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A ReAct-style agent repeatedly asks a model what to do next, runs a tool when requested, and returns the tool’s result to the model until the task is complete. You can write that orchestration yourself, use LangChain’s create_agent as a configurable harness, or build a more explicit workflow with LangGraph. The right choice depends on how much control you need over state, branching, recovery, and review—not on a documented speed or cost advantage.
What happens in an agent loop?
LangChain defines an agent as “a model calling tools in a loop until a given task is complete.” In practice, the application sends the conversation and available tool definitions to a model. The model either requests a tool or returns a final response. When it requests a tool, the application validates and runs that action, adds the result to the conversation, and asks the model what to do next.
- Keep the conversation and tool results in application state.
- Ask the model for its next response, exposing only tools appropriate to the task.
- If the response requests a tool, validate the arguments and permissions before running it.
- Append the tool result and call the model again.
- Stop when the model returns a final answer or an application-defined limit, timeout, or cancellation condition is reached.
This describes the cycle, not a production-ready implementation. Tool-call formats, argument validation, exception handling, loop limits, cancellation, and streaming depend on the model provider and application.
The Tool Desk
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Writing the loop gives your application direct control over each step, but also makes it responsible for the mechanics and failure cases that a higher-level harness can organize.
#1 Best Overall
- Tool selection and permissions: Provide only the tools needed for the current task, and enforce authorization in application code. A model’s request is not itself permission to perform an action.
- Argument validation: Check tool-call structure, required fields, value types, and business constraints before execution.
- Execution and results: Run the tool, capture its output or failure, and return a useful result to the model without exposing secrets.
- Loop termination: Set a budget or other stopping condition so repeated calls cannot run indefinitely.
- Failure behavior: Decide how to handle malformed calls, provider errors, tool failures, repeated requests, timeouts, and side effects.
- Application state: Preserve the conversation and any additional context required to continue correctly.
These details are provider-specific. A conceptual loop alone does not establish safe handling for a particular API or external action.
What LangChain’s create_agent provides
LangChain’s current Python documentation presents create_agent as a configurable harness around a common model-and-tool loop. Its basic configuration accepts a model, tools, and a system prompt; middleware can extend the harness for more advanced behavior. The documented import is from langchain.agents import create_agent. See the LangChain agents documentation for the current interface.
LangChain also describes AgentState as the typed execution context for conversation history and custom state fields used by tools and middleware. That gives you a framework interface for common agent orchestration rather than requiring you to write every loop step yourself. It does not choose safe tools, define useful descriptions, validate external actions, manage credentials, or set appropriate approval boundaries for your application.
The documentation is live and does not identify a release version in the material reviewed. Older examples may use different constructors, so check the documentation and signatures for the exact package version installed in your project.
Rank #3
When explicit LangGraph construction is useful
LangChain’s learning guide says its agents use LangGraph primitives and points to direct LangGraph implementation for deeper customization. The distinction is therefore between a higher-level agent interface and explicitly defining a workflow with underlying graph primitives, rather than between unrelated approaches. See the LangChain learning guide.
In LangGraph, a workflow is represented by nodes, shared state, and decisions or transitions connecting nodes. A node reads the current state and returns updates. That structure can make application-specific stages and routes explicit—for example, classifying a request, retrieving documents, invoking an external action, routing to review, and composing a response. The LangGraph guide to thinking in graphs describes these building blocks and patterns.
Recovery, retries, and human input
The guide distinguishes several cases that should not all be handled the same way:
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- Transient errors: Retry when an error is temporary.
- Errors the model may recover from: Store the error in state and loop back with that context.
- Missing user input: Pause for human input. The guide demonstrates an
interrupt()path. - Unexpected errors: Surface them for debugging rather than silently treating them as normal results.
The guide’s human-input example uses a checkpointer so execution state can be saved at interruption and resumed later. A graph does not automatically have durable persistence configured; that depends on the application’s implementation.
Best Value
Node size is a design trade-off
Smaller nodes can isolate external services, allow different retry handling, make intermediate steps more visible, and limit repeated work if execution resumes after a failure. They also add checkpoints and graph complexity. LangChain presents this as qualitative design guidance, not as a measured performance result.
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| Approach | Best fit | What you control | Trade-off |
|---|---|---|---|
| Manual loop | A custom model/tool cycle where the application should own the orchestration details. | Each model call, tool execution, state update, and stopping condition. | You implement and maintain provider-specific handling for tool calls and failures. |
LangChain create_agent |
A conventional model/tool agent where a configurable harness, prompt, tools, state, and middleware are sufficient. | Harness configuration and application-level tool policies. | Less direct control over workflow-specific transitions than explicit graph construction. |
| Direct LangGraph | A workflow needing explicit stages, conditional routes, recovery behavior, persistence choices, or human-review points. | Nodes, shared state, transitions, and the workflow’s recovery paths. | More workflow structure to design; finer-grained nodes can add checkpoints and complexity. |
Choose create_agent when the standard loop fits and you want to configure rather than hand-build its common orchestration. Consider direct LangGraph when workflow-specific branching, recovery, or review needs to be visible and explicit. A manual loop is reasonable when you need to own the interaction mechanics directly and are prepared to implement the provider-specific details.
The official documentation reviewed here does not establish a winner for implementation time, latency, reliability, or cost. Compare the approaches against your requirements for state and transition control, branching, error and retry behavior, visibility and checkpoint boundaries, and the amount of orchestration code you want to maintain.
Quick Recap
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