A ReAct-style agent repeatedly asks a model what to do next, runs a requested tool, returns the result to the model, and continues until the model finishes or the application stops the run. You can own that cycle yourself, use LangChain’s create_agent harness, or build an explicit workflow with LangGraph. The practical choice is how much control you need over state, routing, recovery, and human review—not a proven difference in speed, cost, or answer quality.
What a ReAct agent loop does
LangChain defines an agent as “a model calling tools in a loop until a given task is complete.” In practical terms, the model chooses whether to respond to the user or request an action from a tool. The application executes approved tool calls, adds their results to the conversation or other state, and asks the model for its next step.
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The loop is not the same as the agent’s harness. The harness includes the prompt, available tools, and any middleware that shape how the model behaves. That distinction matters: choosing a framework can simplify orchestration, but it does not decide which actions are safe or appropriate for your application. LangChain’s Agents documentation describes the current Python agent interface and its configurable harness.
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A manual implementation means your application owns each repeated step and the state that connects them. Conceptually, the cycle is:
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- Keep the conversation and tool-call results in application state.
- Send the model the conversation and only the tool definitions it needs for the task.
- Inspect the model response. If it requests a tool, validate the arguments and permissions before running it.
- Append the tool result to state and call the model again.
- Stop when the model returns a final answer, or when an application-defined budget, timeout, or cancellation condition is reached.
That outline explains the responsibilities; it is not production-ready code. Tool-call payloads and response formats vary by provider, so implementation details must follow the API for the model you use. Your code also needs deliberate behavior for malformed arguments, tool failures, provider errors, repeated calls, and actions with side effects.
Where manual control helps—and costs
Owning the loop gives you direct control over what happens at every step. That can suit a small, conventional workflow or an application with unusual orchestration requirements. The trade-off is that you must build and maintain the state handling, tool dispatch, stopping conditions, and failure behavior yourself. The amount of work depends on the provider API and the workflow; the official documentation does not provide a comparable implementation-time figure.
What LangChain’s create_agent supplies
LangChain’s documented Python entry point is create_agent, configured with a model, tools, and a system prompt. Middleware can extend the harness for more advanced behavior. Its AgentState represents typed execution context, including conversation history and custom state fields needed by tools or middleware.
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LangChain’s learning guide says its agent implementations use LangGraph primitives and points to direct LangGraph implementation for deeper customization. So the choice is not simply between LangChain and unrelated manual code: create_agent is a higher-level interface, while direct LangGraph construction exposes more of the underlying workflow structure. LangChain’s learning guide explains that relationship.
When direct LangGraph construction is useful
LangGraph models an application as nodes, shared state, and decisions or transitions connecting those nodes. A node is a function that reads current state and returns updates. Instead of treating the application as one general agent loop, you can make stages and routes explicit—for example, classify a request, retrieve documents, call an external action, route a case for review, and compose a response.
This structure is useful when workflow-specific branching, recovery, persistence, or human review needs to be part of the design rather than left implicit in a general agent harness. LangChain’s Thinking in LangGraph guide describes the graph model and several ways to handle failures and interruptions.
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Different failures call for different paths
- Transient errors: retry the affected node when the failure may clear on another attempt.
- Errors the model can recover from: store the error in state and return control to the model with that context.
- Missing user input: pause for human input, then resume with the needed information.
- Unexpected errors: surface them for debugging rather than silently treating them as ordinary model output.
The guide demonstrates retry policies and an interrupt() path for human input. It also shows using a checkpointer to save state at an interruption so execution can resume later. Those are documented patterns and capabilities, not a guarantee that a deployment automatically has durable persistence configured.
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Choose node boundaries deliberately
Smaller nodes can isolate external services, support different retry policies, make intermediate steps easier to inspect, and reduce repeated work if a run resumes after failure. They also add checkpoints and graph complexity. LangChain presents this as a design trade-off, not as a measured performance result.
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| Approach | Best fit | What you control | Main trade-off |
|---|---|---|---|
| Manual model/tool loop | A conventional loop where you want to own each orchestration step | Application code for state, tool dispatch, stopping, and failures | You must implement and maintain those loop responsibilities |
LangChain create_agent |
A conventional model/tool agent where its configurable harness is sufficient | Model, tools, system prompt, middleware, and agent state | It provides a higher-level interface, while application-specific safety and action rules remain yours |
| Direct LangGraph construction | A workflow needing explicit stages, conditional routes, recovery, persistence, or human-review points | Graph nodes, shared state, transitions, and interruption or retry paths | More explicit workflow structure brings graph and checkpoint complexity |
Use create_agent when a standard agent loop and its configuration options cover the job. Consider direct LangGraph when the path through the application must be visible and tailored—for example, when different failures need different recovery paths or a human must approve a step. A manual loop remains an option when direct ownership of the orchestration is the point.
Across all three, define tool permissions and side-effect boundaries deliberately. A model requesting an action is not, by itself, a business rule or a safety check. Treat execution of external tools as application code that can fail or cause real-world changes.
What the documentation does—and does not—establish
The official material describes the abstractions and workflow patterns, but does not establish a benchmark winner for implementation time, latency, reliability, or token cost. Those outcomes depend on the model provider, tools, workflow, deployment, and implementation details. The current Python agents page documents from langchain.agents import create_agent, but does not state a release version in the material reviewed; verify the import and signature against the version installed in your project. Older examples may use different constructors.
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