Hermes Agent
An autonomous AI agent framework from NousResearch that supports multiple LLM backends and grows with user needs.
Core frameworks for building AI agents
An autonomous AI agent framework from NousResearch that supports multiple LLM backends and grows with user needs.
AutoGPT is an autonomous AI agent that can complete user-defined tasks end-to-end. It plans and executes steps on its own and is considered a milestone in agent autonomy.
OpenCode is an open-source terminal-based coding agent, cross-platform and compatible with Claude, OpenAI, and many other LLMs, offering an interactive TUI and extensible AI coding experience.
Langflow is a visual AI agent and workflow builder platform with drag-and-drop design, multi-LLM integration, and tool composition to simplify agent development.
Python-ecosystem visual LLM workflow builder emphasizing multi-agent.
Dify is an open-source LLM application development platform with a visual agent orchestration interface, supporting workflows, knowledge bases, and multiple models.
LangChain is the open-source agent engineering platform that unifies model IO, tool calling, RAG, memory and observability under one composable framework.
LangChain is a framework for building applications powered by language models. It provides core capabilities such as chaining, memory management, and agent orchestration, making it a go-to choice for AI agent development.
AI research automation agent by Andrej Karpathy that automatically runs nanochat training research experiments on a single GPU.
OpenHands (formerly OpenDevin) is an autonomous AI agent platform for software development that independently completes code changes, bug fixes, and feature work end-to-end.
OpenHands is an open-source AI software engineering agent platform that can automatically execute development tasks, modify code, and support collaborative iteration.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. Supports LoRA, QLoRA, RLHF and more for building custom agent models.
The Multi-Agent Framework for building the first AI Software Company, enabling natural language programming with multi-role collaboration for automated requirement analysis, design, coding, and testing.
MetaGPT is a multi-agent framework that combines collaborative intelligence with software-engineering SOPs so agents cooperate like a real software company to ship code and docs.
12 Lessons to Get Started Building AI Agents by Microsoft. Hands-on curriculum covering core agent concepts, tool use, and multi-agent collaboration.
Pi Mono is a comprehensive AI agent toolkit including a coding agent CLI, unified LLM API, TUI and web UI libraries, Slack bot, and vLLM pod management for end-to-end agent development.
An open-source agent harness platform providing the best agent toolkit, supporting multiple AI coding agents.
Open Interpreter lets LLMs execute code and control a computer locally, turning natural language into shell, Python, and browser actions on the user machine.
The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code/Codex integration.
Meta's open-source Llama 2 foundational LLM with pretrained and fine-tuned models from 7B to 70B parameters, supporting chat and text completion as a cornerstone of the open LLM ecosystem.
Microsoft Research's open-source multi-agent programming framework.
OpenManus is an open-source AI Agent framework from the MetaGPT team, delivering Manus-like autonomous task execution without any invite code. Supports MCP tool calls and multi-agent flow.
CrewAI is a multi-agent framework for orchestrating role-playing, autonomous AI agents that collaborate like a team to tackle complex tasks.
Role-based multi-agent Python framework for task orchestration.
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How do you know a prompt change is better, not worse? A systematic guide to canary deployment, quality gates, auto-rollback architecture, and continuous behavioral drift monitoring for agents in production.
Most teams evaluate agents by checking a few examples. Real evaluation needs layered metrics, non-rotting datasets, and judges that push back. This article provides runnable code patterns and a practical decision framework.
A systematic comparison of the three categories of agent memory -- working, long-term, and shared -- covering storage media, lifecycle, retrieval methods, typical frameworks, and design patterns, fully addressing agent personalization and multi-agent collaboration engineering.
Exploring how small language models are fine-tuned and deployed for agent workloads at the edge, balancing latency, cost, and accuracy for production AI agents.
A systematic guide to seven tool-call fault tolerance patterns: timeout hierarchy, exponential backoff with jitter, circuit breakers, fallback provider chains, recoverable error classification, structured validation, and idempotency keys -- keeping agents stable in unstable real-world environments.
Most agent workflows fail at the orchestration layer, not the model. A practical comparison of DAG, state machine, and visual builder approaches with production-ready code for error handling, human approval gates, and conditional branching.