Best AI Agent Frameworks and Platforms in 2026
Table of Contents
Quick answer
There is no single best AI agent framework, because frameworks and managed platforms solve different problems. Start with a framework if your engineers want to write the agent logic. Start with a managed platform if you need to run many agents in production without building the infrastructure.
- Run internal agents across models and runtimes: Brainbase
- Precise control over stateful Python agents: LangGraph
- Role-based agent teams: CrewAI
- Microsoft-centered engineering teams: Microsoft Agent Framework
- Multi-language and Google Cloud teams: Google ADK
- Lightweight agents with handoffs: OpenAI Agents SDK
- Managed agents built on Claude: Claude Managed Agents
- AWS or Azure standardization: Amazon Bedrock AgentCore or Microsoft Foundry Agent Service
Disclosure: Brainbase publishes this guide and is one of the options compared. We reviewed public documentation and pricing on November 5, 2026. We did not run a hands-on benchmark.
Framework or managed platform?
A framework decides how your agent thinks and moves through a task. A managed platform decides where it runs and how it is secured, monitored, and improved. Many production teams need both.
Use a framework when the agent is part of your product and your engineers want control over each step. You will usually need to add hosting, identity, permissions, evals, and monitoring yourself.
Use a managed platform when your goal is to operate agents reliably. It should cover sandboxes, auth, audit trails, evals, deployment, and observability, so your team can spend more time on the work the agent does.
The boundary is blurring. LangGraph has a deployment path through LangSmith, CrewAI sells an enterprise platform, and Bedrock AgentCore can host open source frameworks. Decide which layer you need first, then compare products within that layer.
How the options compare
Model choice and deployment freedom separate these options most clearly. Each entry reflects the vendor's public documentation on November 5, 2026. "Not stated" means we didn't find it on the pages reviewed, not that the feature is missing.
| Option | Type | Model choice | Where it runs |
|---|---|---|---|
| Brainbase | Managed platform | Any model ID, across eight harnesses | Managed, private cloud, on-premise, or hybrid |
| LangGraph | Framework and runtime | Commonly uses LangChain model integrations | Your infrastructure, or through LangSmith |
| CrewAI | Framework and platform | Native SDKs and LiteLLM providers | CrewAI cloud, or your VPC or infrastructure on Enterprise |
| Microsoft Agent Framework | Framework | Foundry, OpenAI, Anthropic, Ollama, and others | Your application |
| Google ADK | Framework | Gemini, Claude, OpenAI, local models, and others | Your containers or Google Cloud |
| OpenAI Agents SDK | Framework | Not stated | Your application |
| Claude Managed Agents | Managed platform (beta) | Claude models | Anthropic sandboxes or self-hosted sandboxes |
| Amazon Bedrock AgentCore | Managed platform | Models in or outside Bedrock | Managed AWS services |
| Microsoft Foundry Agent Service | Managed platform | Foundry catalog models | Microsoft Azure |
Which option fits which team
Brainbase
Brainbase is a managed platform for hosting, evaluating, and improving internal agents. One API covers eight harnesses, including Claude Code, Codex, Cursor, and Kafka, and you can change the model separately. It suits teams that don't want to tie agent infrastructure to one model or runtime.
It is less suitable if you want to embed a code library directly in your product and manage everything else yourself.
LangGraph
LangGraph is a low-level orchestration framework and runtime for long-running, stateful agents. It suits engineers who want explicit control, durable execution, human oversight, and memory.
Expect a steeper setup than a managed platform, especially if you need production governance.
CrewAI
CrewAI organizes work with Flows and Crews. A Flow controls the application, and a Crew is a team of role-based agents that handles a complex step. It is a practical choice when the role-based model matches the job.
The free plan has a limit of 50 executions a month, and VPC or self-managed deployment requires Enterprise.
Microsoft Agent Framework
Microsoft Agent Framework is the successor to AutoGen and Semantic Kernel. It offers agents and graph-based workflows in .NET and Python, with Go in public preview. It is a natural fit for Microsoft-centered engineering teams.
Google ADK
Google ADK supports Python, TypeScript, Go, Java, and Kotlin. It includes workflow and multi-agent patterns, and it can run in your own containers or on Google Cloud.
OpenAI Agents SDK
The OpenAI Agents SDK uses handoffs to pass a conversation to a specialist agent. It suits teams that want a light code-first approach.
Claude Managed Agents
Claude Managed Agents provides a hosted harness with built-in tools and managed or self-hosted sandboxes. It suits teams that have standardized on Claude. It is in beta, and the docs say it isn't eligible for Zero Data Retention or HIPAA BAA coverage.
Amazon Bedrock AgentCore
AgentCore combines Runtime, Memory, Gateway, Identity, Observability, Evaluations, Policy, and other components. It works with common frameworks and fits teams that have standardized on AWS.
Microsoft Foundry Agent Service
Foundry Agent Service runs prompt agents and hosted agents, with Entra identity and Azure networking. It suits organizations already built around Azure and Microsoft 365.
How to choose
Choose by answering five questions in order, then test two finalists on one real workflow.
- Who will build and own the agent? Engineers may prefer a framework. Operations teams usually need a platform.
- Where must it run? A private cloud or on-premise requirement can remove several options immediately.
- Will models change? If they will, avoid tying the agent to one provider.
- How will you prove it works? Require evals, traces, and an audit trail before production.
- How many agents will you run? Managing one agent is simple. Managing dozens requires shared identity, permissions, versioning, and monitoring.
For the test, use the same task, tools, data, and success criteria on each finalist. Measure setup time, success rate, cost per successful task, and time to debug a failure.
FAQ
What is the difference between an agent framework and an agent platform?
A framework is code you use to build agent behavior. A platform runs and manages agents in production, often including security, evals, and monitoring.
Is LangChain the same as LangGraph?
No. LangChain's docs say LangGraph focuses on orchestration and can be used with or without LangChain.
Do I need a framework if I use a managed platform?
Not always. A managed platform may provide the runtime you need. You may still keep a framework if your team has already written complex agent logic.
Which option is best for an enterprise?
It depends on your cloud, security, and model needs. Start with deployment and governance requirements, then compare developer experience. Our enterprise platform guide covers that review.
Can I avoid model lock-in?
Yes. Choose an option that keeps model selection separate from the agent's logic, and test a model swap before you commit. An LLM gateway can also help.
Sources
Reviewed November 5, 2026. Check current vendor documentation before you decide.
- Brainbase: Universal Managed Agents API
- LangGraph overview and LangSmith pricing
- CrewAI introduction, LLM connections, and pricing
- Microsoft Agent Framework overview
- Google ADK
- OpenAI Agents SDK handoffs
- Claude Managed Agents overview
- Amazon Bedrock AgentCore FAQs
- Microsoft Foundry Agent Service overview