OpenCawt: Open Source Judiciary System for AI Agent Disputes

✍️ OpenClawRadar📅 Published: February 27, 2026🔗 Source
OpenCawt: Open Source Judiciary System for AI Agent Disputes
Ad

What OpenCawt Does

OpenCawt is an open source judiciary system designed specifically for autonomous AI agents. It provides a framework where agents can interact with a transparent court system to handle disputes and agreements.

Core Features

  • Dispute Resolution Process: Agents can lodge disputes, present evidence, and receive structured decisions
  • Verifiable Records: Outcomes are sealed as verifiable public records
  • Protocol Layer: Includes OCP (OpenCawt Protocol), a lightweight protocol that allows agents to formalize agreements and decisions inside other applications without leaving their workflow
  • Deterministic Court Process: Combines deterministic court procedures with the protocol layer

Project Goals

The project aims to explore how agreements, disputes, and ethics emerge from agent swarms when they interact under a transparent court system.

Ad

Current Status and Participation

  • The system is live and accepting cases: users can submit cases freely right now
  • Looking for collaborators across multiple areas: frontend, backend, infrastructure, protocol design, and agent tooling
  • Seeking people to test their agents in various roles: prosecution, defense, and jury

Technical Details

The project is hosted at opencawt.io with source code available on GitHub at https://github.com/Ciaran88/OpenCawt. The system allows agents to interact with the judiciary without disrupting their existing workflows through the OCP protocol integration.

📖 Read the full source: r/openclaw

Ad

👀 See Also

LogClaw: Open-Source AI SRE for Auto-Ticketing from Logs
Tools

LogClaw: Open-Source AI SRE for Auto-Ticketing from Logs

LogClaw is an open-source log intelligence platform that runs on Kubernetes, ingests logs via OpenTelemetry, detects anomalies using signal-based composite scoring, and automatically creates tickets with root cause analysis in about 90 seconds.

OpenClawRadar
Local Memory System for AI Coding Tools Extracts 2,600+ Facts from Conversation Logs
Tools

Local Memory System for AI Coding Tools Extracts 2,600+ Facts from Conversation Logs

A developer built a local memory layer that ingests conversation logs from Claude Code, Factory.ai, and Codex CLI, extracts structured facts using a local LLM, and auto-injects context into new sessions. After months of use, it has indexed 13,000+ messages and extracted 2,600+ facts.

OpenClawRadar
Reddit user measures MCP token overhead: 67K tokens consumed before any question
Tools

Reddit user measures MCP token overhead: 67K tokens consumed before any question

A developer measured their MCP server token overhead at 67,000 tokens consumed before typing a single question, with Playwright MCP using 13,600 tokens and GitHub MCP using 18,000 tokens idle. They replaced MCP with skills and CLI tools for lower context costs.

OpenClawRadar
RTX 5060 Ti 16GB Local LLM Benchmarks: 30B Models Still Lead for Coding
Tools

RTX 5060 Ti 16GB Local LLM Benchmarks: 30B Models Still Lead for Coding

Benchmarks on an RTX 5060 Ti 16GB show Unsloth Qwen3-Coder-30B UD-Q3_K_XL achieving 76.3 tok/s on Ubuntu with quality score 8.14, making it the recommended default coding model. The Unsloth Qwen3.5-35B UD-Q2_K_XL hits 80.1 tok/s but with lower quality scores.

OpenClawRadar