OptionalageOptionalcallbacksOptionalmemoryOptionalmetadataOptionaltagsWhether to print out response text.
Adds a memory to the agent's long-term memory.
The content of the memory to add.
Optionalnow: DateOptional current date.
Optionalmetadata: Record<string, unknown>Optional metadata for the memory.
Optionalcallbacks: CallbacksOptional Callbacks instance.
The result of adding the memory to the agent's long-term memory.
Convert a runnable to a tool. Return a new instance of RunnableToolLike
which contains the runnable, name, description and schema.
Optionaldescription?: stringThe description of the tool. Falls back to the description on the Zod schema if not provided, or undefined if neither are provided.
Optionalname?: stringThe name of the tool. If not provided, it will default to the name of the runnable.
The Zod schema for the input of the tool. Infers the Zod type from the input type of the runnable.
An instance of RunnableToolLike which is a runnable that can be used as a tool.
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.
Optionaloptions: Partial<RunnableConfig> | Partial<RunnableConfig>[]Either a single call options object to apply to each batch call or an array for each call.
OptionalbatchOptions: RunnableBatchOptions & { An array of RunOutputs, or mixed RunOutputs and errors if batchOptions.returnExceptions is set
Optionaloptions: Partial<RunnableConfig> | Partial<RunnableConfig>[]OptionalbatchOptions: RunnableBatchOptions & { Optionaloptions: Partial<RunnableConfig> | Partial<RunnableConfig>[]OptionalbatchOptions: RunnableBatchOptionsComputes the agent's summary by summarizing the agent's core characteristics given the agent's relevant memories.
OptionalrunManager: 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.
Optionalnow: 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.
Optionalnow: 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.
OptionalrunManager: CallbackManagerForChainRunOptional CallbackManagerForChainRun instance.
The extracted action as a string.
Extracts the observed entity from the given observation.
The observation to extract the entity from.
OptionalrunManager: 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.
OptionalforceOptionalnow?: 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.
Optionalconfig: { Optional configuration object with current date and a boolean to force refresh.
OptionalforceOptionalnow?: DateOptionalrunManager: CallbackManagerForChainRunOptional CallbackManagerForChainRun instance.
The agent's summary as a string.
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.
Return a json-like object representing this chain.
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.
ATTENTION This reference table is for the V2 version of the schema.
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| event | name | chunk | input | output |
+======================+==================+=================================+===============================================+=================================================+
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_start | [model name] | | {'input': 'hello'} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_stream | [model name] | 'Hello' | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_end | [model name] | | 'Hello human!' | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_start | some_runnable | | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_stream | some_runnable | "hello world!, goodbye world!" | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_end | some_runnable | | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_start | [template_name] | | {"question": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
The "on_chain_*" events are the default for Runnables that don't fit one of the above categories.
In addition to the standard events above, users can also dispatch custom events.
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| Attribute | Type | Description |
+===========+======+===========================================================================================================+
| name | str | A user defined name for the event. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
| data | Any | The data associated with the event. This can be anything, though we suggest making it JSON serializable. |
+-----------+------+-----------------------------------------------------------------------------------------------------------+
Here's an example:
import { RunnableLambda } from "@langchain/core/runnables";
import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch";
// Use this import for web environments that don't support "async_hooks"
// and manually pass config to child runs.
// import { dispatchCustomEvent } from "@langchain/core/callbacks/dispatch/web";
const slowThing = RunnableLambda.from(async (someInput: string) => {
// Placeholder for some slow operation
await new Promise((resolve) => setTimeout(resolve, 100));
await dispatchCustomEvent("progress_event", {
message: "Finished step 1 of 2",
});
await new Promise((resolve) => setTimeout(resolve, 100));
return "Done";
});
const eventStream = await slowThing.streamEvents("hello world", {
version: "v2",
});
for await (const event of eventStream) {
if (event.event === "on_custom_event") {
console.log(event);
}
}
OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "autoClose">OptionalstreamOptions: Omit<EventStreamCallbackHandlerInput, "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.
Optionaloptions: Partial<RunnableConfig>OptionalstreamOptions: Omit<LogStreamCallbackHandlerInput, "autoClose">Summarizes memories that are most relevant to an observation.
The observation to summarize related memories for.
OptionalrunManager: 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.
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.
OptionalonCalled after the runnable finishes running, with the Run object.
Optionalconfig: RunnableConfigOptionalonCalled if the runnable throws an error, with the Run object.
Optionalconfig: RunnableConfigOptionalonCalled before the runnable starts running, with the Run object.
Optionalconfig: RunnableConfigAdd retry logic to an existing runnable.
Optionalfields: { OptionalonOptionalstopA new RunnableRetry that, when invoked, will retry according to the parameters.
StaticdeserializeLoad a chain from a json-like object describing it.
Staticis
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