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 es un framework para agentes impulsados por IA centrado en el desarrollo guiado por pruebas automatizado, conversión de diseño a código y orquestación de herramientas externas. Utiliza agentes que escriben, prueban y refinan iterativamente el código fuente basándose en prompts de lenguaje natural y archivos de diseño. El sistema transforma tokens de diseño visual y componentes en código con tipos seguros y linted comparando URLs en vivo contra capturas de pantalla de referencia para garantizar la paridad visual. También proporciona un protocolo para vincular agentes a servicios externos de comercio, búsqueda y gestión de activos para sincronizar datos y expandir las capacidades funcionales. El proyecto cubre capacidades para la generación iterativa de código y pruebas automatizadas, donde los archivos fuente se modifican repetidamente hasta que pasan scripts de prueba específicos. Incluye soporte para conectores del Protocolo de Contexto de Modelo (Model Context Protocol) y una interfaz de línea de comandos para gestionar ajustes operativos y selección de modelos.

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