Give your AI agent
permanent codebase knowledge
Architecture, dependencies, risk areas, hidden coupling. Pre-built and served via MCP so your agent skips the cold start. ~50% fewer tokens. 2.5x fewer tool calls. Same quality answers.
Understands 27 languages natively
Agent Agnostic
One knowledge 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-discovering architecture is a cold start. CodeCortex builds persistent knowledge that eliminates it.
27 Languages
Native tree-sitter extraction across TypeScript, Python, Go, Rust, C, Java, and 21 more. Every symbol, every import, every edge.
13 MCP Tools
8 read + 5 write tools via the Model Context Protocol. Not a middleware between your agent and code. Your agent queries knowledge, then reads source files directly.
Temporal Intelligence
Some files are secretly coupled. Zero imports between them, but they always change together. Only git history reveals what code structure hides.
~50% Fewer Tokens
Tested on a 6,400-file codebase: 43K tokens vs 79K without CodeCortex. 2.5x fewer tool calls. Same answer quality.
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. From the next session on, your agent skips the cold start.
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 already knowing the codebase.
{
"mcpServers": {
"codecortex": {
"command": "codecortex",
"args": ["serve"]
}
}
}