LangGraph pricing<\/strong><\/h3>LangGraph is open-source and free. LangSmith has a free Developer tier with one seat and 5,000 traces\/month. Paid plans start at $39\/seat\/month<\/strong> on Plus, which includes 10,000 traces and one free small serverless deployment.<\/p>LangSmith Deployment for hosting agents incurs additional usage-based charges in addition to the seat fee.<\/p>
2. CrewAI<\/strong><\/h2>\n\t\t\t\n\t\t\t\t<\/path>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure><\/div>Best for:<\/strong> fast multi-agent prototyping and workflows that map naturally to named roles.<\/p>CrewAI is a role-based framework in which you define agents with backstories, goals, and tools, and then organize them into collaborating crews. CrewAI Flows add stateful, event-driven routing around those crews.<\/p>
The role-based metaphor is intuitive enough for non-engineers to understand, which sets it apart from LangGraph’s graph model. That makes it a strong choice for teams that include product managers or domain experts who need to follow the agent logic.<\/p>
While LangGraph gives you precise control over every decision point, CrewAI lets the LLM determine how agents should interact.<\/p>
CrewAI pros<\/strong><\/h3>\nAccessible role-based setup.<\/strong> The role, goal, and backstory setup maps to how people naturally think about team collaboration. <\/li>\n\n\n\nNative MCP and A2A support.<\/strong> Both protocols are built in, giving CrewAI stronger out-of-the-box interoperability than LangGraph, the OpenAI Agents SDK, or PydanticAI, which all lack native A2A.<\/li>\n\n\n\nLarge community.<\/strong> CrewAI has an active open-source community with examples, integrations, and troubleshooting resources.<\/li>\n\n\n\nBuilt-in free tracing since OSS v1.0, <\/strong>no third-party tool required.<\/li>\n<\/ul>CrewAI cons<\/strong><\/h3>\nHigh token consumption in benchmarks.<\/strong> LLM-driven routing between agents uses more tokens than LangGraph’s explicit graph edges or Smolagents’ code-execution approach. For cost-sensitive projects, this adds up.<\/li>\n\n\n\nLimited control over execution flow.<\/strong> As workflows grow in complexity with conditional logic, retries, or fine-grained state management, the role-based metaphor becomes harder to manage. LangGraph may fit these cases better because its transactions are explicit.<\/li>\n<\/ul>CrewAI pricing<\/strong><\/h3>CrewAI is open-source and free. The Basic managed tier is free and includes 50 workflow executions\/month. Enterprise plans with higher execution limits and compliance features are available at custom pricing.<\/p>
3. Microsoft Agent Framework<\/strong><\/h2>Best for:<\/strong> Microsoft ecosystem teams building on Azure.<\/p>Microsoft Agent Framework is a graph-based SDK that merges AutoGen’s multi-agent patterns with Semantic Kernel’s enterprise tooling. It reached v1.0 GA on April 3, 2026.<\/p>
Python and .NET are stable; Go is in public preview with a smaller feature set. The framework supports MCP and A2A, graph-based workflows, checkpointing, approvals, telemetry, and human-in-the-loop patterns.<\/p>
Microsoft Agent Framework has its deepest integration with Microsoft Foundry, but it also supports OpenAI, Anthropic, Amazon Bedrock, Google Gemini, and Ollama.<\/p>
If your team is already on the Microsoft stack, the Foundry integration saves setup time.<\/p>
If you’re not, LangGraph or CrewAI give you more flexibility. Both run across cloud providers or self-hosted infrastructure, reducing Microsoft-specific dependencies.<\/p>
Microsoft Agent Framework pros<\/strong><\/h3>\nApproval and safety primitives are built into the core SDK. <\/strong>The framework supports human approval, middleware, telemetry, and provider-specific content controls, but teams still need to configure safeguards for their use case.<\/li>\n\n\n\nMicrosoft Foundry integration.<\/strong> Deploy, monitor, and manage agents through Microsoft’s platform while retaining support for non-Microsoft model providers.<\/li>\n<\/ul>Microsoft Agent Framework cons<\/strong><\/h3>\nStrongest on the Microsoft stack.<\/strong> Teams outside the Azure and .NET ecosystem get less benefit from the tight integration. LangGraph or CrewAI may be a better fit for cloud-agnostic teams.<\/li>\n\n\n\nSmaller community than the top alternatives.<\/strong> LangGraph and CrewAI have more third-party tutorials, production case studies, and community support. Finding help for edge cases takes more effort.<\/li>\n\n\n\nModerate learning curve.<\/strong> Conversational patterns, selector logic, and the graph workflow engine take time to learn.<\/li>\n<\/ul>Microsoft Agent Framework pricing<\/strong><\/h3>Microsoft Agent Framework is open-source and free. Microsoft Foundry, model endpoints, storage, search, and other connected services may carry separate consumption costs.<\/p>
4. LlamaIndex Workflows<\/strong><\/h2>\n\t\t\t\n\t\t\t\t<\/path>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure><\/div>Best for:<\/strong> RAG-heavy workflows where agents load, parse, index, and retrieve information before acting.<\/p>LlamaIndex Workflows is an event-driven orchestration layer built for document-intensive agent pipelines. Event-driven steps with branching and loops map naturally to document processing tasks.<\/p>
It’s Python only. The standalone TypeScript Workflows are deprecated and were archived on April 30, 2026, so new projects should not treat them as an active TypeScript Option.<\/p>
LlamaIndex Workflows uses a flexible event-driven model, while Haystack uses a component pipeline and offers established document store integrations with databases such as Elasticsearch and Pinecone.<\/p>
In comparison to general-purpose frameworks like LangGraph, LlamaIndex is stronger when documents are at the center of your workflow but less capable for agent tasks that don’t involve document processing.<\/p>
LlamaIndex Workflows pros<\/strong><\/h3>\nDeep data connector ecosystem.<\/strong> Document loaders, parsers, vector stores, and retrieval components plug directly into Workflows, giving document-heavy pipelines a broad set of integrations.<\/li>\n\n\n\nOpenTelemetry-compatible observability.<\/strong> Production monitoring with traceAI can integrate with standard observability stacks.<\/li>\n<\/ul>LlamaIndex Workflows cons<\/strong><\/h3>\nLess capable outside document workflows.<\/strong> If your agents aren’t primarily working with documents, LangGraph, CrewAI, or the OpenAI Agents SDK is a better fit.<\/li>\n\n\n\nMulti-agent support exists, but it is not the framework’s primary design focus. <\/strong>CrewAI, Agno, or LangGraph may be easier for agent-team-first systems.<\/li>\n<\/ul>LlamaIndex Workflows pricing<\/strong><\/h3>LlamaIndex Workflows is open-source and free. The managed document platform is now branded LlamaParse and is priced separately for parsing, extraction, indexing, and document-agent services.<\/p>
LlamaParse includes 10,000 free credits\/month. Starter costs $50\/month <\/strong>with 40,000 included credits, while Pro costs $500\/month <\/strong>with 400,000 included credits; additional usage is credit-based.<\/p>5. OpenAI Agents SDK<\/strong><\/h2>\n\t\t\t\n\t\t\t\t<\/path>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure><\/div>Best for:<\/strong> low-friction, OpenAI-optimized agents.<\/p>The OpenAI Agents SDK is a lightweight framework built around agents, handoffs, guardrails, sessions, human approval, and tracing. It replaced the experimental Swarm project.<\/p>
The OpenAI Agents SDK provides a secure environment for running AI code. It features a built-in sandbox, native model management, and filesystem tools like shell and apply_patch. It also supports multi-agent setups and dedicated code modes.<\/p>
The SDK has official Python and TypeScript implementations. Both support the core agent, handoff, tool, session, streaming, and tracing patterns, though specific feature releases may vary by platform.<\/p>
This framework sits between CrewAI’s simplicity and LangGraph’s control. You get more structure than CrewAI’s LLM-driven routing, and the handoff model lets you see exactly when and why an agent passes work to another.<\/p>
OpenAI Agents SDK pros<\/strong><\/h3>\nMinimal boilerplate to get started.<\/strong> Define an agent, give it tools, and run it. Fewer concepts to learn than LangGraph’s graphs or CrewAI’s role-based setup.<\/li>\n\n\n\nBuilt-in tracing without extra tools.<\/strong> You don’t need a separate product like LangSmith to trace agent execution.<\/li>\n\n\n\nNative sandboxing for code execution.<\/strong> Agents can run code in isolated environments with built-in support for providers like E2B, Modal, and Cloudflare. smolagents supports similar sandbox providers, but you’ll need to install and configure those integrations separately.<\/li>\n<\/ul>OpenAI Agents SDK cons<\/strong><\/h3>\nNo native A2A protocol layer.<\/strong> Cross-framework communication requires external solutions. CrewAI and Microsoft Agent Framework both have native A2A support.<\/li>\n\n\n\nLess mature state management than LangGraph.<\/strong> Sessions and memory were added in April 2026, but they lack LangGraph’s time-travel debugging and fine-grained rewind.<\/li>\n\n\n\nPre-1.0 API stability.<\/strong> The 0.x versioning means breaking changes are possible between releases. LangGraph and Haystack have more stable APIs for production use.<\/li>\n<\/ul>OpenAI Agents SDK pricing<\/strong><\/h3>The OpenAI Agents SDK is open-source and free. LLM API costs apply when using OpenAI models. Non-OpenAI models via LiteLLM carry their own API costs.<\/p>
6. Agno<\/strong><\/h2>\n\t\t\t\n\t\t\t\t<\/path>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure><\/div>Best for:<\/strong> production agent platforms with strong observability needs.<\/p>Agno is a Python agent-platform framework with a full SDK, runtime, and control plane. It was formerly called Phidata and is model-agnostic.<\/p>
Agno supports four delegation modes: coordinate, route, broadcast, and tasks. It also supports nested team structures.<\/p>
Where CrewAI uses roles and backstories to define agent relationships, Agno gives you several explicit ways to distribute work. It is less granular than LangGraph’s node-and-edge model.<\/p>
The AgentOS control plane is the main differentiator. It gives you a centralized layer for monitoring, debugging, and managing agents and workflows.<\/p>
Agno pros<\/strong><\/h3>\nFour delegation modes for teams.<\/strong> Coordinate, route, broadcast, and tasks, plus nested team structures, give you more flexibility than CrewAI’s single role-based model or the OpenAI Agents SDK’s handoff approach.<\/li>\n\n\n\nBuilt-in vector and session memory.<\/strong> Both memory types are part of the core SDK. LangGraph focuses on durable workflow state, while smolagents does not provide the same built-in persistence layer.<\/li>\n\n\n\nAgentOS control plane included.<\/strong> Monitor and manage agents through a centralized platform with a free tier for local use. LangGraph’s comparable tooling offers only a limited free tier, with production-grade features requiring a paid subscription.<\/li>\n<\/ul>Agno cons<\/strong><\/h3>\nFewer production case studies.<\/strong> LangGraph’s Klarna deployment and CrewAI’s Fortune 500 adoption provide more confidence for enterprise teams evaluating risk.<\/li>\n\n\n\nLess structured than graph-based frameworks.<\/strong> More flexibility means more decisions about execution flow, which can be a drawback for teams that prefer LangGraph’s explicit structure.<\/li>\n\n\n\nSmaller third-party ecosystem.<\/strong> Includes many built-in tools and model providers, but less common integrations may require custom work.<\/li>\n<\/ul>Agno pricing<\/strong><\/h3>Agno is open-source and free. AgentOS has a free tier for local use. The Pro plan starts at $150\/month<\/strong> with one live connection, four seats, and unlimited usage. Enterprise pricing is custom.<\/p>7. PydanticAI<\/strong><\/h2>\n\t\t\t\n\t\t\t\t<\/path>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure><\/div>Best for:<\/strong> type-safe, structured outputs.<\/p>PydanticAI uses Python’s type system and Pydantic validation to catch many schema and integration errors earlier in development. It’s built by the Pydantic team and is model-agnostic.<\/p>
If your team already uses Pydantic for data validation, PydanticAI will feel familiar. Agent inputs, dependencies, and structured outputs can use validated Pydantic models, which helps surface schema errors earlier.<\/p>
Where CrewAI and LangGraph focus on orchestration, PydanticAI focuses on correctness. The type system handles output validation directly, so you spend less time writing complex prompts to format responses.<\/p>
Multi-agent support is available through agent delegation, programmatic hand-offs, and graph-based control flow via pydantic-graph<\/strong>. The approach is more code-oriented than CrewAI’s crew of LangGraph’s graph abstraction.<\/p>PydanticAI pros<\/strong><\/h3>\nStructured outputs with validation.<\/strong> The type system validates outputs directly, reducing reliance on prompt wording alone to enforce a schema.<\/li>\n\n\n\nDependency injection for easier testing.<\/strong> Swap out real tools for mocks without changing agent code without changing the main agent logic.<\/li>\n\n\n\nHuman-in-the-loop tool approval.<\/strong> Gate specific tool calls for human review before execution. LangGraph uses a broader interrupt mechanism that can pause workflow execution at defined points.<\/li>\n<\/ul>PydanticAI cons<\/strong><\/h3>\nThe agent framework is Python-only. <\/strong>There’s no TypeScript or JavaScript SDK for building agents with PydanticAI. If your team works primarily in Node.js, you’ll need a different framework.<\/li>\n\n\n\nGrowing but smaller community. <\/strong> CrewAI and LangGraph currently have broader collections of examples and community resources.<\/li>\n<\/ul>PydanticAI pricing<\/strong><\/h3>PydanticAI is open-source and free. Pydantic Logfire has a free Personal tier with 10 million records per month. However, ingestion pauses if the limit is exceeded, and there is no option to pay for overages.<\/p>
Paid plans start at $49\/month<\/strong> on the Team plan, which includes five seats. Unlike the free tier, paid plans allow you to pay for overages at $2 per additional million records instead of pausing ingestion.<\/p>8. Smolagents<\/strong><\/h2>
![\"LangGraph](\"https:\/\/imagedelivery.net\/LqiWLm-3MGbYHtFuUbcBtA\/wp-content\/uploads\/sites\/2\/2026\/08\/image6.png\/w=1024,h=1024,fit=scale-down\")