Persistent memory for AI agents. Store, recall, and share knowledge across sessions.
io.github.AlekseiMarchenko/central-intelligence — MCP Server
This MCP server provides persistent memory for AI agents. It supports storing, recalling, and sharing information across sessions, aimed at agents that integrate MCP. The server description emphasizes that agents forget while CI remembers, and that it returns stored content verbatim.
🛠️ Key Features
Persistent memory for AI agents
Store, recall, and share knowledge across sessions
Works with Claude Code, Cursor, LangChain, CrewAI, and MCP-enabled agents
Content is returned verbatim (no rewrites of saved memories)
Facts are extracted for search
🚀 Use Cases
Maintaining agent context over time
Sharing knowledge between sessions
Enabling search over previously stored information
⚡ Developer Benefits
Integrates with multiple agent frameworks via MCP
Avoids hallucinated rewrites by returning original content
Minimizes “junk memories” and data loss claims per the provided excerpt
⚠️ Limitations
Tool count and specific MCP tool list are not provided in the available data
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.
# 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:
bash
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).
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).
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.
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
bash
# 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.
{"agent_id":"my-agent","content":"User prefers TypeScript over Python","tags":["preference","language"],"scope":"agent"}
POST /memories/recall
json
{"agent_id":"my-agent","query":"what programming language does the user prefer?","limit":5}
Response:
json
{"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
json
{"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
json
{"target_scope":"org"}
GET /usage
Returns memory counts, usage events, and active agents for the authenticated API key.
Self-Hosting
bash
# 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
# Configurecp .env.example .env# Edit .env: set DATABASE_URL and OPENAI_API_KEY# Run
npm run dev:api
