接入你的智能体
發布者自己的指令。不是 PlugMCP 端點。
用我們的金鑰與日誌託管一份副本。
npx -y [email protected]Claude Code 沒有本機指令的安裝連結。複製下方指令並在終端機中執行。
CI_API_KEY=<secret> CI_API_URL=<value> claude mcp add io.github.alekseimarchenko-central-intelligence -- npx -y central-intelligence-mcp@0.1.3在終端機中執行。
{ "mcpServers": { "io.github.alekseimarchenko-central-intelligence": { "command": "npx", "args": [ "-y", "[email protected]" ], "env": { "CI_API_KEY": "<secret>", "CI_API_URL": "<value>" } } }}claude_desktop_config.json
遠端 URL 還是本機指令？ 兩種安裝方式的差異
來自發布者
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.
Persistent memory for AI agents. Store, recall, and share knowledge across sessions.
# 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
Heuristic: If you would write it in a note to your future self, store it in Central Intelligence.
Don't store: secrets, passwords, API keys, PII, large binary files, or ephemeral scratch data.
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.
Five MCP tools give your agent a long-term 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).
Answer model: gpt-5.4-mini. Judge: gpt-4.1-mini. Evaluation harness: lifebench-eval.
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.
Answer model: gpt-5.4-mini. Judge: gpt-4o. Evaluation harness: lifebench-eval.
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.
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.
CI Local reads config files from 5 AI coding platforms and makes them searchable alongside your stored memories:
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).
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.
Add to ~/.claude/settings.json under mcpServers:
{
"central-intelligence": {
"command": "npx",
"args": ["-y", "central-intelligence-mcp"],
"env": {
"CI_API_KEY": "your-api-key"
}
}
}
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"central-intelligence": {
"command": "npx",
"args": ["-y", "central-intelligence-mcp"],
"env": {
"CI_API_KEY": "your-api-key"
}
}
}
}
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.
# 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
Base URL: https://central-intelligence-api.fly.dev
All endpoints require Authorization: Bearer <api-key> header.
curl -X POST https://central-intelligence-api.fly.dev/keys \
-H "Content-Type: application/json" \
-d '{"name": "my-key"}'
{
"agent_id": "my-agent",
"content": "User prefers TypeScript over Python",
"tags": ["preference", "language"],
"scope": "agent"
}
{
"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"
}
]
}
{
"agent_id": "my-agent",
"current_context": "Setting up a new web project for the user",
"max_memories": 5
}
{
"target_scope": "org"
}
Returns memory counts, usage events, and active agents for the authenticated API key.
# 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
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"
}
}
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
See centralintelligence.online/#pricing for the latest.
Contributions welcome. Open an issue or PR.
Persistent memory for AI agents. Store, recall, and share knowledge across sessions.
{ "mcpServers": { "io.github.alekseimarchenko-central-intelligence": { "command": "npx", "args": [ "-y", "[email protected]" ], "env": { "CI_API_KEY": "<secret>", "CI_API_URL": "<value>" } } }}工具
尚無工具清單。遠端伺服器會在首次健康檢查後取得工具清單。
常見問題
在本頁複製適用於 Cursor、Claude Desktop、Claude Code 或 VS Code 的 stdio 設定。它使用發布者的安裝方式，而不是 PlugMCP 代理伺服器。
發布者列出了以下環境變數：CI_API_KEY, CI_API_URL。
Central Intelligence 透過 stdio 在本機執行。
上次檢查時間：2026-11-08。 Persistent memory for AI agents. Store, recall, and share knowledge across sessions.