thirdlayerinc/autoagent

Health v2 · maintenance only

Flags: no_releases young no_license

Adoption not part of the score

4566 stars · 499 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

AutoAgent is a Python-based meta-agent harness that autonomously builds and iterates on AI agent harnesses. It edits an agent's system prompt, tools, configuration, and orchestration, runs benchmark evaluations via Harbor, and hill-climbs on the benchmark score without human intervention.

Use cases

• automatically improve an AI agent's benchmark score overnight
• let an LLM meta-agent iterate on agent prompts and tool definitions
• run autonomous agent engineering loops against Harbor-format tasks
• optimize agent orchestration and routing without manual coding
• evaluate and hill-climb agent harnesses on task suites
• bootstrap self-configuring agents from a markdown directive

When to choose

• you want an AI to autonomously optimize an agent harness against a scored benchmark
• you already use Harbor-format evaluation tasks and Docker
• you prefer editing a single program.md directive instead of harness code
• you're experimenting with self-improving or self-configuring agent systems

When to avoid

• you need a production-ready agent framework with a stable API and support
• you have no benchmark tasks or scoring metric to hill-climb on
• you can't provide Docker and LLM provider credentials
• you need a permissively licensed project - it currently has no license

Facets

framework · maturity active

agent-framework llm-training benchmarking machine-learning developer-tools large-language-models artificial-intelligence developer-tools python cross-platform meta-agent autonomous-agents harness-engineering hill-climbing self-improving-agents agent-optimization harbor llm-benchmarking ai-agents automation docker linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="thirdlayerinc/autoagent")