OpenAI Agents SDK
OpenAI
4.3
Minimal agent framework built around agents, handoffs, guardrails, and built-in run tracing.
Agent FrameworksOpen SourceHybridSupervisedMCP
LangGraph
Graph-based orchestration framework for stateful agents with explicit control flow and durable execution.
4.6
by LangChainUpdated Overview
LangGraph models an agent as a directed graph of nodes and edges rather than a prompt loop. State is a typed object that flows between nodes, edges can branch on that state, and execution can be checkpointed, paused for human approval, and resumed. It is the framework of choice when an agent needs cycles, interrupts, and inspectable state.
Key capabilities
- Typed graph state
- Conditional and cyclic edges
- Checkpointing and resume
- Human-in-the-loop interrupts
- Token and step streaming
- Multi-agent topologies
- MCP tool adapters
Strengths
- Explicit control flow makes agent behavior reviewable instead of emergent
- Checkpointing supports long-running and resumable workflows, including approval gates
- Strong tracing story — you can see which node made which decision on which state
Limitations
- Steeper learning curve than prompt-loop frameworks; graph modeling is real design work
- Verbose for simple agents where a single loop would have done the job
- Ecosystem moves fast enough that examples and APIs drift between versions
The central claim of LangGraph is that agents behave better when their control flow is written down. A prompt-driven loop decides its own next step every turn; a graph constrains the options at each node and makes branching conditions explicit code you can test.
State is the other half. Rather than passing an ever-growing message list, you define a typed state object and describe how each node updates it. That is what makes checkpointing possible — durable state can be persisted, so a run can pause for a human decision on Tuesday and resume on Wednesday without losing context.
Where it fits: workflows with approval steps, retries, and branching outcomes — document processing pipelines, support triage, financial review, anything audited. For a three-tool assistant, the modeling overhead is not worth it. Pair it with MCP to reuse existing tool servers rather than hand-writing integrations.
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CrewAI
4.0
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Agent FrameworksOpen SourceHybridAutonomousMCP
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Anthropic
4.7
Terminal-native coding agent from Anthropic that reads, edits, and runs code directly inside your repository.
Coding AgentsPaidTerminalSupervisedMCP
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