Best Multi-Agent Frameworks in 2025: LangGraph, CrewAI, OpenAI SDK and Google ADK

Building a multi-agent system in 2025 means choosing between at least six production-grade frameworks, each with a fundamentally different philosophy on agent coordination. Choose wrong and you'll rewrite your orchestration layer in six months. This guide cuts through the marketing to compare architectures, trade-offs, and ideal use cases for every major framework available today. If you're new to multi-agent concepts, start with our complete guide to AI agent architectures.

Why Multi-Agent Frameworks Matter

A single-agent system needs a prompt, a model, and maybe some tools. Multi-agent systems need coordination primitives: how agents discover each other, share state, handle failures, and decide who acts next. Building these primitives from scratch means reinventing message passing, state checkpointing, handoff protocols, and failure recovery. Frameworks exist to solve this, so your team can focus on domain logic instead of distributed systems plumbing.

The critical differences between frameworks lie in three areas: orchestration model (graph-based vs. role-based vs. swarm), state management (checkpointed vs. ephemeral vs. event-sourced), and communication pattern (handoffs vs. shared memory vs. message queues). Understanding this maps directly to the orchestration patterns we've covered previously.

The framework landscape has exploded since early 2025. OpenAI released its Agents SDK in March, Google introduced ADK in April, and Anthropic published its Agent SDK alongside Claude 3.5. Meanwhile, LangGraph and CrewAI have matured through multiple production iterations. According to Langfuse's comprehensive framework comparison, LangGraph leads in monthly searches with 27,100, while CrewAI follows with 14,800. But search volume doesn't equal production readiness. Let's examine each framework on its actual technical merits.

OpenAI Agents SDK

Released in March 2025, OpenAI's Agents SDK replaced the experimental Swarm framework with a production-grade toolkit. The core abstraction is the handoff: agents transfer control to each other explicitly, carrying conversation context through the transition. Each agent is defined with instructions, a model reference, tools, and a list of agents it can hand off to. The SDK includes three built-in primitives: Handoffs for agent-to-agent transfer, Guardrails for input/output validation, and Tracing for end-to-end observability of agent chains.

The handoff pattern aligns closely with the orchestrator-worker pattern used in production systems. A triage agent receives user input, determines intent, and transfers to a specialized agent (billing, technical support, account management). The specialized agent can return control or transfer to another agent. Context flows through conversation history, not through explicit state objects.

The SDK is Python-first with no official TypeScript support yet. It's locked to OpenAI models, which limits flexibility but ensures tight integration with GPT-4o and the upcoming GPT-5. Ideal for teams already invested in the OpenAI ecosystem who want minimal abstraction and a clean, opinionated agent transfer model. The trade-off: no model portability, and the handoff pattern can become unwieldy with more than 8-10 agent types.

LangGraph (LangChain)

LangGraph models agent workflows as directed graphs with typed state. Nodes are agents or functions, edges define transitions (including conditional routing), and a shared state object flows through the graph. This graph-based approach gives you explicit, visual control over agent sequencing that no other framework matches. With 27,100 monthly searches, it's the most adopted multi-agent framework by a significant margin.

The standout feature is built-in checkpointing. Every state transition is persisted, enabling time-travel debugging, human-in-the-loop approvals (pause the graph, wait for human input, resume), and mid-execution failure recovery. LangGraph also supports token streaming from any graph node and sub-graph composition, where a complete graph becomes a single node within a parent graph.

LangGraph is model-agnostic: you can plug different LLM providers into different nodes. It integrates with LangSmith for observability, giving you trace-level visibility into every node execution. The trade-off is verbosity. Even simple two-agent flows require defining a state schema, nodes, edges, and compilation. Teams building straightforward sequential workflows may find the graph abstraction overkill. But for complex, branching workflows with conditional routing, retry logic, and human checkpoints, nothing comes close.

CrewAI

CrewAI uses a role-based metaphor that maps to how humans think about teams. Each agent is defined with a role, goal, and backstory. Tasks are assigned to agents and executed within a "crew." The framework supports three process types: sequential (agents run in order), hierarchical (a manager agent delegates to workers), and consensual (agents vote on decisions). With 14,800 monthly searches and an active community, CrewAI is the second most popular framework. See the official CrewAI documentation for the latest API surface.

The biggest strength is developer experience. You can define a working multi-agent system in under 20 lines of Python. CrewAI handles task delegation, output passing between agents, and basic memory. It's model-agnostic, supporting OpenAI, Anthropic, open-source models via Ollama, and any OpenAI-compatible API.

The limitation shows at scale. The abstraction prioritizes simplicity over fine-grained control, which means: no built-in checkpointing for long-running workflows, limited control over agent-to-agent communication (it's mediated through task outputs, not direct messaging), and error handling is coarse-grained. Teams that start with CrewAI for prototyping often migrate to LangGraph when they need production-grade state management and conditional routing.

AutoGen / AG2 (Microsoft)

Microsoft's AutoGen implements conversational agent teams where agents interact through multi-turn conversations. The original AutoGen (v0.2) introduced the concept of agents debating and refining outputs through dialogue. The v0.4 rewrite, now called AG2, rearchitected with an event-driven core, async-first execution, and pluggable orchestration strategies. AG2 introduced GroupChat as its primary coordination pattern: multiple agents in a shared conversation where a selector determines who speaks next.

AutoGen excels at code generation workflows and research tasks where agents need to iterate, critique, and improve each other's outputs. The conversational approach is natural for tasks like: code review (one agent writes, another reviews), content generation (writer + editor + fact-checker), and data analysis (analyst + validator). Microsoft Research actively uses AutoGen in its own projects, which keeps the framework well-maintained.

The trade-off is latency and token cost. Every agent turn in a GroupChat involves a full LLM call with the accumulated conversation history. A 4-agent debate with 5 rounds is 20 LLM calls minimum. This makes AutoGen expensive for high-volume, real-time use cases like customer support. It excels at offline, quality-sensitive workflows where thoroughness matters more than speed.

Google Agent Development Kit (ADK)

Released in April 2025, Google's ADK provides a hierarchical agent tree where a root agent delegates to sub-agents, which can in turn have their own sub-agents. The framework integrates tightly with Vertex AI, Gemini models, and Google Cloud services. The standout feature is native support for the A2A (Agent-to-Agent) protocol, which enables communication between agents from different frameworks. An ADK agent can discover and invoke an agent built with LangGraph or CrewAI through A2A's standardized task interface. See the Google ADK documentation for the full API reference.

ADK also incorporates multimodal capabilities that other frameworks lack. Agents can process images, audio, and video natively through Gemini's multimodal API. This opens use cases like visual inspection agents, voice-based customer support flows, and document understanding pipelines. Session state management is built in, with support for in-memory, database-backed, and Vertex AI-managed persistence.

The framework is the newest in this comparison and its ecosystem is still maturing. Fewer third-party tutorials, integrations, and production case studies compared to LangGraph or CrewAI. Ideal for Google Cloud-native teams, enterprises needing managed infrastructure, and teams building multimodal agent systems.

Claude Agent SDK (Anthropic)

Anthropic's SDK takes a tool-use-first approach where agents are Claude models equipped with tools, including the ability to invoke other agents as tools. The architecture is deliberately simple: an agent loop receives a prompt, calls tools as needed (including sub-agent tools), and returns a structured response. Where other frameworks add abstraction layers, Anthropic keeps the loop minimal and relies on Claude's native capabilities for reasoning, planning, and coordination.

The differentiators are extended thinking (chain-of-thought reasoning visible in the API response), computer use (agents can interact with desktop applications and web browsers), and MCP (Model Context Protocol) for standardized tool discovery across agents. MCP is becoming an industry standard for agent-to-tool communication, supported by VS Code, JetBrains, and multiple third-party platforms.

Safety is built into the architecture through constitutional AI principles. Every agent interaction can be constrained by safety policies evaluated at the model level, not as bolted-on post-processing. Ideal for safety-critical applications (healthcare, finance, legal), teams wanting deep integration with Anthropic's model family, and use cases requiring computer interaction. The trade-off: locked to Claude models, and the SDK is lighter on orchestration features compared to LangGraph.

Comparison Matrix

Here's a side-by-side comparison of the six frameworks across the dimensions that matter most in production deployments.

• Orchestration model — LangGraph: directed graph with conditional edges. CrewAI: role-based crews with process types. OpenAI SDK: explicit handoffs. AutoGen/AG2: conversational GroupChat. Google ADK: hierarchical agent tree. Claude SDK: tool-use chain with sub-agents.
• State persistence — LangGraph: built-in checkpointing with time travel. OpenAI SDK: context variables (ephemeral by default). CrewAI: task outputs passed sequentially. AutoGen/AG2: conversation history (in-memory by default). ADK: session state with pluggable backends. Claude SDK: via MCP servers.
• Model dependency — LangGraph, CrewAI, AutoGen: fully model-agnostic. OpenAI SDK: OpenAI models only. Google ADK: optimized for Gemini but supports others. Claude SDK: Claude models only.
• Learning curve — CrewAI: lowest (role-based DSL, 20 lines to start). OpenAI SDK: low (clean, opinionated API). LangGraph: medium (graph concepts, state schemas). AutoGen/AG2: medium (conversational patterns, selector logic). ADK: medium (Google Cloud ecosystem knowledge). Claude SDK: medium (tool-use patterns, MCP understanding).
• Production readiness — LangGraph: highest (LangSmith observability, checkpointing, streaming). OpenAI SDK: high (built-in tracing and guardrails). Claude SDK: high (safety-first, extended thinking). CrewAI: medium (growing ecosystem, limited checkpointing). AutoGen/AG2: medium (AG2 rewrite maturing). ADK: early (backed by Vertex AI, newest framework).
• Streaming support — LangGraph: per-node token streaming. OpenAI SDK: full streaming. Claude SDK: native streaming with extended thinking. ADK: streaming via Vertex. CrewAI: limited. AutoGen: limited (conversation-based).
• Unique strength — LangGraph: graph visualization and time-travel debugging. CrewAI: fastest prototyping. OpenAI SDK: cleanest handoff model. AutoGen: multi-agent debate and iteration. ADK: A2A protocol and multimodal. Claude SDK: safety, computer use, and MCP.

How to Choose: Decision Framework

Choosing a multi-agent framework isn't a feature checklist exercise. It's an architecture decision that locks in your system for 12-24 months. Here's the decision framework we recommend based on your team context and use case.

If you need complex, branching workflows with human-in-the-loop approvals, choose LangGraph. Its graph-based model gives you deterministic control over every transition, and checkpointing means you can pause, inspect, and resume at any point. This is non-negotiable for regulated industries (finance, healthcare) where you need audit trails of every agent decision.

If you want the fastest path to a working prototype, choose CrewAI. The role-based API maps to natural-language descriptions of your team, and you can have agents running in an afternoon. Plan to re-evaluate if you hit state management or error handling limits.

If your team is already on OpenAI and needs clean agent-to-agent handoffs, choose the OpenAI Agents SDK. It's the most opinionated framework, which is an advantage: fewer decisions, faster implementation, and the tracing/guardrails primitives save weeks of custom development.

If safety and auditability are your top priorities, choose Claude Agent SDK. Constitutional AI constraints at the model level, extended thinking for transparent reasoning, and computer use for automation flows that interact with existing software.

If you need cross-framework interoperability or multimodal agents, choose Google ADK. The A2A protocol means your agents can communicate with agents built on other frameworks, and Gemini's multimodal capabilities open use cases that text-only frameworks can't address.

Now, here's the question most engineering leaders avoid: should I be using a framework at all? Frameworks give you building blocks. They don't give you a production system. The gap between a framework demo and a system handling thousands of concurrent users includes: integration with your existing tools (CRM, helpdesk, billing), observability across agent chains, graceful degradation when models fail, and continuous evaluation of agent quality. If your business isn't building AI infrastructure, that gap represents 3-6 months of engineering time not invested in your core product.

This is the build vs. buy decision every engineering team faces. Building on a framework means owning the orchestration, scaling, monitoring, and integration layers. Platforms like GuruSup exist for precisely this reason: pre-built multi-agent orchestration with 100+ tool integrations, agent-to-agent handoffs, and production observability already solved. GuruSup runs 800+ agents in production with 95% autonomous resolution, which is the kind of outcome that takes a framework team 6-12 months to achieve independently. Our guide on building production multi-agent systems details the full checklist of what "production-ready" actually requires.

FAQ

What is the best multi-agent framework for beginners?

CrewAI has the lowest barrier to entry with its role-based API. You can define agents, tasks, and a crew in under 20 lines of Python. The role/goal/backstory abstraction maps to natural language, making it intuitive for developers new to multi-agent concepts. However, the simplicity abstracts away orchestration details that matter at scale. If you plan to go to production, consider starting with the OpenAI Agents SDK or LangGraph to build a deeper understanding of agent coordination patterns from day one.

Can I use multiple LLM providers in a single multi-agent system?

Yes, and you should. LangGraph, CrewAI, and AutoGen are model-agnostic by design, so you can assign different models to different agents. A common production pattern is model tiering: use a fast, cheap model (GPT-4o-mini, Claude 3 Haiku) for triage and routing agents, and a more capable model (GPT-4o, Claude 3.5 Sonnet) for complex reasoning agents. OpenAI SDK and Claude SDK lock you to their respective providers, though Google ADK supports multiple providers while optimizing for Gemini. Mixing models reduces costs by 40-60% compared to running a single premium model across all agents.

How do I choose between building on a framework and using a platform?

Build on a framework when multi-agent AI is your core product and you need full control over agent logic, model selection, and orchestration. Use a platform when agents complement your existing product (customer support, sales, operations) and your engineering team should focus on domain logic instead of distributed systems infrastructure. The total cost of ownership for a custom multi-agent system — including state management, observability, integration hardening, and continuous evaluation — often exceeds a managed platform's cost by 3-5x in the first year. Most non-AI-native companies get better results faster with a platform.