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 ist ein Framework für KI-gesteuerte Agenten, das sich auf automatisiertes testgetriebenes Entwickeln, Design-zu-Code-Konvertierung und Orchestrierung externer Tools konzentriert. Es nutzt Agenten, die Quellcode basierend auf natürlichsprachlichen Prompts und Designdateien iterativ schreiben, testen und verfeinern. Das System transformiert visuelle Design-Tokens und Komponenten in typsicheren, gelinteten Code, indem es Live-URLs mit Referenz-Screenshots vergleicht, um visuelle Parität sicherzustellen. Es bietet zudem ein Protokoll zur Verknüpfung von Agenten mit externen Handels-, Such- und Asset-Management-Diensten, um Daten zu synchronisieren und funktionale Fähigkeiten zu erweitern. Das Projekt deckt Funktionen für iterative Code-Generierung und automatisiertes Testen ab, bei denen Quelldateien wiederholt modifiziert werden, bis spezifische Testskripte bestehen. Es beinhaltet Unterstützung für Model Context Protocol-Connectors und eine Befehlszeilenschnittstelle zur Verwaltung von Betriebseinstellungen und Modellauswahl.

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