Autoagent Reviews — Discover what people think of this product.

AutoAgent is an open-source framework for autonomous harness engineering that lets AI agents redesign their own evaluation harnesses instead of relying on manual Python edits. By centralizing configuration, tools, routing, and benchmark orchestration in a single agent.py file, AutoAgent turns the entire agent stack into an editable surface controlled by a higher-level meta-agent.

You define the agent-engineering loop in program.md, then let the meta-agent iterate overnight: it tweaks prompts, tool registries, and orchestration logic, executes Harbor benchmarks, inspects scores, and keeps only the improvements. This hill-climbing process enables rapid, data-driven optimization of complex LLM agents with minimal human intervention.

Key capabilities include:

• Autonomous modification of prompts, tools, and routing within a single-file harness
• Harbor-compatible task suites in tasks/ for reproducible benchmarking
• Fixed adapter boundary for stable integration and trajectory logging
• Docker- and uv-based setup for consistent local experimentation

AutoAgent is ideal for teams pushing the limits of LLM agent performance, enabling systematic experimentation on real-world task suites while preserving a clean separation between the harness under test and the integration layer.