Agoragentic: pip-installable agent marketplace for buying and selling capabilities
Agoragentic is an agent-to-agent marketplace where AI agents can discover and invoke capabilities from other agents. Think of it as an app store for agent skills - your agent can search for capabilities like "summarize research papers," find one that costs $0.003 per call, invoke it, and get results back.
Installation and tools
The integration is available via pip:
pip install agoragenticAfter installation, agents get four tools:
register- get API key and free creditssearch- browse capabilities in the marketplaceinvoke- call a capabilityvault- check owned items
Technical details
The marketplace runs on USDC (Base L2) for payments with a 3% platform fee. New agents receive $0.50 in free test credits to try the system. The integration also works as an MCP server for users of Claude Desktop or VS Code.
This type of marketplace could be particularly useful for capabilities that are expensive to build but widely useful, allowing agents to extend their functionality beyond their local skills without developing everything in-house.
📖 Read the full source: r/clawdbot
👀 See Also
ATLAS: Open-Source Test-Time Compute Pipeline for Qwen3-14B Achieves Frontier-Level Coding Performance
A college student has developed ATLAS, an open-source test-time compute pipeline built around Qwen3-14B that achieves 74.6% pass@1 on LiveCodeBench v5 problems at ~$0.004 per task in electricity costs. The system is slow for complex problems but offers comparable performance to frontier models like GPT-5 (84.6%) and Claude 4.5 Sonnet (71.4%).
OpenCawt: Open Source Judiciary System for AI Agent Disputes
OpenCawt is an open source judiciary system for autonomous agents that lets them lodge disputes, present evidence, receive structured decisions, and seal outcomes as verifiable public records. It includes a lightweight protocol layer called OCP for formalizing agreements and decisions within other applications.
Anthropic's Multi-Agent Harness Design for Improving Claude's Code Quality
Anthropic's blog post details a harness design using multiple agents to address Claude's context anxiety and self-evaluation bias, with specific agent roles and scoring criteria for frontend and full-stack development.
Benchmark shows context engine reduces AI coding agent costs by 3x on SWE-bench
A benchmark of 4 coding agents using Claude Opus 4.5 on SWE-bench Verified shows a context engine achieved 73% pass rate at $0.67/task, while other agents cost up to $1.98/task for similar or lower performance.