https://pypi.org/project/csl-core/

CSL-Core is a policy engine that formally verifies AI agent constraints using Z3. Instead of prompting an LLM to behave, you write a small policy in CSL (Constitutional Specification Language), Z3 proves it has no contradictions at compile time, and a deterministic runtime enforces it — completely outside the model. We just shipped a built-in MCP server with 4 tools:

verify_policy: Z3 formal verification in one call

simulate_policy: test any JSON input, get ALLOWED/BLOCKED

explain_policy: human-readable breakdown of any policy

scaffold_policy: describe what you want in English, get a CSL template

This means you can do this from Claude Desktop or Cursor:

"Write me a policy that blocks transfers over $5000 for non-admin users"

→ scaffold generates a CSL template

→ verify proves it has no contradictions

→ simulate tests your edge cases

→ all without leaving your editor

The full loop: From English description to mathematically verified, runtime-enforced policy; happens inside your AI assistant.

Why not just prompt the LLM to enforce rules? We benchmarked GPT-4o, Claude Sonnet 4, and Gemini 2.0 Flash as guardrails with a hardened system prompt. Every model was bypassed by at least one attack (context spoofing, multi-turn role escalation, unicode homoglyphs). CSL-Core blocked all of them; because the LLM never touches the enforcement layer.

Setup:

pip install "csl-core[mcp]"

Claude Desktop config:

{

"mcpServers": {

"csl-core": {

"command": "csl-core-mcp"

}

}

}

Or with Docker:

docker build -t csl-core-mcp .

docker run -i csl-core-mcp

GitHub: https://github.com/Chimera-Protocol/csl-core

Listed on awesome-mcp-servers:

https://github.com/punkpeye/awesome-mcp-servers

Previous Show HN discussion: https://news.ycombinator.com/item?id=46963250

Would love feedback, especially from anyone building agentic systems where safety guarantees matter.