A year ago, building an AI agent meant writing a prompt loop with a few tool calls and hoping it held together. Today, at least a dozen frameworks are competing to be the foundation for your agent architecture, and the landscape is moving fast enough that evaluating them has become a project in itself.
This post is a practical guide to the major agent orchestration frameworks available right now in early 2025. We are not ranking them. There is no single best framework. The right choice depends on your use case, your team's skills, and how much control you need over the agent's behavior. What we will do is break down how each framework thinks about the problem, where it shines, and where it will cost you time.
What Agent Frameworks Actually Solve
Before comparing specific tools, it helps to understand why frameworks exist at all. You can build an agent with nothing more than an LLM API call in a while loop. Many teams start this way. The problems surface when you need that agent to be reliable.
Agent frameworks solve a common set of infrastructure problems that every team rebuilds from scratch without them.
Tool orchestration. Your agent needs to call external APIs, query databases, search the web, or execute code. Frameworks provide a standardized way to register tools, validate inputs and outputs, and handle failures.
State and memory. Agents that forget what happened three steps ago make bad decisions. Frameworks manage conversation history, working memory, and long-term context so you do not have to build your own state machine.
Planning and execution loops. The core loop of an agent (observe, think, act, repeat) sounds simple but gets complicated fast. What happens when a tool call fails? When the agent gets stuck in a loop? When it needs human approval before proceeding? Frameworks provide structure around these patterns.
Multi-agent coordination. Some tasks benefit from multiple specialized agents working together. One agent researches, another analyzes, a third writes. Coordinating these agents requires handoff protocols, shared context, and conflict resolution. This is hard to get right from scratch.
Observability. When your agent produces a wrong answer, you need to trace the reasoning chain, see which tools it called, and understand where it went off track. Frameworks provide logging, tracing, and debugging tools that make this possible.
If your agent is a simple single-turn tool caller, you may not need a framework at all. But the moment you need multi-step reasoning, tool chaining, memory, or coordination between agents, the engineering cost of building this infrastructure yourself exceeds the cost of learning a framework.
The Frameworks
LangChain and LangGraph
LangChain is the most widely adopted framework in the space, with the largest ecosystem of integrations, tools, and community resources. But the LangChain of early 2025 is not the same product that launched in 2022. The team has been explicit: if you are building agents, use LangGraph, not LangChain's legacy agent abstractions.
LangGraph models agent workflows as directed graphs. You define nodes (each representing an LLM call, tool invocation, or conditional branch) and edges (transitions between them). This gives you explicit, visual control over the execution flow. When something fails, you can point to the exact node where it went wrong.
Strengths. The ecosystem is unmatched. Hundreds of integrations, extensive documentation, and a large community mean you are unlikely to hit a problem nobody has solved before. LangGraph's graph-based approach makes complex workflows transparent and debuggable. LangSmith provides strong observability for tracing, evaluation, and monitoring. Model-agnostic by design.
Weaknesses. LangChain's rapid evolution has left a trail of deprecated APIs and breaking changes that frustrate developers upgrading existing projects. The abstraction layers can feel heavy for simple use cases. LangGraph introduces real complexity for beginners: the graph-based mental model is powerful but has a steeper learning curve than alternatives. The dependency footprint is large.
Best for. Teams building complex, multi-step agent workflows that need precise control over execution paths. Organizations that want the safety net of a large ecosystem and community. Projects where observability and debugging are critical from day one.
CrewAI
CrewAI takes a fundamentally different approach to agent orchestration. Instead of graphs and nodes, you think in terms of crews, roles, and tasks. You define agents with specific roles ("Researcher," "Writer," "Analyst"), assign them tasks, and let the crew collaborate to produce a result.
This organizational metaphor is intuitive for teams coming from a business process background. If you can describe the workflow as "first the researcher gathers data, then the analyst reviews it, then the writer produces the report," CrewAI maps that directly.
Strengths. The mental model is the most accessible of any framework. You can have a working multi-agent system in remarkably few lines of code. CrewAI is built from scratch as a standalone framework (independent of LangChain), which keeps the dependency footprint light. Role-based task delegation is natural for business workflows. Growing community with strong momentum.
Weaknesses. The simplicity that makes CrewAI approachable can become a limitation for complex workflows. Fine-grained control over execution flow is harder to achieve compared to LangGraph's explicit graph model. Production observability and enterprise features are still maturing. Memory support is more limited than some alternatives. For highly custom agent architectures, you may find yourself working around the framework rather than with it.
Best for. Teams that want to get a multi-agent prototype running quickly. Business process automation where the workflow maps naturally to roles and tasks. Projects where development speed matters more than fine-grained orchestration control.
OpenAI Agents SDK
The OpenAI Agents SDK, released this month as a production-ready successor to the experimental Swarm project, is the most minimalist framework on this list. It ships with four primitives: Agents, Handoffs, Guardrails, and Tracing. That is it.
Strengths. The barrier to entry is the lowest of any framework. You can have a working agent in under 20 lines of Python. Built-in tracing and observability from the start is a differentiator; most frameworks treat this as an afterthought. Handoffs provide a clean pattern for multi-agent delegation. Despite the name, the SDK is provider-agnostic and supports over 100 LLMs through the Chat Completions API. The dual Python and TypeScript support is unique in this space.
Weaknesses. Minimalism is a double-edged sword. The SDK intentionally omits features that other frameworks include: no built-in RAG, no complex workflow orchestration, no visual debugging. You are expected to build these yourself or pull in other libraries. For complex multi-step workflows, you may outgrow the SDK quickly and end up layering additional abstractions on top. The "agents" branding invites comparison with more full-featured frameworks that solve a broader set of problems.
Best for. Teams already in the OpenAI ecosystem that want a lightweight starting point. Projects where you want to keep the framework layer thin and own the orchestration logic yourself. Rapid prototyping where you need a working agent fast and plan to evaluate more opinionated frameworks later.
Microsoft AutoGen
AutoGen is Microsoft's framework for multi-agent conversation systems. Where LangGraph thinks in graphs and CrewAI thinks in roles, AutoGen thinks in conversations. Agents communicate through message passing, and the framework manages turn-taking, message routing, and conversation state.
Strengths. The conversation-based architecture is natural for use cases where agents need to debate, iterate, or negotiate. Strong human-in-the-loop support, meaning you can inject human approval or feedback at any point in the agent conversation. Deep integration with the Microsoft ecosystem (Azure, Semantic Kernel). Good support for code generation and execution workflows.
Weaknesses. The conversation metaphor can feel forced for workflows that are not inherently conversational. Debugging multi-agent conversations is harder than debugging explicit graphs because the execution path is emergent rather than defined. The framework is undergoing significant architectural changes as Microsoft works toward merging AutoGen with Semantic Kernel, which creates uncertainty about the long-term API surface.
Best for. Teams in the Microsoft ecosystem. Use cases that genuinely benefit from agent-to-agent conversation (debate, collaborative reasoning, iterative refinement). Projects that need strong human-in-the-loop patterns.
Microsoft Semantic Kernel
Semantic Kernel is Microsoft's enterprise-grade framework, and it occupies a different niche than AutoGen. Where AutoGen is research-oriented and conversation-first, Semantic Kernel is production-oriented and workflow-first. It is the framework that underpins Microsoft 365 Copilot.
Strengths. Enterprise-grade reliability and security. Multi-language support (Python, C#, Java), which is rare in this space and critical for organizations with .NET or Java codebases. Plugin-based architecture makes it easy to assemble reusable skills. The strongest integration with Azure services of any framework. Battle-tested in production at massive scale through Copilot.
Weaknesses. Tightly coupled with the Microsoft ecosystem. The enterprise focus means the developer experience can feel more rigid and ceremonial than lighter frameworks. Smaller community compared to LangChain. Less flexibility for unconventional agent architectures.
Best for. Enterprise organizations with significant Microsoft infrastructure. Teams building on .NET or Java that want first-class support. Projects where stability, compliance, and enterprise governance outweigh flexibility.
Pydantic AI
Pydantic AI brings the type-safety philosophy of Pydantic to agent development. If your team already uses Pydantic for data validation (and if you are writing Python APIs, you almost certainly do), the mental model is immediately familiar.
We wrote about Pydantic AI in detail in our post on building smarter, type-safe AI agents. The short version: it treats structured outputs as a first-class concern, using Pydantic models to define and validate what agents produce. This eliminates an entire category of bugs where agents return malformed data.
Strengths. Type safety and validation are baked in, not bolted on. Model-agnostic by design, so you can swap providers without rewriting agent logic. The structured output approach pairs naturally with any framework that consumes typed data. Lightweight and composable rather than monolithic.
Weaknesses. Pydantic AI is more focused than the other frameworks on this list. It is not a full orchestration layer. You get strong type-safe agent interactions, but you will likely combine it with another framework (LangGraph, CrewAI) for complex multi-step workflows. The community is smaller and the ecosystem is younger.
Best for. Python teams that prioritize type safety and structured outputs. Projects where you want a provider-agnostic abstraction layer between your application and the LLM. Teams that plan to compose Pydantic AI with a separate orchestration framework.
LlamaIndex
LlamaIndex started as a data connector for LLMs and has evolved into a full agent framework, but its DNA is still retrieval-first. If your agents need to reason over large volumes of documents, structured data, or knowledge graphs, LlamaIndex has the deepest capabilities in this area.
Strengths. The best RAG (retrieval-augmented generation) pipeline of any framework on this list. Sophisticated document ingestion, chunking, indexing, and retrieval. Strong support for structured data and knowledge graphs. Agent capabilities have matured significantly, with support for tool use, planning, and multi-step reasoning on top of the retrieval layer.
Weaknesses. If your use case is not data-heavy, LlamaIndex's retrieval-first design may feel like overhead. The agent orchestration features, while improving, are less mature than LangGraph or CrewAI for complex multi-agent workflows. The framework is strongest when the agent's primary job is to find and synthesize information, rather than execute multi-step business processes.
Best for. RAG-heavy applications where agents need to search, synthesize, and reason over large document collections. Enterprise knowledge management use cases. Projects where the quality of information retrieval is the primary success metric.
How to Choose
Rather than picking the framework with the most features, start with the shape of your problem.
If you need precise control over complex workflows: LangGraph. The graph-based model gives you explicit, debuggable execution paths.
If you want multi-agent collaboration fast: CrewAI. The role-based model maps naturally to business processes and gets you to a working prototype quickly.
If you want the thinnest possible framework layer: OpenAI Agents SDK. Four primitives, minimal opinions, maximum flexibility to build your own abstractions.
If you are in the Microsoft ecosystem: Semantic Kernel for production workloads, AutoGen for research and conversational agent patterns.
If type safety and structured outputs are paramount: Pydantic AI, likely combined with another orchestration framework.
If your agents are primarily searching and reasoning over documents: LlamaIndex.
The Portability Question
Regardless of which framework you choose, the most important architectural decision you can make right now is to keep your agent logic decoupled from the framework itself.
This space is moving fast. LangChain's API surface has changed dramatically in the past year. Microsoft is merging AutoGen and Semantic Kernel. OpenAI just shipped a brand new SDK. The framework you choose today may not be the framework you want in 12 months.
Design your agents so the core business logic (the prompts, the tool definitions, the evaluation criteria) lives in framework-agnostic code. Use Pydantic models for structured inputs and outputs. Keep your orchestration layer as a thin wrapper that can be swapped without rewriting the logic underneath. We covered this pattern in our Pydantic AI post, and it applies regardless of which framework you build on.
The frameworks are converging on similar patterns: tool calling, memory management, execution loops, and multi-agent handoffs. The specific APIs differ, but the underlying concepts are stabilizing. If you build around those concepts rather than around a specific framework's abstractions, switching costs stay manageable.
What Comes Next
Frameworks solve the building problem. The next challenge is the deployment and management problem: where do your agents run in production, how do you monitor them, who governs what they can access? That is a different category of tooling entirely, and it is evolving just as quickly. We will cover agent platforms in a future post.
For now, the practical advice is straightforward. Pick two or three frameworks that match your use case. Build a proof of concept in each (budget two to three days per framework). Evaluate based on actual code, not marketing. And design for the possibility that you will need to switch.
We have been building AI-powered applications with these frameworks for clients across industries, and the framework evaluation process is one of the first things we work through in any new engagement. If you are building agents and need help choosing the right foundation or getting from prototype to production, let's talk about your project.
