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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 48 of 165 tools
Structured LLM outputs with validation
Instructor is the most popular Python library for extracting structured, validated data from large language models, with over 3 million monthly downloads and ports across Python, TypeScript, Go, Ruby, Elixir, and Rust. It uses Pydantic models to define output schemas and automatically handles validation, retries, and error correction when the LLM output does not match. Instructor patches existing client libraries instead of replacing them, preserving full access to the underlying API.
BEAM/Elixir-native framework for durable multi-agent systems
Jido is an Elixir-native AI agent framework that leverages the BEAM virtual machine's concurrency and fault-tolerance for building durable, distributed multi-agent systems. It provides primitives for agent lifecycle management, skill composition, and message-based coordination. Designed for teams running Elixir in production who need agent capabilities. Apache-2.0 with 1,600+ GitHub stars.
JavaScript framework for building and visualizing multi-agent workflows on a Kanban board
KaibanJS is an MIT-licensed JavaScript framework for defining AI agents, tasks, tools, and teams, then orchestrating their work through a Kanban-inspired runtime and visual board. It can run inside Node.js, React, or Next.js projects, supports custom UIs and headless workflows, and provides real-time task-state visibility for multi-agent applications.
Control plane for autonomous AI agents
Keycard is the control plane for autonomous agents, providing identity verification, policy enforcement, and scoped access management. Resolves agent identity, enforces security policies, and issues time-limited resource-specific access tokens. Provides full visibility into every agent action with drift detection, automatic remediation, and integrations with Datadog, Linear, GitHub, and other services for agent-driven incident response and security operations.
Coding data, MCP integrations, and sandboxed tool-use environments
Klavis AI combines long-horizon coding and agentic data environments for post-training, Strata and MCP integrations for connecting AI agents to external APIs and SaaS tools, and MCP Sandbox for agent training and evaluation.
Kotlin-native AI agent framework by JetBrains with MCP support
Koog is JetBrains official Kotlin-native framework for building predictable, fault-tolerant AI agents. It provides structured agent workflows with MCP protocol support, type-safe tool definitions, and deterministic execution patterns designed for JVM production environments. As the first production-grade Kotlin agent framework, it fills the gap in a JVM ecosystem dominated by Python-based alternatives and integrates naturally with existing Kotlin and Java backend infrastructure.
RAG-based document QA with multi-user support and agent reasoning
Kotaemon is an open-source RAG-powered document question-answering interface backed by Cinnamon AI. It supports multi-user workspaces with access controls, advanced retrieval pipelines including hybrid search and knowledge graph extraction, and agentic reasoning for complex multi-step queries. The web UI handles PDFs, Office documents, and images with citations pointing to exact source passages, making it suitable for both individual research and team knowledge management.
Visual framework for building multi-agent AI apps
LangFlow is an open-source visual framework for building multi-agent AI apps with drag-and-drop. Built on LangChain, it lets developers compose chains, agents, and RAG pipelines by connecting modular components visually. Features real-time interaction, Python customization, one-click deployment, and export to LangChain code. Supports all major LLM providers, vector stores, and tools. With 146K+ GitHub stars, it bridges visual prototyping and production deployment.
Multi-agent programming framework inspired by the Actor model
Langroid is a lightweight Python framework from CMU and UW-Madison researchers for building LLM applications using a multi-agent programming paradigm inspired by the Actor Framework. Agents are first-class citizens that encapsulate LLM state, vector stores, and tools, then collaborate via message passing through hierarchical task delegation. With 3,900+ GitHub stars, Langroid works with any LLM provider and does not depend on LangChain or other frameworks.
Twelve-lesson curriculum for building AI coding agents from scratch
Learn Claude Code is a comprehensive educational framework with 12 structured lessons teaching AI coding agent construction from first principles. With over 46,000 GitHub stars and 7,000 forks, it provides complete runnable Python implementations progressing from a 50-line bash agent to a 550-line skills agent with modular extensibility and subagent orchestration.
Memory-first coding agent that learns over time
Letta Code is a memory-first CLI coding agent that persists across sessions and improves over time. Unlike stateless tools like Claude Code or Codex, it works with long-lived agents remembering your codebase, preferences, and past interactions. Model-agnostic supporting Claude, GPT, Gemini, and more. Features git-backed context repositories, skill learning from experience, sleep-time reflection, and subagent orchestration. #1 model-agnostic OSS harness on Terminal-Bench.
Open-source real-time voice, video, and AI agent infrastructure
LiveKit is an open-source WebRTC infrastructure platform for building real-time voice, video, and data applications. It powers ChatGPT Advanced Voice Mode and serves as the foundation for AI agent voice interactions. LiveKit provides server-side SDKs for Go, Python, Node.js, and Rust alongside client SDKs for every major platform. The Agents framework lets developers build voice-enabled AI assistants with speech-to-text, LLM processing, and text-to-speech pipelines in Python or Node.js.
Enterprise-grade RAG and MCP knowledge base with one-click deployment
MaxKB is an enterprise-grade RAG platform with 21,000+ GitHub stars from the 1Panel team. It provides one-click deployment of knowledge bases with built-in LLM integration, MCP support, and a streamlined approach to document ingestion and retrieval that prioritizes operational simplicity over configuration complexity.
SQL-native memory infrastructure for AI agents and applications
Memori is an AI memory engine that provides persistent, queryable memory for agents and applications using SQL-native storage. It stores structured memories with semantic search, temporal awareness, and relationship tracking, enabling AI systems to remember user preferences, past interactions, and contextual facts across sessions. With 12,900 GitHub stars, it offers a database-native approach to the agent memory problem.
Multi-agent framework simulating a software company
MetaGPT is an open-source multi-agent framework with 56K+ GitHub stars that simulates a software company by assigning roles — product manager, architect, engineer, QA — to AI agents collaborating through structured SOPs. Given a one-line requirement, it outputs user stories, competitive analysis, data structures, APIs, and working code. Features incremental development, human feedback integration, and experience-based learning for continuous improvement across iterations.
Unified Python/.NET framework for multi-agent AI
Microsoft Agent Framework is Microsoft's official unified SDK for building multi-agent AI workflows in Python and .NET. It consolidates Semantic Kernel and AutoGen into a single framework with MCP tool integration, graph-based workflows, human-in-the-loop patterns, and multi-agent orchestration. The framework reached Release Candidate status in February 2026 and is Microsoft's recommended path for production agent development.
Open-source platform for managing AI coding agents as teammates
Multica is an open-source managed agents platform that lets you assign coding tasks to AI agents like Claude Code and Codex as if they were team members. It provides a unified dashboard for task assignment, execution monitoring, and skill reuse across local and cloud compute environments. With multi-workspace support, team-level isolation, and reusable skill compounding, Multica turns autonomous coding agents into organized, trackable development resources.
Secure sandboxed runtime for AI agent execution
NVIDIA OpenShell provides kernel-level isolation for AI agent workloads with Landlock, seccomp, and network namespace sandboxing. Announced at GTC 2026 with 17 enterprise partners including Adobe, Atlassian, SAP, and Salesforce, it offers declarative YAML policy enforcement, L7 HTTP inspection, and GPU passthrough — purpose-built to contain the blast radius when autonomous coding agents interact with filesystems and networks.
Framework for converting MCP servers into autonomous AI agents with UI
Nanobot transforms MCP servers into full autonomous agents by adding a planning layer, conversation memory, and web-based UI on top of MCP tool capabilities. It enables building agents that combine multiple MCP servers with LLM reasoning to complete multi-step tasks. Features MCP-UI for browser-based interaction and supports any MCP-compatible tools as agent capabilities.
Browser automation framework turning websites into action APIs
Notte is a browser automation framework for AI agents that converts any website into a structured action API. Instead of scraping pages for text, Notte lets agents interact with sites — clicking buttons, filling forms, and navigating flows. Built with hybrid AI-plus-deterministic scripting, it includes digital personas, CAPTCHA solving, and proxy management for reliable automation at scale.
Microsoft's screen parsing model for GUI agent interaction
OmniParser is Microsoft's open-source screen parsing toolkit that converts GUI screenshots into structured, actionable data for AI agents. It detects interactive UI elements like buttons, input fields, and icons, then generates grounded descriptions that enable language models to interact with any desktop or web application. Accumulated over 24,000 GitHub stars as a foundational layer for computer-use agents.
Open-source async coding agent you can run in your own sandbox
Open-source framework from LangChain AI for building your organization's internal coding agent — the same pattern Stripe's Minions, Ramp's Inspect, and Coinbase's Cloudbot follow. Built on LangGraph and Deep Agents, Open SWE runs each task in an isolated cloud sandbox (Modal, Daytona, Runloop, or LangSmith), invokes from Slack, Linear, or GitHub, orchestrates subagents, and opens pull requests autonomously — customizable end-to-end for your codebase and conventions.
Lightweight multi-agent handoff framework by OpenAI
OpenAI Swarm is an experimental lightweight framework for building multi-agent systems with handoff patterns. Agents are defined as simple Python functions with instructions and tool lists, and can hand off conversations to other specialized agents. Designed to be minimal and educational rather than production-ready — demonstrates patterns for agent coordination without heavy abstractions. Runs on OpenAI's Chat Completions API with function calling for tool use and agent transitions.
Rust-based agent OS with built-in security, WASM sandboxing, and multi-agent runtime
OpenFang is an open-source agent operating system built in Rust that provides a secure multi-agent runtime with WASM sandboxing, auditability layers, and multi-channel communication. It goes beyond typical orchestration SDKs by treating agent security and operational isolation as first-class concerns, making it suitable for teams deploying agents in environments where trust boundaries and audit trails matter.
Local-first personal AI agent with memory trees, desktop integrations, and private workspace context.
OpenHuman is an open-source, local-first personal AI agent from TinyHumans. It combines a desktop app, persistent memory trees, Obsidian-compatible storage, OAuth integrations, and local model support into a private assistant harness. It is most interesting for users who want agentic workflows and long-term memory without handing every context detail to a fully cloud-hosted assistant.
Open-source general AI agent framework from the MetaGPT team
OpenManus is an open-source framework for building general-purpose AI agents, developed by core contributors from the MetaGPT community. It provides a modular architecture with planning agents, reactive agents, and tool-calling agents that can execute code, browse the web, search for information, and handle files. Built as the open alternative to Manus AI, it gained over 55,000 GitHub stars and supports multi-agent collaboration with real-time execution feedback.
Enterprise-grade sandbox for AI agent code execution
OpenSandbox is an open-source sandbox platform from Alibaba providing secure, isolated execution environments for AI coding agents. It supports Python, Java, JavaScript, and C# SDKs with a unified Sandbox Protocol for custom runtimes. Integrates with Docker and Kubernetes, offering isolation through gVisor, Kata Containers, and Firecracker microVMs with per-sandbox network controls.
Structured generation for LLMs
Outlines is an open-source Python library for structured text generation that guarantees LLM outputs conform to a defined schema or format. It constrains the model's token selection at each step so only tokens leading to valid output are considered, eliminating fragile post-processing. Supports multiple-choice constraints, regex patterns, JSON Schema, and type-safe Pydantic models — helping teams extract reliable structured data from any LLM.
Vectorless, reasoning-based RAG that reads documents like a human expert — no vector DB, no chunking.
PageIndex is a vectorless, reasoning-based RAG system that builds hierarchical tree indexes from long documents and uses LLMs to navigate them like a human expert would. Instead of chunking text and comparing embeddings, it constructs a table-of-contents-style structure and reasons its way to the right sections — no vector database required. Available as an open-source Python package, cloud API, MCP server, and chat platform.
Behavioral control layer for reliable customer-facing AI agents
Parlant is an open-source framework that adds behavioral governance to conversational AI agents. Instead of relying on prompt engineering alone, it lets teams define explicit policies, conversation guidelines, and behavioral rules that agents follow predictably across multi-turn interactions. Parlant sits between the LLM and the user-facing interface, enforcing consistent agent behavior for customer support, sales, and service automation use cases.
Open-source framework for real-time voice and multimodal AI agents
Pipecat is an open-source framework with 11,000+ GitHub stars for building real-time voice and multimodal AI agents. Developed by Daily.co, it manages the STT to LLM to TTS pipeline with sub-second latency, integrating with AWS Bedrock, NVIDIA NIM, and AssemblyAI for production-grade voice agent deployment.
Build codebase context graphs and AI agents for engineering
Engineering context graph and agent platform that parses codebases into living knowledge graphs for automated SDLC tasks. Connects to repositories, issue trackers, and team communications to empower AI agents with deep architecture understanding for debugging, PR generation, and system design. Offers an open-source core with full IDE extensions, CLI integrations, and enterprise on-premises deployment options.
Low-code multi-agent framework with chat integrations
PraisonAI is an open-source low-code multi-agent framework with 6K+ GitHub stars for building AI agent teams through simple YAML configuration. Define agent roles, goals, and tools in YAML and PraisonAI handles orchestration. Features built-in integrations with WhatsApp, Telegram, Discord, and Slack for deploying conversational agents. Supports both CrewAI and AutoGen as backend orchestrators, RAG capabilities, and a web UI for monitoring agent interactions in real-time.
Build and evaluate LLM apps end-to-end
Prompt Flow is Microsoft's open-source development suite for building, testing, evaluating, and deploying LLM-based applications end-to-end. It links LLM calls, prompts, Python code, and other tools into executable flows defined in YAML, with a VS Code extension providing a visual flow designer. The tool supports tracing LLM interactions for debugging, running batch evaluations with quality metrics against larger datasets, and integrating tests into CI/CD pipelines before production deployment.
Production RAG engine with hybrid search and knowledge graphs
R2R is a production-grade RAG engine from SciPhi AI that combines hybrid search with knowledge graph extraction and agentic retrieval capabilities. It provides a complete pipeline from document ingestion through retrieval and generation, supporting vector, keyword, and graph-based search strategies. The managed API and self-hosted options make it accessible for both rapid prototyping and production deployments requiring advanced retrieval beyond simple vector similarity.
All-in-one multimodal RAG framework
RAG-Anything is an all-in-one multimodal RAG framework from the University of Hong Kong that processes text, images, tables, and equations through a unified pipeline built on LightRAG. It constructs multi-modal knowledge graphs by extracting multimodal entities and establishing cross-modal relationships. The VLM-Enhanced Query mode integrates visual content into large language models for deeper document understanding beyond plain text retrieval.
Deep document understanding RAG engine
RAGFlow is an open-source RAG engine with 76K+ GitHub stars that provides deep document understanding for building knowledge-based AI applications. Optimizes chunking for 20+ document types including PDFs, Word docs, presentations, and images using layout-aware parsing. Features template-based chunking strategies, citation with source references, multi-recall retrieval combining keyword and semantic search, and a visual knowledge base management interface with drag-and-drop document upload.
Version control for AI coding-agent actions
Re_gent is an open-source version-control layer for AI coding-agent activity. Instead of only reviewing the final Git diff, it records what the agent attempted, changed, and executed along the way so teams can trace, undo, and govern autonomous coding work. It fits Claude Code, Codex, Cursor, and multi-agent teams that need an audit trail between prompt and pull request.
Durable execution engine for workflows and AI agents
Restate is a durable execution engine that provides reliable workflow orchestration for AI agents and backend services. It runs as a single binary with no external dependencies, delivering sub-50ms latency and 94K+ actions per second. Supports TypeScript, Python, Go, Java, and Kotlin SDKs with built-in retries, sagas, and virtual object state. MIT licensed with 3,700+ GitHub stars.
Build modular, scalable LLM applications in Rust
Open-source Rust library for building scalable, modular, and ergonomic LLM-powered applications. Rig unifies 20+ model providers (OpenAI, Anthropic, Mistral, DeepSeek, Ollama, and more) and 10+ vector stores behind one trait-based interface, supports completion and embedding workflows, multi-turn streaming, and transcription/audio/image generation, with full GenAI Semantic Convention compatibility and WASM-ready core library — production agentic infra for Rust teams.
Visual AI agent builder by Ironclad
Rivet is an open-source visual AI programming environment and TypeScript library by Ironclad for building, debugging, and deploying AI agents through a node-based graph editor. Complex LLM prompt chains become visual networks with real-time data flow inspection at every node, team collaboration on agent graphs, and a TypeScript library for embedding finished graphs in any application. Transforms agent development from text-editing into visual engineering.
Open-source AI coworker with persistent memory and tool use
Rowboat is an open-source AI coworker platform that provides persistent memory, tool use, and multi-agent orchestration in a chat-based interface. It enables teams to build AI assistants that remember context across sessions, access internal tools and databases, and coordinate specialized sub-agents for complex workflows. Over 9,300 GitHub stars.
Multi-agent model API that orchestrates frontier models behind one OpenAI-compatible endpoint
Sakana Fugu is a hosted model-provider API that exposes a learned multi-agent system as one OpenAI-compatible model. It dynamically routes coding, code review, research, and reasoning tasks across a frontier-model pool, with Fugu for lower-latency work and Fugu Ultra for harder workloads where answer quality matters more than cost or speed.
Validated agent skills and database connectors for AI co-scientist workflows
Scientific Agent Skills is an open-source library of 165+ validated agent skills and 100+ scientific database connectors. It equips AI agents across Claude Code, Cursor, and Codex with specialized workflows for biology, chemistry, and research.
Microsoft's AI orchestration SDK for .NET, Python, and Java
Microsoft's open-source AI SDK that lets you combine AI models with conventional programming. Supports plugins, planners, memory, and function calling with availability for .NET, Python, and Java. Designed for enterprise developers building AI-powered applications within the Microsoft ecosystem, offering deep integration with Azure AI services and existing business logic.
Visual agent builder with 1000+ integrations
Sim is an open-source platform for building, deploying, and orchestrating AI agents with a visual workflow editor. Connects 1,000+ integrations and LLMs with drag-and-drop canvas design, AI-assisted Copilot for generating nodes from natural language, and built-in knowledge base for RAG. Trusted by 100K+ builders. Includes 11 pre-built workflow templates for quick deployment.
Hugging Face's lightweight agent framework
smolagents is Hugging Face's lightweight agent framework for building AI agents that can use tools, write and execute code, and collaborate in multi-agent setups. Designed for simplicity with minimal abstractions — agents are just LLMs that write Python code to orchestrate tool calls rather than using JSON-based function calling. Supports any LLM provider, integrates with Hugging Face Hub for sharing tools and agents, and runs with as few as 1,000 lines of core library code.
Alibaba's Spring framework for building AI applications in Java
Spring AI Alibaba is Alibaba's open-source framework that brings AI capabilities to Java Spring Boot applications. It provides auto-configuration for AI model providers, RAG pipeline components, agent frameworks, and tool integration following Spring conventions. With 9,100 GitHub stars and 220+ contributors, it is the most mature AI framework for Java enterprise developers building production AI features.