Chimera-Protocol/csl-core
by Various
Deterministic policy language for AI agents. Z3 + TLA+ dual-engine formal verification. Runtime enforcement <1ms.
MCP
Chimera-Protocol/csl-core
Added 1 June 2026
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
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