Cortex

Self-organizing graph memory for AI agents. One binary. Zero dependencies.

Cortex is an embedded temporal graph memory that auto-links knowledge, decays what's irrelevant, detects contradictions, and synthesises context briefings on demand. Think SQLite, but for agent memory.

Why Cortex?

Your agents each know something. Cortex discovers what they know together.

Most agent memory systems are glorified key-value stores with a vector index. Cortex is a self-organizing knowledge graph: it wires itself, forgets what's stale, resolves contradictions, computes trust from topology, and tells each agent exactly what it needs to know.

Self-organizing -- the auto-linker runs in the background, discovering relationships via embedding similarity, shared entities, temporal proximity, and configurable structural rules. You store facts; Cortex finds the connections.

Temporal -- every node carries validity windows (valid_from, valid_until) and lifecycle expiry (expires_at). Query "facts true on January 15th" or "facts that expired before June." Stale knowledge is filtered, not deleted.

Trust from topology -- confidence is not a field you set. It's computed from the graph structure: corroboration across agents, contradiction count, source track record, access reinforcement, and freshness. Like PageRank for knowledge.

Briefing synthesis -- "what does my agent need to know?" generates a structured context document from the graph. Configurable roles, agent or cross-agent scope, contradiction alerts.

Embedded -- single Rust binary, redb storage (ACID, mmap), HNSW vector index. No external dependencies. No server to manage. cargo install and go.

Quick Start

Install

# Linux / macOS -- one line, no Rust or protoc needed
curl -sSf https://raw.githubusercontent.com/MikeSquared-Agency/cortex/main/install.sh | sh

# Via Cargo
cargo install cortex-memory

# Docker
docker run -p 9090:9090 -p 9091:9091 mikesquared/cortex:latest

5 Minutes to Memory

# Create a project
cortex init

# Start the server
cortex serve

# Store some knowledge
cortex node create --kind fact --title "The API uses JWT auth" --importance 0.7

# Store with temporal validity
cortex node create --kind fact --title "Rate limit is 1000/min" \
  --valid-from 2026-01-01T00:00:00Z

# Search
cortex search "authentication"

# Get a briefing for your agent
cortex briefing my-agent

# Get a cross-agent briefing
cortex briefing my-agent --scope shared

# Check trust score for a node
cortex trust <node-id>

Use with AI Agents (MCP)

Cortex speaks MCP natively. One command gives your AI agent persistent, self-organizing memory.

Claude Code

# Start Cortex in the background
cortex serve &

# Add to Claude Code
claude mcp add cortex -- cortex mcp

Claude Code now has 7 memory tools: cortex_store, cortex_search, cortex_recall, cortex_briefing, cortex_traverse, cortex_relate, cortex_observe.

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "cortex": {
      "command": "cortex",
      "args": ["mcp"]
    }
  }
}

Windsurf

Add to your MCP config:

{
  "mcpServers": {
    "cortex": {
      "command": "cortex",
      "args": ["mcp"]
    }
  }
}

VS Code (Copilot)

Add to .vscode/mcp.json:

{
  "servers": {
    "cortex": {
      "command": "cortex",
      "args": ["mcp"]
    }
  }
}

Without installing the binary

Use the Node.js bridge (no Rust needed):

npx cortex-mcp-bridge

Or with a remote Cortex server:

{
  "mcpServers": {
    "cortex": {
      "command": "node",
      "args": ["/path/to/cortex-mcp-bridge.js"],
      "env": {
        "CORTEX_URL": "http://your-server:9091"
      }
    }
  }
}

What your agent gets

Tool	Purpose
cortex_store	Remember facts, decisions, goals, events, patterns
cortex_search	Semantic search by meaning
cortex_recall	Hybrid search (semantic + graph structure)
cortex_briefing	"What do I need to know?" context document
cortex_traverse	Explore how concepts connect in the graph
cortex_relate	Explicitly link related knowledge
cortex_observe	Record performance metrics for prompt selection

The briefing tool supports scope: agent (default), shared (cross-agent), or unified (multi-agent overview for orchestrators).

Prompt Management

Cortex includes a built-in prompt versioning system with branching, inheritance, and context-aware selection.

# Import prompts from a migration file
cortex prompt migrate prompts.json

# List all prompts
cortex prompt list

# Get the resolved HEAD of a prompt (with inherited sections)
cortex prompt get my-prompt

# Bind a prompt to an agent
cortex agent bind my-agent my-prompt --weight 0.9

# Select the best variant for current context (epsilon-greedy)
cortex agent select my-agent --task-type coding --sentiment 0.3

# Record performance and auto-update weights
cortex agent observe my-agent \
  --variant-id UUID --variant-slug my-prompt \
  --sentiment-score 0.8 --task-outcome success

# Check performance metrics
cortex prompt performance my-prompt

As a Library (Python)

from cortex_memory import Cortex

cx = Cortex("localhost:9090")
cx.store("decision", "Use FastAPI", body="Async + type hints", importance=0.8)

results = cx.search("backend framework")
print(cx.briefing("my-agent"))

# Cross-agent briefing
print(cx.briefing("my-agent", scope="shared"))

# Store an entity
cx.store_entity("Anthropic", entity_type="company", aliases=["anthropic-ai"])

Embedded in Rust

use cortex_core::{Cortex, LibraryConfig};

let cx = Cortex::open("./memory.redb", LibraryConfig::default())?;
cx.store(Node::fact("The API uses JWT auth", 0.7))?;
let results = cx.search("authentication", 5)?;

Documentation

• Quick Start
• MCP Setup -- Claude Code, Cursor, Windsurf, VS Code
• Configuration Reference
• CLI Reference
• Python SDK
• gRPC API
• HTTP API
• Prompt System
• Architecture

Concepts

• Self-Organizing Memory -- how Cortex discovers what your agents know together
• Graph Model -- nodes, edges, temporal validity, entities
• Trust Scoring -- PageRank for knowledge
• Entity Resolution -- cross-agent discovery via shared entities
• Briefings -- role-based context synthesis with scope
• Auto-Linker -- configurable rules, entity promotion
• Decay and Memory -- edge decay vs temporal validity
• Metadata Conventions -- well-known metadata keys

Guides

• Coding Agent -- memory for coding agents
• Research Agent -- memory for research agents
• Browser Agent -- memory for browser agents
• Multi-Agent -- shared memory, entities, scoped briefings

How Cortex Compares

	Cortex	Mem0	Zep/Graphiti	Cognee	Letta	Engram
Embedded (no server)	Yes	No	No	No	No	Yes
Self-organizing graph	Yes	No	Partial	Partial	No	No
Auto-linking	Yes	No	No	No	No	No
Temporal validity	Yes	No	Yes	No	No	No
Knowledge decay	Yes	No	No	Partial	No	Yes
Contradiction detection	Yes	No	Yes	No	No	No
Trust from topology	Yes	No	No	No	No	No
Briefing synthesis	Yes	No	No	No	No	No
Entity resolution	Yes	Yes	Yes	Yes	No	No
Cross-agent discovery	Yes	Yes	No	Yes	No	No
Single binary	Yes	No	No	No	No	Yes
Rust (performance)	Yes	No	No	No	No	No (Go)
Open source (MIT)	Yes	Partial	Partial	Yes	Yes	Yes

Cortex occupies a unique position: embedded AND self-organizing. Every competitor requires external infrastructure or manual curation. Cortex does neither.

Graph Visualisation

Cortex ships a live graph explorer. Start the server and open http://localhost:9091/viz:

• Force-directed layout with nodes coloured by kind
• Node size reflects importance score
• Click any node for full details (title, body, metadata, connections)
• Search, filter by kind, filter by minimum importance
• Export as SVG, PNG, or JSON

Architecture

┌────────────────────────────────────────────────┐
│              Your Application                  │
│       AI Agent(s)    SDK / gRPC / MCP          │
└──────────────────┬─────────────────────────────┘
                   │
┌──────────────────▼─────────────────────────────┐
│                   Cortex                       │
│  gRPC :9090           HTTP :9091               │
│  ┌───────────┐  ┌────────────┐  ┌──────────┐  │
│  │  Storage   │  │   Graph    │  │   HNSW   │  │
│  │  (redb)    │  │   Engine   │  │   Index  │  │
│  └───────────┘  └────────────┘  └──────────┘  │
│  ┌───────────┐  ┌────────────┐  ┌──────────┐  │
│  │ Auto-Link  │  │  Briefing  │  │  Trust   │  │
│  │ + Entities │  │  + Scope   │  │  Engine  │  │
│  └───────────┘  └────────────┘  └──────────┘  │
│  ┌───────────┐  ┌────────────┐  ┌──────────┐  │
│  │  Prompt    │  │ Selection  │  │ Retention│  │
│  │  Resolver  │  │  Engine    │  │ + Expiry │  │
│  └───────────┘  └────────────┘  └──────────┘  │
└────────────────────────────────────────────────┘

Integration Guides

• LangChain -- Use Cortex as a LangChain memory backend
• CrewAI -- Share memory across a multi-agent team
• OpenClaw / Warren -- Native integration with Warren

Examples

examples/
  langchain-agent/     LangChain agent with Cortex memory
  crewai-team/         CrewAI multi-agent with shared Cortex
  personal-assistant/  Simple assistant with briefings
  rag-pipeline/        Cortex as a RAG backend
  rust-embedded/       Rust app using cortex-core directly

Contributing

See CONTRIBUTING.md. All contributions welcome.

License

MIT -- see LICENSE.