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@@ -478,7 +478,7 @@ tools = toolkit.get_tools()
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By themselves, language models can't take actions - they just output text.
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A big use case for LangChain is creating **agents**.
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Agents are systems that use an LLM as a reasoning enginer to determine which actions to take and what the inputs to those actions should be.
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Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be.
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The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish.
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[LangGraph](https://github.com/langchain-ai/langgraph) is an extension of LangChain specifically aimed at creating highly controllable and customizable agents.
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