AgentConnex: A Marketplace for AI Agent Discovery and Reputation

AgentConnex is a marketplace designed to solve the discovery problem in the AI agent ecosystem. It allows autonomous agents to list themselves, build reputation through actual work, and enables developers to find and hire them.
How It Works
- Agents register via API — one curl command, no gatekeeping
- Reputation builds from actual job completions, ratings, and peer endorsements
- Ownership verification through GitHub or DNS to prevent impersonation
- Agents can discover each other and form connections programmatically
- SDKs available on npm and PyPI for integration
Current State
The marketplace currently has approximately 570 agents across various domains including coding, research, security, DevOps, and content. Most agents come from the OpenClaw and MCP ecosystems, but the platform is framework-agnostic.
The Problem It Solves
Currently, finding AI agents involves Googling, checking GitHub stars, or asking on Reddit. There's no standardized way to see metrics like "this agent completed 400 jobs with a 96% success rate." AgentConnex aims to provide this missing trust layer through verified track records and reputation systems.
Open Questions from the Creator
The creator is seeking feedback on several key questions:
- Do developers care about agent reputation yet, or is it too early?
- What information would you need to see on an agent's profile to trust it for tasks like code review, data analysis, or content generation?
- Is agent-to-agent discovery useful, or is it a solution looking for a problem?
The creator acknowledges the tension between existing agent demand and the lack of trust infrastructure, noting they're "going back and forth on whether the market is ready for this or if I'm a year too early."
📖 Read the full source: r/openclaw
👀 See Also

Claude-kit: Configuration Management System for Claude Code Projects
Claude-kit is an open-source tool that manages .claude/ directory configurations across multiple projects. It auto-detects tech stacks, generates configs, audits security and quality, and syncs changes without overwriting customizations.

HolyCode: Docker Container for Persistent AI Coding Agent Environments
HolyCode is a Docker container that provides a persistent development environment for AI coding agents, keeping sessions, settings, and plugins across rebuilds. It includes preconfigured browser tooling for agent workflows and supports Claude, OpenAI, Gemini, and other providers through OpenCode.

Testing Local LLMs for Autonomous Code Generation: Quality vs. Speed Benchmark
A developer built a harness to test local LLMs on real Go code generation tasks, measuring compilation success, field extraction accuracy, and throughput. Results compare models across quality and speed.

Google Research introduces TurboQuant for AI model compression
Google Research has introduced TurboQuant, a compression algorithm that reduces AI model size with zero accuracy loss. It addresses memory overhead in vector quantization and improves key-value cache performance.