Summary
Hokmah gives AI coding agents persistent architectural memory through three graph structures: TransitionGraph (Markov model of which files change together), IdeaGraph (16 relation types between concepts), and WorldModel (file tree and dependencies). You connect a GitHub repo, it builds the graph, then you can analyze refactoring impact without sending your whole codebase to an LLM. The free tier includes impact analysis with risk scores and affected files. Pro tier adds test and code generation that uses 40x fewer tokens by traversing the graph instead of dumping context. Works over streamable-http, so no local installation needed unless you want to self-host against your own Hokmah backend.
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Hokmah MCP Server
AI Agent with Architectural Memory — MCP Server
Gives any AI coding agent persistent understanding of codebases via TransitionGraph, IdeaGraph, and WorldModel. Analyze impact, generate tests, write code — all from the graph.
Quick Start (30 seconds)
Add to your editor's MCP config (Cursor, Claude Code, VS Code, Windsurf, Cline, JetBrains):
{
"mcpServers": {
"hokmah": {
"type": "streamable-http",
"url": "https://hokmah.dev/mcp"
}
}
}
Then ask your agent: "analyze the impact of refactoring the auth module in github.com/owner/repo"
Available Tools
| Tool | Tier | Description |
|---|---|---|
hokmah_analyze | FREE | Impact analysis, risk score, affected files, architectural invariants |
hokmah_connect_project | FREE | Connect a GitHub repo, build the architectural graph |
hokmah_connect_mcp | FREE | Connect an external MCP server for orchestration |
hokmah_generate_tests | PRO | Test generation from the graph (40x fewer tokens) |
hokmah_generate_code | PRO | Code generation with architectural memory |
How It Works
Hokmah builds a persistent architectural graph from your codebase:
- TransitionGraph — Markov model of code changes (which files change together)
- IdeaGraph — 16 relation types between concepts
- WorldModel — File tree, dependencies, symbols
When you ask "what's the impact of changing X?", Hokmah traverses the graph instead of sending your entire codebase to an LLM. That's why analyze is free (zero LLM tokens) and generate uses 40x fewer tokens.
Pricing
- Free —
hokmah_analyze+hokmah_connect_project+hokmah_connect_mcp(unlimited) - Pro —
hokmah_generate_tests+hokmah_generate_code(BYOK — bring your own LLM key)
Get a Pro key at hokmah.dev.
Editor Setup
- Cursor — Settings → MCP → Add server → paste config
- Claude Desktop —
~/Library/Application Support/Claude/claude_desktop_config.json - Claude Code —
claude mcp add hokmah --transport streamable-http --url https://hokmah.dev/mcp - VS Code (Copilot) —
.vscode/mcp.jsonin project root - Windsurf —
~/.windsurf/mcp.json - Cline — Settings → MCP Servers → Add
- JetBrains — Settings → Tools → AI Assistant → MCP Servers
Self-Hosting
The hosted server at https://hokmah.dev/mcp is the recommended way to use Hokmah. To run the server yourself against your own Hokmah backend:
pip install -r requirements.txt
cp pro_keys.example.json pro_keys.json # edit with your real PRO keys
HOKMAH_API_BASE=http://localhost:8000 python mcp_server.py
Environment variables:
HOKMAH_API_BASE— upstream Hokmah API (defaulthttp://localhost:8000)HOKMAH_MCP_PORT— port to listen on (default8001)HOKMAH_PRO_KEYS— path to the PRO keys JSON file (default/home/vpm/mcp-server/pro_keys.json)
A reference systemd unit is provided in hokmah-mcp.service.
Built by
Catalyst AI Research · Haifa, Israel
License
MIT
Featured
Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
