CrewAI alternatives: 7 frameworks to try in 2026

Teams look for CrewAI alternatives for one of three reasons. They want finer control over how agents hand work to each other, they need a different language or cloud, or they want to run on a runtime their platform team already supports. CrewAI itself is still a reasonable pick. Its docs describe Crews for agent teams and Flows for stateful, resumable workflows, and its site says the Basic plan is free with 50 workflow executions per month.

This guide compares 7 alternatives on what each vendor says about itself today. Pricing and license details come from each vendor's own site, fetched in November 2026. Where a page does not state a price or a license, we say so instead of guessing.

CrewAI alternatives: a brief overview

• LangGraph: Best for explicit, low-level control of agent state, with human-in-the-loop and streaming built in.
• Microsoft Agent Framework: Best for .NET teams and AutoGen migrations. Microsoft calls it the direct successor to AutoGen and Semantic Kernel.
• OpenAI Agents SDK: Best for small, handoff-based agent systems built mostly on OpenAI models.
• Google ADK: Best for teams that want one agent framework across Python, TypeScript, Go, Java and Kotlin.
• Strands Agents: Best for teams on AWS Bedrock that want Swarm, Graph and Workflow patterns.
• Pydantic AI: Best for Python teams that want typed outputs and durable execution.
• Mastra: Best for TypeScript teams that want agents, workflows and memory in one framework.

When to leave CrewAI, and when to stay

Switching frameworks means rewriting your orchestration code and re-testing every flow. Do it only when one of these is true.

• You need to control exactly which step runs next, and role-based crews fight you.
• You must run in .NET, Go, Java or TypeScript and CrewAI does not fit your stack.
• Your cloud vendor ships a runtime your security team has already approved.
• You need durable execution that survives restarts, and you do not want to build it.

If none apply, stay. Evals, tool permissions and failure handling matter more than the framework, and they follow you to any framework. Our guide to why AI agent pilots never reach production covers that side, and CrewAI vs LangGraph is the head-to-head if you are already down to two.

1. LangGraph, best for explicit control over agent state

LangChain describes LangGraph as "an agent runtime and low-level orchestration framework." You define the graph, so you decide which node runs next. That is the opposite of handing a crew a goal and letting it negotiate.

Key features

• Single-agent, multi-agent and hierarchical control flows from low-level primitives
• Human-in-the-loop approval checks on agent actions
• Built-in memory across sessions
• Token-by-token streaming

Best for

• Engineering teams that want to see and test every transition
• Workflows where an approval step must block a risky action

Pricing

• The library is MIT-licensed and free to use
• LangSmith, the separate hosted product, has a Developer plan at $0 per seat per month with up to 5k base traces, and a Plus plan at $39 per seat per month with up to 10k base traces and one free Serverless (Small) deployment
• Enterprise pricing is custom

Pros

• Explicit graphs are easier to debug than emergent agent chatter
• Free open-source core with a separate paid tracing and deployment product

Cons

• More code to write than a crew definition
• Hosted deployment is a separate product with metered compute and database rates

See the CrewAI vs LangGraph comparison for a deeper side-by-side.

2. Microsoft Agent Framework, best for .NET teams and AutoGen migrations

Microsoft's docs say Agent Framework "is the direct successor" to Semantic Kernel and AutoGen, created by the same teams. It combines AutoGen's agent abstractions with Semantic Kernel's session state, type safety, middleware and telemetry, and adds graph-based workflows.

Key features

• Agents with Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI and Ollama clients, among others
• Workflows that connect agents and functions through explicit execution paths
• A Harness Agent with planning, context compaction and tool approval
• MCP clients for tool integration

Best for

• Teams already on .NET, or using Microsoft Foundry and Azure OpenAI clients
• Existing AutoGen or Semantic Kernel users, with migration guides in the docs

Pricing

• The overview page lists no price

Pros

• Supports .NET, Python and Go
• Its docs state plainly that if you can write a function for the task, you should not use an agent

Cons

• The Go version is in public preview and does not implement declarative agents, CodeAct or functional workflows
• The docs warn that third-party systems you connect are your responsibility

3. OpenAI Agents SDK, best for lightweight handoff-based agents

The SDK is a lightweight package that its docs call a production-ready successor to the experimental Swarm project. The core pieces are agents, handoffs between agents, and guardrails that validate inputs and outputs.

Key features

• Built-in agent loop that runs until the task finishes
• Sessions for persistent working memory
• Tracing for debugging and monitoring
• Realtime and voice agents
• MCP server tool calling

Best for

• Small agent systems where a few agents pass work to each other
• Teams already on the OpenAI Responses API

Pricing

• The docs page lists no price and does not state a license

Pros

• Few concepts to learn
• Docs list LiteLLM and Any-LLM adapters for non-OpenAI models

Cons

• The Python docs are the most detailed. A separate TypeScript SDK has its own docs at openai.github.io/openai-agents-js
• Defaults to the Responses API for OpenAI models, so the smoothest path favors one provider

4. Google ADK, best for multi-language teams

Google's Agent Development Kit is an open-source framework for building, debugging and deploying agents. It supports Python, TypeScript, Go, Java and Kotlin, which is the widest language list here.

Key features

• Graph workflows that mix deterministic code with AI reasoning (new in ADK 2.0)
• Multi-agent orchestration and evaluation
• An Agents CLI to scaffold, test, evaluate and deploy
• A web dev UI for trying agents
• Session, memory and artifact context management

Best for

• Organizations with services in several languages
• Teams deploying to Google Cloud through Agent Runtime, Cloud Run or GKE

Pricing

• The docs page lists no price. You can containerize ADK and run it yourself, or deploy to Google Cloud

Pros

• Model-agnostic, with Gemini access and adapters for other providers and local models
• Self-hosting is an option

Cons

• The docs page does not name the license, so check the repository before adopting
• Best-documented deployment paths point at Google Cloud

5. Strands Agents, best for AWS Bedrock teams

Strands is an open-source toolkit for production agents, available in Python and TypeScript. Its harness runs the agent loop, and you attach tools, model providers, memory, sessions and hooks.

Key features

• @tool decorator for turning functions into tools, plus MCP servers
• Multi-agent patterns named on the site: Agents as Tools, Swarm, Graph and Workflow, plus Agent2Agent (A2A) communication
• Hooks, interventions and context management
• Strands Evals for scoring and simulating agent behavior (Python only)

Best for

• Teams on AWS who want Bedrock as a first-class provider
• Builders who want several multi-agent patterns in one library

Pricing

• The site lists no price. The license is Apache, based on the LICENSE.APACHE link on the page

Pros

• Works with Bedrock, Anthropic, OpenAI, Google and Ollama, among others
• Lists several multi-agent patterns, so you can pick the simplest one

Cons

• Strands Box, its agent sandbox, is in preview
• Evals are Python-only

6. Pydantic AI, best for typed outputs and durable runs

Pydantic AI describes itself as a typed, extensible agent loop where swapping models is a string change. Agents return validated results and tool arguments are validated before your code runs.

Key features

• Type-safe dependency injection
• OpenTelemetry-native instrumentation
• Pydantic Evals and Pydantic Graph for evaluation and typed control flow
• Durable execution on engines including Temporal, DBOS, Prefect, Restate and AWS Lambda
• Built-in human-in-the-loop approval

Best for

• Python teams that treat agents like any other typed service
• Long-running agents that must survive restarts and waits

Pricing

• The docs page lists no price and does not state the license
• The page recommends Pydantic Logfire for observability. Its pricing was not checked for this article

Pros

• Nine listed durable execution engines, five of them co-maintained with the vendor teams
• Supports OpenAI, Anthropic, Google, Bedrock, Azure AI Foundry, Groq, Mistral, xAI and Ollama

Cons

• Python only
• You assemble more of the system yourself than with a crew-style framework

7. Mastra, best for TypeScript teams

Mastra is an open-source TypeScript framework covering agents, workflows, memory and observability. Workflows use typed steps, branches and retries, and can pause for human approval.

Key features

• Model routing to 90+ providers through one interface
• Memory with conversation threads and semantic recall
• Agents and workflows exposed as API endpoints
• MCP server support
• Tracing, cost tracking and evals

Best for

• Full-stack JavaScript teams who do not want a Python service in the stack

Pricing

• The core framework is Apache 2.0. Enterprise features are source-available under the Mastra Enterprise License
• Mastra Platform, per mastra.ai/pricing: Starter $0 per month (100K observability events, then $10 per 100K; 24 CPU hours, then $0.35 per hour; 15-day retention; unlimited users, deployments and projects)
• Teams is $250 per month (1M events, then $8 per 100K; 250 CPU hours, then $0.25 per hour; 6-month retention; SSO and SOC 2 docs). Enterprise pricing is custom
• A free self-hosted option under Apache 2.0 is also listed

Pros

• Everything runs in TypeScript
• Typed workflow steps with retries

Cons

• No Python support
• Platform usage is metered on events and CPU hours beyond the Starter allowances

For more TypeScript options, see our list of the best TypeScript AI agent frameworks.

How to choose

Run the same small task through your top two before committing. A refund-approval flow with one tool call and one human approval step exposes most differences in an afternoon. For wider lists, see 12 best multi-agent frameworks for production and 11 best Python AI agent frameworks, and for design trade-offs read multi-agent system architecture patterns.

Need help getting an agent into production?

Picking the framework is the easy part. The hard part is evals, tool permissions and monitoring. AY Automate builds agents with evals, sandboxed permission limits and human approval gates, and runs them as an ongoing partnership. See our AI agent development service, read how to choose an AI agent development service, or book a free consultation and bring the workflow you want to automate.

For a wider survey, see our AI agent frameworks compared guide and the list of open-source agent frameworks.

FAQ

What is the best CrewAI alternative?

It depends on your stack. LangGraph suits teams that want explicit control, Microsoft Agent Framework suits .NET teams, Pydantic AI suits typed Python, and Mastra suits TypeScript. Test your top two on one real workflow before you decide.

Is there a free open-source alternative to CrewAI?

Yes. LangGraph is MIT-licensed, Strands Agents is Apache-licensed per its site, and Mastra's core is Apache 2.0. Other frameworks here do not state a license on the pages we checked, so read each repository's LICENSE file. Model usage and hosting cost money regardless.

How much does CrewAI cost?

CrewAI's pricing page lists a free Basic plan with 50 workflow executions per month and an Enterprise plan with custom pricing. It gives no other numbers.

Is AutoGen still an option?

Microsoft's Agent Framework docs call Agent Framework the direct successor to AutoGen and Semantic Kernel and link migration guides. New projects on Microsoft's stack should start there.

Should I use CrewAI or LangGraph?

Use CrewAI when a role-based crew matches your problem and you want to start fast. Use LangGraph when you need to define every transition and approval step yourself. Our CrewAI vs LangGraph guide goes deeper.

Can I use these frameworks with models other than OpenAI?

Mostly yes. Microsoft Agent Framework, Google ADK, Strands, Pydantic AI and Mastra each list multiple providers. The OpenAI Agents SDK defaults to the Responses API but lists LiteLLM and Any-LLM adapters for other providers.

Do I need a framework at all?

Not always. Microsoft's own docs say that if you can write a function to handle the task, do that instead of using an agent.