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Agent Frameworks
Discover the top Agent Frameworks in 2026. Compare architecture, pricing tiers, performance benchmarks, and open-source developer alternatives.
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An agent framework is the library you build an LLM application with — it owns control flow, state, tool dispatch and retries so you are not hand-rolling a while-loop around a chat completion. You need one the moment your feature has to make several model calls in sequence, remember what happened between them, and recover when step four throws.
Most of these entries are not direct competitors. Almost all of the choice collapses into two questions, and neither is answered by a feature table.
The first is what shape your control flow actually has. If it branches, pauses for a human, and has to survive a process restart, you want an explicit graph with checkpointing — LangGraph, which scores 86 in LangGraph, is built around exactly that model. If the work decomposes into roles that hand off to each other, CrewAI's role abstraction (81, CrewAI) is a shorter path. If what you need is a typed function that returns validated structured output, Pydantic AI (85) is closer to ordinary Python than either.
The second question is which layer you are missing, because a third of this category does not compete with the orchestrators at all. Mem0 (88) is a memory store. E2B (87) is a Firecracker sandbox for running model-written code. Browser Use (85, MIT) and Stagehand (85) give an agent a browser. LlamaIndex (87) is retrieval and document parsing first, orchestration second. Adding one of these to a framework you already have is usually the right move; replacing your framework to get one is not.
Two things are worth carrying into the decision. Most tools here are open source, so by default you can read the control loop before you depend on it. And most do not yet carry a scored review, so a card without a score has not been assessed yet; it has not scored badly.
The lifecycle event that defines this category in 2026 is the end of the managed-thread era. OpenAI's Assistants API shut down on 26 August 2026, and OpenAI's own migration guidance points to the Responses API and the Agents SDK. Retired tools are not shown in the list below.
Start from the shape of your control flow, then check whether you need a framework at all or just a layer.
showing 21 of 165 tools
AWS open-source SDK for building model-driven AI agents
Strands Agents is an open-source SDK from AWS that takes a model-driven approach to building AI agents. Developers define a prompt, model, and tools, and the LLM handles planning and orchestration autonomously. Supports Amazon Bedrock, Anthropic, OpenAI, Gemini, Ollama, and more. Powers Amazon Q Developer and AWS Glue in production. Available in Python and TypeScript with native MCP support.
Open-source generalist AI agent for browser and code tasks
Suna is an open-source generalist AI agent that can autonomously browse the web, write and execute code, manage files, and interact with external services. It features a real-time browser automation engine, an isolated code execution sandbox, and integrations with popular APIs. Designed as an open-source alternative to commercial AI agent platforms. Over 9,000 GitHub stars with rapid community growth.
Open-source Firecracker sandboxes for long-running AI agents
Superserve is an open-source sandbox infrastructure layer for AI agents that need durable computers instead of short-lived shells. It runs isolated Firecracker microVMs, supports pause, resume, snapshot, fork, preview URLs, MCP connectivity, SDK/API control, Docker workloads, and self-hosting, while the hosted service adds pay-as-you-go agent sandboxes for teams.
Multi-agent orchestration with 10+ swarm patterns
Swarms is an enterprise-grade multi-agent orchestration framework with 6,100+ GitHub stars that provides 10+ swarm patterns including sequential, concurrent, hierarchical, mixture-of-agents, and graph-based workflows. The SwarmRouter lets teams switch between orchestration strategies by changing a single parameter. It supports MCP for tool integration, multi-model providers via Anthropic, OpenAI, and local models, and includes an AutoSwarmBuilder that generates agents from task descriptions.
Code-first agent framework for data analytics tasks
TaskWeaver is Microsoft's open-source code-first agent framework that converts natural language requests into executable Python code for data analytics and workflow automation. Unlike text-based agent frameworks, it preserves rich in-memory data structures like DataFrames across conversation turns, supports custom algorithm plugins as callable functions, and verifies generated code before execution. It includes a Planner for task decomposition and a Code Interpreter for generation and execution.
Terminal guardrail runtime and policy-as-code engine for AI coding agents
Tirith is an open-source terminal security and policy-as-code runtime for AI agents and developers. It intercepts shell commands in real time to block homograph attacks, pipe-to-shell exploits, dotfile tampering, and destructive executions.
Open-source observability and self-healing layer for AI agents
TraceRoot is a YC S25-backed open-source observability platform purpose-built for AI agents and LLM apps. It combines OpenTelemetry-compatible tracing with an agentic debugging runtime that reads your source code, correlates failures with recent commits, and proposes fix PRs automatically. BYOK support spans seven LLM providers; the entire stack runs self-hosted via Docker Compose, with TraceRoot Cloud available for managed deployments.
Agentic AI security posture management
Trent AI is a specialized security platform for agentic AI applications providing AI Security Posture Management that compounds with every development cycle. Scans, judges, mitigates, and evaluates AI agent security detecting threats traditional tools miss including prompt injection attacks, tool misuse, unintended autonomous actions, data exfiltration through agent chains, and privilege escalation. Offers continuous assessment with remediation plan execution through Claude Code.
Autonomous coding agents that ship while you sleep
Twill is an autonomous coding agent platform that implements features, fixes bugs, and ships pull requests without manual intervention. Uses structured workflow of research, planning, human review, implementation in isolated sandbox, AI code review, then merge. Supports custom agent configurations with multiple LLM providers, isolated dev environments for verification, and integrations with GitHub, Linear, Sentry, Notion, and cloud platforms for end-to-end engineering automation.
ByteDance's open-source multimodal desktop agent with vision-based GUI automation
UI-TARS Desktop is ByteDance's open-source multimodal AI agent that automates desktop and browser interactions using computer vision rather than DOM selectors or accessibility APIs. Powered by the UI-TARS vision model, it can understand and operate any graphical interface by looking at screenshots, making it capable of automating applications that traditional browser automation tools cannot reach, including native desktop apps and complex web UIs.
MCP-native personal context vault for keeping AI agents aligned with your work, voice, and projects.
Unabyss is a personal context headquarters for AI agents. It syncs sources such as email, Slack, Notion, Drive, meetings, and professional profiles into structured context files that can be served to MCP-capable clients. The strongest angle is not generic note taking; it is permissioned, reusable context for Claude, Cursor, custom agents, and other tools that otherwise need the same background explained repeatedly.
Agent framework with native MCP server support
Upsonic is an agent framework designed for building reliable AI agents with native Model Context Protocol (MCP) server support. It provides structured task execution, tool calling orchestration, and built-in reliability patterns for production agent deployments. Features include automatic error recovery, structured output validation, and seamless integration with MCP-compatible tools and data sources for enterprise agentic workflows.
TypeScript-first AI agent framework with built-in observability
VoltAgent is an open-source TypeScript AI agent framework with built-in observability, RAG support, memory management, and MCP integration. It provides a structured approach to building production AI agents in the Node.js ecosystem with agent debugging tools, sub-agent orchestration, and tool management. Over 7,000 GitHub stars and 150K+ weekly npm downloads.
Enterprise RAG framework by Tencent
WeKnora is a Tencent-developed LLM-powered knowledge management and Q&A framework for enterprise document understanding and semantic retrieval. Supports 10+ document formats including PDF, Word, Excel, and images with seamless IM platform integration for WeCom, Feishu, Slack, and Telegram. Offers Quick Q&A mode using RAG pipelines and Intelligent Reasoning mode with ReACT agents for complex multi-step reasoning tasks across organizational knowledge bases.
Context engineering platform for AI agents with temporal knowledge graphs
Zep is a context engineering platform that assembles relationship-aware context for AI agents from conversations, business data, documents, and events. It maintains a temporal knowledge graph that automatically extracts entities and relationships, tracking how context evolves over time. Zep delivers formatted context blocks optimized for LLMs with sub-200ms latency, integrating with LangChain, LlamaIndex, AutoGen, and Google ADK through Python, TypeScript, and Go SDKs.
Google's official Agent Development Kit for building AI agents in Go
adk-go is Google's official Agent Development Kit for the Go programming language, providing the tools and abstractions needed to build production AI agents. It supports tool calling, multi-turn conversations, structured outputs, and integration with Google's Gemini models. With 7,300 GitHub stars and Apache 2.0 license, it brings first-class AI agent development capabilities to the Go ecosystem.
Lightweight OS for running AI agents in-process
agentOS is a portable open-source operating system for AI agents that delivers ~6ms cold starts at 32x lower cost than traditional sandboxes. Powered by WebAssembly and V8 isolates, it runs agents like Claude Code and Codex directly inside your process with granular permissions and host-managed tool access for S3, GitHub, and databases. Available as a simple npm package with no special infrastructure or vendor lock-in required.
Persistent memory layer for AI coding agents — keeps Claude Code, Codex, Cursor, and any MCP agent in context across sessions
agentmemory is an open-source MCP server that gives AI coding agents persistent, cross-session memory. Built on hybrid vector-graph search, it achieves 95.2% recall on the LongMemEval-S benchmark while using up to 92% fewer context tokens than naive context injection. Works out of the box with Claude Code, Codex, Cursor, Windsurf, Cline, OpenCode, Kilo Code, Hermes, and any MCP client through 51 MCP tools plus 12 hooks and 4 skills.
AI chatbot framework for WeChat with multi-model and plugin support
chatgpt-on-wechat is an open-source framework for deploying AI chatbots on WeChat, the dominant messaging platform in China. It supports OpenAI, Claude, Gemini, Qwen, and local models through a plugin architecture. Features group chat management, image generation, voice messages, and knowledge base integration. Over 42,700 GitHub stars reflecting massive adoption in the Chinese developer community.
Filesystem-first framework for durable AI agents
Eve is Vercel's filesystem-first TypeScript framework for building durable AI agents as ordinary project files. It combines Markdown instructions and skills, typed tools, channels, connections, subagents, schedules, sandboxes, and evals with Vercel's agent runtime so teams can ship deployable agents without hand-rolling orchestration. The current beta fits Vercel-native backend agent projects.
Composable agent orchestration via MCP servers
mcp-agent is an open-source framework with 8K+ GitHub stars for building AI agents that leverage MCP (Model Context Protocol) servers as composable tool providers. Agents connect to multiple MCP servers simultaneously, gaining access to diverse capabilities without custom integrations. Supports multi-agent workflows, parallel tool execution, and automatic server discovery. Designed to make MCP the universal interface between AI agents and external tools, databases, and services.