Best CrewAI Alternatives in 2026: 12 Agent Frameworks Compared

Best CrewAI Alternatives in 2026: 12 Agent Frameworks Compared

Turn this article into takeaways for your work.

Each assistant summarizes the article only for you and suggests best practices for your work.

If you want more control over agent state than CrewAI's crews give you, start with LangGraph. For a lighter Python or TypeScript build, look at the OpenAI Agents SDK or Pydantic AI. For a .NET or enterprise Microsoft stack, Microsoft Agent Framework is the answer, and for TypeScript-only teams it's Mastra. This list covers 12 options, judged on published license, language, GitHub activity and the pricing each vendor puts on its own page.

CrewAI is a good framework with a clear idea: you describe agents as roles, give them tasks, and let them hand work to each other. People leave for ordinary reasons. They want graph-level control over execution, a language other than Python, a vendor with a bigger balance sheet behind it, or no framework at all. For the wider landscape, see our roundups of multi-agent frameworks and AI agent frameworks for developers.

Key Facts

  • CrewAI's GitHub repository shows 60.4k stars, 9.0k forks and an MIT license, so licensing isn't a reason to leave.
  • CrewAI's pricing page lists two plans: a free plan with 50 workflow executions a month and 2 automations, and a custom-priced Enterprise plan.
  • Microsoft Agent Framework reached version 1.0 on April 3, 2026 for .NET and Python, combining Semantic Kernel's foundations with AutoGen's orchestration.
  • Microsoft's AutoGen README says the project is archived and that Microsoft is not providing bug fixes, security fixes, new features or support.
  • LangGraph (44.3k stars) describes itself as a low-level orchestration framework for stateful agents, the opposite end of the abstraction spectrum from CrewAI's role-based crews.

Quick Comparison Table

Framework Best For Language License Core Framework Cost
LangGraph Explicit control over agent state and flow Python MIT Free
OpenAI Agents SDK Lightweight multi-agent workflows Python MIT Free
Microsoft Agent Framework .NET and Python enterprise agents .NET, Python MIT Free
Google ADK Code-first Python agents, evaluation and deployment Python Apache 2.0 Free
Pydantic AI Typed agents in Python Python MIT Free
Mastra TypeScript-native agents and workflows TypeScript Apache 2.0 (ee/ code separately licensed) Free (Mastra Platform paid)
Agno Fast Python agent builds Python Apache 2.0 Free
Strands Agents Model-agnostic agents with Bedrock support Python, TypeScript Apache 2.0 Free
LlamaIndex Document-centric agent workflows Python MIT Free
AG2 Community continuation of AutoGen Python Apache 2.0 Free
smolagents Minimal agents that write code Python Apache 2.0 Free
n8n Visual workflows with AI steps Node (visual builder) Sustainable Use License Free self-hosted, cloud from 20 EUR/mo

Where CrewAI Stands Today

CrewAI's own documentation frames the product around two ideas. Flows are the backbone of an application, defining steps, state and control flow. Crews are teams of autonomous agents, each with a role, a goal and tools, that a Flow delegates complex tasks to. The docs recommend starting with a Flow for production apps and using Crews inside Flow steps where a team of agents helps.

How CrewAI Flows and Crews Work

The commercial side is narrower than most roundups assume. The pricing page shows a free plan with 50 workflow executions a month and 2 automations, and an Enterprise plan with custom pricing. Enterprise adds SSO (Microsoft Entra, Okta), role-based access control, PII redaction, policies, and a choice of CrewAI cloud, dedicated VPC or your own infrastructure. The page doesn't state a billing term or per-seat price for either plan.

CrewAI Plan Price Included Notes
Free (Basic) Free 50 workflow executions/month, 2 automations Studio editor, GitHub integration, tracing, community support
Enterprise Custom Executions sized to workflow SSO, RBAC, PII redaction, 45-day onboarding, dedicated support

If your team needs to run production crews and hits the 50-execution cap, you're in an Enterprise sales conversation, not a self-serve upgrade. That's a real reason to price out the open-source route or a competitor. Our step-by-step CrewAI build tutorial shows what the framework looks like in practice.

Language and Licensing, Side by Side

Framework Primary Language License Stars (GitHub)
CrewAI (baseline) Python MIT 60.4k
LangGraph Python MIT 44.3k
OpenAI Agents SDK Python MIT 30.5k
Microsoft Agent Framework .NET, Python MIT 14.5k
Google ADK Python Apache 2.0 22.1k
Pydantic AI Python MIT 21.0k
Mastra TypeScript Apache 2.0 plus enterprise license for ee/ code 29.3k
Agno Python Apache 2.0 43.0k
Strands Agents Python Apache 2.0 9.0k
LlamaIndex Python MIT 52.8k
AG2 Python Apache 2.0 5.0k
smolagents Python Apache 2.0 30.2k
n8n TypeScript/Node Sustainable Use License 209.4k

Stars track age and marketing reach more than fit, so treat them as a rough maintenance signal. The one license difference worth reading twice is n8n's: its repository describes it as fair-code under the Sustainable Use License plus an Enterprise License, which isn't the same as MIT or Apache for commercial redistribution. Mastra's repository is Apache 2.0 for the core, with code in ee/ directories under a Mastra Enterprise License that needs a valid license for production use.

Maintenance and Activity

Framework Commits on main What to Know
CrewAI 3,125 Active, funded by the commercial platform
LangGraph 7,264 Active, sits in the LangChain ecosystem
OpenAI Agents SDK 2,548 Active, vendor-maintained
Microsoft Agent Framework 3,792 1.0 since April 2026
Google ADK 5,114 Active, vendor-maintained
Pydantic AI 5,146 Active
Mastra 22,553 Very active, funded team
Agno 6,323 Active
Strands Agents 3,192 Active
LlamaIndex 7,962 Active, now positioned as a document processing platform
AG2 5,665 Active, independent fork of AutoGen
smolagents 1,059 Smaller, Hugging Face maintained
AutoGen (Microsoft) n/a Archived, successor is Microsoft Agent Framework

1. LangGraph: Explicit Control Over State and Flow

LangGraph is the usual first stop for teams that outgrow CrewAI's role metaphor. Its repository calls it a low-level orchestration framework for stateful agents. You define nodes and edges, decide exactly when control passes between them, and keep state explicit. That's more code than a crew definition, and it's also why debugging is easier when an agent loops or stalls.

LangGraph for Stateful Agent Workflows

The paid layer is LangSmith, not LangGraph. LangChain's pricing page lists Developer at $0 per seat per month with 5,000 base traces, Plus at $39 per seat per month with 10,000 base traces, and a custom annual Enterprise plan. Our LangGraph build guide walks through a first graph.

LangGraph
Best for Teams that want to see and control every state transition
Not ideal for Quick prototypes where role-based delegation is enough
License MIT
Watch for More boilerplate than CrewAI; observability costs live in LangSmith

2. OpenAI Agents SDK: Lightweight Multi-Agent Workflows

The OpenAI Agents SDK describes itself as a lightweight, powerful framework for multi-agent workflows. It has fewer concepts to learn than CrewAI, and it's MIT-licensed with about 30.5k stars. The framework itself is free; your cost is model calls. Our OpenAI agents guide covers the migration from the older Assistants API.

OpenAI Agents SDK
Best for Teams already on OpenAI models who want minimal abstraction
Not ideal for Teams that want a hosted control plane and visual editor
License MIT
Watch for You assemble your own deployment and monitoring

3. Microsoft Agent Framework: The Enterprise Successor

Microsoft's 1.0 announcement dated April 3, 2026 says the framework unifies Semantic Kernel's enterprise-ready foundations with AutoGen's orchestrations in one open-source SDK for .NET and Python. Its repository is MIT-licensed and describes support for building, orchestrating and deploying agents and multi-agent workflows.

If you were weighing CrewAI against AutoGen, note that the AutoGen README says the project is archived, that Microsoft isn't providing fixes or support, and that Microsoft Agent Framework is its enterprise-ready successor.

Microsoft Agent Framework
Best for .NET shops and Microsoft-heavy enterprises
Not ideal for Teams that want the biggest third-party tutorial base
License MIT
Watch for Newest of the major frameworks; fewer community examples

4. Google ADK: Code-First Agents With Evaluation Built In

Google's Agent Development Kit for Python is an Apache 2.0, code-first toolkit for building, evaluating and deploying agents. It has about 22.1k stars. It's free to use; any deployment cost comes from the infrastructure you run it on. Choose it if evaluation is a first-class requirement or you already deploy on Google Cloud.

Google ADK
Best for Teams that want built-in evaluation and a vendor-backed roadmap
Not ideal for Teams wanting a visual editor like CrewAI Studio
License Apache 2.0
Watch for Check language support for your stack before committing

5. Pydantic AI: Typed Agents in Python

Pydantic AI is MIT-licensed and describes itself as typed end to end, covering agents, realtime voice, image generation and embeddings across models. If your team already trusts Pydantic for validation, the mental model carries over. It's a better fit for a single well-typed agent than for CrewAI-style crews of many roles.

Pydantic AI
Best for Python teams that value type safety and validation
Not ideal for Role-playing crews with loose, conversational handoffs
License MIT
Watch for Younger ecosystem than LangChain's

6. Mastra: TypeScript-Native Agents

Mastra calls itself the modern TypeScript framework for AI-powered applications and agents. It's the clearest pick if CrewAI's Python-only nature is your blocker. Its pricing page lists a Starter plan at $0 per month and a Teams plan at $250 per month, plus custom Enterprise, all metered on observability events, CPU time and storage.

Mastra Platform Plan Price Included
Starter $0/month 100K observability events, 24 CPU hours, 15-day retention
Teams $250/month 1M observability events, 250 CPU hours, 6-month retention, SSO
Enterprise Custom Custom volume and retention, RBAC, audit logs, SLAs

Self-hosting the open-source framework is free; a self-hosted Enterprise tier is custom-priced with a flat annual fee, according to the same page.

Mastra
Best for TypeScript teams building agents inside a Node stack
Not ideal for Python-first data teams
License Apache 2.0 core, enterprise license for ee/ code
Watch for Metered platform costs grow with CPU time and events

7. Agno: Fast Python Agent Builds

Agno is Apache 2.0 with about 43.0k stars and calls itself a self-driving agent platform. It's a direct Python rival for CrewAI with a different structure and a growing community. Evaluate it if CrewAI's abstractions feel heavy for the agent you're shipping.

Agno
Best for Python teams wanting a quick path from prototype to agent
Not ideal for Teams needing a vendor-backed enterprise support contract
License Apache 2.0
Watch for Smaller enterprise footprint than Microsoft or Google options

8. Strands Agents: Model-Agnostic and Bedrock-Friendly

The Strands Agents SDK is Apache 2.0 and supports Python and TypeScript. Its README lists first-class support for Amazon Bedrock, Anthropic, OpenAI and Gemini, with Ollama in the quickstart and custom providers possible. At about 9.0k stars it's smaller than the others, but it fits AWS-leaning teams well.

Strands Agents
Best for AWS-centric teams that still want model choice
Not ideal for Teams wanting the largest community
License Apache 2.0
Watch for Smaller third-party content than LangChain or CrewAI

9. LlamaIndex: Agent Workflows Around Documents

LlamaIndex has about 52.8k stars and an MIT license. Its repository now describes it as a document processing platform for AI, with an open-source framework for agentic apps. Pick it when your agents mostly read, parse and retrieve documents. See our LlamaIndex alternatives and LangChain vs LlamaIndex comparisons.

LlamaIndex
Best for Retrieval-heavy agents over messy documents
Not ideal for Pure multi-agent role orchestration
License MIT
Watch for Commercial layer is aimed at document parsing

10. AG2: The Community Continuation of AutoGen

AG2 is Apache 2.0 and describes itself as formerly AutoGen. It has about 5.0k stars and 5,665 commits. It suits teams that already built on AutoGen's conversational multi-agent model and want to keep that API outside Microsoft's roadmap.

AG2
Best for Existing AutoGen users who don't want to migrate yet
Not ideal for New projects wanting a major vendor behind them
License Apache 2.0
Watch for Much smaller community than the original AutoGen had

11. smolagents: Minimal Agents That Write Code

Hugging Face's smolagents is Apache 2.0, has about 30.2k stars, and describes itself as a barebones library for agents that think in code. It's the smallest option here, which is the point. Use it to learn or to keep a dependency footprint tiny.

smolagents
Best for Small projects and research where code-writing agents fit
Not ideal for Large production orchestration with governance needs
License Apache 2.0
Watch for Fewer enterprise features; only 1,059 commits

12. n8n: Visual Workflows With AI Steps

n8n isn't an agent framework in the code sense. It's a fair-code workflow platform with native AI capabilities, 209.4k stars, and a Sustainable Use License. It's the pick for teams who like CrewAI Studio's visual angle but want broader automation. Its pricing page lists cloud plans billed annually: Starter at 20 EUR per month with 2.5K executions, Pro at 50 EUR per month with 10K executions, and Business at 667 EUR per month with 40K executions (self-hosted only, per the page). No monthly-billed prices are listed. Our n8n agent guide shows a build.

n8n Plan Price (billed annually) Executions/month
Starter 20 EUR/month 2.5K
Pro 50 EUR/month 10K
Business 667 EUR/month 40K
Enterprise Contact sales Custom
n8n
Best for Ops and automation teams adding AI steps to workflows
Not ideal for Teams needing MIT or Apache licensing
License Sustainable Use License plus Enterprise License
Watch for Executions are the meter, not agent runs

What the Paid Layer Costs

Product Entry Paid Tier Price What It Meters
CrewAI Enterprise Custom Workflow executions sized to your workflow
LangSmith (LangGraph) Plus $39/seat/month Traces beyond 10,000/month
Mastra Platform Teams $250/month Observability events, CPU hours, storage
n8n Cloud Starter 20 EUR/month billed annually Executions
OpenAI Agents SDK None Free Model API usage
Microsoft Agent Framework None Free Model and hosting usage

Most of these frameworks are free. The bill comes from observability, hosting or model calls, so size the meter against your real volume. If tracing is your bottleneck, our LLM observability roundup prices the options.

How to Choose: Decision Framework

If You Need Pick Why
Full control of state and flow LangGraph Explicit nodes and edges
Minimal abstraction on OpenAI models OpenAI Agents SDK Small surface area
.NET or Microsoft stack Microsoft Agent Framework 1.0 for .NET and Python
Type-safe single agent Pydantic AI Built on Pydantic validation
TypeScript only Mastra Native TypeScript framework
Evaluation and Google Cloud fit Google ADK Code-first, evaluation included
AWS and Bedrock Strands Agents First-class Bedrock support
Document-heavy agents LlamaIndex Parsing and retrieval focus
Existing AutoGen code AG2 Keeps the AutoGen model alive
Visual workflows with AI steps n8n Low-code builder
Tiny code-writing agents smolagents Barebones library
Python agents, different style Agno Direct CrewAI alternative

Frequently Asked Questions

What to Do Next

Pick two candidates from the decision table and build the same small workflow in each. Measure how much time goes to fighting the framework versus building your product. If neither wins, call the provider SDK directly.

About the author

Camellia

Camellia

Principal Product Marketing Strategist

Camellia is Principal Product Marketing Strategist at Rework, helping B2B buyers pick the right software with confidence. With 6+ years in product marketing and 150+ SaaS tools evaluated across CRM, project management, and sales engagement, Camellia turns competitive intelligence into clear, honest comparisons. Readers get vendor evaluations they can trust to cut through marketing noise and decide faster.