Bundles

Bundled components are based on standard Langflow functionality, so you add them to your flows and configure them in much the same way as the standard components. This documentation summarizes each bundled component and its parameters. For details about provider-specific aspects of bundled components, this documentation provides links to relevant component provider documentation.

Agent bundles

Agents use LLMs as a brain to analyze problems and select external tools.

For more information, see Agents.

CrewAI bundles

This bundle represents Agents of CrewAI allowing for the creation of specialized AI agents with defined roles goals and capabilities within a crew.

For more information, see the CrewAI agents documentation.

Parameters

Inputs

Name	Display Name	Info
role	Role	The role of the agent.
goal	Goal	The objective of the agent.
backstory	Backstory	The backstory of the agent.
tools	Tools	The tools at the agent's disposal.
llm	Language Model	The language model that runs the agent.
memory	Memory	This determines whether the agent should have memory or not.
verbose	Verbose	This enables verbose output.
allow_delegation	Allow Delegation	This determines whether the agent is allowed to delegate tasks to other agents.
allow_code_execution	Allow Code Execution	This determines whether the agent is allowed to execute code.
kwargs	kwargs	Additional keyword arguments for the agent.

Outputs

Name	Display Name	Info
output	Agent	The constructed CrewAI Agent object.

Hierarchical Crew

This component represents a group of agents managing how they should collaborate and the tasks they should perform in a hierarchical structure. This component allows for the creation of a crew with a manager overseeing the task execution.

For more information, see the CrewAI hierarchical crew ocumentation.

Parameters

Inputs

Name	Display Name	Info
agents	Agents	The list of Agent objects representing the crew members.
tasks	Tasks	The list of HierarchicalTask objects representing the tasks to be executed.
manager_llm	Manager LLM	The language model for the manager agent.
manager_agent	Manager Agent	The specific agent to act as the manager.
verbose	Verbose	This enables verbose output for detailed logging.
memory	Memory	The memory configuration for the crew.
use_cache	Use Cache	This enables caching of results.
max_rpm	Max RPM	This sets the maximum requests per minute.
share_crew	Share Crew	This determines if the crew information is shared among agents.
function_calling_llm	Function Calling LLM	The language model for function calling.

Outputs

Name	Display Name	Info
crew	Crew	The constructed Crew object with hierarchical task execution.

Sequential crew

This component represents a group of agents with tasks that are executed sequentially. This component allows for the creation of a crew that performs tasks in a specific order.

For more information, see the CrewAI sequential crew documentation.

Parameters

Inputs

Name	Display Name	Info
tasks	Tasks	The list of SequentialTask objects representing the tasks to be executed.
verbose	Verbose	This enables verbose output for detailed logging.
memory	Memory	The memory configuration for the crew.
use_cache	Use Cache	This enables caching of results.
max_rpm	Max RPM	This sets the maximum requests per minute.
share_crew	Share Crew	This determines if the crew information is shared among agents.
function_calling_llm	Function Calling LLM	The language model for function calling.

Outputs

Name	Display Name	Info
crew	Crew	The constructed Crew object with sequential task execution.

Sequential task agent

This component creates a CrewAI Task and its associated Agent allowing for the definition of sequential tasks with specific agent roles and capabilities.

For more information, see the CrewAI sequential agents documentation.

Parameters

Inputs

Name	Display Name	Info
role	Role	The role of the agent.
goal	Goal	The objective of the agent.
backstory	Backstory	The backstory of the agent.
tools	Tools	The tools at the agent's disposal.
llm	Language Model	The language model that runs the agent.
memory	Memory	This determines whether the agent should have memory or not.
verbose	Verbose	This enables verbose output.
allow_delegation	Allow Delegation	This determines whether the agent is allowed to delegate tasks to other agents.
allow_code_execution	Allow Code Execution	This determines whether the agent is allowed to execute code.
agent_kwargs	Agent kwargs	The additional kwargs for the agent.
task_description	Task Description	The descriptive text detailing the task's purpose and execution.
expected_output	Expected Task Output	The clear definition of the expected task outcome.
async_execution	Async Execution	The boolean flag indicating asynchronous task execution.
previous_task	Previous Task	The previous task in the sequence for chaining.

Outputs

Name	Display Name	Info
task_output	Sequential Task	The list of SequentialTask objects representing the created tasks.

CSV Agent

This component creates a CSV agent from a CSV file and LLM.

For more information, see the Langchain CSV agent documentation.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use for the agent.
path	File	The path to the CSV file.
agent_type	String	The type of agent to create.

Outputs

Name	Type	Description
agent	AgentExecutor	The CSV agent instance.

OpenAI Tools Agent

This component creates an OpenAI Tools Agent.

For more information, see the Langchain OpenAI agent documentation.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use.
tools	List of Tools	The tools to give the agent access to.
system_prompt	String	The system prompt to provide context to the agent.
input_value	String	The user's input to the agent.
memory	Memory	The memory for the agent to use for context persistence.
max_iterations	Integer	The maximum number of iterations to allow the agent to execute.
verbose	Boolean	This determines whether to print out the agent's intermediate steps.
handle_parsing_errors	Boolean	This determines whether to handle parsing errors in the agent.

Outputs

Name	Type	Description
agent	AgentExecutor	The OpenAI Tools agent instance.
output	String	The output from executing the agent on the input.

OpenAPI Agent

This component creates an agent for interacting with OpenAPI services.

For more information, see the Langchain OpenAPI toolkit documentation.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use.
openapi_spec	String	The OpenAPI specification for the service.
base_url	String	The base URL for the API.
headers	Dict	The optional headers for API requests.
agent_executor_kwargs	Dict	The optional parameters for the agent executor.

Outputs

Name	Type	Description
agent	AgentExecutor	The OpenAPI agent instance.

SQL Agent

This component creates an agent for interacting with SQL databases.

For more information, see the Langchain SQL agent documentation.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use.
database	Database	The SQL database connection.
top_k	Integer	The number of results to return from a SELECT query.
use_tools	Boolean	This determines whether to use tools for query execution.
return_intermediate_steps	Boolean	This determines whether to return the agent's intermediate steps.
max_iterations	Integer	The maximum number of iterations to run the agent.
max_execution_time	Integer	The maximum execution time in seconds.
early_stopping_method	String	The method to use for early stopping.
verbose	Boolean	This determines whether to print the agent's thoughts.

Outputs

Name	Type	Description
agent	AgentExecutor	The SQL agent instance.

Tool Calling Agent

This component creates an agent for structured tool calling with various language models.

For more information, see the Langchain tool calling documentation.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use.
tools	List[Tool]	The list of tools available to the agent.
system_message	String	The system message to use for the agent.
return_intermediate_steps	Boolean	This determines whether to return the agent's intermediate steps.
max_iterations	Integer	The maximum number of iterations to run the agent.
max_execution_time	Integer	The maximum execution time in seconds.
early_stopping_method	String	The method to use for early stopping.
verbose	Boolean	This determines whether to print the agent's thoughts.

Outputs

Name	Type	Description
agent	AgentExecutor	The tool calling agent instance.

XML Agent

This component creates an XML Agent using LangChain.

The agent uses XML formatting for tool instructions to the Language Model.

For more information, see the Langchain XML Agent documentation.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use for the agent.
user_prompt	String	The custom prompt template for the agent with XML formatting instructions.
tools	List[Tool]	The list of tools available to the agent.

Outputs

Name	Type	Description
agent	AgentExecutor	The XML Agent instance.

Embedding models bundles

Embedding model components in Langflow generate text embeddings using the selected Large Language Model.

For more information, see Embedding models.

For more information on a specific embedding model bundle, see the provider's documentation.

AI/ML

This component generates embeddings using the AI/ML API.

Parameters

Inputs

Name	Type	Description
model_name	String	The name of the AI/ML embedding model to use.
aiml_api_key	SecretString	The API key required for authenticating with the AI/ML service.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance of AIMLEmbeddingsImpl for generating embeddings.

Amazon Bedrock Embeddings

This component is used to load embedding models from Amazon Bedrock.

Parameters

Inputs

Name	Type	Description
credentials_profile_name	String	The name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information.
model_id	String	The ID of the model to call, such as amazon.titan-embed-text-v1. This is equivalent to the modelId property in the list-foundation-models API.
endpoint_url	String	The URL to set a specific service endpoint other than the default AWS endpoint.
region_name	String	The AWS region to use, such as us-west-2. Falls back to the AWS_DEFAULT_REGION environment variable or region specified in ~/.aws/config if not provided.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Amazon Bedrock.

Astra DB vectorize

important

This component is deprecated as of Langflow version 1.1.2. Instead, use the Astra DB vector store component.

Connect this component to the Embeddings port of the Astra DB vector store component to generate embeddings.

This component requires that your Astra DB database has a collection that uses a vectorize embedding provider integration. For more information and instructions, see Embedding Generation.

Parameters

Inputs

Name	Display Name	Info
provider	Embedding Provider	The embedding provider to use.
model_name	Model Name	The embedding model to use.
authentication	Authentication	The name of the API key in Astra that stores your vectorize embedding provider credentials. (Not required if using an Astra-hosted embedding provider.)
provider_api_key	Provider API Key	As an alternative to authentication, directly provide your embedding provider credentials.
model_parameters	Model Parameters	Additional model parameters.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Astra vectorize.

Azure OpenAI Embeddings

This component generates embeddings using Azure OpenAI models.

Parameters

Inputs

Name	Type	Description
Model	String	The name of the model to use. Default: text-embedding-3-small.
Azure Endpoint	String	Your Azure endpoint, including the resource, such as https://example-resource.azure.openai.com/.
Deployment Name	String	The name of the deployment.
API Version	String	The API version to use, with options including various dates.
API Key	String	The API key required to access the Azure OpenAI service.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Azure OpenAI.

Cloudflare Workers AI Embeddings

This component generates embeddings using Cloudflare Workers AI models.

Parameters

Inputs

Name	Display Name	Info
account_id	Cloudflare account ID	Find your Cloudflare account ID.
api_token	Cloudflare API token	Create an API token.
model_name	Model Name	List of supported models.
strip_new_lines	Strip New Lines	Whether to strip new lines from the input text.
batch_size	Batch Size	The number of texts to embed in each batch.
api_base_url	Cloudflare API base URL	The base URL for the Cloudflare API.
headers	Headers	Additional request headers.

Outputs

Name	Display Name	Info
embeddings	Embeddings	An instance for generating embeddings using Cloudflare Workers.

Cohere Embeddings

This component is used to load embedding models from Cohere.

Parameters

Inputs

Name	Type	Description
cohere_api_key	String	The API key required to authenticate with the Cohere service.
model	String	The language model used for embedding text documents and performing queries. Default: embed-english-v2.0.
truncate	Boolean	Whether to truncate the input text to fit within the model's constraints. Default: False.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Cohere.

Embedding similarity

This component computes selected forms of similarity between two embedding vectors.

Parameters

Inputs

Name	Display Name	Info
embedding_vectors	Embedding Vectors	A list containing exactly two data objects with embedding vectors to compare.
similarity_metric	Similarity Metric	Select the similarity metric to use. Options: "Cosine Similarity", "Euclidean Distance", "Manhattan Distance".

Outputs

Name	Display Name	Info
similarity_data	Similarity Data	A data object containing the computed similarity score and additional information.

Google generative AI embeddings

This component connects to Google's generative AI embedding service using the GoogleGenerativeAIEmbeddings class from the langchain-google-genai package.

Parameters

Inputs

Name	Display Name	Info
api_key	API Key	The secret API key for accessing Google's generative AI service. Required.
model_name	Model Name	The name of the embedding model to use. Default: "models/text-embedding-004".

Outputs

Name	Display Name	Info
embeddings	Embeddings	The built GoogleGenerativeAIEmbeddings object.

Hugging Face Embeddings

note

This component is deprecated as of Langflow version 1.0.18. Instead, use the Hugging Face Embeddings Inference component.

This component loads embedding models from HuggingFace.

Use this component to generate embeddings using locally downloaded Hugging Face models. Ensure you have sufficient computational resources to run the models.

Parameters

Inputs

Name	Display Name	Info
Cache Folder	Cache Folder	The folder path to cache HuggingFace models.
Encode Kwargs	Encoding Arguments	Additional arguments for the encoding process.
Model Kwargs	Model Arguments	Additional arguments for the model.
Model Name	Model Name	The name of the HuggingFace model to use.
Multi Process	Multi-Process	Whether to use multiple processes.

Outputs

Name	Display Name	Info
embeddings	Embeddings	The generated embeddings.

Hugging Face embeddings inference

This component generates embeddings using Hugging Face Inference API models and requires a Hugging Face API token to authenticate. Local inference models do not require an API key.

Use this component to create embeddings with Hugging Face's hosted models, or to connect to your own locally hosted models.

Parameters

Inputs

Name	Display Name	Info
API Key	API Key	The API key for accessing the Hugging Face Inference API.
API URL	API URL	The URL of the Hugging Face Inference API.
Model Name	Model Name	The name of the model to use for embeddings.

Outputs

Name	Display Name	Info
embeddings	Embeddings	The generated embeddings.

Connect the Hugging Face component to a local embeddings model

To run an embeddings inference locally, see the HuggingFace documentation.

To connect the local Hugging Face model to the Hugging Face embeddings inference component and use it in a flow, follow these steps:

1. Create a Vector store RAG flow. There are two embeddings models in this flow that you can replace with Hugging Face embeddings inference components.
2. Replace both OpenAI embeddings model components with Hugging Face model components.
3. Connect both Hugging Face components to the Embeddings ports of the Astra DB vector store components.
4. In the Hugging Face components, set the Inference Endpoint field to the URL of your local inference model. The API Key field is not required for local inference.
5. Run the flow. The local inference models generate embeddings for the input text.

IBM watsonx embeddings

This component generates text using IBM watsonx.ai foundation models.

To use IBM watsonx.ai embeddings components, replace an embeddings component with the IBM watsonx.ai component in a flow.

An example document processing flow looks like the following:

This flow loads a PDF file from local storage and splits the text into chunks.

The IBM watsonx embeddings component converts the text chunks into embeddings, which are then stored in a Chroma DB vector store.

The values for API endpoint, Project ID, API key, and Model Name are found in your IBM watsonx.ai deployment. For more information, see the Langchain documentation.

Default models

The component supports several default models with the following vector dimensions:

• sentence-transformers/all-minilm-l12-v2: 384-dimensional embeddings
• ibm/slate-125m-english-rtrvr-v2: 768-dimensional embeddings
• ibm/slate-30m-english-rtrvr-v2: 768-dimensional embeddings
• intfloat/multilingual-e5-large: 1024-dimensional embeddings

The component automatically fetches and updates the list of available models from your watsonx.ai instance when you provide your API endpoint and credentials.

Parameters

Inputs

Name	Display Name	Info
url	watsonx API Endpoint	The base URL of the API.
project_id	watsonx project id	The project ID for your watsonx.ai instance.
api_key	API Key	The API Key to use for the model.
model_name	Model Name	The name of the embedding model to use.
truncate_input_tokens	Truncate Input Tokens	The maximum number of tokens to process. Default: 200.
input_text	Include the original text in the output	Determines if the original text is included in the output. Default: True.

Outputs

Name	Display Name	Info
embeddings	Embeddings	An instance for generating embeddings using watsonx.ai.

LM Studio Embeddings

This component generates embeddings using LM Studio models.

Parameters

Inputs

Name	Display Name	Info
model	Model	The LM Studio model to use for generating embeddings.
base_url	LM Studio Base URL	The base URL for the LM Studio API.
api_key	LM Studio API Key	The API key for authentication with LM Studio.
temperature	Model Temperature	The temperature setting for the model.

Outputs

Name	Display Name	Info
embeddings	Embeddings	The generated embeddings.

MistralAI

This component generates embeddings using MistralAI models.

Parameters

Inputs

Name	Type	Description
model	String	The MistralAI model to use. Default: "mistral-embed".
mistral_api_key	SecretString	The API key for authenticating with MistralAI.
max_concurrent_requests	Integer	The maximum number of concurrent API requests. Default: 64.
max_retries	Integer	The maximum number of retry attempts for failed requests. Default: 5.
timeout	Integer	The request timeout in seconds. Default: 120.
endpoint	String	The custom API endpoint URL. Default: https://api.mistral.ai/v1/).

Outputs

Name	Type	Description
embeddings	Embeddings	A MistralAIEmbeddings instance for generating embeddings.

NVIDIA

This component generates embeddings using NVIDIA models.

Parameters

Inputs

Name	Type	Description
model	String	The NVIDIA model to use for embeddings, such as nvidia/nv-embed-v1.
base_url	String	The base URL for the NVIDIA API. Default: https://integrate.api.nvidia.com/v1.
nvidia_api_key	SecretString	The API key for authenticating with NVIDIA's service.
temperature	Float	The model temperature for embedding generation. Default: 0.1.

Outputs

Name	Type	Description
embeddings	Embeddings	A NVIDIAEmbeddings instance for generating embeddings.

Ollama embeddings

This component generates embeddings using Ollama models.

For a list of Ollama embeddings models, see the Ollama documentation.

To use this component in a flow, connect Langflow to your locally running Ollama server and select an embeddings model.

1. In the Ollama component, in the Ollama Base URL field, enter the address for your locally running Ollama server. This value is set as the OLLAMA_HOST environment variable in Ollama. The default base URL is http://127.0.0.1:11434.
2. To refresh the server's list of models, click .
3. In the Ollama Model field, select an embeddings model. This example uses all-minilm:latest.
4. Connect the Ollama embeddings component to a flow. For example, this flow connects a local Ollama server running a all-minilm:latest embeddings model to a Chroma DB vector store to generate embeddings for split text.

For more information, see the Ollama documentation.

Parameters

Inputs

Name	Type	Description
Ollama Model	String	The name of the Ollama model to use. Default: llama2.
Ollama Base URL	String	The base URL of the Ollama API. Default: http://localhost:11434.
Model Temperature	Float	The temperature parameter for the model. Adjusts the randomness in the generated embeddings.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using Ollama.

OpenAI Embeddings

This component is used to load embedding models from OpenAI.

Parameters

Inputs

Name	Type	Description
OpenAI API Key	String	The API key to use for accessing the OpenAI API.
Default Headers	Dict	The default headers for the HTTP requests.
Default Query	NestedDict	The default query parameters for the HTTP requests.
Allowed Special	List	The special tokens allowed for processing. Default: [].
Disallowed Special	List	The special tokens disallowed for processing. Default: ["all"].
Chunk Size	Integer	The chunk size for processing. Default: 1000.
Client	Any	The HTTP client for making requests.
Deployment	String	The deployment name for the model. Default: text-embedding-3-small.
Embedding Context Length	Integer	The length of embedding context. Default: 8191.
Max Retries	Integer	The maximum number of retries for failed requests. Default: 6.
Model	String	The name of the model to use. Default: text-embedding-3-small.
Model Kwargs	NestedDict	Additional keyword arguments for the model.
OpenAI API Base	String	The base URL of the OpenAI API.
OpenAI API Type	String	The type of the OpenAI API.
OpenAI API Version	String	The version of the OpenAI API.
OpenAI Organization	String	The organization associated with the API key.
OpenAI Proxy	String	The proxy server for the requests.
Request Timeout	Float	The timeout for the HTTP requests.
Show Progress Bar	Boolean	Whether to show a progress bar for processing. Default: False.
Skip Empty	Boolean	Whether to skip empty inputs. Default: False.
TikToken Enable	Boolean	Whether to enable TikToken. Default: True.
TikToken Model Name	String	The name of the TikToken model.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using OpenAI.

Text embedder

This component generates embeddings for a given message using a specified embedding model.

Parameters

Inputs

Name	Display Name	Info
embedding_model	Embedding Model	The embedding model to use for generating embeddings.
message	Message	The message for which to generate embeddings.

Outputs

Name	Display Name	Info
embeddings	Embedding Data	A data object containing the original text and its embedding vector.

VertexAI Embeddings

This component is a wrapper around Google Vertex AI Embeddings API.

Parameters

Inputs

Name	Type	Description
credentials	Credentials	The default custom credentials to use.
location	String	The default location to use when making API calls. Default: us-central1.
max_output_tokens	Integer	The token limit determines the maximum amount of text output from one prompt. Default: 128.
model_name	String	The name of the Vertex AI large language model. Default: text-bison.
project	String	The default GCP project to use when making Vertex API calls.
request_parallelism	Integer	The amount of parallelism allowed for requests issued to VertexAI models. Default: 5.
temperature	Float	Tunes the degree of randomness in text generations. Should be a non-negative value. Default: 0.
top_k	Integer	How the model selects tokens for output. The next token is selected from the top k tokens. Default: 40.
top_p	Float	Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top p value. Default: 0.95.
tuned_model_name	String	The name of a tuned model. If provided, model_name is ignored.
verbose	Boolean	This parameter controls the level of detail in the output. When set to True, it prints internal states of the chain to help debug. Default: False.

Outputs

Name	Type	Description
embeddings	Embeddings	An instance for generating embeddings using VertexAI.

Language model bundles

Language model components in Langflow generate text using the selected Large Language Model.

For more information, see Language models.

For more information on a specific model bundle, see the provider's documentation.

AIML

This component creates a ChatOpenAI model instance using the AIML API.

For more information, see AIML documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens. Range: 0-128000.
model_kwargs	Dictionary	Additional keyword arguments for the model.
model_name	String	The name of the AIML model to use. Options are predefined in AIML_CHAT_MODELS.
aiml_api_base	String	The base URL of the AIML API. Defaults to https://api.aimlapi.com.
api_key	SecretString	The AIML API Key to use for the model.
temperature	Float	Controls randomness in the output. Default: 0.1.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

Amazon Bedrock

This component generates text using Amazon Bedrock LLMs.

For more information, see Amazon Bedrock documentation.

Parameters

Inputs

Name	Type	Description
model_id	String	The ID of the Amazon Bedrock model to use. Options include various models.
aws_access_key	SecretString	AWS Access Key for authentication.
aws_secret_key	SecretString	AWS Secret Key for authentication.
aws_session_token	SecretString	The session key for your AWS account.
credentials_profile_name	String	Name of the AWS credentials profile to use.
region_name	String	AWS region name. Default: us-east-1.
model_kwargs	Dictionary	Additional keyword arguments for the model.
endpoint_url	String	Custom endpoint URL for the Bedrock service.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatBedrock configured with the specified parameters.

Anthropic

This component allows the generation of text using Anthropic Chat and Language models.

For more information, see the Anthropic documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens. Default: 4096.
model	String	The name of the Anthropic model to use. Options include various Claude 3 models.
anthropic_api_key	SecretString	Your Anthropic API key for authentication.
temperature	Float	Controls randomness in the output. Default: 0.1.
anthropic_api_url	String	Endpoint of the Anthropic API. Defaults to https://api.anthropic.com if not specified (advanced).
prefill	String	Prefill text to guide the model's response (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatAnthropic configured with the specified parameters.

Azure OpenAI

This component generates text using Azure OpenAI LLM.

For more information, see the Azure OpenAI documentation.

Parameters

Inputs

Name	Type	Description
Model Name	String	Specifies the name of the Azure OpenAI model to be used for text generation.
Azure Endpoint	String	Your Azure endpoint, including the resource.
Deployment Name	String	Specifies the name of the deployment.
API Version	String	Specifies the version of the Azure OpenAI API to be used.
API Key	SecretString	Your Azure OpenAI API key.
Temperature	Float	Specifies the sampling temperature. Defaults to 0.7.
Max Tokens	Integer	Specifies the maximum number of tokens to generate. Defaults to 1000.
Input Value	String	Specifies the input text for text generation.
Stream	Boolean	Specifies whether to stream the response from the model. Defaults to False.

Outputs

Name	Type	Description
model	LanguageModel	An instance of AzureOpenAI configured with the specified parameters.

Cohere

This component generates text using Cohere's language models.

For more information, see the Cohere documentation.

Parameters

Inputs

Name	Type	Description
Cohere API Key	SecretString	Your Cohere API key.
Max Tokens	Integer	Specifies the maximum number of tokens to generate. Defaults to 256.
Temperature	Float	Specifies the sampling temperature. Defaults to 0.75.
Input Value	String	Specifies the input text for text generation.

Outputs

Name	Type	Description
model	LanguageModel	An instance of the Cohere model configured with the specified parameters.

DeepSeek

This component generates text using DeepSeek's language models.

For more information, see the DeepSeek documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	Maximum number of tokens to generate. Set to 0 for unlimited. Range: 0-128000.
model_kwargs	Dictionary	Additional keyword arguments for the model.
json_mode	Boolean	If True, outputs JSON regardless of passing a schema.
model_name	String	The DeepSeek model to use. Default: deepseek-chat.
api_base	String	Base URL for API requests. Default: https://api.deepseek.com.
api_key	SecretString	Your DeepSeek API key for authentication.
temperature	Float	Controls randomness in responses. Range: [0.0, 2.0]. Default: 1.0.
seed	Integer	Number initialized for random number generation. Use the same seed integer for more reproducible results, and use a different seed number for more random results.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

Google Generative AI

This component generates text using Google's Generative AI models.

For more information, see the Google Generative AI documentation.

Parameters

Inputs

Name	Type	Description
Google API Key	SecretString	Your Google API key to use for the Google Generative AI.
Model	String	The name of the model to use, such as "gemini-pro".
Max Output Tokens	Integer	The maximum number of tokens to generate.
Temperature	Float	Run inference with this temperature.
Top K	Integer	Consider the set of top K most probable tokens.
Top P	Float	The maximum cumulative probability of tokens to consider when sampling.
N	Integer	Number of chat completions to generate for each prompt.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatGoogleGenerativeAI configured with the specified parameters.

Groq

This component generates text using Groq's language models.

1. To use this component in a flow, connect it as a Model in a flow like the Basic prompting flow, or select it as the Model Provider if you're using an Agent component.

2. In the Groq API Key field, paste your Groq API key. The Groq model component automatically retrieves a list of the latest models. To refresh your list of models, click .
3. In the Model field, select the model you want to use for your LLM. This example uses llama-3.1-8b-instant, which Groq recommends for real-time conversational interfaces.
4. In the Prompt component, enter:

_10

You are a helpful assistant who supports their claims with sources.

5. Click Playground and ask your Groq LLM a question. The responses include a list of sources.

For more information, see the Groq documentation.

Parameters

Inputs

Name	Type	Description
groq_api_key	SecretString	API key for the Groq API.
groq_api_base	String	Base URL path for API requests. Default: https://api.groq.com.
max_tokens	Integer	The maximum number of tokens to generate.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.1.
n	Integer	Number of chat completions to generate for each prompt.
model_name	String	The name of the Groq model to use. Options are dynamically fetched from the Groq API.
tool_mode_enabled	Bool	If enabled, the component only displays models that work with tools.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatGroq configured with the specified parameters.

Hugging Face API

This component sends requests to the Hugging Face API to generate text using the model specified in the Model ID field.

The Hugging Face API is a hosted inference API for models hosted on Hugging Face, and requires a Hugging Face API token to authenticate.

In this example based on the Basic prompting flow, the Hugging Face API model component replaces the Open AI model. By selecting different hosted models, you can see how different models return different results.

1. Create a Basic prompting flow.

2. Replace the OpenAI model component with a Hugging Face API model component.

3. In the Hugging Face API component, add your Hugging Face API token to the API Token field.

4. Open the Playground and ask a question to the model, and see how it responds.

5. Try different models, and see how they perform differently.

For more information, see the Hugging Face documentation.

Parameters

Inputs

Name	Type	Description
model_id	String	The model ID from Hugging Face Hub. For example, "gpt2", "facebook/bart-large".
huggingfacehub_api_token	SecretString	Your Hugging Face API token for authentication.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7.
max_new_tokens	Integer	Maximum number of tokens to generate. Default: 512.
top_p	Float	Nucleus sampling parameter. Range: [0.0, 1.0]. Default: 0.95.
top_k	Integer	Top-k sampling parameter. Default: 50.
model_kwargs	Dictionary	Additional keyword arguments to pass to the model.

Outputs

Name	Type	Description
model	LanguageModel	An instance of HuggingFaceHub configured with the specified parameters.

IBM watsonx.ai

This component generates text using IBM watsonx.ai foundation models.

To use IBM watsonx.ai model components, replace a model component with the IBM watsonx.ai component in a flow.

An example flow looks like the following:

The values for API endpoint, Project ID, API key, and Model Name are found in your IBM watsonx.ai deployment. For more information, see the Langchain documentation.

Parameters

Inputs

Name	Type	Description
url	String	The base URL of the watsonx API.
project_id	String	Your watsonx Project ID.
api_key	SecretString	Your IBM watsonx API Key.
model_name	String	The name of the watsonx model to use. Options are dynamically fetched from the API.
max_tokens	Integer	The maximum number of tokens to generate. Default: 1000.
stop_sequence	String	The sequence where generation should stop.
temperature	Float	Controls randomness in the output. Default: 0.1.
top_p	Float	Controls nucleus sampling, which limits the model to tokens whose probability is below the top_p value. Range: Default: 0.9.
frequency_penalty	Float	Controls frequency penalty. A positive value decreases the probability of repeating tokens, and a negative value increases the probability. Range: Default: 0.5.
presence_penalty	Float	Controls presence penalty. A positive value increases the likelihood of new topics being introduced. Default: 0.3.
seed	Integer	A random seed for the model. Default: 8.
logprobs	Boolean	Whether to return log probabilities of output tokens or not. Default: True.
top_logprobs	Integer	The number of most likely tokens to return at each position. Default: 3.
logit_bias	String	A JSON string of token IDs to bias or suppress.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatWatsonx configured with the specified parameters.

LMStudio

This component generates text using LM Studio's local language models.

For more information, see LM Studio documentation.

Parameters

Inputs

Name	Type	Description
base_url	String	The URL where LM Studio is running. Default: "http://localhost:1234".
max_tokens	Integer	Maximum number of tokens to generate in the response. Default: 512.
temperature	Float	Controls randomness in the output. Range: [0.0, 2.0]. Default: 0.7.
top_p	Float	Controls diversity via nucleus sampling. Range: [0.0, 1.0]. Default: 1.0.
stop	List[String]	List of strings that stop generation when encountered.
stream	Boolean	Whether to stream the response. Default: False.
presence_penalty	Float	Penalizes repeated tokens. Range: [-2.0, 2.0]. Default: 0.0.
frequency_penalty	Float	Penalizes frequent tokens. Range: [-2.0, 2.0]. Default: 0.0.

Outputs

Name	Type	Description
model	LanguageModel	An instance of LMStudio configured with the specified parameters.

Maritalk

This component generates text using Maritalk LLMs.

For more information, see Maritalk documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens. Default: 512.
model_name	String	The name of the Maritalk model to use. Options: sabia-2-small, sabia-2-medium. Default: sabia-2-small.
api_key	SecretString	The Maritalk API Key to use for authentication.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.5.
endpoint_url	String	The Maritalk API endpoint. Default: https://api.maritalk.com.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatMaritalk configured with the specified parameters.

Mistral

This component generates text using MistralAI LLMs.

For more information, see Mistral AI documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens (advanced).
model_name	String	The name of the Mistral AI model to use. Options include open-mixtral-8x7b, open-mixtral-8x22b, mistral-small-latest, mistral-medium-latest, mistral-large-latest, and codestral-latest. Default: codestral-latest.
mistral_api_base	String	The base URL of the Mistral API. Defaults to https://api.mistral.ai/v1 (advanced).
api_key	SecretString	The Mistral API Key to use for authentication.
temperature	Float	Controls randomness in the output. Default: 0.5.
max_retries	Integer	Maximum number of retries for API calls. Default: 5 (advanced).
timeout	Integer	Timeout for API calls in seconds. Default: 60 (advanced).
max_concurrent_requests	Integer	Maximum number of concurrent API requests. Default: 3 (advanced).
top_p	Float	Nucleus sampling parameter. Default: 1 (advanced).
random_seed	Integer	Seed for random number generation. Default: 1 (advanced).
safe_mode	Boolean	Enables safe mode for content generation (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatMistralAI configured with the specified parameters.

Novita AI

This component generates text using Novita AI's language models.

For more information, see Novita AI documentation.

Parameters

Inputs

Name	Type	Description
api_key	SecretString	Your Novita AI API Key.
model	String	The id of the Novita AI model to use.
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7.
top_p	Float	Controls the nucleus sampling. Range: [0.0, 1.0]. Default: 1.0.
frequency_penalty	Float	Controls the frequency penalty. Range: [0.0, 2.0]. Default: 0.0.
presence_penalty	Float	Controls the presence penalty. Range: [0.0, 2.0]. Default: 0.0.

Outputs

Name	Type	Description
model	LanguageModel	An instance of Novita AI model configured with the specified parameters.

NVIDIA

This component generates text using NVIDIA LLMs.

For more information, see NVIDIA AI documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens (advanced).
model_name	String	The name of the NVIDIA model to use. Default: mistralai/mixtral-8x7b-instruct-v0.1.
base_url	String	The base URL of the NVIDIA API. Default: https://integrate.api.nvidia.com/v1.
nvidia_api_key	SecretString	The NVIDIA API Key for authentication.
temperature	Float	Controls randomness in the output. Default: 0.1.
seed	Integer	The seed controls the reproducibility of the job (advanced). Default: 1.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatNVIDIA configured with the specified parameters.

Ollama

This component generates text using Ollama's language models.

To use this component in a flow, connect Langflow to your locally running Ollama server and select a model.

1. In the Ollama component, in the Base URL field, enter the address for your locally running Ollama server. This value is set as the OLLAMA_HOST environment variable in Ollama. The default base URL is http://127.0.0.1:11434.
2. To refresh the server's list of models, click .
3. In the Model Name field, select a model. This example uses llama3.2:latest.
4. Connect the Ollama model component to a flow. For example, this flow connects a local Ollama server running a Llama 3.2 model as the custom model for an Agent component.

For more information, see the Ollama documentation.

Parameters

Inputs

Name	Type	Description
Base URL	String	Endpoint of the Ollama API.
Model Name	String	The model name to use.
Temperature	Float	Controls the creativity of model responses.

Outputs

Name	Type	Description
model	LanguageModel	An instance of an Ollama model configured with the specified parameters.

OpenAI

This component generates text using OpenAI's language models.

For more information, see OpenAI documentation.

Parameters

Inputs

Name	Type	Description
api_key	SecretString	Your OpenAI API Key.
model	String	The name of the OpenAI model to use. Options include "gpt-3.5-turbo" and "gpt-4".
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7.
top_p	Float	Controls the nucleus sampling. Range: [0.0, 1.0]. Default: 1.0.
frequency_penalty	Float	Controls the frequency penalty. Range: [0.0, 2.0]. Default: 0.0.
presence_penalty	Float	Controls the presence penalty. Range: [0.0, 2.0]. Default: 0.0.

Outputs

Name	Type	Description
model	LanguageModel	An instance of OpenAI model configured with the specified parameters.

OpenRouter

This component generates text using OpenRouter's unified API for multiple AI models from different providers.

For more information, see OpenRouter documentation.

Parameters

Inputs

Name	Type	Description
api_key	SecretString	Your OpenRouter API key for authentication.
site_url	String	Your site URL for OpenRouter rankings (advanced).
app_name	String	Your app name for OpenRouter rankings (advanced).
provider	String	The AI model provider to use.
model_name	String	The specific model to use for chat completion.
temperature	Float	Controls randomness in the output. Range: [0.0, 2.0]. Default: 0.7.
max_tokens	Integer	The maximum number of tokens to generate (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

Perplexity

This component generates text using Perplexity's language models.

For more information, see Perplexity documentation.

Parameters

Inputs

Name	Type	Description
model_name	String	The name of the Perplexity model to use. Options include various Llama 3.1 models.
max_output_tokens	Integer	The maximum number of tokens to generate.
api_key	SecretString	The Perplexity API Key for authentication.
temperature	Float	Controls randomness in the output. Default: 0.75.
top_p	Float	The maximum cumulative probability of tokens to consider when sampling (advanced).
n	Integer	Number of chat completions to generate for each prompt (advanced).
top_k	Integer	Number of top tokens to consider for top-k sampling. Must be positive (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatPerplexity configured with the specified parameters.

Qianfan

This component generates text using Qianfan's language models.

For more information, see Qianfan documentation.

SambaNova

This component generates text using SambaNova LLMs.

For more information, see Sambanova Cloud documentation.

Parameters

Inputs

Name	Type	Description
sambanova_url	String	Base URL path for API requests. Default: https://api.sambanova.ai/v1/chat/completions.
sambanova_api_key	SecretString	Your SambaNova API Key.
model_name	String	The name of the Sambanova model to use. Options include various Llama models.
max_tokens	Integer	The maximum number of tokens to generate. Set to 0 for unlimited tokens.
temperature	Float	Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.07.

Outputs

Name	Type	Description
model	LanguageModel	An instance of SambaNova model configured with the specified parameters.

VertexAI

This component generates text using Vertex AI LLMs.

For more information, see Google Vertex AI documentation.

Parameters

Inputs

Name	Type	Description
credentials	File	JSON credentials file. Leave empty to fall back to environment variables. File type: JSON.
model_name	String	The name of the Vertex AI model to use. Default: "gemini-1.5-pro".
project	String	The project ID (advanced).
location	String	The location for the Vertex AI API. Default: "us-central1" (advanced).
max_output_tokens	Integer	The maximum number of tokens to generate (advanced).
max_retries	Integer	Maximum number of retries for API calls. Default: 1 (advanced).
temperature	Float	Controls randomness in the output. Default: 0.0.
top_k	Integer	The number of highest probability vocabulary tokens to keep for top-k-filtering (advanced).
top_p	Float	The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Default: 0.95 (advanced).
verbose	Boolean	Whether to print verbose output. Default: False (advanced).

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatVertexAI configured with the specified parameters.

xAI

This component generates text using xAI models like Grok.

For more information, see the xAI documentation.

Parameters

Inputs

Name	Type	Description
max_tokens	Integer	Maximum number of tokens to generate. Set to 0 for unlimited. Range: 0-128000.
model_kwargs	Dictionary	Additional keyword arguments for the model.
json_mode	Boolean	If True, outputs JSON regardless of passing a schema.
model_name	String	The xAI model to use. Default: grok-2-latest.
base_url	String	Base URL for API requests. Default: https://api.x.ai/v1.
api_key	SecretString	Your xAI API key for authentication.
temperature	Float	Controls randomness in the output. Range: [0.0, 2.0]. Default: 0.1.
seed	Integer	Controls reproducibility of the job.

Outputs

Name	Type	Description
model	LanguageModel	An instance of ChatOpenAI configured with the specified parameters.

Memory bundles

AstraDBChatMemory Component

This component creates an AstraDBChatMessageHistory instance, which stores and retrieves chat messages using Astra DB, a cloud-native database service.

Parameters

Inputs

Name	Type	Description
collection_name	String	The name of the Astra DB collection for storing messages. Required.
token	SecretString	The authentication token for Astra DB access. Required.
api_endpoint	SecretString	The API endpoint URL for the Astra DB service. Required.
namespace	String	The optional namespace within Astra DB for the collection.
session_id	MessageText	The unique identifier for the chat session. Uses the current session ID if not provided.

Outputs

Name	Type	Description
message_history	BaseChatMessageHistory	An instance of AstraDBChatMessageHistory for the session.

CassandraChatMemory Component

This component creates a CassandraChatMessageHistory instance, enabling storage and retrieval of chat messages using Apache Cassandra or DataStax Astra DB.

Parameters

Inputs

Name	Type	Description
database_ref	MessageText	The contact points for the Cassandra database or Astra DB database ID. Required.
username	MessageText	The username for Cassandra. Leave empty for Astra DB.
token	SecretString	The password for Cassandra or the token for Astra DB. Required.
keyspace	MessageText	The keyspace in Cassandra or namespace in Astra DB. Required.
table_name	MessageText	The name of the table or collection for storing messages. Required.
session_id	MessageText	The unique identifier for the chat session. Optional.
cluster_kwargs	Dictionary	Additional keyword arguments for the Cassandra cluster configuration. Optional.

Outputs

Name	Type	Description
message_history	BaseChatMessageHistory	An instance of CassandraChatMessageHistory for the session.

Mem0 Chat Memory

The Mem0 Chat Memory component retrieves and stores chat messages using Mem0 memory storage.

Parameters

Inputs

Name	Display Name	Info
mem0_config	Mem0 Configuration	The configuration dictionary for initializing the Mem0 memory instance.
ingest_message	Message to Ingest	The message content to be ingested into Mem0 memory.
existing_memory	Existing Memory Instance	An optional existing Mem0 memory instance.
user_id	User ID	The identifier for the user associated with the messages.
search_query	Search Query	The input text for searching related memories in Mem0.
mem0_api_key	Mem0 API Key	The API key for the Mem0 platform. Leave empty to use the local version.
metadata	Metadata	The additional metadata to associate with the ingested message.
openai_api_key	OpenAI API Key	The API key for OpenAI. Required when using OpenAI embeddings without a provided configuration.

Outputs

Name	Display Name	Info
memory	Mem0 Memory	The resulting Mem0 Memory object after ingesting data.
search_results	Search Results	The search results from querying Mem0 memory.

Redis Chat Memory

This component retrieves and stores chat messages from Redis.

Parameters

Inputs

Name	Display Name	Info
host	hostname	The IP address or hostname.
port	port	The Redis Port Number.
database	database	The Redis database.
username	Username	The Redis username.
password	Password	The password for the username.
key_prefix	Key prefix	The key prefix.
session_id	Session ID	The unique session identifier for the message.

Outputs

Name	Display Name	Info
memory	Memory	The Redis chat message history object.

ZepChatMemory Component

This component creates a ZepChatMessageHistory instance, enabling storage and retrieval of chat messages using Zep, a memory server for Large Language Models (LLMs).

Parameters

Inputs

Name	Type	Description
url	MessageText	The URL of the Zep instance. Required.
api_key	SecretString	The API Key for authentication with the Zep instance.
api_base_path	Dropdown	The API version to use. Options include api/v1 or api/v2.
session_id	MessageText	The unique identifier for the chat session. Optional.

Outputs

Name	Type	Description
message_history	BaseChatMessageHistory	An instance of ZepChatMessageHistory for the session.