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  2. docs.langchain.com docs.langchain.com
    Graph API concepts - Docs by LangChain
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    1. AliJalili 15 Dec 2025
      Tools give agents the ability to take actions. Agents go beyond simple model-only tool binding by facilitating: Multiple tool calls in sequence (triggered by a single prompt) Parallel tool calls when appropriate Dynamic tool selection based on previous results Tool retry logic and error handling State persistence across tool calls

      When you bind tools directly to a Model, the model makes a single, stateless decision. It suggests the best tool for the immediate prompt and then stops.

      The Agent, however, uses its loop (often ReAct: Reason, Act, Observe) to execute complex strategies

      Model vs Agent
    2. AliJalili 15 Dec 2025
      An LLM Agent runs tools in a loop to achieve a goal. An agent runs until a stop condition is met - i.e., when the model emits a final output or an iteration limit is reached.

      The difference lies in autonomy and execution flow: A Model with Tools (via direct binding/function calling) is a single, stateless step where the LLM merely suggests the best tool and its arguments, requiring the developer to manually execute the tool and initiate any subsequent calls. In contrast, an Agent with Tools leverages an Agent Executor to manage a dynamic, multi-step loop (e.g., ReAct), where the LLM acts as the planner, deciding which tool to call next, and the Executor automatically runs the tool, feeds the observation back to the model, and repeats the cycle until the complex, multi-step goal is autonomously achieved.

      Model vs Agent
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    docs.langchain.com/oss/python/langchain/retrieval