Whether to print out response text.
Optional ageOptional callbacksOptional memoryOptional metadataOptional tagsAdds a memory to the agent's long-term memory.
The content of the memory to add.
Optional now: DateOptional current date.
Optional metadata: Record<string, unknown>Optional metadata for the memory.
Optional callbacks: CallbacksOptional Callbacks instance.
The result of adding the memory to the agent's long-term memory.
Optional config: (RunnableConfig | Callbacks)[]Use .batch() instead. Will be removed in 0.2.0.
This feature is deprecated and will be removed in the future.
It is not recommended for use.
Call the chain on all inputs in the list
Assigns new fields to the dict output of this runnable. Returns a new runnable.
Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently.
Array of inputs to each batch call.
Optional options: Partial<RunnableConfig> | Partial<RunnableConfig>[]Either a single call options object to apply to each batch call or an array for each call.
Optional batchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optional options: Partial<RunnableConfig> | Partial<RunnableConfig>[]Optional batchOptions: RunnableBatchOptions & { Optional options: Partial<RunnableConfig> | Partial<RunnableConfig>[]Optional batchOptions: RunnableBatchOptionsBind arguments to a Runnable, returning a new Runnable.
A new RunnableBinding that, when invoked, will apply the bound args.
Optional config: RunnableConfig | CallbacksOptional tags: string[]Use .invoke() instead. Will be removed in 0.2.0.
Run the core logic of this chain and add to output if desired.
Wraps _call and handles memory.
Creates a new LLMChain with the given prompt and the agent's language model, verbosity, output key, and memory.
The prompt to use for the LLMChain.
A new LLMChain instance.
Computes the agent's summary by summarizing the agent's core characteristics given the agent's relevant memories.
Optional runManager: CallbackManagerForChainRunOptional CallbackManagerForChainRun instance.
The computed summary as a string.
Generates a dialogue response to the given observation.
The observation to generate a dialogue response for.
Optional now: DateOptional current date.
A boolean indicating whether to continue the dialogue and the output string.
Generates a reaction to the given observation.
The observation to generate a reaction for.
Optional now: DateOptional current date.
A boolean indicating whether to continue the dialogue and the output string.
Extracts the action of the given entity from the given observation.
The observation to extract the action from.
The name of the entity to extract the action for.
Optional runManager: CallbackManagerForChainRunOptional CallbackManagerForChainRun instance.
The extracted action as a string.
Extracts the observed entity from the given observation.
The observation to extract the entity from.
Optional runManager: CallbackManagerForChainRunOptional CallbackManagerForChainRun instance.
The extracted entity as a string.
Returns a full header of the agent's status, summary, and current time.
Optional configuration object with current date and a boolean to force refresh.
Optional forceOptional now?: DateThe full header as a string.
Gets the agent's summary, which includes the agent's name, age, traits, and a summary of the agent's core characteristics. The summary is updated periodically through probing the agent's memories.
Optional config: { Optional configuration object with current date and a boolean to force refresh.
Optional forceOptional now?: DateOptional runManager: CallbackManagerForChainRunOptional CallbackManagerForChainRun instance.
The agent's summary as a string.
Invoke the chain with the provided input and returns the output.
Input values for the chain run.
Optional options: RunnableConfigPromise that resolves with the output of the chain run.
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
Pick keys from the dict output of this runnable. Returns a new runnable.
Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.
A runnable, function, or object whose values are functions or runnables.
A new runnable sequence.
Optional config: RunnableConfig | CallbacksUse .invoke() instead. Will be removed in 0.2.0.
Return a json-like object representing this chain.
Stream output in chunks.
Optional options: Partial<RunnableConfig>A readable stream that is also an iterable.
Generate a stream of events emitted by the internal steps of the runnable.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event: string - Event names are of the format: on_[runnable_type]_(start|stream|end).name: string - The name of the runnable that generated the event.run_id: string - Randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.tags: string[] - The tags of the runnable that generated the event.metadata: Record<string, any> - The metadata of the runnable that generated the event.data: Record<string, any>Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
| event | name | chunk | input | output |
|---|---|---|---|---|
| on_llm_start | [model name] | {'input': 'hello'} | ||
| on_llm_stream | [model name] | 'Hello' OR AIMessageChunk("hello") | ||
| on_llm_end | [model name] | 'Hello human!' | ||
| on_chain_start | format_docs | |||
| on_chain_stream | format_docs | "hello world!, goodbye world!" | ||
| on_chain_end | format_docs | [Document(...)] | "hello world!, goodbye world!" | |
| on_tool_start | some_tool | {"x": 1, "y": "2"} | ||
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | ||
| on_tool_end | some_tool | {"x": 1, "y": "2"} | ||
| on_retriever_start | [retriever name] | {"query": "hello"} | ||
| on_retriever_chunk | [retriever name] | {documents: [...]} | ||
| on_retriever_end | [retriever name] | {"query": "hello"} | {documents: [...]} | |
| on_prompt_start | [template_name] | {"question": "hello"} | ||
| on_prompt_end | [template_name] | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.
Optional options: Partial<RunnableConfig>Optional streamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Summarizes memories that are most relevant to an observation.
The observation to summarize related memories for.
Optional runManager: CallbackManagerForChainRunOptional CallbackManagerForChainRun instance.
The summarized memories as a string.
Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
Bind config to a Runnable, returning a new Runnable.
New configuration parameters to attach to the new runnable.
A new RunnableBinding with a config matching what's passed.
Create a new runnable from the current one that will try invoking other passed fallback runnables if the initial invocation fails.
Other runnables to call if the runnable errors.
A new RunnableWithFallbacks.
Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.
The object containing the callback functions.
Optional onCalled after the runnable finishes running, with the Run object.
Optional config: RunnableConfigOptional onCalled if the runnable throws an error, with the Run object.
Optional config: RunnableConfigOptional onCalled before the runnable starts running, with the Run object.
Optional config: RunnableConfigAdd retry logic to an existing runnable.
Optional fields: { Optional onOptional stopA new RunnableRetry that, when invoked, will retry according to the parameters.
Static deserializeLoad a chain from a json-like object describing it.
Static isGenerated using TypeDoc
Implementation of a generative agent that can learn and form new memories over time. It extends the BaseChain class, which is a generic sequence of calls to components, including other chains.
Example