Tools for AI agents to test, fix and optimise your codebase

Observability and enforcement for AI agent harnesses. Capture every run and runtime reliability with policy enforcement. 40 built-in policies, a local dashboard, no account required with a generous free cloud plan

AI said it finished. Flyto2 shows the proof.

Open-source customer money path for usage-based SaaS — authorize customer spend before paid work runs.

🔨 Kyoko is the all-in-one, fully local tool for debugging and improving your AI agents.

Simple, modular, and observable Go framework for backend applications.

Agent skills for Odoo addon development and OCA module migration

Use cultivar to test your Agent Skills, run them in sandboxes, and across different agents.

Privacy Code Scanner and Dataflow Context Engine for AI coding agents

Run a task with AI as a flow of steps you keep, reuse, and refine, not a one-off chat.

A plugin-based gateway that orchestrates other MCPs and allows developers to build upon it enterprise-grade agents.

Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.

Cross-platform .NET performance engineering skill for coding agents, covering CPU, memory, GC, benchmarking, concurrency, startup, native profiling, GPU rendering, and production diagnostics on macOS, Windows, and Linux.

The open-source TypeScript framework for building AI workflows and agents. Designed for Claude Code describe what you want, Claude builds it, with all the best practices already in place.

Feed your agent papers and half-formed ideas — it links them into a system design you can defend. Markdown keeps the record; a visual canvas makes it readable. An Agent Skill for Claude Code & any SKILL.md-compatible agent.

AI coding agent with one Python core and three front-ends — headless CLI, Textual TUI, and an Electron desktop. Works with any OpenAI-compatible API, with risk-tiered permissions, event-sourced replayable sessions, and a fail-closed OS-level sandbox.

The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.