Schema definitions for representing agent actions, observations, and return values.
The schema definitions are provided for backwards compatibility.
New agents should be built using the
langchain library, which provides a
simpler and more flexible way to define agents.
See docs on building agents.
Agents use language models to choose a sequence of actions to take.
A basic agent works in the following manner:
The schemas for the agents themselves are defined in langchain.agents.agent.
Serializable base class.
This class is used to serialize objects to JSON.
It relies on the following methods and properties:
is_lc_serializable: Is this class serializable?
By design, even if a class inherits from Serializable, it is not serializable
by default. This is to prevent accidental serialization of objects that should
not be serialized.
get_lc_namespace: Get the namespace of the LangChain object.
During deserialization, this namespace is used to identify the correct class to instantiate.
Please see the Reviver class in langchain_core.load.load for more details.
During deserialization an additional mapping is handle classes that have moved or been renamed across package versions.
lc_secrets: A map of constructor argument names to secret ids.
lc_attributes: List of additional attribute names that should be included
as part of the serialized representation.
Message from an AI.
An AIMessage is returned from a chat model as a response to a prompt.
This message represents the output of the model and consists of both the raw output as returned by the model and standardized fields (e.g., tool calls, usage metadata) added by the LangChain framework.
Base abstract message class.
Messages are the inputs and outputs of a chat model.
Examples include HumanMessage,
AIMessage, and
SystemMessage.
Message for passing the result of executing a tool back to a model.
FunctionMessage are an older version of the ToolMessage schema, and
do not contain the tool_call_id field.
The tool_call_id field is used to associate the tool call request with the
tool call response. Useful in situations where a chat model is able
to request multiple tool calls in parallel.
Message from the user.
A HumanMessage is a message that is passed in from a user to the model.
Represents a request to execute an action by an agent.
The action consists of the name of the tool to execute and the input to pass to the tool. The log is used to pass along extra information about the action.
Representation of an action to be executed by an agent.
This is similar to AgentAction, but includes a message log consisting of
chat messages.
This is useful when working with ChatModels, and is used to reconstruct
conversation history from the agent's perspective.
Result of running an AgentAction.
Final return value of an ActionAgent.
Agents return an AgentFinish when they have reached a stopping condition.
