langchain_experimental API Reference

langchain_experimental.autonomous_agents

Classes

autonomous_agents.autogpt.memory.AutoGPTMemory	Memory for AutoGPT.
autonomous_agents.autogpt.output_parser.AutoGPTAction(...)	Action returned by AutoGPTOutputParser.
autonomous_agents.autogpt.output_parser.AutoGPTOutputParser	Output parser for AutoGPT.
autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser	Base Output parser for AutoGPT.
autonomous_agents.autogpt.prompt.AutoGPTPrompt	Prompt for AutoGPT.
autonomous_agents.baby_agi.baby_agi.BabyAGI	Controller model for the BabyAGI agent.
autonomous_agents.baby_agi.task_creation.TaskCreationChain	Chain generating tasks.
autonomous_agents.baby_agi.task_execution.TaskExecutionChain	Chain to execute tasks.
autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain	Chain to prioritize tasks.
autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain	Chain to execute tasks.
autonomous_agents.hugginggpt.task_planner.BasePlanner	Create a new model by parsing and validating input data from keyword arguments.
autonomous_agents.hugginggpt.task_planner.PlanningOutputParser	Create a new model by parsing and validating input data from keyword arguments.
autonomous_agents.hugginggpt.task_planner.TaskPlaningChain	Chain to execute tasks.
autonomous_agents.hugginggpt.task_planner.TaskPlanner	Create a new model by parsing and validating input data from keyword arguments.

Functions

autonomous_agents.autogpt.output_parser.preprocess_json_input(...)	Preprocesses a string to be parsed as json.
autonomous_agents.autogpt.prompt_generator.get_prompt(tools)	Generates a prompt string.
autonomous_agents.hugginggpt.repsonse_generator.load_response_generator(llm)
autonomous_agents.hugginggpt.task_planner.load_chat_planner(llm)

langchain_experimental.cpal

Classes

cpal.constants.Constant(value[, names, ...])

Enum for constants used in the CPAL.

langchain_experimental.generative_agents

Generative Agents primitives.

Classes

generative_agents.generative_agent.GenerativeAgent	An Agent as a character with memory and innate characteristics.
generative_agents.memory.GenerativeAgentMemory	Memory for the generative agent.

langchain_experimental.llms

Experimental LLM wrappers.

Classes

llms.anthropic_functions.AnthropicFunctions	Create a new model by parsing and validating input data from keyword arguments.
llms.anthropic_functions.TagParser()	A heavy-handed solution, but it's fast for prototyping.
llms.jsonformer_decoder.JsonFormer	Jsonformer wrapped LLM using HuggingFace Pipeline API.
llms.llamaapi.ChatLlamaAPI	Create a new model by parsing and validating input data from keyword arguments.
llms.rellm_decoder.RELLM	RELLM wrapped LLM using HuggingFace Pipeline API.

Functions

llms.jsonformer_decoder.import_jsonformer()	Lazily import jsonformer.
llms.rellm_decoder.import_rellm()	Lazily import rellm.

langchain_experimental.pal_chain

Implements Program-Aided Language Models.

As in https://arxiv.org/pdf/2211.10435.pdf.

This is vulnerable to arbitrary code execution: https://github.com/hwchase17/langchain/issues/5872

Classes

pal_chain.base.PALChain

Implements Program-Aided Language Models (PAL).

Functions

langchain_experimental.plan_and_execute

Classes

plan_and_execute.agent_executor.PlanAndExecute	Plan and execute a chain of steps.
plan_and_execute.executors.base.BaseExecutor	Base executor.
plan_and_execute.executors.base.ChainExecutor	Chain executor.
plan_and_execute.planners.base.BasePlanner	Base planner.
plan_and_execute.planners.base.LLMPlanner	LLM planner.
plan_and_execute.planners.chat_planner.PlanningOutputParser	Planning output parser.
plan_and_execute.schema.BaseStepContainer	Base step container.
plan_and_execute.schema.ListStepContainer	List step container.
plan_and_execute.schema.Plan	Plan.
plan_and_execute.schema.PlanOutputParser	Plan output parser.
plan_and_execute.schema.Step	Step.
plan_and_execute.schema.StepResponse	Step response.

Functions

plan_and_execute.executors.agent_executor.load_agent_executor(...)	Load an agent executor.
plan_and_execute.planners.chat_planner.load_chat_planner(llm)	Load a chat planner.

langchain_experimental.prompts

Functions

langchain_experimental.sql

Chain for interacting with SQL Database.

Classes

sql.base.SQLDatabaseChain	Chain for interacting with SQL Database.
sql.base.SQLDatabaseSequentialChain	Chain for querying SQL database that is a sequential chain.

langchain_experimental.tot

Classes

tot.base.ToTChain	A Chain implementing the Tree of Thought (ToT).
tot.checker.ToTChecker	Tree of Thought (ToT) checker.
tot.prompts.CheckerOutputParser	Create a new model by parsing and validating input data from keyword arguments.
tot.prompts.JSONListOutputParser	Class to parse the output of a PROPOSE_PROMPT response.
tot.thought.Thought	Create a new model by parsing and validating input data from keyword arguments.
tot.thought.ThoughtValidity(value[, names, ...])
tot.thought_generation.BaseThoughtGenerationStrategy	Base class for a thought generation strategy.
tot.thought_generation.ProposePromptStrategy	Propose thoughts sequentially using a "propose prompt".
tot.thought_generation.SampleCoTStrategy	Sample thoughts from a Chain-of-Thought (CoT) prompt.