Agent-Forge

Agent-Forge runs a Karpathy-style propose → eval → score → commit-or-revert loop over a mutable, git-versioned AGENT.md specification — so agents iteratively improve against a measurable eval suite without human intervention between iterations.

It is framework-agnostic (LangChain, LangGraph, CrewAI, AutoGen, or raw SDKs), using an LLM-judge eval harness with held-out validation to guard against overfitting, and git as the safety net: every proposed change is committed or reverted on its score. Built on Python 3.12+ with FastAPI, SQLAlchemy 2.0, Pydantic v2, a Celery + Redis pipeline, pgvector/sentence-transformers retrieval, and MCP — containerized with Docker Compose and fronted by a Next.js trust dashboard.

• Git-native improvement loop with full audit trail
• LLM-judge evals + held-out validation
• Framework-agnostic adapters · pgvector memory · Celery orchestration

Forge agents worth trusting. 201 commits.