The best open-source AI agent framework is the one that matches your runtime, reliability needs and team stack. LangGraph is the strongest general default for stateful orchestration, Pydantic AI is the cleanest Python-first choice, Mastra leads for TypeScript product teams, and CrewAI remains the easiest way to prototype role-based agent teams.
This ranking compares installable frameworks and SDKs used to build agent applications. Coding agents, browser operators and companion frontends are covered separately because they are finished applications or specialist layers, not direct substitutes for a general orchestration framework. If you need implementation help rather than a library comparison, see AI Haven's AI agent development services.
Quick verdict
Choose LangGraph when long-running state and explicit workflow control matter most. Choose Pydantic AI for typed Python services, Mastra for TypeScript and Next.js products, CrewAI for fast role-based prototypes, and Microsoft Agent Framework when Azure or .NET is already your operating environment.
Choose the agent architecture
Open-source agent frameworks optimize for different engineering tradeoffs
Choose by orchestration model, developer experience, deployment needs, and ecosystem maturity rather than headline popularity.
Stateful production workflows
Graph-based control, persistence, durable execution and human review are core concepts rather than add-ons.
Main risk
LangGraph’s explicit graph and state model adds architectural complexity for simple agents.
Typed Python agent services
Pydantic validation, structured outputs and a familiar Python application model make correctness easier to enforce.
Main risk
Pydantic AI is Python-first and does not provide a complete deployment platform by itself.
TypeScript and Next.js products
Agents, workflows, memory, RAG, evaluation and observability live in one TypeScript-first ecosystem.
Main risk
Mastra’s fast-moving TypeScript ecosystem can introduce API churn and framework coupling.
Key Takeaways
- LangGraph is the best overall choice when an agent must pause, resume, branch, preserve state or include human approval.
- Pydantic AI 2.0 is the strongest Python-first option for teams that value type safety, validation, structured outputs and clean service boundaries.
- Mastra is the most complete TypeScript-first framework in this ranking for web product teams that want workflows, memory, RAG, evals and observability together.
- CrewAI is easier to prototype with than graph-oriented runtimes, but its role-based abstractions require more discipline as workflows become complex.
- Microsoft Agent Framework is the supported Microsoft direction for new enterprise projects; Microsoft's original AutoGen repository is now in maintenance mode.
- Google ADK and Strands Agents both support multiple languages, while OpenAI Agents SDK offers a deliberately smaller Python and TypeScript abstraction.
- LlamaIndex and Haystack belong in the comparison when retrieval and document pipelines are the center of the product, not merely one tool attached to an agent.
How We Ranked These Frameworks
- Runtime reliability: State, checkpoints, retries, resumability, human review and predictable control flow.
- Developer experience: Documentation, type safety, debugging, testing and how much framework-specific abstraction a team must learn.
- Production tooling: Tracing, evaluation, observability, deployment options and operational controls.
- Model and tool flexibility: Provider choice, MCP support, structured outputs, tool execution and integration depth.
- Project health: Current official documentation, public repository status, release direction and clearly stated open-source licensing.
- Use-case fit: General orchestration ranks separately from RAG pipelines, coding agents, browser tools and end-user frontends.
What Counts As An Open-Source AI Agent Framework?
For this guide, a framework must provide an installable open-source core for coordinating model calls, tools, state, workflows or multiple agents. A public repository alone is not enough: the project must be usable as a developer library or SDK and have an active path for new applications.
Hosted control planes and enterprise modules may use different terms from the open-source core. The license shown below refers to the main repository checked on July 23, 2026; teams should still inspect package-level and enterprise-directory terms before shipping a commercial product.
Quick Comparison Table
- Rank
- 1
- Framework
- LangGraph
- Stack
- Python / TypeScript
- Core License
- MIT
- Best For
- Durable stateful orchestration
- Main Trade-off
- More architecture than a simple agent loop
- Rank
- 2
- Framework
- Pydantic AI
- Stack
- Python
- Core License
- MIT
- Best For
- Typed production services
- Main Trade-off
- Python-only
- Rank
- 3
- Framework
- Mastra
- Stack
- TypeScript
- Core License
- Apache 2.0 core
- Best For
- Next.js and Node products
- Main Trade-off
- Younger ecosystem
- Rank
- 4
- Framework
- CrewAI
- Stack
- Python
- Core License
- MIT
- Best For
- Role-based multi-agent teams
- Main Trade-off
- Crew abstractions can become rigid
- Rank
- 5
- Framework
- Microsoft Agent Framework
- Stack
- Python / .NET
- Core License
- MIT
- Best For
- Microsoft enterprise stacks
- Main Trade-off
- Most compelling inside Azure and .NET
- Rank
- 6
- Framework
- Google ADK
- Stack
- Python / TS / Go / Java / Kotlin
- Core License
- Apache 2.0
- Best For
- Google Cloud and Gemini agents
- Main Trade-off
- Broad platform surface
- Rank
- 7
- Framework
- OpenAI Agents SDK
- Stack
- Python / TypeScript
- Core License
- MIT
- Best For
- Lightweight handoffs and tool loops
- Main Trade-off
- OpenAI-first architecture
- Rank
- 8
- Framework
- Strands Agents
- Stack
- Python / TypeScript
- Core License
- Apache 2.0
- Best For
- Model-driven portable agents
- Main Trade-off
- Newer unified SDK ecosystem
- Rank
- 9
- Framework
- Agno
- Stack
- Python
- Core License
- Apache 2.0
- Best For
- Agent teams and runtime platforms
- Main Trade-off
- More platform than small apps need
- Rank
- 10
- Framework
- LlamaIndex Workflows
- Stack
- Python / TypeScript
- Core License
- MIT
- Best For
- Data and RAG agents
- Main Trade-off
- Data-centric rather than general runtime-first
- Rank
- 11
- Framework
- Haystack
- Stack
- Python
- Core License
- Apache 2.0
- Best For
- RAG pipelines and retrieval
- Main Trade-off
- Pipeline-first and Python-centric
- Rank
- 12
- Framework
- AG2
- Stack
- Python
- Core License
- Apache 2.0
- Best For
- Conversational multi-agent research
- Main Trade-off
- AutoGen lineage can confuse project selection
| Rank | Framework | Stack | Core License | Best For | Main Trade-off |
|---|---|---|---|---|---|
| 1 | LangGraph | Python / TypeScript | MIT | Durable stateful orchestration | More architecture than a simple agent loop |
| 2 | Pydantic AI | Python | MIT | Typed production services | Python-only |
| 3 | Mastra | TypeScript | Apache 2.0 core | Next.js and Node products | Younger ecosystem |
| 4 | CrewAI | Python | MIT | Role-based multi-agent teams | Crew abstractions can become rigid |
| 5 | Microsoft Agent Framework | Python / .NET | MIT | Microsoft enterprise stacks | Most compelling inside Azure and .NET |
| 6 | Google ADK | Python / TS / Go / Java / Kotlin | Apache 2.0 | Google Cloud and Gemini agents | Broad platform surface |
| 7 | OpenAI Agents SDK | Python / TypeScript | MIT | Lightweight handoffs and tool loops | OpenAI-first architecture |
| 8 | Strands Agents | Python / TypeScript | Apache 2.0 | Model-driven portable agents | Newer unified SDK ecosystem |
| 9 | Agno | Python | Apache 2.0 | Agent teams and runtime platforms | More platform than small apps need |
| 10 | LlamaIndex Workflows | Python / TypeScript | MIT | Data and RAG agents | Data-centric rather than general runtime-first |
| 11 | Haystack | Python | Apache 2.0 | RAG pipelines and retrieval | Pipeline-first and Python-centric |
| 12 | AG2 | Python | Apache 2.0 | Conversational multi-agent research | AutoGen lineage can confuse project selection |
12 Best Open-Source AI Agent Frameworks Reviewed
LangGraph
Best overall for stateful production agents
- Stack
- Python / TypeScript
- License
- MIT
- Architecture
- Graph runtime
- Current status
- Actively maintained
LangGraph is the strongest default when agent behavior must be explicit, inspectable and recoverable. Its graph model makes loops, branching, interrupts and approval steps visible instead of hiding them inside a high-level autonomous-agent abstraction.
Persistence, durable execution, streaming, memory and human-in-the-loop controls make it especially useful for long-running workflows. The trade-off is that teams must design state and transitions deliberately, which is unnecessary for a small tool-calling assistant.
Strengths
- Explicit state and control flow
- Durable execution and checkpointing
- Strong human-review patterns
- Python and TypeScript implementations
Trade-offs
- More architectural work up front
- Graph concepts add a learning curve
- Deployment services are separate from the open-source runtime
Pydantic AI
Best Python framework for typed agent services
- Stack
- Python
- License
- MIT
- Architecture
- Typed agents + graphs
- Current status
- 2.0 stable
Pydantic AI brings the conventions that made Pydantic and FastAPI familiar to agent development: typed dependencies, validated outputs, explicit model interfaces and application code that remains recognizable to Python engineers.
Version 2.0 adds a current stable baseline for new projects. Durable execution integrations, graph APIs, MCP support, evaluation tooling and observability give it more depth than a thin structured-output wrapper, while still keeping types and testability at the center.
Strengths
- Excellent Python ergonomics
- Strong structured-output validation
- Model-agnostic interfaces
- Durable execution integrations
Trade-offs
- Python-only core
- Some durability relies on external runtimes
- Less natural for JavaScript product teams
Mastra
Best TypeScript framework for agent products
- Stack
- TypeScript
- License
- Apache 2.0 core
- Architecture
- Agents + workflows
- Current status
- Actively maintained
Mastra is the most complete TypeScript-first choice for teams building agents inside Node, Next.js or modern web applications. Agents, workflows, memory, RAG, model routing, evaluation and observability share one ecosystem.
Its breadth reduces the need to assemble a separate framework for every concern. The core is Apache 2.0, while enterprise-designated directories use separate terms, so commercial teams should check the exact packages they intend to deploy.
Strengths
- TypeScript-first design
- Integrated workflows, memory and RAG
- Built-in evaluation and observability concepts
- Good fit for web product teams
Trade-offs
- Younger than LangGraph
- Enterprise directories have separate terms
- Broad surface can be more than a simple app needs
CrewAI
Best for fast role-based multi-agent prototypes
- Stack
- Python
- License
- MIT
- Architecture
- Crews + flows
- Current status
- Actively maintained
CrewAI remains one of the easiest frameworks to explain and prototype with: define agents with roles and goals, assign tasks and coordinate the crew. That model is effective for research, content, operations and delegation-heavy proof-of-concepts.
Flows provide a more structured path beyond a free-form crew, but production teams still need to watch state, retries, evaluation and observability as workflows expand. The framework is strongest when role boundaries are genuinely useful rather than decorative.
Strengths
- Fast multi-agent prototyping
- Intuitive role and task model
- Useful flow abstraction
- Large example ecosystem
Trade-offs
- Role abstractions can become rigid
- Complex control flow needs discipline
- Not the lowest-level runtime
Microsoft Agent Framework
Best for Azure, .NET and Microsoft enterprise stacks
- Stack
- Python / .NET
- License
- MIT
- Architecture
- Unified agent framework
- Current status
- Supported successor
Microsoft Agent Framework is now the Microsoft path to evaluate for new agent projects. It unifies lessons from Semantic Kernel and AutoGen behind a supported Python and .NET programming model, with enterprise integration as its clearest advantage.
That status matters: Microsoft's original AutoGen repository is in maintenance mode and directs new users here. Existing Semantic Kernel and AutoGen teams should use the official migration guidance rather than treating all three projects as equivalent current choices.
Strengths
- Official Microsoft successor path
- Python and .NET support
- Strong enterprise integration direction
- Migration guidance from AutoGen and Semantic Kernel
Trade-offs
- Less compelling outside Microsoft stacks
- Large enterprise surface
- Migration status must be understood carefully
Google Agent Development Kit
Best for Google Cloud and Gemini-oriented agents
- Stack
- Python / TS / Go / Java / Kotlin
- License
- Apache 2.0
- Architecture
- Code-first agent SDK
- Current status
- ADK 2.0 current
Google ADK is a code-first framework for building, evaluating and deploying agent systems. Its language coverage is now unusually broad: Python, TypeScript, Go, Java and Kotlin are all represented in the official documentation.
ADK is attractive for teams already operating on Google Cloud or building heavily around Gemini, but the framework is not limited to a single model. Its deployment and enterprise story are strengths; smaller projects may find the platform surface broader than necessary.
Strengths
- Five documented language options
- Strong evaluation and deployment direction
- Google Cloud integration
- Open-source code-first core
Trade-offs
- Broad ecosystem takes time to evaluate
- Most natural inside Google Cloud
- Language features may not be identical
OpenAI Agents SDK
Best lightweight SDK for handoffs and tool use
- Stack
- Python / TypeScript
- License
- MIT
- Architecture
- Agent loop + handoffs
- Current status
- Actively maintained
OpenAI Agents SDK keeps its abstraction deliberately small: agents, tools, handoffs, guardrails, sessions and tracing. It is easier to adopt than a full graph runtime when those primitives already match the application.
Official Python and TypeScript SDKs now exist. The design is naturally OpenAI-first even though model integrations can be broader, so teams should compare its portability and state requirements against LangGraph or Strands before standardizing.
Strengths
- Small, understandable abstraction
- Python and TypeScript SDKs
- Handoffs and guardrails
- Built-in tracing concepts
Trade-offs
- OpenAI-first design
- Less durable orchestration than LangGraph
- Platform story is intentionally narrower
Strands Agents
Best portable model-driven agent SDK
- Stack
- Python / TypeScript
- License
- Apache 2.0
- Architecture
- Model-driven harness
- Current status
- Unified SDK monorepo
Strands Agents provides a model-driven agent harness with Python and TypeScript SDKs in one repository. Its current positioning emphasizes provider flexibility, execution controls, observability hooks, guardrails, MCP, streaming and multi-agent patterns.
AWS and Bedrock are natural deployment fits, but the official project documents Anthropic, OpenAI, Gemini, Ollama and custom providers as well. It is a credible current option for teams that want more runtime control than a tiny SDK without adopting a graph model.
Strengths
- Python and TypeScript SDKs
- Model and cloud flexibility
- MCP and multi-agent support
- Hooks, guardrails and observability
Trade-offs
- Newer ecosystem
- Default quickstart is AWS-oriented
- Smaller third-party knowledge base than older frameworks
Agno
Best for agent teams and runtime platforms
- Stack
- Python
- License
- Apache 2.0
- Architecture
- Agents + teams + workflows
- Current status
- Actively maintained
Agno is designed for teams building an agent platform rather than a single prompt-and-tool loop. Its concepts span individual agents, multi-agent teams, workflows, memory, knowledge and runtime operations.
That platform ambition is the reason to choose it and the reason to be cautious. It can consolidate a broad Python agent stack, but a small product may be easier to maintain with Pydantic AI or a focused SDK.
Strengths
- Broad agent-platform primitives
- Teams and workflows
- Memory and knowledge features
- Apache-licensed core
Trade-offs
- Can be overkill for small applications
- Platform concepts add surface area
- Smaller ecosystem than the leaders
LlamaIndex Workflows
Best for data-intensive and RAG agents
- Stack
- Python / TypeScript
- License
- MIT
- Architecture
- Data framework + workflows
- Current status
- Actively maintained
LlamaIndex is the strongest option here when data access is the product's central challenge. Its ecosystem focuses on ingestion, indexes, retrieval, document processing and workflows that connect agents to enterprise knowledge.
It can build agentic systems, but its center of gravity remains data and RAG. Choose it over a general runtime when retrieval quality, connectors and document workflows matter more than elaborate multi-agent coordination.
Strengths
- Deep data and retrieval ecosystem
- Strong document workflow fit
- Python and TypeScript options
- Extensive connector story
Trade-offs
- Data-centric architecture
- Retrieval still requires evaluation and tuning
- Not the simplest general agent loop
Haystack
Best for explicit production RAG pipelines
- Stack
- Python
- License
- Apache 2.0
- Architecture
- Component pipelines
- Current status
- Actively maintained
Haystack uses explicit component pipelines for retrieval, routing, generation and agent behavior. It is a mature fit for teams that want the data path to remain visible and testable rather than hidden inside an autonomous-agent abstraction.
Its agent capabilities are most compelling when combined with Haystack's established retrieval and pipeline strengths. Python-only teams building search, question-answering or enterprise knowledge systems should compare it directly with LlamaIndex.
Strengths
- Clear component pipeline model
- Mature retrieval architecture
- Good production RAG fit
- Apache-licensed core
Trade-offs
- Python-centric
- Less general-purpose than orchestration-first frameworks
- Pipeline design can be verbose
AG2
Best for conversational multi-agent experimentation
- Stack
- Python
- License
- Apache 2.0
- Architecture
- Conversational agents
- Current status
- Active community project
AG2 is the active community-governed continuation of AutoGen-style conversational multi-agent patterns. It remains useful for research workflows, group chat, human participation and systems where agent-to-agent conversation is the main abstraction.
Do not confuse AG2 with Microsoft's original AutoGen repository or Microsoft Agent Framework. New Microsoft-platform projects should evaluate Agent Framework; teams specifically invested in the community AutoGen lineage can evaluate AG2 on its own merits.
Strengths
- Flexible multi-agent conversations
- Human participation patterns
- Active community governance
- Broad model experimentation
Trade-offs
- Lineage and naming can confuse buyers
- Conversation is not always the right workflow abstraction
- Python-only
Framework Capability Matrix
Agent framework capabilities at a glance
| Tool | Python | TypeScript | State / Durable Flow | Multi-Agent | RAG Fit | MCP | Provider Flexibility |
|---|---|---|---|---|---|---|---|
| LangGraph | ✓ | ✓ | Excellent | ✓ | Integrations | ✓ | High |
| Pydantic AI | ✓ | ✗ | Integrations | ✓ | Custom | ✓ | High |
| Mastra | ✗ | ✓ | Workflows | ✓ | ✓ | ✓ | High |
| CrewAI | ✓ | ✗ | Flows | ✓ | Tools | ✓ | High |
| Microsoft Agent Framework | ✓ | ✗ | Strong | ✓ | Connectors | ✓ | Broad |
| Google ADK | ✓ | ✓ | Strong | ✓ | Tools | ✓ | Broad |
| OpenAI Agents SDK | ✓ | ✓ | Sessions | ✓ | Custom | ✓ | OpenAI-first |
| Strands Agents | ✓ | ✓ | Agent state | ✓ | Tools | ✓ | High |
| Agno | ✓ | ✗ | Workflows | ✓ | ✓ | ✓ | High |
| LlamaIndex | ✓ | ✓ | Workflows | ✓ | ✓ | ✓ | High |
| Haystack | ✓ | ✗ | Pipelines | Agent loops | ✓ | Integrations | High |
| AG2 | ✓ | ✗ | Conversation state | ✓ | Tools | Integrations | High |
Which Framework Should You Choose?
- Decision
- Long-running production workflow
- Recommended Pick
- LangGraph
- Why
- Explicit state, checkpoints, interrupts and durable execution.
- Also Compare
- Pydantic AI for typed Python services
- Decision
- Typed Python API or backend
- Recommended Pick
- Pydantic AI
- Why
- Validation, structured outputs and clean dependency boundaries.
- Also Compare
- LangGraph for more complex state machines
- Decision
- Next.js or Node product
- Recommended Pick
- Mastra
- Why
- The broadest integrated TypeScript-first agent product stack.
- Also Compare
- LangGraph JS or OpenAI Agents SDK TS
- Decision
- Fast role-based prototype
- Recommended Pick
- CrewAI
- Why
- Roles, tasks and crews are quick to model and explain.
- Also Compare
- AG2 for conversation-led collaboration
- Decision
- Azure or .NET organization
- Recommended Pick
- Microsoft Agent Framework
- Why
- Supported Microsoft successor with enterprise integration.
- Also Compare
- Google ADK for multi-language cloud teams
- Decision
- Portable Python and TypeScript SDK
- Recommended Pick
- Strands Agents
- Why
- Provider-flexible agent harness with MCP and observability hooks.
- Also Compare
- OpenAI Agents SDK for a smaller OpenAI-first surface
- Decision
- Data or document agent
- Recommended Pick
- LlamaIndex
- Why
- Retrieval, indexing and document connectors are central.
- Also Compare
- Haystack for explicit component pipelines
| Decision | Recommended Pick | Why | Also Compare |
|---|---|---|---|
| Long-running production workflow | LangGraph | Explicit state, checkpoints, interrupts and durable execution. | Pydantic AI for typed Python services |
| Typed Python API or backend | Pydantic AI | Validation, structured outputs and clean dependency boundaries. | LangGraph for more complex state machines |
| Next.js or Node product | Mastra | The broadest integrated TypeScript-first agent product stack. | LangGraph JS or OpenAI Agents SDK TS |
| Fast role-based prototype | CrewAI | Roles, tasks and crews are quick to model and explain. | AG2 for conversation-led collaboration |
| Azure or .NET organization | Microsoft Agent Framework | Supported Microsoft successor with enterprise integration. | Google ADK for multi-language cloud teams |
| Portable Python and TypeScript SDK | Strands Agents | Provider-flexible agent harness with MCP and observability hooks. | OpenAI Agents SDK for a smaller OpenAI-first surface |
| Data or document agent | LlamaIndex | Retrieval, indexing and document connectors are central. | Haystack for explicit component pipelines |
Head-to-Head Shortlist
LangGraph vs Pydantic AI
Choose LangGraph when workflow topology, state transitions and resumability are the hardest problems. Choose Pydantic AI when application correctness, typed dependencies and validated model outputs matter more than a graph-first runtime. They can also complement each other in a Python architecture.
Mastra vs LangGraph JS vs OpenAI Agents SDK TS
Mastra provides the broadest TypeScript product framework. LangGraph JS is stronger for explicit durable orchestration. OpenAI Agents SDK TS is the lightest choice when agents, handoffs, tools and guardrails already describe the whole application.
CrewAI vs Microsoft Agent Framework vs AG2
CrewAI is the fastest role-based prototype. Microsoft Agent Framework is the supported enterprise direction for Azure and .NET. AG2 is the community option for conversation-centered AutoGen-style experimentation; it is not the replacement Microsoft recommends for new AutoGen projects.
LlamaIndex vs Haystack
LlamaIndex offers a broad data framework and connector ecosystem for knowledge agents. Haystack is especially strong when you want explicit, composable Python pipelines for retrieval and generation. Both deserve evaluation when RAG is the product core.
Specialized Agent Tools That Are Not Direct Framework Substitutes
- Tool or SDK
- Deep Agents
- Category
- Higher-level harness
- Best Fit
- Long-running research and coding tasks
- Why It Is Separate
- It is an opinionated layer in the LangGraph ecosystem, not a separate low-level runtime category.
- Tool or SDK
- Browser Use
- Category
- Browser agent library
- Best Fit
- Web navigation and action workflows
- Why It Is Separate
- Its primary job is browser control rather than general orchestration.
- Tool or SDK
- OpenHands
- Category
- Coding-agent application
- Best Fit
- Repository and software tasks
- Why It Is Separate
- It is a working coding agent, not a framework for arbitrary agent products.
- Tool or SDK
- Letta
- Category
- Stateful agent platform
- Best Fit
- Persistent memory-driven agents
- Why It Is Separate
- Memory and long-lived agent identity are its defining product layer.
- Tool or SDK
- Vercel AI SDK
- Category
- Application toolkit
- Best Fit
- AI-enabled TypeScript interfaces and tool loops
- Why It Is Separate
- It is broader than agents and should be compared separately unless its agent abstractions are the main requirement.
- Tool or SDK
- smolagents
- Category
- Minimal agent library
- Best Fit
- Small experiments and code agents
- Why It Is Separate
- Its intentionally small abstraction targets a different level of complexity.
- Tool or SDK
- SillyTavern
- Category
- End-user frontend
- Best Fit
- Character chat and roleplay
- Why It Is Separate
- It is a user-facing application layer, not a general agent-development framework.
| Tool or SDK | Category | Best Fit | Why It Is Separate |
|---|---|---|---|
| Deep Agents | Higher-level harness | Long-running research and coding tasks | It is an opinionated layer in the LangGraph ecosystem, not a separate low-level runtime category. |
| Browser Use | Browser agent library | Web navigation and action workflows | Its primary job is browser control rather than general orchestration. |
| OpenHands | Coding-agent application | Repository and software tasks | It is a working coding agent, not a framework for arbitrary agent products. |
| Letta | Stateful agent platform | Persistent memory-driven agents | Memory and long-lived agent identity are its defining product layer. |
| Vercel AI SDK | Application toolkit | AI-enabled TypeScript interfaces and tool loops | It is broader than agents and should be compared separately unless its agent abstractions are the main requirement. |
| smolagents | Minimal agent library | Small experiments and code agents | Its intentionally small abstraction targets a different level of complexity. |
| SillyTavern | End-user frontend | Character chat and roleplay | It is a user-facing application layer, not a general agent-development framework. |
Interoperability Checks Before You Commit
- MCP: Confirm whether support is native, adapter-based or limited to particular transports.
- Agent-to-agent protocols: Check whether cross-agent communication is a real requirement or unnecessary architecture.
- Observability: Verify OpenTelemetry, tracing and evaluation export paths before production adoption.
- Durability: Distinguish in-memory session state from checkpointed execution that can recover after failure.
- License boundaries: Inspect enterprise directories, hosted services and individual packages in addition to the root license.
- Provider portability: Run a small model-switch test before accepting a framework's model-agnostic claim.
Frequently Asked Questions
What is the best open-source AI agent framework in 2026?
LangGraph is the best overall choice for stateful production workflows because durable execution, persistence, streaming and human review are central to its runtime. Pydantic AI is better for typed Python services, while Mastra is the stronger TypeScript product framework.
Which open-source agent framework is best for Python?
Pydantic AI is the best Python-first choice when type safety and validated outputs lead the decision. LangGraph is better when complex state transitions and resumable workflows are the harder engineering problem.
Which AI agent framework is best for TypeScript?
Mastra is the most complete TypeScript-first framework in this ranking. LangGraph JS is stronger for graph-based orchestration, while OpenAI Agents SDK TS is better for a smaller OpenAI-centered abstraction.
Is AutoGen still recommended for new projects?
Microsoft's original AutoGen repository is in maintenance mode and directs new projects to Microsoft Agent Framework. AG2 remains an active community project based on the AutoGen lineage, but it should be evaluated separately from Microsoft's supported successor.
Are all parts of these frameworks open source?
The licenses shown here refer to each main repository or open-source core. Hosted platforms, enterprise folders, deployment services and individual packages can have different terms, so commercial teams should review the exact code they plan to use.
Do I need a multi-agent framework?
Usually not at the beginning. A single agent with clear tools and deterministic workflow steps is easier to test and operate. Add multiple agents only when role separation, parallel work or independent context produces a measurable benefit.
Final Verdict
LangGraph is the best overall open-source AI agent framework for serious stateful workflows in 2026. Pydantic AI is the best typed Python framework, Mastra is the strongest TypeScript product framework, CrewAI is the fastest role-based prototype option, and Microsoft Agent Framework is the supported choice for new Microsoft-stack projects.
Choose LlamaIndex or Haystack when retrieval is the product core. Choose OpenAI Agents SDK for a small OpenAI-first surface, Strands Agents for a portable Python and TypeScript harness, Google ADK for multi-language Google Cloud teams, Agno for a broader Python agent platform, and AG2 only when conversation-centered community AutoGen patterns are specifically what you need.