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
Code Decisions: Open Source Claude Plugin Captures Technical Decisions
Code Decisions is an open source Claude Code plugin that captures technical decisions from conversations and surfaces them when affected files are edited. It writes decisions as markdown files to .claude/decisions/ with an affects field pointing to governed files.
SMELT compiler reduces OpenClaw workspace token usage by up to 95%
SMELT compiles OpenClaw workspace markdown files into a denser runtime form, sending only relevant content to AI models. Benchmarks show token reductions from 76.1% to 95.5% on queries, avoiding reprocessing of static files like USER.md and SOUR.md on every message.
onWatch: Open-source local API quota tracker with SQLite storage
onWatch is a local-first API quota tracker that stores all data in a local SQLite database with no cloud service, telemetry, or account creation. It's a single binary (~13MB) that runs as a background daemon using <50MB RAM and serves a dashboard on localhost.
Claude Code Limiter: Self-Hosted Rate Limiter for Shared Claude Code Subscriptions
claude-code-limiter is a self-hostable tool that adds per-user rate limits to shared Claude Code subscriptions, featuring per-model quotas, credit budgets, sliding 24h windows, time-of-day rules, and a real-time dashboard.