5 repository-uri
AI agents that generate, analyze, and debug source code across multiple programming languages.
Distinct from Code Analysis and Debugging: No existing candidate captures AI agent code generation capabilities; closest is Code Analysis and Debugging which focuses on static analysis tools, not agent-driven code creation.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Code Generation Agents. Refine with filters or upvote what's useful.
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
Translates natural-language problems into executable Python code and submits solutions for automated grading.
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
Generates, analyzes, and debugs code in multiple programming languages to support software development.
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
Produces implementation based on defined architectural boundaries and outcomes to ensure project consistency.
Micro-agent is a framework for AI-driven agents focused on automated test-driven development, design-to-code conversion, and external tool orchestration. It utilizes agents that iteratively write, test, and refine source code based on natural language prompts and design files. The system transforms visual design tokens and components into type-safe, linted code by comparing live URLs against reference screenshots to ensure visual parity. It also provides a protocol for linking agents to external commerce, search, and asset management services to synchronize data and expand functional capabili
Implements an AI agent that iteratively writes, tests, and refines source code from natural language prompts and design files.
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
Uses detailed technical specifications to direct agents and ensure generated code aligns with project standards.