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dengyier/OpenWorkProof

by Various

Open protocol for AI agent work contracts and verifiable execution — authorization, evidence, and acceptance for multi-agent systems

MCP

dengyier/OpenWorkProof

Added 3 Sept 2026

#agent-governance #agent-infrastructure #agent-protocol #ai-agents #audit #authorization #ed25519 #llm-agents

Overview

OpenWorkProof is an open protocol for defining AI agent work contracts and verifying their execution. It standardizes authorization, evidence, and acceptance steps in multi-agent workflows. The protocol is implemented in Python and available on GitHub.

Best for

Best for
Developers building multi-agent systems that need explicit contracts and verifiable task completion.

Use cases

  • Formalize agreements between autonomous agents before task execution
  • Collect and verify evidence of agent actions for audit trails
  • Automate acceptance criteria checks in multi-agent pipelines

Notes

OpenWorkProof is an open protocol for defining AI agent work contracts and verifying their execution. It standardizes authorization, evidence, and acceptance steps in multi-agent workflows. The protocol is implemented in Python and available on GitHub.

134 stars on GitHub. Last updated 2026-08-30. Licensed Apache-2.0.

Use cases

  • Formalize agreements between autonomous agents before task execution
  • Collect and verify evidence of agent actions for audit trails
  • Automate acceptance criteria checks in multi-agent pipelines

Pros

  • Open protocol enables cross-tool interoperability
  • Focuses on verifiable execution rather than opaque agent behavior
  • Lightweight Python implementation suitable for integration

Cons

  • Early-stage project with limited community adoption (134 stars)
  • No documentation or examples provided in the available facts
  • Protocol scope may require custom extensions for real-world workflows

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

Pros

  • Open protocol enables cross-tool interoperability
  • Focuses on verifiable execution rather than opaque agent behavior
  • Lightweight Python implementation suitable for integration

Cons

  • Early-stage project with limited community adoption (134 stars)
  • No documentation or examples provided in the available facts
  • Protocol scope may require custom extensions for real-world workflows
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