Give your AI agent
permanent codebase memory
Architecture, dependencies, patterns, history — pre-digested and served via MCP. Your agent starts every session already understanding your codebase.
Understands 27+ languages natively
Agent Agnostic
One memory layer.
Every agent.
Switch between Claude Code, Cursor, Codex, and Gemini without losing context. CodeCortex speaks MCP — the universal protocol for AI tools. Your knowledge persists, no matter which agent you use.
Why It Works
Knowledge that compounds,
not context that burns
Every session your agent spends re-reading files is a session wasted. CodeCortex builds structured knowledge that gets richer over time.
27 Languages
Native tree-sitter extraction across TypeScript, Python, Go, Rust, C, Java, and 21 more. Every symbol, every import, every edge.
14 MCP Tools
Your agent doesn't read files — it queries knowledge. 9 read + 5 write tools via the Model Context Protocol.
Temporal Intelligence
Some files are secretly coupled — zero imports, but they always change together. Git history reveals what code structure hides.
85% Token Reduction
Stop feeding your agent raw files. Structured knowledge delivers full codebase understanding in ~4,300 tokens vs ~37,800 from scanning.
Deep Understanding
A living map
of your codebase
Six layers of knowledge — from raw structure to architectural decisions — stored as flat files in .codecortex/. No database. No cloud. Commit it to git.
Battle-Tested
We threw the Linux kernel at it
93,000 files. 5.3 million symbols. 30 years of git history. The world's largest open-source codebase — understood in 90 seconds. Zero crashes.
Zero Config
One prompt. That's it.
Paste this into your AI agent. It installs CodeCortex, analyzes your codebase, and wires up MCP — your agent remembers everything from the next session onward.
Install codecortex-ai globally, then initialize it on this project. After init completes, add the MCP server config so I can use CodeCortex tools in future sessions.
Steps:
1. Run: npm install -g codecortex-ai
2. Run: codecortex init
3. Add this to my MCP config (claude_desktop_config.json or .cursor/mcp.json):
{
"mcpServers": {
"codecortex": {
"command": "codecortex",
"args": ["serve"]
}
}
}
4. Confirm it worked by running: codecortex statusQuick Start
From amnesia to understanding
in three commands
Analyze your codebase
Discovers files, extracts symbols with tree-sitter, builds the dependency graph, and analyzes git history for hidden coupling.
$ codecortex initStart the MCP server
Starts a local MCP server over stdio. Any MCP-compatible AI agent can connect and read your codebase knowledge.
$ codecortex serveConnect your agent
Add the MCP config to Claude Code, Cursor, or any agent. It starts every session with full codebase understanding.
{
"mcpServers": {
"codecortex": {
"command": "codecortex",
"args": ["serve"]
}
}
}