The best AI agent frameworks in 2026
The best AI agent frameworks in 2026
We reviewed 7 AI agent frameworks across orchestration, observability, and production readiness. See how LangGraph, CrewAI, Microsoft Agent Framework, and others compare.
June 6, 2026
Your agent works in local testing. Then you ship it, and something subtle breaks. The wrong tool gets picked. A long-running conversation loses context. Token spend triples because an agent gets stuck in a loop you cannot reproduce.
A framework earns the label "best" if it helps you prevent those failures and diagnose them fast when they happen. We have seen this pattern across thousands of teams shipping agents. The framework you choose determines what you can build quickly. The observability and evaluation layer you pair it with determines whether what you build keeps working once it ships.
We evaluated seven options across developer experience during prototyping, production reliability, observability and debugging support, ecosystem integrations, and pricing transparency, so you can match the right framework to your stack, not just your prototype.
This guide compares seven frameworks: LangChain, CrewAI, Microsoft Agent Framework, LlamaIndex Workflows, Google ADK, OpenAI Agents SDK, and Mastra.
TL;DR
- Choose LangChain if you need an open-source framework for rapid prototyping across model providers, paired with LangGraph for stateful multi-agent orchestration, Deep Agents for long-running workflows, and LangSmith for enterprise-grade observability and evaluation across the full application lifecycle.
- Choose CrewAI if you need role-based multi-agent prototypes up and running quickly with an intuitive mental model.
- Choose Microsoft Agent Framework if you're on the Microsoft stack and want the unified successor to AutoGen and Semantic Kernel, with graph-based workflows, responsible AI guardrails available through Azure AI Foundry, and Python + .NET runtimes at 1.0 GA.
- Choose LlamaIndex Workflows if you need event-driven orchestration for document-heavy, data-intensive pipelines.
- Choose Google ADK if you're GCP-native and want an opinionated, batteries-included agent runtime with built-in debugging UIs.
- Choose OpenAI Agents SDK if you need tightly scoped assistants and clean multi-agent delegation with minimal abstraction.
- Choose Mastra if you're a TypeScript team building production agents and want workflows, memory, and a Studio environment in one package.
The best AI agent frameworks at a glance
| Tool | Type | Open Source | Best For |
|---|---|---|---|
| LangChain | LLM application framework | Yes (MIT) | Fast prototyping of complex agentic workflows |
| LangGraph | Agent runtime | Yes (MIT) | Complex agents that require precision |
| Deep Agents | Agent harness | Yes (MIT) | Long-running workflows |
| CrewAI | Multi-agent orchestration framework | Yes (MIT) | Rapid prototyping of role-based agent workflows |
| Microsoft Agent Framework | Multi-agent orchestration framework | Yes (MIT) | Unified successor to AutoGen + Semantic Kernel for Microsoft stack |
| LlamaIndex (Workflows) | Agent workflow framework | Yes (MIT) | Document-centric, event-driven multi-agent systems |
| Google ADK (Agent Development Kit) | Agent development framework | Yes (Apache 2.0) | GCP-native teams seeking opinionated agent runtimes |
| OpenAI Agents SDK | Multi-agent workflow SDK | Yes (MIT) | Tightly scoped assistants and delegation workflows |
| Mastra | AI agent application framework | Partial | TypeScript teams building production custom agents |
What makes a great AI agent framework?
The best agent frameworks give developers clear primitives for tool calling, state management, and inter-agent communication without hiding what's happening underneath. Abstraction is only useful when it accelerates the right decisions; abstraction that obscures failure modes costs more in debugging time than it saves in setup time, which is why the most trusted frameworks expose enough internals to reason about agent behavior at every step.
Production readiness separates frameworks that work in demos from those that hold up under real workloads. Durable execution, reliable state persistence, and predictable error handling matter far more once an agent is handling real user requests than during prototyping. Frameworks that require bolting on external systems like Temporal or Redis just to achieve basic reliability push that complexity onto the team rather than absorbing it.
The right choice depends on what your team is optimizing for: speed to first working prototype, control over complex multi-agent state, language ecosystem fit, or depth of cloud provider integration. A framework that's excellent for a Python team building document-heavy pipelines may be a poor fit for a .NET enterprise team or a TypeScript shop shipping production agents, which is why evaluating against your actual stack matters more than evaluating against benchmarks.
How we evaluated these tools
We reviewed technical documentation, official GitHub repositories, and public pricing pages for each framework, then analyzed community feedback from Reddit, Hacker News, and GitHub Issues to surface real-world friction points that documentation rarely surfaces. Every limitation cited in this guide traces to a specific community source.
We compared frameworks across several dimensions: developer experience during prototyping, production reliability, observability and debugging support, and ecosystem integrations.
Every solution was assessed on the capabilities they actually document and ship, not on roadmap claims.
We believe in LangChain, but we have done our best to give every tool here a fair assessment. If LangChain is not the right fit, one of these alternatives probably is.
LangChain
Quick Facts:
- Type: Open-source framework for building LLM applications
- Company: LangChain
- Open Source: Yes (MIT)
- GitHub: 134k stars at
github.com/langchain-ai/langchain
LangChain is the most widely adopted open-source framework for building AI agents and LLM applications, with ~134k GitHub stars and more than 1,000 pre-built integrations connecting models to data systems, vector databases, and external APIs.
Its core value is breadth: teams can swap model providers with a one-line code change, compose chains and agents from modular components, and move from a working prototype to a production-grade system without switching frameworks.
The framework pairs natively with LangGraph for stateful, cyclic multi-agent orchestration, Deep Agents for long-running workflows, and with LangSmith for systematic debugging in production.
Who should use LangChain?
LangChain fits teams that need to move quickly across a broad set of agentic use cases, from RAG pipelines to tool-calling agents to multi-step workflows, without committing early to a single model provider. It's a strong fit for teams that expect to iterate heavily on prompts and model choices during development, and for organizations that want a single framework that pairs with LangSmith for observability, evaluation, and deployment across the application's entire lifecycle.
Standout features
- Provider abstraction: Swap between OpenAI, Anthropic, Google Gemini, AWS Bedrock, and others without rewriting application logic
- LangGraph pairing: Native support for our separate orchestration framework, built for stateful, cyclic multi-agent systems with loops and human-in-the-loop control
- Deep Agents pairing: Native support for our open source agent harness built for long-running tasks. It handles planning, context management, and multi-agent orchestration for complex work like research and coding
- LangSmith pairing: Our framework-agnostic agent engineering platform for tracing, evaluation, deployment. It offers systematic debugging, capturing costs, latency, and response quality at every step.
- 1,000+ integrations: Community-maintained connectors for vector databases, document loaders, tools, and APIs via
langchain-community - Composable primitives: Modular components including text splitters, retrievers, and output parsers that work as standalone utilities outside full chain architectures
FAQ
Q: Does LangChain work with models outside of OpenAI?
Yes. LangChain's model provider abstraction layer supports OpenAI, Anthropic, Google Gemini, AWS Bedrock, Hugging Face, and many others. Switching providers typically requires changing one line of code.
Q: What's the difference between LangChain, LangGraph, and Deep Agents?
LangChain is the broader framework for building LLM applications, including chains, retrievers, and tool-calling agents. LangGraph is a separate, lower-level orchestration framework for building stateful multi-agent systems. Deep Agents is an agent harness for long-running workflows, such as coding and research agents.
Q: Is LangChain suitable for production, or just prototyping?
LangChain works in production, but teams often find that the abstraction layers that accelerate prototyping require careful management at scale.
CrewAI
Quick Facts:
- Type: Multi-agent orchestration framework
- Company: crewAI
- Open Source: Yes (MIT)
- GitHub: ~49.2k stars at
github.com/crewAIInc/crewAI - Website: crewai.com
CrewAI is a standalone multi-agent orchestration framework built around a role-based mental model where each agent has a defined persona, a set of tools, and a specific task within a larger crew. The framework is designed for speed of initial setup: developers consistently report that the abstractions are intuitive enough to get a working multi-agent prototype running faster than with most alternatives.
The framework supports OpenAI as the default model provider, with explicit support for local runtimes via Ollama, and integrates with a range of tools including web scraping, PostgreSQL, MongoDB Vector Search, Qdrant, and Weaviate.
Who should use CrewAI?
CrewAI fits teams that need a working multi-agent prototype quickly and whose workflows map naturally to distinct agent roles with clear task boundaries. It's a good choice for automation use cases like email triage, content publishing pipelines, and research workflows where distinct tasks are managed by individual agents.
Standout features
- Role-based agent model: Each agent has a defined persona, goal, and backstory.
- Broad tool integrations: Official connectors for various tools and databases.
- Rapid prototyping: Intuitive abstractions that reduce time from concept to prototype.
- MCP support: Full Model Context Protocol (MCP) client support across various transports.
FAQ
Q: Is CrewAI reliable enough for production use?
CrewAI can work in production for well-scoped workflows, but community feedback highlights gaps in tool-calling reliability.
Q: Does CrewAI support models other than OpenAI?
Yes, but with varying reliability. CrewAI supports Ollama for local runtimes and community members have deployed it with non-OpenAI providers.
Microsoft Agent Framework
Quick Facts:
- Type: Multi-agent orchestration framework and SDK
- Company: Microsoft
- Open Source: Yes (MIT)
- GitHub: ~9.6k stars at
github.com/microsoft/agent-framework
Microsoft Agent Framework is the unified successor to AutoGen and Semantic Kernel. It combines AutoGen's conversational multi-agent abstractions with Semantic Kernel's enterprise features.
The framework supports Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Amazon Bedrock, Google Gemini, and Ollama out of the box.
Who should use Microsoft Agent Framework?
It fits enterprise teams already invested in the Microsoft stack who want first-party orchestration with integrated observability.
Standout features
- Multi-agent orchestration patterns: Sequential, concurrent, handoff, and group chat patterns.
- Azure AI Foundry integration: Guardrails that keep agents on-task and protect sensitive data.
- Protocol support: Native MCP support in core.
FAQ
Q: What happens to existing AutoGen and Semantic Kernel projects?
Existing applications will continue to receive bug fixes during the support window while teams plan migrations.
LlamaIndex (Workflows)
Quick Facts:
- Type: Agent workflow and orchestration framework
- Company: LlamaIndex
- Open Source: Yes (MIT)
- GitHub: 347 stars at
github.com/run-llama/llama-agents
LlamaIndex Workflows is an event-driven orchestration layer for building multi-agent systems without requiring a separate domain-specific language.
Who should use LlamaIndex (Workflows)?
It fits developers building document-centric, data-intensive multi-agent systems who want event-driven orchestration in plain code.
Standout features
- Event-driven orchestration: Typed event models for composable and inspectable agent pipelines.
- LlamaIndex data ecosystem integration: Direct access to document extraction through LlamaCloud.
FAQ
Q: How mature is LlamaIndex Workflows for production multi-agent systems?
The framework carries documented risks around certain abstractions, including handoff failures.
Google ADK (Agent Development Kit)
Quick Facts:
- Type: Agent development framework and SDK
- Company: Google
- Open Source: Yes (Apache 2.0)
- GitHub: 19k stars at
github.com/google/adk-python
Google ADK is designed to make it fast to build, debug, and deploy AI agents on Google Cloud infrastructure.
Who should use Google ADK?
It fits GCP-native teams who want an opinionated, end-to-end agent runtime with built-in debugging tooling.
Standout features
- Built-in debugging UI: For inspecting agent execution without setting up external tooling.
- Protocol support: MCP and A2A protocols.
FAQ
Q: Is Google ADK suitable for teams not using Google Cloud?
It can run outside GCP, but its benefits diminish outside the GCP ecosystem.
OpenAI Agents SDK
Quick Facts:
- Type: Multi-agent workflow SDK
- Company: OpenAI
- Open Source: Yes (MIT)
- GitHub: 22.2k stars at
github.com/openai/openai-agents-python
The OpenAI Agents SDK is designed for building multi-agent workflows using OpenAI's model APIs.
Who should use OpenAI Agents SDK?
It fits developers building tightly scoped assistants or delegation-based agent workflows.
Standout features
- Minimal API surface: Low abstraction design makes agent execution easy to reason about.
- Clean handoff primitives: Built-in multi-agent delegation patterns.
FAQ
Q: What are the real costs of running the OpenAI Agents SDK in production?
Costs are driven by OpenAI API usage.
Mastra
Quick Facts:
- Type: AI agent and application framework for TypeScript
- Company: Mastra
- Open Source: Partial
- GitHub: ~23k stars at
github.com/mastra-ai/mastra
Mastra is designed to give JavaScript and TypeScript developers a path to building production agents without assembling separate libraries.
Who should use Mastra?
It fits TypeScript-heavy teams building production custom agents.
Standout features
- TypeScript-first developer experience: Designed for TypeScript environments.
- Batteries-included platform: Reduces dependency on external tooling.
FAQ
Q: Is Mastra a good choice for teams coming from Python-first frameworks?
Expect a meaningful context switch in how workflows and memory are modeled.
The full LangChain ecosystem
Every framework helps you build agents. We built the LangChain ecosystem to cover an AI agent's entire application lifecycle as a connected set of products:
Use your favorite framework and you'll always get full trace visibility, whether you're building with the LangChain framework, LangGraph, Deep Agents, the OpenAI Agents SDK, Microsoft Agent Framework, Mastra, or custom code.
The information provided in this article is accurate at the time of publication. Tool capabilities, pricing, and availability may change. Always verify current specifications on official websites.