This is powered by a language model and a prompt.
The inputs to this chain are:List of available toolsUser inputAny previously executed steps (intermediate_steps)This chain then returns either the next action to take or the final response to send to the user (AgentAction or AgentFinish).Different agents have different prompting styles for reasoning, different ways of encoding input, and different ways of parsing the output.
For a full list of agent types see agent typesTools‚ÄãTools are functions that an agent calls.
There are two important considerations here:Giving the agent access to the right toolsDescribing the tools in a way that is most helpful to the agentWithout both, the agent you are trying to build will not work.
If you don't give the agent access to a correct set of tools, it will never be able to accomplish the objective.
If you don't describe the tools properly, the agent won't know how to properly use them.LangChain provides a wide set of tools to get started, but also makes it easy to define your own (including custom descriptions).
For a full list of tools, see hereToolkits‚ÄãOften the set of tools an agent has access to is more important than a single tool.
For this LangChain provides the concept of toolkits - groups of tools needed to accomplish specific objectives.
There are generally around 3-5 tools in a toolkit.LangChain provides a wide set of toolkits to get started.
For a full list of toolkits, see hereAgentExecutor‚ÄãThe agent executor is the runtime for an agent.
This is what actually calls the agent and executes the actions it chooses.
The core idea of agents is to use an LLM to choose a sequence of actions to take.
The core idea of agents is to use an LLM to choose a sequence of actions to take. ->: This is powered by a language model and a prompt.
The inputs to this chain are:List of available toolsUser inputAny previously executed steps (intermediate_steps)This chain then returns either the next action to take or the final response to send to the user (AgentAction or AgentFinish).Different agents have different prompting styles for reasoning, different ways of encoding input, and different ways of parsing the output.
For a full list of agent types see agent typesTools‚ÄãTools are functions that an agent calls.
There are two important considerations here:Giving the agent access to the right toolsDescribing the tools in a way that is most helpful to the agentWithout both, the agent you are trying to build will not work.
If you don't give the agent access to a correct set of tools, it will never be able to accomplish the objective.
If you don't describe the tools properly, the agent won't know how to properly use them.LangChain provides a wide set of tools to get started, but also makes it easy to define your own (including custom descriptions).
For a full list of tools, see hereToolkits‚ÄãOften the set of tools an agent has access to is more important than a single tool.
For this LangChain provides the concept of toolkits - groups of tools needed to accomplish specific objectives.
There are generally around 3-5 tools in a toolkit.LangChain provides a wide set of toolkits to get started.
For a full list of toolkits, see hereAgentExecutor‚ÄãThe agent executor is the runtime for an agent.
This is what actually calls the agent and executes the actions it chooses.