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Chimera-Protocol/csl-core

by Various

Deterministic policy language for AI agents. Z3 + TLA+ dual-engine formal verification. Runtime enforcement <1ms.

C

MCP

Chimera-Protocol/csl-core

Added 1 June 2026

#agent-framework #ai-governance #ai-safety #deterministic-ai #formal-verification #hallucination-prevention #langchain #llm-safety

Overview

CSL-core is a deterministic policy language for AI agents, using Z3 and TLA+ dual-engine formal verification to enforce constraints at runtime. It checks agent actions against predefined policies with sub-millisecond latency, ensuring behavior stays within safe bounds.

Best for

Best for
Developers building safety-critical AI agents that need formal runtime policy enforcement

Use cases

  • Define safety guardrails for autonomous agent actions
  • Verify agent behavior against formal specifications at runtime
  • Enforce compliance policies in multi-agent systems

Notes

CSL-core is a deterministic policy language for AI agents, using Z3 and TLA+ dual-engine formal verification to enforce constraints at runtime. It checks agent actions against predefined policies with sub-millisecond latency, ensuring behavior stays within safe bounds.

14 stars on GitHub. Last updated 2026-05-31. Licensed Apache-2.0.

Use cases

  • Define safety guardrails for autonomous agent actions
  • Verify agent behavior against formal specifications at runtime
  • Enforce compliance policies in multi-agent systems

Pros

  • Dual-engine verification (Z3 + TLA+) provides strong formal guarantees
  • Sub-millisecond runtime enforcement enables real-time use
  • Deterministic policy language reduces ambiguity in agent behavior

Cons

  • Small community (14 stars) means limited support and documentation
  • Requires understanding of formal methods (Z3, TLA+) to use effectively
  • Python-only implementation may limit integration with non-Python stacks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Dual-engine verification (Z3 + TLA+) provides strong formal guarantees
  • Sub-millisecond runtime enforcement enables real-time use
  • Deterministic policy language reduces ambiguity in agent behavior

Cons

  • Small community (14 stars) means limited support and documentation
  • Requires understanding of formal methods (Z3, TLA+) to use effectively
  • Python-only implementation may limit integration with non-Python stacks