The open-source AI agent framework landscape in 2026 is both richer and more turbulent than it was even twelve months ago. The year began with two major transitions: Microsoft moved AutoGen into maintenance mode and merged it with Semantic Kernel into the new Microsoft Agent Framework (GA April 2026), while OpenAI archived its experimental Swarm library and redirected users to the production-grade Agents SDK. LangGraph hit 1.0 GA. CrewAI crossed the 1.0 threshold. And TypeScript-native frameworks like Mastra and Vercel AI SDK surged past 20,000 GitHub stars, proving that the agent revolution is not Python’s alone. For context on how these frameworks fit into the broader agent landscape, see our Complete Guide to AI Agents.
This guide is for developers and technical leaders who need to cut through the noise. We compare eight frameworks across eight criteria — language support, agent types, key features, learning curve, production readiness, best use case, GitHub stars, and 2026 momentum — with deep dives into each. The goal is not to crown a winner but to help you choose the right tool for your use case, team, and stack.
Quick links: Comparison Table · LangChain / LangGraph · AutoGen / AG2 · CrewAI · OpenAI Agents SDK · Haystack · Semantic Kernel · Mastra · Vercel AI SDK · How to Choose · FAQ
Comparison Table
The table below compares all eight frameworks across eight essential dimensions. Star counts are approximate and sourced from GitHub and third-party trackers as of early June 2026. Production readiness reflects consensus across multiple independent comparisons, not vendor claims.
| Framework | Language(s) | Agent Types | Key Features | Learning Curve | Production Readiness | Best Use Case | ~ GitHub Stars |
|---|---|---|---|---|---|---|---|
| LangChain / LangGraph | Python, JavaScript | Single, Multi, Hierarchical, Swarm | Stateful graphs, checkpointing, memory, human-in-the-loop, LangSmith tracing | Advanced | Mature | Complex stateful workflows, enterprise orchestration | 137k / 33k |
| AutoGen (AG2) | Python | Multi, Conversational, GroupChat | Event-driven, async messaging, code execution sandboxes | Intermediate | Maintenance Mode | Legacy multi-agent research systems (use MAF for new builds) | ~48k |
| CrewAI | Python | Multi, Hierarchical, Role-based | Role/Goal/Backstory agents, sequential & hierarchical processes, Flows, MCP native | Beginner | Stable | Rapid multi-agent prototyping, marketing automation | ~38k |
| OpenAI Agents SDK | Python, TypeScript | Single, Multi (handoff) | Handoff delegation, guardrails, tracing, sandboxed execution, provider-agnostic (100+ LLMs) | Beginner | Stable | Delegation chains, TypeScript/Next.js teams, rapid prototyping | ~19k |
| Haystack | Python | Single, Multi (pipeline agents) | Typed pipelines, 50+ document stores, RAG-native, multimodal, agentic pipelines | Intermediate | Stable | Production RAG, semantic search, question answering | ~22k |
| Semantic Kernel | .NET, Python, Java | Single, Multi, Planner | Enterprise SDK, Azure integration, OpenTelemetry, A2A protocol, plugin architecture | Intermediate | Mature | .NET enterprise teams, Azure-native AI applications | ~28k |
| Mastra | TypeScript | Single, Multi, Graph-based | Graph workflows (then/branch/parallel), RAG, MCP, evals, 4-tier memory, 81+ providers | Intermediate | Stable | TypeScript-native production agents, integrated framework | ~21k |
| Vercel AI SDK | TypeScript, JavaScript | Single, Multi (tools-based) | Streaming, React hooks, 2.8M weekly downloads, Next.js native, provider-agnostic | Beginner | Mature | Web app AI features, React/Next.js teams, chatbots | ~20k |
A note on star counts: Star counts are a lagging indicator of community size — not of production readiness. LangGraph has roughly one-quarter the stars of LangChain but more verified enterprise deployments. AutoGen has ~48k stars but is in maintenance mode. Choose by mental model and production track record, not by GitHub popularity. For a practical, ranked view of the tools built on these frameworks, see our Top 20 Open Source AI Agent Tools guide.
Deep Dives
1. LangChain / LangGraph
The most mature agent ecosystem, for teams that need ultimate control.
LangChain is the granddaddy of the open-source LLM application ecosystem — 137,000 GitHub stars, 3,900+ contributors, and 281,000 dependent repositories as of mid-2026. But for agents specifically, LangGraph is the framework that matters. Released as a standalone library in 2024 and reaching 1.0 GA in October 2025, LangGraph models agent behavior as a directed state graph: nodes are computation steps, edges are conditional transitions, and checkpointers provide persistent state with Postgres or Redis backends.
LangGraph’s power lies in its explicitness. You define every state transition. You can pause workflows mid-execution for human approval. You can rewind to any checkpoint during debugging. This makes it the go-to for enterprises — confirmed deployments include Klarna (853 employee-equivalent agents, saving $60M), Uber (~21,000 developer-hours saved), LinkedIn, Cisco, JPMorgan, and Elastic. LangSmith provides monitoring and tracing for observability at scale.
The trade-off is complexity. LangGraph has a steep learning curve — expect a multi-day ramp-up before you’re productive. For teams that don’t need stateful orchestration, LangGraph is overkill. But for production systems where failure is expensive and audit trails are mandatory, nothing else in the open-source ecosystem matches its depth.
2026 momentum: Deep Agents (launched March 2026) adds built-in planning, filesystem-based context management, and sub-agent spawning on top of LangGraph — pushing it further toward batteries-included, without sacrificing the underlying graph model.
2. AutoGen / AG2
Microsoft’s multi-agent pioneer — now in maintenance mode.
AutoGen was the framework that sparked the multi-agent revolution. Originally from Microsoft Research, it introduced event-driven, conversational multi-agent systems where agents collaborate through message-passing rather than rigid pipelines. At its peak in 2025, AutoGen amassed ~55,000 GitHub stars and proved that multi-agent setups could outperform single-agent solutions on benchmarks like GAIA.
But 2026 brought a major reset. In early 2026, Microsoft announced that AutoGen was entering maintenance mode — bug fixes only, no new features. The team merged AutoGen’s orchestration ideas with Semantic Kernel’s production infrastructure into the Microsoft Agent Framework (MAF), which reached 1.0 GA on April 3, 2026. MAF ships as a unified SDK for .NET and Python under Microsoft.Agents.AI, with Semantic Kernel as the foundation layer and AutoGen-style graph workflows on top.
The community fork AG2 continues AutoGen development independently, but its long-term trajectory is uncertain. For new projects, the unambiguous guidance from Microsoft and independent analysts is: start with MAF, not AutoGen.
Best remaining use case: Teams with existing AutoGen 0.2 or 0.4 deployments that aren’t ready to migrate, or researchers who need AutoGen’s specific conversational multi-agent paradigm for academic work. For everyone else, the migration path leads to MAF or to alternative frameworks.
3. CrewAI
The simplest path to multi-agent orchestration.
If LangGraph is a precision instrument, CrewAI is a power tool for multi-agent workflows. The framework’s mental model is intuitive: define agents with roles, goals, and backstories, then assign them tasks in a sequential or hierarchical process. A working multi-agent crew can be scaffolded in under 10 minutes via the CLI — the fastest time-to-value of any framework in this comparison.
CrewAI hit 1.0 GA in October 2025 and has since added significant capabilities: CrewAI Flows (event-driven workflows with @start, @listen, and @router decorators for complex branching), native MCP server support (v1.10.x), and streaming tool calls. The crewAIInc/crewAI repository has grown to approximately 38,000 GitHub stars.
CrewAI’s strength is accessibility. The role-based abstraction maps naturally to how teams think about delegation — researcher, writer, reviewer — making it popular for content generation pipelines, marketing automation, customer service triage, and rapid prototypes. The company claims ~1.4 billion automations per month and 60% Fortune 500 adoption (though these figures are not independently audited).
The trade-off: CrewAI is less suited for deeply stateful, long-running agents that need persistent memory across sessions. For those use cases, LangGraph’s checkpointing model is more appropriate. But for teams that need to orchestrate multiple agents quickly without building a state machine from scratch, CrewAI remains the most approachable option.
4. OpenAI Agents SDK
Lightweight, official, and provider-agnostic.
OpenAI’s Agents SDK, released in March 2025, is the successor to the experimental Swarm library (archived March 2025). It’s a minimalist, open-source toolkit built around a single elegant primitive: the handoff. Agents can delegate tasks to other agents, enabling triage-and-specialist architectures with remarkably little code.
Despite the name, the SDK is provider-agnostic — it works with 100+ LLMs, not just OpenAI models. It ships for both Python (v0.17.3 as of May 2026) and TypeScript (v0.8.3 as of April 2026), making it one of the few frameworks with first-class support in both ecosystems. Key features include built-in guardrails (parallel input validation that halts on failure), tracing via OpenAI’s observability platform, and — as of April 2026 — sandboxed code execution with providers like Modal, E2B, Cloudflare, and Vercel.
The Agents SDK has approximately 19,000 GitHub stars (Python repo) and roughly 10.3 million monthly PyPI downloads. Its learning curve is the gentlest of any framework here: if you can write a Python function and decorate it as a tool, you can build an agent.
Best for: Teams that want a lightweight, official toolkit without the abstraction overhead of LangChain or CrewAI. Particularly strong for delegation-heavy workflows (triage → specialist → response) and for TypeScript/Next.js teams that want the same SDK across frontend and backend. The main limitation is that the handoff model, while elegant, is less expressive than LangGraph’s state graphs for complex branching and looping workflows.
5. Haystack
The RAG specialist with growing agent capabilities.
Haystack, built by Berlin-based deepset (~$45.6M raised), occupies a distinct niche: it is the framework you choose when retrieval quality is the primary constraint. While other frameworks treat RAG as a feature, Haystack was built around it from day one. Its pipeline architecture — where components (Retriever, Ranker, PromptBuilder, Generator) are composed into typed, directed graphs — maps directly to the structure of production search and question-answering systems.
The Haystack 2.x rewrite modernized the framework significantly, adding agentic pipelines, multimodal support (text + images), and a growing component ecosystem. With approximately 22,000 GitHub stars and an Apache 2.0 license, Haystack provides 50+ document store integrations, hybrid retrieval strategies, and a REST API for deployment.
Haystack’s agent capabilities are structured as “agentic pipelines” — agents that can reason, use tools (including Haystack components as tools), and iterate within the pipeline framework. This is a different mental model from LangGraph’s freeform graphs or CrewAI’s role-playing, but it’s well-suited for use cases where the primary workflow is retrieval-centric and agents assist within that pipeline.
Best for: Production RAG systems where retrieval precision and pipeline predictability matter more than open-ended agent autonomy. Teams building semantic search, enterprise knowledge management, or customer support chatbots with grounded answers. Not the best choice for purely conversational multi-agent systems where retrieval is secondary. Deepset also offers a managed cloud platform (Haystack Enterprise) for teams that want a hosted solution.
6. Semantic Kernel
The enterprise-grade .NET SDK — now part of Microsoft Agent Framework.
Semantic Kernel (SK) is Microsoft’s open-source AI orchestration SDK, purpose-built for the .NET ecosystem with additional support for Python and Java. As of mid-2026, it has approximately 28,000 GitHub stars and has become the default answer for enterprise .NET teams asking “how do we build AI agents without leaving our stack?”
SK’s architecture centers on plugins (reusable AI functions, equivalent to tools in other frameworks), planners (agents that chain plugins to accomplish goals), and memories (vector-backed semantic storage). It is model-agnostic, supporting OpenAI, Azure OpenAI, Anthropic, Google, and local models. Enterprise features include OpenTelemetry integration for observability, Azure AI Foundry deployment, and support for Google’s Agent-to-Agent (A2A) protocol for cross-framework interoperability.
The major 2026 development was SK’s merger with AutoGen into the Microsoft Agent Framework (MAF) 1.0, which shipped GA on April 3, 2026. In MAF, SK provides the production foundation (stability, telemetry, enterprise integration) while AutoGen contributes the multi-agent orchestration patterns. The unified Microsoft.Agents.AI SDK ships as first-class packages for both .NET and Python with identical API shapes.
Best for: .NET and C# enterprise teams building AI agents on Azure. Organizations that need deep integration with the Microsoft ecosystem — Azure AI Foundry, Microsoft 365, Power Platform. Teams that value long-term support stability over cutting-edge experimentation. The main limitation is that SK’s Python experience, while solid, is secondary to its .NET-native design — Python-first teams may find other frameworks more idiomatic.
7. Mastra
The TypeScript-native contender with graph-based workflows.
Mastra represents the new wave of TypeScript-first agent frameworks. Built by the team behind Gatsby (YC W25, $13M seed round), Mastra provides an integrated, opinionated framework where agents, workflows, RAG, memory, and evaluation live in a single coherent package — no stitching together of separate libraries required.
Mastra’s workflow engine models agent orchestration as composable graphs with then(), branch(), and parallel() primitives, plus suspend/resume for human-in-the-loop patterns. The .network() method turns any agent into a router that delegates to sub-agents. Memory is structured across four tiers: message history, working memory, semantic recall, and RAG — a more comprehensive model than most Python frameworks offer. MCP support is built-in, and Mastra connects to 81 providers covering 2,436+ models via the Vercel AI SDK.
With approximately 21,000 GitHub stars and accelerating adoption (Replit used Mastra to improve Agent 3 task success from 80% to 96%), Mastra is carving out a position as “the most complete TypeScript agent framework.” The framework has attracted enterprise users including Marsh McLennan (75,000 employees) and SoftBank.
Best for: TypeScript teams that want a single integrated framework rather than assembling agents from separate libraries. Developers who value type safety, IDE autocomplete, and Zod schema validation in their agent pipelines. Projects where observability (built-in tracing and eval harness) and MCP connectivity are requirements from day one. The main limitation is that Mastra’s ecosystem is younger than the Python equivalents — fewer community contributions, third-party integrations, and Stack Overflow answers — though the trajectory is steeply upward.
8. Vercel AI SDK
The web developer’s agent toolkit — massive adoption, streaming-first.
The Vercel AI SDK is not a traditional “agent framework” in the same sense as LangGraph or CrewAI, but it is the most downloaded TypeScript AI toolkit by an enormous margin: 2.8 million weekly npm downloads and approximately 20,000 GitHub stars. Built by the creators of Next.js, the SDK is designed to add AI features to web applications with minimal friction.
Its architectural philosophy is streaming-first. The useChat and useCompletion React hooks handle the full lifecycle of AI interactions — streaming responses, tool calls, loading states, and error handling — with a few lines of code. The SDK is provider-agnostic, supporting OpenAI, Anthropic, Google, Mistral, and dozens of others through a unified interface. As of 2026, the SDK has grown agentic capabilities: the generateText and streamText functions support tool calling, multi-step reasoning, and structured output generation, effectively enabling single-agent workflows.
The SDK’s agent capabilities are lighter than dedicated frameworks — you won’t find built-in multi-agent orchestration, persistent memory, or human-in-the-loop checkpointing. But for the most common use case in web applications — an AI feature that uses tools, streams responses, and generates structured data — the Vercel AI SDK is faster to implement than any Python alternative.
Best for: React and Next.js developers adding AI chat, tool use, or structured generation to web applications. Projects where streaming UX is a priority. Teams that want the DX advantages of TypeScript-native tooling. Not the right choice for complex multi-agent systems or backend-only agent deployments — pair it with LangGraph.js or Mastra for those cases.
How to Choose a Framework
The right framework depends on your team’s language, your use case’s complexity, and your production requirements. Here’s a practical decision guide:
Choose by Language
- Python-first team → LangGraph, CrewAI, or OpenAI Agents SDK. LangGraph for complex stateful workflows, CrewAI for rapid multi-agent prototyping, OpenAI Agents SDK for lightweight delegation chains.
- TypeScript/JavaScript-first team → Mastra or Vercel AI SDK. Mastra for full agent applications with RAG and evals, Vercel AI SDK for web app AI features.
- .NET enterprise team → Semantic Kernel / Microsoft Agent Framework. The only framework with first-class .NET support and deep Azure integration.
- Mixed Python + TypeScript → OpenAI Agents SDK (ships for both) or LangGraph (LangGraph.js for TypeScript).
Choose by Complexity
- Simple single-agent with tools → OpenAI Agents SDK or Vercel AI SDK. Both minimize boilerplate for the most common agent patterns.
- Multi-agent orchestration, fast prototype → CrewAI. Role-based abstractions get you from idea to working crew in under 10 minutes.
- Complex stateful workflows, human-in-the-loop → LangGraph. The checkpointing and graph model are unmatched for production-critical systems.
- RAG-first with agent augmentation → Haystack. When retrieval quality is more important than agent autonomy.
Choose by Production Posture
- Enterprise, long-term support → LangGraph or Semantic Kernel/MAF. Both have 1.0 releases, stable APIs, and verified enterprise deployments.
- Startup, rapid iteration → CrewAI or Mastra. Fastest time-to-value, growing fast, backed by venture funding.
- Lightweight, low commitment → OpenAI Agents SDK. Minimal dependencies, works with any LLM provider.
Special Cases
- Building on Azure → Microsoft Agent Framework (Semantic Kernel + AutoGen merged). Native Azure AI Foundry deployment, Entra ID auth, OpenTelemetry.
- Building on Vercel/Next.js → Vercel AI SDK. Native streaming, React hooks, Edge runtime support.
- Building a coding agent → Consider LangGraph Deep Agents for planning + sub-agent spawning, or Mastra for TypeScript-native tool execution.
If you’re migrating from AutoGen: The official successor is the Microsoft Agent Framework (GA April 2026). For teams not on the Microsoft stack, CrewAI or LangGraph are the most common migration targets based on community discussion.
Frequently Asked Questions
Which framework has the most GitHub stars?
LangChain has the most stars at approximately 137,000, followed by AutoGen (~48k) and CrewAI (~38k). However, star counts are not a reliable measure of production readiness — LangGraph (~33k stars) has more verified enterprise deployments than any framework with more stars. AutoGen, despite ~48k stars, is now in maintenance mode.
What’s the difference between LangChain and LangGraph?
LangChain is the broader ecosystem — a platform for building LLM applications with chains, agents, tools, and output parsing. LangGraph is a specific library within that ecosystem focused on stateful, graph-based agent orchestration with checkpointing and human-in-the-loop patterns. For agent-specific work in 2026, LangGraph is the recommended starting point within the LangChain ecosystem.
Is AutoGen dead?
Not dead, but in maintenance mode as of early 2026. Microsoft is no longer adding features to AutoGen and has merged its orchestration concepts into the Microsoft Agent Framework (MAF). The community fork AG2 continues development, but for new projects, MAF or alternative frameworks are recommended.
Which framework is best for beginners?
CrewAI has the gentlest learning curve for multi-agent systems — its role-based abstraction is intuitive and the CLI scaffolds working crews in minutes. For single-agent applications, the OpenAI Agents SDK is similarly approachable with minimal boilerplate.
Can I use these frameworks with local/open-source models?
Yes, all eight frameworks are provider-agnostic or support multiple providers. LangGraph, OpenAI Agents SDK, Mastra, and Vercel AI SDK all support 80+ LLM providers including local models via Ollama, vLLM, or similar. Haystack and Semantic Kernel have built-in support for local models. CrewAI supports any model via LiteLLM integration.
Which framework is most “production-ready”?
LangGraph consistently ranks #1 in production-readiness across independent comparisons, with confirmed enterprise deployments at Klarna, Uber, Cisco, LinkedIn, JPMorgan, and Elastic. Semantic Kernel (via Microsoft Agent Framework) is the most production-ready option for .NET/Azure teams, with GA 1.0 guarantees and long-term support commitments.
Should I build agents in Python or TypeScript?
Python remains the dominant language for AI agent development, with the deepest ecosystem of frameworks, tools, and community resources. TypeScript is the fastest-growing alternative and is the better choice if your application stack is already JavaScript/TypeScript or if you’re building agents that integrate deeply with web applications. The gap is narrowing rapidly — frameworks like Mastra and Vercel AI SDK are closing the feature parity gap with their Python counterparts.
How do these compare to hosted platforms like Dify or LangSmith?
This guide focuses on code-first frameworks — libraries and SDKs you integrate into your own application. Hosted platforms like Dify (~143k stars, visual builder), LangSmith (observability), and deepset Cloud (managed Haystack) operate at a higher level of abstraction. They’re often complementary: you might build agents with LangGraph and monitor them with LangSmith, or use CrewAI for orchestration and Dify for the end-user interface.