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rafapra3008/cervellaswarm

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Lingua Universale -- a verification language for AI agent protocols. Like mypy for multi-agent communication. 3696 tests, zero deps.

R

MCP

rafapra3008/cervellaswarm

Added 1 June 2026

#ai-agents #formal-verification #mcp #programming-language #protocol-verification #session-types

Overview

A verification language for AI agent protocols similar to how mypy works for Python. It uses 3696 tests to check multi-agent communication with zero external dependencies.

Best for

Best for
Developers constructing custom multi-agent systems that need strict protocol verification

Use cases

  • Verify communication protocols between autonomous AI agents
  • Ensure type safety in multi-agent message exchanges
  • Test agent interactions without runtime overhead

Notes

A verification language for AI agent protocols similar to how mypy works for Python. It uses 3696 tests to check multi-agent communication with zero external dependencies.

8 stars on GitHub. Last updated 2026-05-27. Licensed Apache-2.0.

Use cases

  • Verify communication protocols between autonomous AI agents
  • Ensure type safety in multi-agent message exchanges
  • Test agent interactions without runtime overhead

Pros

  • Extensive test suite (3696 tests) provides high verification coverage
  • Zero dependencies keeps the tool lightweight and easy to integrate
  • Focused specifically on multi-agent protocol correctness

Cons

  • Very low community adoption (8 GitHub stars) suggests limited real-world use
  • Lack of integrations with popular agent frameworks may require custom setup
  • Documentation and examples likely minimal given the early stage

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

Pros

  • Extensive test suite (3696 tests) provides high verification coverage
  • Zero dependencies keeps the tool lightweight and easy to integrate
  • Focused specifically on multi-agent protocol correctness

Cons

  • Very low community adoption (8 GitHub stars) suggests limited real-world use
  • Lack of integrations with popular agent frameworks may require custom setup
  • Documentation and examples likely minimal given the early stage