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.
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.
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"
| 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 |
Hokmah builds a persistent architectural graph from your codebase:
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.
hokmah_analyze + hokmah_connect_project + hokmah_connect_mcp (unlimited)hokmah_generate_tests + hokmah_generate_code (BYOK — bring your own LLM key)Get a Pro key at hokmah.dev.
~/Library/Application Support/Claude/claude_desktop_config.jsonclaude mcp add hokmah --transport streamable-http --url https://hokmah.dev/mcp.vscode/mcp.json in project root~/.windsurf/mcp.jsonThe 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 (default http://localhost:8000)HOKMAH_MCP_PORT — port to listen on (default 8001)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.
Catalyst AI Research · Haifa, Israel
MIT
io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
io.github.mikerawsonnz/llm-orchestration-agent
io.github.mikerawsonnz/authenticated-llm-agent
labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent