jennyzzt/dgm

Health v2 · maintenance only

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2254 stars · 441 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Darwin Gödel Machine (DGM) is a research implementation of a self-improving AI agent that iteratively rewrites its own codebase and validates each modification against coding benchmarks like SWE-bench and Polyglot. It uses foundation models (OpenAI/Anthropic APIs) within a Docker-based evaluation loop to evolve agents via open-ended evolution.

Use cases

• run self-improving coding agent experiments
• evolve an agent that modifies its own code
• benchmark coding agents on SWE-bench
• research open-ended evolution for AI agents
• reproduce the Darwin Gödel Machine paper
• evaluate LLM agents on Polyglot benchmark

When to choose

• you want to reproduce or extend the DGM self-improving agent research
• you need an experimental framework for evolving coding agents against benchmarks
• you're studying open-ended evolution and self-modification in LLM agents

When to avoid

• you need a production-ready coding assistant
• you can't provide GPU/API access or Docker infrastructure
• you want a stable, supported library rather than research code

Facets

library · maturity experimental

agent-framework llm-inference benchmarking machine-learning artificial-intelligence large-language-models python self-improving-agents open-ended-evolution swe-bench darwin-godel-machine research-code autonomous-coding-agents ai-agents research docker linux macos

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