal, temporal) and memory-based 2-way search run in parallel. A query type classifier routes each question to the best retrieval path, and results are fused with Reciprocal Rank Fusion and reranked with a local cross-encoder model. Config files from all supported platforms are parsed, embedded, and cached locally.

Memory Scopes

| Scope | Visible to | Use case | |-------|-----------|----------| | agent | Only the agent that stored it | Personal context, session continuity | | user | All agents serving the same user | User preferences, cross-tool context | | org | All agents in the organization | Shared knowledge, team decisions |

MCP Server Setup

Claude Code

Add to ~/.claude/settings.json under mcpServers:

{
  "central-intelligence": {
    "command": "npx",
    "args": ["-y", "central-intelligence-mcp"],
    "env": {
      "CI_API_KEY": "your-api-key"
    }
  }
}

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "central-intelligence": {
      "command": "npx",
      "args": ["-y", "central-intelligence-mcp"],
      "env": {
        "CI_API_KEY": "your-api-key"
      }
    }
  }
}

Any MCP-Compatible Client

The MCP server is published as central-intelligence-mcp on npm. Point your MCP client to it with the CI_API_KEY environment variable set.

CLI Usage

# Install globally
npm install -g central-intelligence-local

# Get API key + auto-configure AI tools
ci signup

# Open local memory dashboard
ci dashboard

# Sync local memories to cloud
ci sync

# Audit memory health (duplicates, staleness, health score)
ci audit

# Import from ChatGPT data export
ci chatgpt-import conversations.json

# Export/import memory bundles
ci export -o memories.json
ci import memories.json

REST API

Base URL: https://central-intelligence-api.fly.dev

All endpoints require Authorization: Bearer <api-key> header.

Create API Key

curl -X POST https://central-intelligence-api.fly.dev/keys \
  -H "Content-Type: application/json" \
  -d '{"name": "my-key"}'

POST /memories/remember

{
  "agent_id": "my-agent",
  "content": "User prefers TypeScript over Python",
  "tags": ["preference", "language"],
  "scope": "agent"
}

POST /memories/recall

{
  "agent_id": "my-agent",
  "query": "what programming language does the user prefer?",
  "limit": 5
}

Response:

{
  "memories": [
    {
      "id": "uuid",
      "content": "User prefers TypeScript over Python",
      "relevance_score": 0.434,
      "tags": ["preference", "language"],
      "scope": "agent",
      "created_at": "2026-03-22T21:42:34.590Z"
    }
  ]
}

POST /memories/context

{
  "agent_id": "my-agent",
  "current_context": "Setting up a new web project for the user",
  "max_memories": 5
}

DELETE /memories/:id

POST /memories/:id/share

{
  "target_scope": "org"
}

GET /usage

Returns memory counts, usage events, and active agents for the authenticated API key.

Self-Hosting

# Clone and install
git clone https://github.com/AlekseiMarchenko/central-intelligence.git
cd central-intelligence
npm install

# Set up PostgreSQL
createdb central_intelligence
psql -d central_intelligence -f packages/api/src/db/schema.sql

# Configure
cp .env.example .env
# Edit .env: set DATABASE_URL and OPENAI_API_KEY

# Run
npm run dev:api

Deploy to Fly.io

fly apps create my-ci-api
fly postgres create --name my-ci-db
fly postgres attach my-ci-db
fly secrets set OPENAI_API_KEY=sk-...
fly deploy

Then point the MCP server to your instance:

{
  "env": {
    "CI_API_KEY": "your-key",
    "CI_API_URL": "https://your-app.fly.dev"
  }
}

Architecture

central-intelligence/
├── packages/
│   ├── api/            # Backend API (Hono + PostgreSQL + pgvector)
│   │   ├── src/
│   │   │   ├── db/           # Schema, migrations (facts, entities, pgvector, hybrid)
│   │   │   ├── middleware/   # Auth, rate limiting, billing, x402 payments
│   │   │   ├── routes/       # REST endpoints, dashboard, docs, demo
│   │   │   └── services/     # Core logic:
│   │   │       ├── memories.ts          # Store + v2 hybrid recall (pgvector + BM25 + RRF + reranker)
│   │   │       ├── rerank.ts            # bge-reranker-v2-m3 (local ONNX), Cohere API fallback
│   │   │       ├── embeddings.ts        # OpenAI text-embedding-3-small
│   │   │       ├── encryption.ts        # AES-256-GCM at rest
│   │   │       ├── date-parser.ts       # Temporal extraction from memory content
│   │   │       ├── auth.ts              # API key validation
│   │   │       ├── fact-extraction.ts   # [Enterprise] Structured fact decomposition via GPT-4o-mini
│   │   │       ├── entity-resolution.ts # [Enterprise] Trigram + co-occurrence entity merging
│   │   │       ├── observations.ts      # [Enterprise] Auto-synthesized higher-level facts
│   │   │       └── query-decompose.ts   # [Enterprise] Query expansion via GPT-4o-mini
│   │   └── tests/        # Vitest
│   ├── mcp-server/     # MCP server (npm: central-intelligence-mcp)
│   ├── cli/            # Cloud CLI (npm: central-intelligence-cli, legacy)
│   ├── local/          # Local memory with cross-tool config parsing
│   ├── node-sdk/       # Node.js/TypeScript SDK (npm: central-intelligence-sdk)
│   ├── python-sdk/     # Python SDK (PyPI: central-intelligence)
│   └── openclaw-skill/ # OpenClaw skill file
├── .github/workflows/  # CI (typecheck + test) + Deploy (Fly.io)
├── benchmark/          # LifeBench VM (self-contained Fly machine)
├── db/                 # Custom Postgres image with pgvector baked in
├── landing/            # Landing page
├── Dockerfile          # API container (non-root, ONNX model pre-cached)
├── fly.toml            # Fly.io config (iad region, health checks)
└── README.md

Pricing

| Tier | Price | Memories | Agents | |------|-------|----------|--------| | Free | $0 | 500 | Unlimited | | Pro | $29/mo | 50,000 | Unlimited | | Team | $99/mo | 500,000 | Unlimited |

See centralintelligence.online/#pricing for the latest.

Contributing

Contributions welcome. Open an issue or PR.

License

Apache 2.0