Awesome GitHub RepositoriesCode Generation Agents

Evolver is a self-evolving AI agent framework that uses gene expression programming to autonomously improve agent behaviors through a continuous five-step loop of scanning, selecting, mutating, validating, and solidifying. It functions as an auditable evolution system that records every mutation and selection step, and can translate natural-language problems into executable Python code for automated grading and evaluation. The framework distinguishes itself through a distributed architecture that enables multiple agents to collaborate and share learned experiences across a network. It operate

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

Potpie is an LLM codebase analysis platform and multi-agent orchestration framework designed to act as an AI software engineer. It parses repositories into a structured code knowledge graph, enabling AI agents to perform multi-hop reasoning, dependency tracing, and grounded technical analysis across large codebases. The system distinguishes itself through a spec-driven development framework where agents generate detailed technical specifications and architecture plans before implementing multi-file code changes. It utilizes a durable execution engine to coordinate specialized AI personas for

Micro-agent est un framework pour agents pilotés par l'IA axé sur le développement piloté par les tests (TDD) automatisé, la conversion design-vers-code et l'orchestration d'outils externes. Il utilise des agents qui écrivent, testent et affinent de manière itérative le code source basé sur des prompts en langage naturel et des fichiers de design. Le système transforme les jetons de design visuel et les composants en code typé et linté en comparant des URLs en direct avec des captures d'écran de référence pour assurer la parité visuelle. Il fournit également un protocole pour lier les agents à des services externes de commerce, de recherche et de gestion d'assets pour synchroniser les données et étendre les capacités fonctionnelles. Le projet couvre des capacités pour la génération de code itérative et les tests automatisés, où les fichiers sources sont modifiés à plusieurs reprises jusqu'à ce que des scripts de test spécifiques passent. Il inclut la prise en charge des connecteurs Model Context Protocol et une interface en ligne de commande pour gérer les paramètres opérationnels et la sélection de modèles.

Agent-OS is an LLM multi-agent orchestration framework and AI software development lifecycle tool designed to coordinate specialized agents through shared workspaces and structured task lists. It functions as an agentic application bootstrapper and technical specification engine, providing the infrastructure to guide the process from product requirements to automated coding and deployment. The system distinguishes itself through spec-driven development, using detailed technical specifications and layered context injection to ensure generated code aligns with project standards. It employs a ma