LangGraph
LangChain
4.6
Graph-based orchestration framework for stateful agents with explicit control flow and durable execution.
Agent FrameworksOpen SourceHybridSupervisedMCP
OpenAI Agents SDK
Minimal agent framework built around agents, handoffs, guardrails, and built-in run tracing.
4.3
by OpenAIUpdated Overview
The OpenAI Agents SDK keeps its primitive count deliberately low: an agent with instructions and tools, handoffs to transfer control to another agent, guardrails that validate inputs and outputs, and tracing over every run. It favors readable Python over configuration, and works with MCP servers as a tool source.
Key capabilities
- Agent and tool primitives
- Handoffs between agents
- Input and output guardrails
- Built-in run tracing
- Structured output typing
- MCP server support
- Session state handling
Strengths
- Small enough to read end to end, which makes debugging tractable
- Guardrails are a first-class concept rather than something you bolt on later
- Tracing ships in the box, so run inspection does not require extra infrastructure
Limitations
- Best-supported path assumes OpenAI models; other providers work but with less polish
- No durable checkpointing, so long-running approval workflows need external state
- Handoff model is less expressive than a graph when control flow gets genuinely complex
The SDK is a reaction to framework bloat. Four concepts carry the whole model: agents that hold instructions and tools, handoffs that pass control, guardrails that check inputs and outputs, and tracing that records what happened. There is no DSL and no configuration layer — it is ordinary Python you can step through in a debugger.
Handoffs deserve attention because they are implemented as tools. Transferring control to a specialist agent is just another tool call, which means routing decisions are made by the model with the same machinery as everything else. That is elegant, and it is also the design’s ceiling: complex branching gets harder to reason about than an explicit graph.
Where it fits: triage-and-specialist patterns, customer-facing assistants that need output validation, and any team that values reading the framework source over learning its abstractions. Start with our OpenAI API tutorial if you are new to the platform.
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CrewAI
4.0
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Agent FrameworksOpen SourceHybridAutonomousMCP
Cursor Agent
Anysphere
4.5
Agent mode inside the Cursor editor, planning and applying multi-file changes without leaving the IDE.
Coding AgentsFreemiumIDESupervisedMCP
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