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name: Deploy to GitHub Pages
on:
push:
branches:
- main
# Review gh actions docs if you want to further define triggers, paths, etc
# https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#on
jobs:
deploy:
name: Deploy to GitHub Pages
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/setup-node@v3
with:
node-version: 18
cache: npm
cache-dependency-path: ./docs/package-lock.json
- name: Install dependencies
run: cd docs && npm install
- name: Build website
run: cd docs && npm run build
# Popular action to deploy to GitHub Pages:
# Docs: https://github.com/peaceiris/actions-gh-pages#%EF%B8%8F-docusaurus
- name: Deploy to GitHub Pages
uses: peaceiris/actions-gh-pages@v3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
# Build output to publish to the `gh-pages` branch:
publish_dir: ./docs/build
# The following lines assign commit authorship to the official
# GH-Actions bot for deploys to `gh-pages` branch:
# https://github.com/actions/checkout/issues/13#issuecomment-724415212
# The GH actions bot is used by default if you didn't specify the two fields.
# You can swap them out with your own user credentials.

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name: pre-release
on:
pull_request:
types:
- closed
branches:
- dev
paths:
- "pyproject.toml"
env:
POETRY_VERSION: "1.5.1"
jobs:
if_release:
if: |
${{ github.event.pull_request.merged == true }}
&& ${{ contains(github.event.pull_request.labels.*.name, 'pre-release') }}
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Install poetry
run: pipx install poetry==$POETRY_VERSION
- name: Set up Python 3.10
uses: actions/setup-python@v4
with:
python-version: "3.10"
cache: "poetry"
- name: Build project for distribution
run: make build
- name: Check Version
id: check-version
run: |
echo version=$(poetry version --short) >> $GITHUB_OUTPUT
- name: Create Release
uses: ncipollo/release-action@v1
with:
artifacts: "dist/*"
token: ${{ secrets.GITHUB_TOKEN }}
draft: false
generateReleaseNotes: true
tag: v${{ steps.check-version.outputs.version }}
commit: main
- name: Publish to PyPI
env:
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
run: |
poetry publish

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- "pyproject.toml"
env:
POETRY_VERSION: "1.4.0"
POETRY_VERSION: "1.5.1"
jobs:
if_release:

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# Poetry
.testenv/*
langflow.db
.githooks/prepare-commit-msg
langchain.db
.langchain.db
# docusaurus
.docusaurus/

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# Read the Docs configuration file for Sphinx projects
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
# Required
version: 2
# Set the OS, Python version and other tools you might need
build:
os: ubuntu-22.04
tools:
python: "3.11"
# You can also specify other tool versions:
# nodejs: "19"
# rust: "1.64"
# golang: "1.19"
# Build documentation in the "docs/" directory with Sphinx
sphinx:
configuration: docs/conf.py
# Optionally build your docs in additional formats such as PDF and ePub
# formats:
# - pdf
# - epub
# Optional but recommended, declare the Python requirements required
# to build your documentation
# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
# python:
# install:
# - requirements: docs/requirements.txt

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# Website
This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
### Installation
```
$ yarn
```
### Local Development
```
$ yarn start
```
This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
### Build
```
$ yarn build
```
This command generates static content into the `build` directory and can be served using any static contents hosting service.
### Deployment
Using SSH:
```
$ USE_SSH=true yarn deploy
```
Not using SSH:
```
$ GIT_USER=<Your GitHub username> yarn deploy
```
If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.

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module.exports = {
presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
};

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# Agents
Agents are components that use reasoning to make decisions and take actions, designed to autonomously perform tasks or provide services with some degree of “freedom” (or agency). They combine the power of LLM chaining processes with access to external tools such as APIs to interact with applications and accomplish tasks.
---
### AgentInitializer
The `AgentInitializer` component is a quick way to construct a zero-shot agent from a language model (LLM) and tools.
**Params**
- **LLM:** Language Model to use in the `AgentInitializer`.
- **Memory:** Used to add memory functionality to an agent. It allows the agent to store and retrieve information from previous conversations.
- **Tools:** Tools that the agent will have access to.
- **Agent:** The type of agent to be instantiated. Current supported: `zero-shot-react-description`, `react-docstore`, `self-ask-with-search,conversational-react-description` and `openai-functions`.
---
### CSVAgent
A `CSVAgent` is an agent that is designed to interact with CSV (Comma-Separated Values) files. CSV files are a common format for storing tabular data, where each row represents a record and each column represents a field. The CSV agent can perform various tasks, such as reading and writing CSV files, processing the data, and generating tables. It can extract information from the CSV file, manipulate the data, and perform operations like filtering, sorting, and aggregating.
**Params**
- **LLM:** Language Model to use in the `CSVAgent`.
- **path:** The file path to the CSV data.
---
### JSONAgent
The `JSONAgent` deals with JSON (JavaScript Object Notation) data. Similar to the CSVAgent, it works with a language model (LLM) and a toolkit designed for JSON manipulation. This agent can iteratively explore a JSON blob to find the information needed to answer the user's question. It can list keys, get values, and navigate through the structure of the JSON object.
**Params**
- **LLM:** Language Model to use in the `JSONAgent`.
- **Toolkit:** Toolkit that the agent will have access to.
---
### SQLAgent
A `SQLAgent` is an agent that is designed to interact with SQL databases. It is capable of performing various tasks, such as querying the database, retrieving data, and executing SQL statements. The agent can provide information about the structure of the database, including the tables and their schemas. It can also perform operations like inserting, updating, and deleting data in the database. The SQL agent is a helpful tool for managing and working with SQL databases efficiently.
**Params**
- **LLM:** Language Model to use in the `SQLAgent`.
- **database_uri:** A string representing the connection URI for the SQL database.
---
### VectorStoreAgent
The `VectorStoreAgent` is designed to work with a vector store – a data structure used for storing and querying vector-based representations of data. The `VectorStoreAgent` can query the vector store to find relevant information based on user inputs.
**Params**
- **LLM:** Language Model to use in the `VectorStoreAgent`.
- **Vector Store Info:** `VectorStoreInfo` to use in the `VectorStoreAgent`.
---
### VectorStoreRouterAgent
The `VectorStoreRouterAgent` is a custom agent that takes a vector store router as input. It is typically used when there’s a need to retrieve information from multiple vector stores. These can be connected through a `VectorStoreRouterToolkit` and sent over to the `VectorStoreRouterAgent`. An agent configured with multiple vector stores can route queries to the appropriate store based on the context.
**Params**
- **LLM:** Language Model to use in the `VectorStoreRouterAgent`.
- **Vector Store Router Toolkit:** `VectorStoreRouterToolkit` to use in the `VectorStoreRouterAgent`.
---
### ZeroShotAgent
The `ZeroShotAgent` is an agent that uses the ReAct framework to determine which tool to use based solely on the tool's description. It can be configured with any number of tools and requires a description for each tool. The agent is designed to be the most general-purpose action agent. It uses an `LLMChain` to determine which actions to take and in what order.
**Params**
- **Allowed Tools:** Tools that the agent will have access to.
- **LLM Chain:** LLM Chain to be used by the agent.

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
# Chains
Chains, in the context of language models, refer to a series of calls made to a language model. It allows for the output of one call to be used as the input for another call. Different types of chains allow for different levels of complexity. Chains are useful for creating pipelines and executing specific scenarios.
---
### CombineDocsChain
The `CombineDocsChain` incorporates methods to combine or aggregate loaded documents for question-answering functionality.
:::info
Works as a proxy of LangChain’s [documents](https://python.langchain.com/docs/modules/chains/document/) chains generated by the `load_qa_chain` function.
:::
**Params**
- **LLM:** Language Model to use in the chain.
- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) chain (“stuff" as in "to stuff" or "to fill") is the most straightforward of *the* document chains. It takes a list of documents, inserts them all into a prompt, and passes that prompt to an LLM. This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combined documents chain to get a single output (the Reduce step). It can optionally first compress or collapse the mapped documents to make sure that they fit in the combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) chain runs an initial prompt on each document that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest-scoring response is returned.
- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. There are also certain tasks that are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
---
### ConversationChain
The `ConversationChain` is a straightforward chain for interactive conversations with a language model, making it ideal for chatbots or virtual assistants. It allows for dynamic conversations, question-answering, and complex dialogues.
**Params**
- **LLM:** Language Model to use in the chain.
- **Memory:** Default memory store.
- **input_key:** Used to specify the key under which the user input will be stored in the conversation memory. It allows you to provide the user's input to the chain for processing and generating a response.
- **output_key:** Used to specify the key under which the generated response will be stored in the conversation memory. It allows you to retrieve the response using the specified key.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can be helpful for debugging and understanding the chain's behavior. If set to False, it will suppress the verbose output — defaults to `False`.
---
### ConversationalRetrievalChain
The `ConversationalRetrievalChain` extracts information and provides answers by combining document search and question-answering abilities.
:::info
A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
:::
**Params**
- **LLM:** Language Model to use in the chain.
- **Memory:** Default memory store.
- **Retriever:** The retriever used to fetch relevant documents.
- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) chain (“stuff" as in "to stuff" or "to fill") is the most straightforward of *the* document chains. It takes a list of documents, inserts them all into a prompt, and passes that prompt to an LLM. This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combined documents chain to get a single output (the Reduce step). It can optionally first compress or collapse the mapped documents to make sure that they fit in the combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) chain runs an initial prompt on each document that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest-scoring response is returned.
- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. There are also certain tasks that are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
---
### LLMChain
The `LLMChain` is a straightforward chain that adds functionality around language models. It combines a prompt template with a language model. To use it, create input variables to format the prompt template. The formatted prompt is then sent to the language model, and the generated output is returned as the result of the `LLMChain`.
**Params**
- **LLM:** Language Model to use in the chain.
- **Memory:** Default memory store.
- **Prompt**: Prompt template object to use in the chain.
- **output_key:** This parameter is used to specify which key in the LLM output dictionary should be returned as the final output. By default, the `LLMChain` returns both the input and output key values — defaults to `text`.
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
---
### LLMMathChain
The `LLMMathChain` combines a language model (LLM) and a math calculation component. It allows the user to input math problems and get the corresponding solutions.
The `LLMMathChain` works by using the language model with an `LLMChain` to understand the input math problem and generate a math expression. It then passes this expression to the math component, which evaluates it and returns the result.
**Params**
- **LLM:** Language Model to use in the chain.
- **LLMChain:** LLM Chain to use in the chain.
- **Memory:** Default memory store.
- **input_key:** Used to specify the input value for the mathematical calculation. It allows you to provide the specific values or variables that you want to use in the calculation — defaults to `question`.
- **output_key:** Used to specify the key under which the output of the mathematical calculation will be stored. It allows you to retrieve the result of the calculation using the specified key — defaults to `answer`.
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
---
### RetrievalQA
`RetrievalQA` is a chain used to find relevant documents or information to answer a given query. The retriever is responsible for returning the relevant documents based on the query, and the QA component then extracts the answer from those documents. The retrieval QA system combines the capabilities of both the retriever and the QA component to provide accurate and relevant answers to user queries.
:::info
A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
:::
**Params**
- **Combine Documents Chain:** Chain to use to combine the documents.
- **Memory:** Default memory store.
- **Retriever:** The retriever used to fetch relevant documents.
- **input_key:** This parameter is used to specify the key in the input data that contains the question. It is used to retrieve the question from the input data and pass it to the question-answering model for generating the answer — defaults to `query`.
- **output_key:** This parameter is used to specify the key in the output data where the generated answer will be stored. It is used to retrieve the answer from the output data after the question-answering model has generated it — defaults to `result`.
- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
---
### SQLDatabaseChain
The `SQLDatabaseChain` finds answers to questions using a SQL database. It works by using the language model to understand the SQL query and generate the corresponding SQL code. It then passes the SQL code to the SQL database component, which executes the query on the database and returns the result.
**Params**
- **Db:** SQL Database to connect to.
- **LLM:** Language Model to use in the chain.
- **Prompt:** Prompt template to translate natural language to SQL.

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# Embeddings
Embeddings are vector representations of text that capture the semantic meaning of the text. They are created using text embedding models and allow us to think about the text in a vector space, enabling us to perform tasks like semantic search, where we look for pieces of text that are most similar in the vector space.
---
### CohereEmbeddings
Used to load [Cohere’s](https://cohere.com/) embedding models.
**Params**
- **cohere_api_key:** Holds the API key required to authenticate with the Cohere service.
- **model:** The language model used for embedding text documents and performing queries —defaults to `embed-english-v2.0`.
- **truncate:** Used to specify whether or not to truncate the input text. Truncation is useful when dealing with long texts that exceed the model's maximum input length. By truncating the text, the user can ensure that it fits within the model's constraints.
---
### HuggingFaceEmbeddings
Used to load [HuggingFace’s](https://huggingface.co) embedding models.
**Params**
- **cache_folder:** Used to specify the folder where the embeddings will be cached. When embeddings are computed for a text, they can be stored in the cache folder so that they can be reused later without the need to recompute them. This can improve the performance of the application by avoiding redundant computations.
- **encode_kwargs:** Used to pass additional keyword arguments to the encoding method of the underlying HuggingFace model. These keyword arguments can be used to customize the encoding process, such as specifying the maximum length of the input sequence or enabling truncation or padding.
- **model_kwargs:** Used to customize the behavior of the model, such as specifying the model architecture, the tokenizer, or any other model-specific configuration options. By using `model_kwargs`, the user can configure the HuggingFace model according to specific needs and preferences.
- **model_name:** Used to specify the name or identifier of the HuggingFace model that will be used for generating embeddings. It allows users to choose a specific pre-trained model from the Hugging Face model hub — defaults to `sentence-transformers/all-mpnet-base-v2`.
---
### OpenAIEmbeddings
Used to load [OpenAI’s](https://openai.com/) embedding models.
**Params**
- **chunk_size:** Determines the maximum size of each chunk of text that is processed for embedding. If any of the incoming text chunks exceeds `chunk_size` characters, it will be split into multiple chunks of size `chunk_size` or less before being embedded — defaults to `1000`.
- **deployment:** Used to specify the deployment name or identifier of the text embedding model. It allows the user to choose a specific deployment of the model to use for embedding. When the deployment is provided, this can be useful when the user has multiple deployments of the same model with different configurations or versions — defaults to `text-embedding-ada-002`.
- **embedding_ctx_length:** This parameter determines the maximum context length for the text embedding model. It specifies the number of tokens that the model considers when generating embeddings for a piece of text — defaults to `8191` (this means that the model will consider up to 8191 tokens when generating embeddings).
- **max_retries:** Determines the maximum number of times to retry a request if the model provider returns an error from their API — defaults to `6`.
- **model:** Defines which pre-trained text embedding model to use — defaults to `text-embedding-ada-002`.
- **openai_api_base:** Refers to the base URL for the Azure OpenAI resource. It is used to configure the API to connect to the Azure OpenAI service. The base URL can be found in the Azure portal under the user Azure OpenAI resource.
- **openai_api_key:** Is used to authenticate and authorize access to the OpenAI service.
- **openai_api_type:** Is used to specify the type of OpenAI API being used, either the regular OpenAI API or the Azure OpenAI API. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate API service.
- **openai_api_version:** Is used to specify the version of the OpenAI API being used. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate version of the OpenAI API service.
- **openai_organization:** Is used to specify the organization associated with the OpenAI API key. If not provided, the default organization associated with the API key will be used.
- **openai_proxy:** Proxy enables better budgeting and cost management for making OpenAI API calls, including more transparency into pricing.
- **request_timeout:** Used to specify the maximum amount of time, in milliseconds, to wait for a response from the OpenAI API when generating embeddings for a given text.
- **tiktoken_model_name:** Used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name.

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# LLMs
(coming soon)

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# Loaders
(coming soon)

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# Memories
(coming soon)

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# Prompts
A prompt refers to the input given to a language model. It is constructed from multiple components and can be parametrized using prompt templates. A prompt template is a reproducible way to generate prompts and allow for easy customization through input variables.
---
### PromptTemplate
The `PromptTemplate` component allows users to create prompts and define variables that provide control over instructing the model. The template can take in a set of variables from the end user and generates the prompt once the conversation is initiated.
:::info
Once a variable is defined in the prompt template, it becomes a component input of its own. Check out [Prompt Customization](../guidelines/prompt-customization.mdx) to learn more.
:::
- **template:** Template used to format an individual request.

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# Text Splitters
(coming soon)

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# Toolkits
(coming soon)

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# Tools
(coming soon)

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# Utilities
(coming soon)

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# Vector Stores
(coming soon)

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# Wrappers
(coming soon)

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# Community
## 🤖 Join **Langflow** Discord server
Join us to ask questions and showcase your projects.
Let's bring together the building blocks of AI integration!
Langflow [Discord](https://discord.gg/EqksyE2EX9) server.
---
## 🐦 Stay tunned for **Langflow** on Twitter
Follow [@logspace_ai](https://twitter.com/logspace_ai) on **Twitter** to get the latest news about **Langflow**.
---
## ⭐️ Star **Langflow** on GitHub
You can "star" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
By adding a star, other users will be able to find it more easily and see that it has been already useful for others.
---
## 👀 Watch the GitHub repository for releases
You can "watch" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc.
Then you can try and help them solve those questions.
---
Thanks! 🚀

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# GitHub Issues
Our [issues](https://github.com/logspace-ai/langflow/issues) page is kept up to date
with bugs, improvements, and feature requests. There is a taxonomy of labels to help
with sorting and discovery of issues of interest.
If you're looking for help with your code, consider posting a question on the
[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions). Please
understand that we won't be able to provide individual support via email. We
also believe that help is much more valuable if it's **shared publicly**,
so that more people can benefit from it.
- **Describing your issue:** Try to provide as many details as possible. What
exactly goes wrong? _How_ is it failing? Is there an error?
"XY doesn't work" usually isn't that helpful for tracking down problems. Always
remember to include the code you ran and if possible, extract only the relevant
parts and don't just dump your entire script. This will make it easier for us to
reproduce the error.
- **Sharing long blocks of code or logs:** If you need to include long code,
logs or tracebacks, you can wrap them in `<details>` and `</details>`. This
[collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow.
## Issue labels
[See this page](https://github.com/logspace-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests.

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# How to contribute?
👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/logspace-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.
As an open-source project in a rapidly developing field, we are extremely open
to contributions, whether in the form of a new feature, improved infra, or better documentation.
To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
Please do not try to push directly to this repo unless you are a maintainer.
---
## Local development
You can develop Langflow using docker compose, or locally.
We provide a .vscode/launch.json file for debugging the backend in VSCode, which is a lot faster than using docker compose.
Setting up hooks:
```bash
make init
```
This will install the pre-commit hooks, which will run `make format` on every commit.
It is advised to run `make lint` before pushing to the repository.
---
## Run locally
Langflow can run locally by cloning the repository and installing the dependencies. We recommend using a virtual environment to isolate the dependencies from your system.
Before you start, make sure you have the following installed:
- Poetry (>=1.4)
- Node.js
Then install the dependencies and start the development server for the backend:
```bash
make install_backend
make backend
```
And the frontend:
```bash
make frontend
```
---
## Docker compose
The following snippet will run the backend and frontend in separate containers. The frontend will be available at `localhost:3000` and the backend at `localhost:7860`.
```bash
docker compose up --build
# or
make dev build=1
```

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# Deploy on Google Cloud Platform
## Run Langflow from a New Google Cloud Project
This guide will help you set up a Langflow development VM in a Google Cloud Platform project using Google Cloud Shell.
:::note
When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
:::
## Standard VM
[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment.
---
## Spot/Preemptible Instance
[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts.
---
## Pricing (approximate)
> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator)
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- |
| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs |
| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance |
| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month |

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# Deploy on Jina AI Cloud
Langflow integrates with langchain-serve to provide a one-command deployment to [Jina AI Cloud](https://github.com/jina-ai/langchain-serve).
Start by installing `langchain-serve` with
```bash
pip install -U langchain-serve
```
Then, run:
```bash
langflow --jcloud
```
```text
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://<your-app>.wolf.jina.ai/
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
```
**Complete (example) output:**
```text
🚀 Deploying Langflow server on Jina AI Cloud
╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
│ │
│ ID langflow-e3dd8820ec │
│ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai │
│ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec │
│ │
╰──────────────────────────────────────────────────────────────────────────╯
╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
│ App ID │ langflow-e3dd8820ec │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ Phase │ Serving │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ Endpoint │ wss://langflow-e3dd8820ec.wolf.jina.ai │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ App logs │ dashboards.wolf.jina.ai │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ Swagger UI │ https://langflow-e3dd8820ec.wolf.jina.ai/docs │
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
│ OpenAPI JSON │ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json │
╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
```
## API Usage (with python)
You can use Langflow directly on your browser or the API endpoints on Jina AI Cloud to interact with the server.
```python
import requests
BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict"
FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468"
# You can tweak the flow by adding a tweaks dictionary
# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
TWEAKS = {
"ChatOpenAI-g4jEr": {},
"ConversationChain-UidfJ": {}
}
def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
"""
Run a flow with a given message and optional tweaks.
:param message: The message to send to the flow
:param flow_id: The ID of the flow to run
:param tweaks: Optional tweaks to customize the flow
:return: The JSON response from the flow
"""
api_url = f"{BASE_API_URL}/{flow_id}"
payload = {"message": message}
if tweaks:
payload["tweaks"] = tweaks
response = requests.post(api_url, json=payload)
return response.json()
# Setup any tweaks you want to apply to the flow
print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
```
```json
{
"result": "Great choice! Bangalore in the 1920s was a vibrant city with a rich cultural and political scene. Here are some suggestions for things to see and do:\n\n1. Visit the Bangalore Palace - built in 1887, this stunning palace is a perfect example of Tudor-style architecture. It was home to the Maharaja of Mysore and is now open to the public.\n\n2. Attend a performance at the Ravindra Kalakshetra - this cultural center was built in the 1920s and is still a popular venue for music and dance performances.\n\n3. Explore the neighborhoods of Basavanagudi and Malleswaram - both of these areas have retained much of their old-world charm and are great places to walk around and soak up the atmosphere.\n\n4. Check out the Bangalore Club - founded in 1868, this exclusive social club was a favorite haunt of the British expat community in the 1920s.\n\n5. Attend a meeting of the Indian National Congress - founded in 1885, the INC was a major force in the Indian independence movement and held many meetings and rallies in Bangalore in the 1920s.\n\nHope you enjoy your trip to 1920s Bangalore!"
}
```
:::info
Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the **[langchain-serve](https://github.com/jina-ai/langchain-serve)** repository.
:::

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# Buffer Memory
For certain applications, retaining past interactions is crucial. For that, chains and agents may accept a memory component as one of their input parameters. The `ConversationBufferMemory` component is one of them. It stores messages and extracts them into variables.
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/buffer-memory.png",
}}
/>
#### <a target="\_blank" href="json_files/Buffer_Memory.json" download>Download Flow</a>
:::note LangChain Components 🦜🔗
- [`ConversationBufferMemory`](https://python.langchain.com/docs/modules/memory/how_to/buffer)
- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
:::

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# Conversation Chain
This example shows how to instantiate a simple `ConversationChain` component using a Language Model (LLM). Once the Node Status turns green 🟢, the chat will be ready to take in user messages. Here, we used `ChatOpenAI` to act as the required LLM input, but you can use any LLM for this purpose.
:::info
Make sure to always get the API key from the provider.
:::
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/basic-chat.png",
}}
/>
#### <a target="\_blank" href="json_files/Basic_Chat.json" download>Download Flow</a>
:::note LangChain Components 🦜🔗
- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
:::

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# CSV Loader
The `VectoStoreAgent` component retrieves information from one or more vector stores. This example shows a `VectoStoreAgent` connected to a CSV file through the `Chroma` vector store. Process description:
- The `CSVLoader` loads a CSV file into a list of documents.
- The extracted data is then processed by the `CharacterTextSplitter`, which splits the text into small, meaningful chunks (usually sentences).
- These chunks feed the `Chroma` vector store, which converts them into vectors and stores them for fast indexing.
- Finally, the agent accesses the information of the vector store through the `VectorStoreInfo` tool.
:::info
The vector store is used for efficient semantic search, while `VectorStoreInfo` carries information about it, such as its name and description. Embeddings are a way to represent words, phrases, or any entities in a vector space. Learn more about them [here](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
:::
:::tip
Once you build this flow, ask questions about the data in the chat interface (e.g., number of rows or columns).
:::
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/csv-loader.png",
}}
/>
#### <a target="\_blank" href="json_files/CSV_Loader.json" download>Download Flow</a>
:::note LangChain Components 🦜🔗
- [`CSVLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv)
- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`VectorStoreAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
:::

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
# 📚 How to Upload Examples?
We welcome all examples that can help our community learn and explore Langflow's capabilities.
Langflow Examples is a repository on [GitHub](https://github.com/logspace-ai/langflow_examples) that contains examples of flows that people can use for inspiration and learning.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/community-examples.png",
}}
/>
</div>
To upload examples, please follow these steps:
1. **Create a Flow:** First, create a flow using Langflow. You can use any of the available templates or create a new flow from scratch.
2. **Export the Flow:** Once you have created a flow, export it as a JSON file. Make sure to give your file a descriptive name and include a brief description of what it does.
3. **Submit a Pull Request:** Finally, submit a pull request (PR) to the examples repo. Make sure to include your JSON file in the PR.
If your example uses any third-party libraries or packages, please include them in your PR and make sure that your example follows the [**⛓️ Langflow Code Of Conduct**](https://github.com/logspace-ai/langflow/blob/dev/CODE_OF_CONDUCT.md).

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# MidJourney Prompt Chain
The `MidJourneyPromptChain` can be used to generate imaginative and detailed MidJourney prompts.
For example, type something like:
```bash
Dragon
```
And get a response such as:
```text
Imagine a mysterious forest, the trees are tall and ancient, their branches reaching up to the sky. Through the darkness, a dragon emerges from the shadows, its scales shimmering in the moonlight. Its wingspan is immense, and its eyes glow with a fierce intensity. It is a majestic and powerful creature, one that commands both respect and fear.
```
:::tip
Notice that the `ConversationSummaryMemory` stores a summary of the conversation over time. Try using it to create better prompts as the conversation goes on.
:::
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/midjourney-prompt-chain.png",
}}
/>
#### <a target="\_blank" href="json_files/MidJourney_Prompt_Chain.json" download>Download Flow</a>
:::note LangChain Components 🦜🔗
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`ConversationSummaryMemory`](https://python.langchain.com/docs/modules/memory/how_to/summary)
:::

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# Multiple Vector Stores
The example below shows an agent operating with two vector stores built upon different data sources.
The `TextLoader` loads a TXT file, while the `WebBaseLoader` pulls text from webpages into a document format to accessed downstream. The `Chroma` vector stores are created analogous to what we have demonstrated in our [CSV Loader](/examples/csv-loader.mdx) example. Finally, the `VectorStoreRouterAgent` constructs an agent that routes between the vector stores.
:::info
Get the TXT file used [here](https://github.com/hwchase17/chat-your-data/blob/master/state_of_the_union.txt).
:::
URL used by the `WebBaseLoader`:
```txt
https://pt.wikipedia.org/wiki/Harry_Potter
```
:::tip
When you build the flow, request information about one of the sources. The agent should be able to use the correct source to generate a response.
:::
:::info
Learn more about Multiple Vector Stores [here](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore?highlight=Multiple%20Vector%20Stores#multiple-vectorstores).
:::
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/multiple-vectorstores.png",
}}
/>
#### <a target="\_blank" href="json_files/Multiple_Vector_Stores.json" download>Download Flow</a>
:::note LangChain Components 🦜🔗
- [`WebBaseLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base)
- [`TextLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/unstructured_file)
- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`VectorStoreRouterToolkit`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
- [`VectorStoreRouterAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
:::

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# Python Function
Langflow allows you to create a customized tool using the `PythonFunction` connected to a `Tool` component. In this example, Regex is used in Python to validate a pattern.
```python
import re
def is_brazilian_zipcode(zipcode: str) -> bool:
pattern = r"\d{5}-?\d{3}"
# Check if the zip code matches the pattern
if re.match(pattern, zipcode):
return True
return False
```
:::tip
When a tool is called, it is often desirable to have its output returned directly to the user. You can do this by setting the **return_direct** flag for a tool to be True.
:::
The `AgentInitializer` component is a quick way to construct an agent from the model and tools.
:::info
The `PythonFunction` is a custom component that uses the LangChain 🦜🔗 tool decorator. Learn more about it [here](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools).
:::
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/python-function.png",
}}
/>
#### <a target="\_blank" href="json_files/Python_Function.json" download>Download Flow</a>
:::note LangChain Components 🦜🔗
- [`PythonFunctionTool`](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools)
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
- [`AgentInitializer`](https://python.langchain.com/docs/modules/agents/)
:::

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# Serp API Tool
The [Serp API](https://serpapi.com/) (Search Engine Results Page) allows developers to scrape results from search engines such as Google, Bing and Yahoo, and can be used as in Langflow through the `Search` component.
:::info
To use the Serp API, you first need to sign up [Serp API](https://serpapi.com/) for an API key on the provider's website.
:::
Here, the `ZeroShotPrompt` component specifies a prompt template for the `ZeroShotAgent`. Set a _Prefix_ and _Suffix_ with rules for the agent to obey. In the example, we used default templates.
The `LLMChain` is a simple chain that takes in a prompt template, formats it with the user input, and returns the response from an LLM.
:::tip
In this example, we used [`ChatOpenAI`](https://platform.openai.com/) as the LLM, but feel free to experiment with other Language Models!
:::
The `ZeroShotAgent` takes the `LLMChain` and the `Search` tool as inputs, using the tool to find information when necessary.
:::info
Learn more about the Serp API [here](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi).
:::
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/serp-api-tool.png",
}}
/>
#### <a target="\_blank" href="json_files/SerpAPI_Tool.json" download>Download Flow</a>
:::note LangChain Components 🦜🔗
- [`ZeroShotPrompt`](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/)
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`LLMChain`](https://python.langchain.com/docs/modules/chains/foundational/llm_chain)
- [`Search`](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi)
- [`ZeroShotAgent`](https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent)
:::

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# 🎨 Creating Flows
## Compose
Creating flows with Langflow is easy. Drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://python.langchain.com/docs/modules/) to choose from, including LLMs, prompt serializers, agents, and chains.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/langflow_canvas.png",
}}
/>
## Fork
The easiest way to start with Langflow is by forking a **community example**. Forking an example stores a copy in your project collection, allowing you to edit and save the modified version as a new flow.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_fork.mp4" />
</div>
## Build
Building a flow means validating if the components have prerequisites fulfilled and are properly instantiated. When a chat message is sent, the flow will run for the first time, executing the pipeline.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_build.mp4" />
</div>

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# 🤗 HuggingFace Spaces
A fully featured version of Langflow can be accessed via HuggingFace spaces with no installation required.
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/hugging-face.png",
}}
/>
</div>
Check out Langflow on [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow).

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@ -0,0 +1,15 @@
# 📦 How to install?
## Installation
You can install Langflow from pip:
```bash
pip install langflow
```
Next, run:
```bash
langflow
```

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Chat Interface
Langflow’s chat interface provides a user-friendly experience and functionality to interact with the model and customize the prompt. The sidebar brings options that allow users to view and edit pre-defined prompt variables. This feature facilitates quick experimentation by enabling the modification of variable values right in the chat.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
Notice that editing variables in the chat interface take place temporarily and won’t change their original value in the components once the chat is closed.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface2.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
To view the complete prompt in its original, structured format, click the "Display Prompt" option. This feature lets you see the prompt exactly as it entered the model.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface3.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
In the chat interface, you can redefine which variable should be interpreted as the chat input. This gives you control over these inputs and allows dynamic and creative interactions.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface4.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>

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import ThemedImage from '@theme/ThemedImage';
import useBaseUrl from '@docusaurus/useBaseUrl';
import ZoomableImage from '/src/theme/ZoomableImage.js';
import ReactPlayer from 'react-player';
# Collection
A collection is a snapshot of the flows available in the database. You can download your entire collection for local storage and upload it anytime for future use.
<div style={{ marginBottom: '20px', display: 'flex', justifyContent: 'center' }}>
<ReactPlayer playing controls url='/videos/langflow_collection.mp4'
/>
</div>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Component
Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain [documentation](https://docs.langchain.com/docs/category/components) section.
### Component's Features
<div style={{ marginBottom: "20px" }}>
During the flow creation process, you will notice handles (colored circles)
attached to one or both sides of a component. These handles represent the
availability to connect to other components, while their colors are type hints
(hover over a handle to see connection details).
</div>
<div style={{ marginBottom: "20px" }}>
For example, if you select a <code>ConversationChain</code> component, you
will see orange <span style={{ color: "orange" }}>o</span> and purple{" "}
<span style={{ color: "purple" }}>o</span> input handles. They indicate that
this component accepts an LLM and a Memory component as inputs. The red
asterisk <span style={{ color: "red" }}>*</span> means that at least one input
of that type is required.
</div>
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/single-compenent.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
<div style={{ marginBottom: "20px" }}>
On the top right corner, you will find the component status icon 🔴. Make the
necessary connections, build the flow (⚡ zap icon on the bottom right of the
canvas) and once the validation is completed, the status of each validated
component should light green 🟢. Hover over the component status to reveal the
outputs going through it in case of success, or the detected error in case of
failure.
</div>
---
### Component's Parameters
Langflow components can be edited in the component settings button. Hide parameters to reduce complexity and keep the canvas clean and intuitive for experimentation.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_parameters.mp4" />
</div>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Features
<div style={{ marginBottom: "20px" }}>
When you click for New Project, you will see on the top left corner of the
screen, some options such as <strong>Import</strong>, <strong>Export</strong>,{" "}
<strong>Code</strong> and <strong>Save</strong>, as displayed in the image
below:
</div>
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/features.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
<div style={{ marginBottom: "20px" }}>
Further down, we will explain each of these options.
</div>
---
### Import and Export
Flows can be exported and imported as JSON files.
:::caution
Watch out for API keys being stored in local files.
:::
---
### Code
The Code button shows snippets to use your flow as a Python object or an API.
**Python Code**
Through the Langflow package, you can load a flow from a JSON file and use it as a LangChain object.
```py
from langflow import load_flow_from_json
flow = load_flow_from_json("path/to/flow.json")
# Now you can use it like any chain
flow("Hey, have you heard of Langflow?")
```
**API**
Once you save a flow, the API endpoint is created with your latest changes. Click the "code" button to use that flow as an API. You can post-adjust component parameters using the global variable TWEAKS.
The example below shows a Python script making a POST request to a local API endpoint, which gets a prediction based on the message input.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_api.mp4" />
</div>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Prompt Customization
The prompt template allows users to create prompts and define variables that provide control over instructing the model.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
Variables can be used to define instructions, questions, context, inputs, or examples for the model and can be created with any chosen name in curly brackets, e.g., `{variable_name}`. They act as placeholders for parts of the text that can be easily modified.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization2.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
Once inserted, these variables are immediately recognized as new fields in the prompt component. Here, you can define their values within the component itself or leave a field empty to be adjusted over the chat interface.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization3.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
You can also use documents or output parsers as prompt variables. By plugging them into prompt handles, they’ll disable and feed that input field.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization4.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>
With this, users can interact with documents, webpages, or any other type of content directly from the prompt, which allows for seamless integration of external resources with the language model.
If working with an interactive (chat-like) flow, remember to keep one of the input variables empty to behave as the chat input.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization5.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
</div>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Building chatbots with System Message
## Overview
In this guide, we will modify the "Basic Chat with Prompt and History" example, integrating the ChatPromptTemplate with the SystemMessagePromptTemplate and HumanMessagePromptTemplate components. By following these steps, you'll be able to build a personalized chatbot that can interpret and respond based on user-defined System messages.
## Interactive Guide
<iframe
src="https://app.tango.us/app/embed/f05bb3a7-4ceb-4b61-921f-628563b562f6?iframe=true"
sandbox="allow-scripts allow-top-navigation-by-user-activation allow-popups allow-same-origin"
security="restricted"
title="Step-by-Step Instructions to Customize and Build a Chatbot with custom System Message"
width="100%"
height="800px"
referrerpolicy="strict-origin-when-cross-origin"
frameborder="0"
webkitallowfullscreen="webkitallowfullscreen"
mozallowfullscreen="mozallowfullscreen"
allowfullscreen="allowfullscreen"
></iframe>
## Step-by-Step Instructions
1. Navigate to the "Community Examples" section.
2. Locate the "Basic Chat with Prompt and History" example, and click on "Fork Example".
3. Once in the editor, find the "PromptTemplate" component and remove it.
4. Now, add these three components: ChatPromptTemplate, SystemMessagePromptTemplate, and HumanMessagePromptTemplate.
> **Note:** Remember to set the model to gpt-3.5-turbo-0613 or the most up-to-date version. The latest models have improved capabilities to comprehend System messages.
5. Open the "Prompt" field on the SystemMessagePromptTemplate component.
6. Enter the text: `You are a {role} that {behavior}.`
7. Save your changes by clicking on "Check & Save".
8. Define the 'role' variable by typing "obedient assistant".
9. Next, navigate to the HumanMessagePromptTemplate and open the "Prompt" field.
10. Here, simply enter `{input}`.
11. Save these changes by clicking on "Check & Save".
12. Now, you should see your flow populated with the variables you defined.
13. In the Memory component, set the 'Input Key' to "input".
> **Tip:** When using a Memory component with multiple variables, it's crucial to specify which variable should be used to generate the conversation history.
14. Click on the "Build" button to implement your changes.
15. Open the chat interface to test your modifications.
16. You should now be able to see and use the defined variables in the chat interface.
17. Click on 'role' to examine the variable you established in the canvas.
18. Now, let's define the 'behavior' variable.
19. Enter the text: "writes the word 'Langflow' at the end of every sentence."
20. Test your chatbot by typing "How can you help me?"
21. If everything was set up correctly, your chatbot should respond appropriately, following the defined behavior.
22. Congratulations! You have successfully customized and built your chatbot.
By following these instructions, you have created a dynamic chatbot capable of understanding and responding based on custom system messages, enhancing the user experience and interaction. Enjoy your personalized assistant!

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Integrating documents with prompt variables
## Overview
This guide takes you through the process of augmenting the "Basic Chat with Prompt and History" example. You'll learn how to embed documents as context into the PromptTemplate component utilizing a WebBaseLoader.
## Interactive Guide
<iframe
src="https://app.tango.us/app/embed/76578e84-a700-4b3b-a6ef-8a0350472781?iframe=true"
sandbox="allow-scripts allow-top-navigation-by-user-activation allow-popups allow-same-origin"
security="restricted"
title="Loading a document into a PromptTemplate variable"
width="100%"
height="800px"
referrerpolicy="strict-origin-when-cross-origin"
frameborder="0"
webkitallowfullscreen="webkitallowfullscreen"
mozallowfullscreen="mozallowfullscreen"
allowfullscreen="allowfullscreen"
></iframe>
## Step-by-Step Instructions
1. Start by navigating to the "Community Examples" section.
2. Find the "Basic Chat with Prompt and History" example and click on "Fork Example".
3. In the editor, open the "Template" field.
4. Here, introduce the `{context}` variable, placing it somewhere before the "Current conversation:" text.
5. Once done, save your changes by clicking on "Check & Save".
6. Next, open the search bar and type "web".
7. Drag and drop a WebBaseLoader (or any other loader of your choice) onto the canvas.
8. Connect this loader to the `{context}` variable that we just added.
9. In the "Web Page" field, enter "https://langflow.org/how-upload-examples".
10. Now, click on "ConversationBufferMemory".
11. In the "Input Key" field, enter "text" to define the Chat variable.
> **Tip:** When defining more than one variable and using a Memory component, it's crucial to specify which variable should be used to create the conversation history.
12. Click on the "Build" button to implement your changes.
13. Open the chat interface to test your modifications.
14. Try asking something like, "How do I upload examples?"
15. Click on "Display Prompt" to view your template.
16. Now, you can see what the model used as a basis to generate its response.
By following these instructions, you have successfully loaded a document into a PromptTemplate variable, allowing for more enriched and context-aware chat responses. This customization enhances user interaction by integrating relevant document content into the chat flow.

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# 👋 Welcome to Langflow
Langflow is an easy way to prototype [LangChain](https://github.com/hwchase17/langchain) flows. The drag-and-drop feature allows quick and effortless experimentation, while the built-in chat interface facilitates real-time interaction. It provides options to edit prompt parameters, create chains and agents, track thought processes, and export flows.
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/new_langflow2.gif",
}}
/>
</div>

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const lightCodeTheme = require("prism-react-renderer/themes/github");
// With JSDoc @type annotations, IDEs can provide config autocompletion
/** @type {import('@docusaurus/types').DocusaurusConfig} */
(
module.exports = {
title: "Langflow Documentation",
tagline: "Langflow is a GUI for LangChain, designed with react-flow",
favicon: "img/favicon.ico",
url: "https://logspace-ai.github.io",
baseUrl: "/",
onBrokenLinks: "throw",
onBrokenMarkdownLinks: "warn",
organizationName: "logspace-ai",
projectName: "langflow",
trailingSlash: false,
customFields: {
mendableAnonKey: process.env.MENDABLE_ANON_KEY,
},
i18n: {
defaultLocale: "en",
locales: ["en"],
},
presets: [
[
"@docusaurus/preset-classic",
/** @type {import('@docusaurus/preset-classic').Options} */
({
docs: {
routeBasePath: "/",
sidebarPath: require.resolve("./sidebars.js"),
path: "docs",
// sidebarPath: 'sidebars.js',
},
theme: {
customCss: require.resolve("./src/css/custom.css"),
},
}),
],
],
plugins: [
["docusaurus-node-polyfills", { excludeAliases: ["console"] }],
"docusaurus-plugin-image-zoom",
// ....
async function myPlugin(context, options) {
return {
name: "docusaurus-tailwindcss",
configurePostCss(postcssOptions) {
// Appends TailwindCSS and AutoPrefixer.
postcssOptions.plugins.push(require("tailwindcss"));
postcssOptions.plugins.push(require("autoprefixer"));
return postcssOptions;
},
};
},
],
themeConfig:
/** @type {import('@docusaurus/preset-classic').ThemeConfig} */
({
navbar: {
hideOnScroll: true,
title: "Langflow",
logo: {
alt: "Langflow",
src: "img/chain.png",
},
items: [
// right
{
position: "right",
href: "https://github.com/logspace-ai/langflow",
position: "right",
className: "header-github-link",
target: "_blank",
rel: null,
},
{
position: "right",
href: "https://twitter.com/logspace_ai",
position: "right",
className: "header-twitter-link",
target: "_blank",
rel: null,
},
{
position: "right",
href: "https://discord.gg/EqksyE2EX9",
position: "right",
className: "header-discord-link",
target: "_blank",
rel: null,
},
],
},
tableOfContents: {
minHeadingLevel: 2,
maxHeadingLevel: 5,
},
colorMode: {
defaultMode: "light",
disableSwitch: true,
respectPrefersColorScheme: false,
},
announcementBar: {
content:
'⭐️ If you like ⛓️Langflow, star it on <a target="_blank" rel="noopener noreferrer" href="https://github.com/logspace-ai/langflow">GitHub</a>! ⭐️',
backgroundColor: "#B53D38", //Mustard Yellow #D19900 #D4B20B - Salmon #E9967A
textColor: "#fff",
isCloseable: false,
},
footer: {
links: [],
copyright: `Copyright © ${new Date().getFullYear()} Logspace.`,
},
zoom: {
selector: ".markdown :not(a) > img:not(.no-zoom)",
background: {
light: "rgba(240, 240, 240, 0.9)",
},
config: {},
},
prism: {
theme: lightCodeTheme,
},
}),
}
);

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declare module '*.module.scss' {
const classes: { readonly [key: string]: string };
export default classes;
}
declare module '@theme/*';
declare module '@components/*';
declare module '@docusaurus/*';

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{
"name": "docusaurus",
"version": "0.0.0",
"private": true,
"scripts": {
"docusaurus": "docusaurus",
"start": "docusaurus start",
"build": "docusaurus build",
"swizzle": "docusaurus swizzle",
"deploy": "docusaurus deploy",
"clear": "docusaurus clear",
"serve": "docusaurus serve",
"write-translations": "docusaurus write-translations",
"write-heading-ids": "docusaurus write-heading-ids"
},
"dependencies": {
"@babel/preset-react": "^7.22.3",
"@docusaurus/core": "2.4.1",
"@docusaurus/plugin-ideal-image": "^2.4.1",
"@docusaurus/preset-classic": "2.4.1",
"@docusaurus/theme-classic": "^2.4.1",
"@docusaurus/theme-search-algolia": "^2.4.1",
"@mdx-js/react": "^1.6.22",
"@mendable/search": "^0.0.114",
"@pbe/react-yandex-maps": "^1.2.4",
"@prismicio/client": "^7.0.1",
"@uiball/loaders": "^1.2.6",
"autoprefixer": "^10.4.14",
"clsx": "^1.2.1",
"docusaurus-plugin-image-zoom": "^0.1.4",
"jquery": "^3.7.0",
"medium-zoom": "^1.0.8",
"node-fetch": "^3.3.1",
"path-browserify": "^1.0.1",
"postcss": "^8.4.24",
"prism-react-renderer": "^1.3.5",
"react": "^17.0.2",
"react-dom": "^17.0.2",
"react-images": "^0.6.7",
"react-medium-image-zoom": "^5.1.6",
"react-player": "^2.12.0",
"react-transition-group": "^4.4.5",
"remark-parse": "^10.0.2",
"swizzle": "^1.1.0",
"tailwindcss": "^3.3.2"
},
"devDependencies": {
"@docusaurus/module-type-aliases": "2.4.1",
"css-loader": "^6.8.1",
"docusaurus-node-polyfills": "^1.0.0",
"node-sass": "^9.0.0",
"sass": "^1.62.1",
"ts-loader": "^9.4.3"
},
"browserslist": {
"production": [
">0.5%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
},
"engines": {
"node": ">=16.14"
}
}

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@ -0,0 +1,18 @@
module.exports = function(context, options) {
return {
name: 'loaders',
configureWebpack(config, isServer) {
return {
module: {
rules: [
{
test: /\.(gif|png|jpe?g|svg)$/i,
exclude: /\.(mdx?)$/i,
use: ['file-loader', { loader: 'image-webpack-loader' }],
},
],
},
};
},
};
};

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module.exports = {
docs: [
{
type: "category",
label: "Getting Started",
collapsed: false,
items: [
"index",
"getting-started/installation",
"getting-started/hugging-face-spaces",
"getting-started/creating-flows",
],
},
{
type: "category",
label: "Guidelines",
collapsed: false,
items: [
"guidelines/components",
"guidelines/features",
"guidelines/collection",
"guidelines/prompt-customization",
"guidelines/chat-interface",
],
},
{
type: "category",
label: "Component Reference",
collapsed: false,
items: [
"components/agents",
"components/chains",
"components/embeddings",
"components/llms",
"components/loaders",
"components/memories",
"components/prompts",
"components/text-splitters",
"components/toolkits",
"components/tools",
"components/vector-stores",
"components/wrappers",
],
},
{
type: "category",
label: "Step-by-Step Guides",
collapsed: false,
items: ["guides/loading_document", "guides/chatprompttemplate_guide"],
},
// {
// type: 'category',
// label: 'Components',
// collapsed: false,
// items: [
// 'components/agents', 'components/chains', 'components/loaders', 'components/embeddings', 'components/llms',
// 'components/memories', 'components/prompts','components/text-splitters', 'components/toolkits', 'components/tools',
// 'components/utilities', 'components/vector-stores', 'components/wrappers',
// ],
// },
{
type: "category",
label: "Examples",
collapsed: false,
items: [
"examples/conversation-chain",
"examples/buffer-memory",
"examples/midjourney-prompt-chain",
"examples/csv-loader",
"examples/serp-api-tool",
"examples/multiple-vectorstores",
"examples/python-function",
"examples/how-upload-examples",
],
},
{
type: "category",
label: "Deployment",
collapsed: false,
items: ["deployment/gcp-deployment", "deployment/jina-deployment"],
},
{
type: "category",
label: "Contributing",
collapsed: false,
items: [
"contributing/how-contribute",
"contributing/github-issues",
"contributing/community",
],
},
],
};

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for line in $(cat spell_check_results.txt); do
echo "Adding $line to cspell.config.yaml"
echo " - $line" >> cspell.config.yaml
done

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# Using cspell, we'll loop over each subdirectory inside ./docs and check every mdx file for spelling errors.
# If there is an error, we'll write the word to an output file
# prep
if [ -f spell_check_results.txt ]; then
rm spell_check_results.txt
fi
cd docs
# first check, over the mdx files in the root directory
find . -maxdepth 1 -type f -name "*.mdx" -exec cspell --words-only {} \; >> ../output.txt
# loop over each subdirectory and any directories inside
for dir in */; do
find $dir -type d -exec cspell --words-only {}/*.mdx \; >> ../output.txt
done
# loop over each line in the output file and prune duplications
cd ../
awk '!a[$0]++' output.txt > spell_check_results.txt
rm output.txt
# check the number of lines in spell_check_results.txt
lines=$(wc -l < spell_check_results.txt)
echo "There are $lines spelling errors or unknown words."

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/**
* Any CSS included here will be global. The classic template
* bundles Infima by default. Infima is a CSS framework designed to
* work well for content-centric websites.
*/
:root {
--ifm-background-color: var(--token-primary-bg-c);
--ifm-navbar-link-hover-color: initial;
--ifm-navbar-padding-vertical: 0;
--ifm-navbar-item-padding-vertical: 0;
--ifm-font-family-base: -apple-system, BlinkMacSystemFont, Inter, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI emoji';
--ifm-font-family-monospace: 'SFMono-Regular', 'Roboto Mono', Consolas, 'Liberation Mono', Menlo, Courier, monospace;
}
.theme-doc-sidebar-item-category.menu__list-item:not(:first-child) {
margin-top: 1.5rem!important;
}
.docusaurus-highlight-code-line {
background-color: rgba(0, 0, 0, 0.1);
display: block;
margin: 0 calc(-1 * var(--ifm-pre-padding));
padding: 0 var(--ifm-pre-padding);
}
.diagonal-box {
transform: skewY(-6deg);
}
.diagonal-content {
transform: skewY(6deg);
}
[class^='announcementBar'] {
z-index: 10;
}
.showcase {
background-color: #fff;
}
.showcase-border {
border-color: rgba(243, 244, 246, 1);
}
.text-description {
color: rgba(107, 114, 128, 1);
}
p {
text-align: justify;
}
/* apply */
#hero-apply {
z-index: -1;
background-image: linear-gradient(
var(--ifm-footer-background-color),
var(--ifm-navbar-background-color)
);
}
.apply-form {
background-image: linear-gradient(#fff, #f5f5fa);
max-width: 600px;
}
.apply-text {
color: #36395a;
}
/* index */
#hero {
background-image: linear-gradient(
var(--ifm-footer-background-color),
var(--ifm-navbar-background-color)
);
}
/**
* Hero component title overrides to match other heading styles
*/
.hero-title {
color: rgb(28, 30, 33);
font-family: var(--ifm-heading-font-family);
}
h1 {
font-size: 26px;
}
h2 {
font-size: 22px;
}
h3 {
font-size: 18px;
}
body {
font-size: 16px;
}
.docsearch-logo {
color: #21243d;
}
.apply-button:hover {
color: #fff;
}
/* GitHub */
.header-github-link:hover {
opacity: 0.6;
}
.header-github-link:before {
content: '';
width: 24px;
height: 24px;
display: flex;
background: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E")
no-repeat;
}
/* Twitter */
.header-twitter-link:hover {
opacity: 0.6;
}
.header-twitter-link::before {
content: '';
width: 24px;
height: 24px;
display: flex;
background: url("/logos/twitter.svg");
background-size: contain;
}
/* Discord */
.header-discord-link:hover {
opacity: 0.6;
}
.header-discord-link::before {
content: '';
width: 24px;
height: 24px;
display: flex;
background: url("/logos/discord.svg");
background-size: contain;
}
/* Images */
.image-rendering-crisp {
image-rendering: crisp-edges;
/* alias for google chrome */
image-rendering: -webkit-optimize-contrast;
}
.image-rendering-pixel {
image-rendering: pixelated;
}
.img-center {
display: flex;
justify-content: center;
width: 100%,
}
.resized-image {
width: 400px;
}
/* Reduce width on mobile for Mendable Search */
@media (max-width: 767px) {
.mendable-search {
width: 200px;
}
}
@media (max-width: 500px) {
.mendable-search {
width: 150px;
}
}
@media (max-width: 380px) {
.mendable-search {
width: 140px;
}
}

23
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/**
* CSS files with the .module.css suffix will be treated as CSS modules
* and scoped locally.
*/
.heroBanner {
padding: 4rem 0;
text-align: center;
position: relative;
overflow: hidden;
}
@media screen and (max-width: 996px) {
.heroBanner {
padding: 2rem;
}
}
.buttons {
display: flex;
align-items: center;
justify-content: center;
}

7
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---
title: Markdown page example
---
# Markdown page example
You don't need React to write simple standalone pages.

51
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import React from "react";
import Footer from "@theme-original/Footer";
import { MendableFloatingButton } from "@mendable/search";
import useDocusaurusContext from "@docusaurus/useDocusaurusContext";
export default function FooterWrapper(props) {
const iconSpan1 = React.createElement(
"img",
{
src: "/img/chain.png",
style: { width: "40px" },
},
null
);
const icon = React.createElement(
"div",
{
style: {
color: "#000000",
fontSize: "22px",
width: "48px",
height: "48px",
margin: "0px",
padding: "0px",
display: "flex",
alignItems: "center",
justifyContent: "center",
textAlign: "center",
},
},
[iconSpan1]
);
const {
siteConfig: { customFields },
} = useDocusaurusContext();
const mendableFloatingButton = React.createElement(MendableFloatingButton, {
floatingButtonStyle: { color: "#000000", backgroundColor: "#f6f6f6" },
anon_key: customFields.mendableAnonKey, // Mendable Search Public ANON key, ok to be public
showSimpleSearch: true,
icon: icon,
});
return (
<>
<Footer />
{mendableFloatingButton}
</>
);
}

5
docs/src/theme/SearchBar.js Normal file
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// By default, the classic theme does not provide any SearchBar implementation
// If you swizzled this, it is your responsibility to provide an implementation
// Tip: swizzle the SearchBar from the Algolia theme for inspiration:
// npm run swizzle @docusaurus/theme-search-algolia SearchBar
export {default} from '@docusaurus/Noop';

47
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import React, { useState, useEffect } from 'react';
import ThemedImage from '@theme/ThemedImage';
import useBaseUrl from '@docusaurus/useBaseUrl';
const ZoomableImage = ({ alt, sources }) => {
const [isFullscreen, setIsFullscreen] = useState(false);
const toggleFullscreen = () => {
setIsFullscreen(!isFullscreen);
};
const handleKeyPress = (event) => {
if (event.key === 'Escape') {
setIsFullscreen(false);
}
};
useEffect(() => {
if (isFullscreen) {
document.addEventListener('keydown', handleKeyPress);
} else {
document.removeEventListener('keydown', handleKeyPress);
}
return () => {
document.removeEventListener('keydown', handleKeyPress);
};
}, [isFullscreen]);
return (
<div
className={`zoomable-image ${isFullscreen ? 'fullscreen' : ''}`}
onClick={toggleFullscreen}
>
<ThemedImage
className="zoomable-image-inner"
alt={alt}
sources={{
light: useBaseUrl(sources.light),
dark: useBaseUrl(sources.dark),
}}
/>
</div>
);
};
export default ZoomableImage;

0
docs/static/.nojekyll vendored Normal file
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1
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langflow.org

101
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Index,Organization Id,Name,Website,Country,Description,Founded,Industry,Number of employees
1,FAB0d41d5b5d22c,Ferrell LLC,https://price.net/,Papua New Guinea,Horizontal empowering knowledgebase,1990,Plastics,3498
2,6A7EdDEA9FaDC52,"Mckinney, Riley and Day",http://www.hall-buchanan.info/,Finland,User-centric system-worthy leverage,2015,Glass / Ceramics / Concrete,4952
3,0bFED1ADAE4bcC1,Hester Ltd,http://sullivan-reed.com/,China,Switchable scalable moratorium,1971,Public Safety,5287
4,2bFC1Be8a4ce42f,Holder-Sellers,https://becker.com/,Turkmenistan,De-engineered systemic artificial intelligence,2004,Automotive,921
5,9eE8A6a4Eb96C24,Mayer Group,http://www.brewer.com/,Mauritius,Synchronized needs-based challenge,1991,Transportation,7870
6,cC757116fe1C085,Henry-Thompson,http://morse.net/,Bahamas,Face-to-face well-modulated customer loyalty,1992,Primary / Secondary Education,4914
7,219233e8aFF1BC3,Hansen-Everett,https://www.kidd.org/,Pakistan,Seamless disintermediate collaboration,2018,Publishing Industry,7832
8,ccc93DCF81a31CD,Mcintosh-Mora,https://www.brooks.com/,Heard Island and McDonald Islands,Centralized attitude-oriented capability,1970,Import / Export,4389
9,0B4F93aA06ED03e,Carr Inc,http://ross.com/,Kuwait,Distributed impactful customer loyalty,1996,Plastics,8167
10,738b5aDe6B1C6A5,Gaines Inc,http://sandoval-hooper.com/,Uzbekistan,Multi-lateral scalable protocol,1997,Outsourcing / Offshoring,9698
11,AE61b8Ffebbc476,Kidd Group,http://www.lyons.com/,Bouvet Island (Bouvetoya),Proactive foreground paradigm,2001,Primary / Secondary Education,7473
12,eb3B7D06cCdD609,Crane-Clarke,https://www.sandoval.com/,Denmark,Front-line clear-thinking encryption,2014,Food / Beverages,9011
13,8D0c29189C9798B,"Keller, Campos and Black",https://www.garner.info/,Liberia,Ameliorated directional emulation,2020,Museums / Institutions,2862
14,D2c91cc03CA394c,Glover-Pope,http://www.silva.biz/,United Arab Emirates,Persevering contextually-based approach,2013,Medical Practice,9079
15,C8AC1eaf9C036F4,Pacheco-Spears,https://aguilar.com/,Sweden,Secured logistical synergy,1984,Maritime,769
16,b5D10A14f7a8AfE,Hodge-Ayers,http://www.archer-elliott.com/,Honduras,Future-proofed radical implementation,1990,Facilities Services,8508
17,68139b5C4De03B4,"Bowers, Guerra and Krause",http://www.carrillo-nicholson.com/,Uganda,De-engineered transitional strategy,1972,Primary / Secondary Education,6986
18,5c2EffEfdba2BdF,Mckenzie-Melton,http://montoya-thompson.com/,Hong Kong,Reverse-engineered heuristic alliance,1998,Investment Management / Hedge Fund / Private Equity,4589
19,ba179F19F7925f5,Branch-Mann,http://www.lozano.com/,Botswana,Adaptive intangible frame,1999,Architecture / Planning,7961
20,c1Ce9B350BAc66b,Weiss and Sons,https://barrett.com/,Korea,Sharable optimal functionalities,2011,Plastics,5984
21,8de40AC4e6EaCa4,"Velez, Payne and Coffey",http://burton.com/,Luxembourg,Mandatory coherent synergy,1986,Wholesale,5010
22,Aad86a4F0385F2d,Harrell LLC,http://www.frey-rosario.com/,Guadeloupe,Reverse-engineered mission-critical moratorium,2018,Construction,2185
23,22aC3FFd64fD703,"Eaton, Reynolds and Vargas",http://www.freeman.biz/,Monaco,Self-enabling multi-tasking process improvement,2014,Luxury Goods / Jewelry,8987
24,5Ec4C272bCf085c,Robbins-Cummings,http://donaldson-wilkins.com/,Belgium,Organic non-volatile hierarchy,1991,Pharmaceuticals,5038
25,5fDBeA8BB91a000,Jenkins Inc,http://www.kirk.biz/,South Africa,Front-line systematic help-desk,2002,Insurance,1215
26,dFfD6a6F9AC2d9C,"Greene, Benjamin and Novak",http://www.kent.net/,Romania,Centralized leadingedge moratorium,2012,Museums / Institutions,4941
27,4B217cC5a0674C5,"Dickson, Richmond and Clay",http://everett.com/,Czech Republic,Team-oriented tangible complexity,1980,Real Estate / Mortgage,3122
28,88b1f1cDcf59a37,Prince-David,http://thompson.com/,Christmas Island,Virtual holistic methodology,1970,Banking / Mortgage,1046
29,f9F7bBCAEeC360F,Ayala LLC,http://www.zhang.com/,Philippines,Open-source zero administration hierarchy,2021,Legal Services,7664
30,7Cb3AeFcE4Ba31e,Rivas Group,https://hebert.org/,Australia,Open-architected well-modulated capacity,1998,Logistics / Procurement,4155
31,ccBcC32adcbc530,"Sloan, Mays and Whitehead",http://lawson.com/,Chad,Face-to-face high-level conglomeration,1997,Civil Engineering,365
32,f5afd686b3d05F5,"Durham, Allen and Barnes",http://chan-stafford.org/,Zimbabwe,Synergistic web-enabled framework,1993,Mechanical or Industrial Engineering,6135
33,38C6cfC5074Fa5e,Fritz-Franklin,http://www.lambert.com/,Nepal,Automated 4thgeneration website,1972,Hospitality,4516
34,5Cd7efccCcba38f,Burch-Ewing,http://cline.net/,Taiwan,User-centric 4thgeneration system engine,1981,Venture Capital / VC,7443
35,9E6Acb51e3F9d6F,"Glass, Barrera and Turner",https://dunlap.com/,Kyrgyz Republic,Multi-channeled 3rdgeneration open system,2020,Utilities,2610
36,4D4d7E18321eaeC,Pineda-Cox,http://aguilar.org/,Bolivia,Fundamental asynchronous capability,2010,Human Resources / HR,1312
37,485f5d06B938F2b,"Baker, Mccann and Macdonald",http://www.anderson-barker.com/,Kenya,Cross-group user-facing focus group,2013,Legislative Office,1638
38,19E3a5Bf6dBDc4F,Cuevas-Moss,https://dodson-castaneda.net/,Guatemala,Extended human-resource intranet,1994,Music,9995
39,6883A965c7b68F7,Hahn PLC,http://newman.com/,Belarus,Organic logistical leverage,2012,Electrical / Electronic Manufacturing,3715
40,AC5B7AA74Aa4A2E,"Valentine, Ferguson and Kramer",http://stuart.net/,Jersey,Centralized secondary time-frame,1997,Non - Profit / Volunteering,3585
41,decab0D5027CA6a,Arroyo Inc,https://www.turner.com/,Grenada,Managed demand-driven website,2006,Writing / Editing,9067
42,dF084FbBb613eea,Walls LLC,http://www.reese-vasquez.biz/,Cape Verde,Self-enabling fresh-thinking installation,1989,Investment Management / Hedge Fund / Private Equity,1678
43,A2D89Ab9bCcAd4e,"Mitchell, Warren and Schneider",https://fox.biz/,Trinidad and Tobago,Enhanced intangible time-frame,2021,Capital Markets / Hedge Fund / Private Equity,3816
44,77aDc905434a49f,Prince PLC,https://www.watts.com/,Sweden,Profit-focused coherent installation,2016,Individual / Family Services,7645
45,235fdEFE2cfDa5F,Brock-Blackwell,http://www.small.com/,Benin,Secured foreground emulation,1986,Online Publishing,7034
46,1eD64cFe986BBbE,Walton-Barnett,https://ashley-schaefer.com/,Western Sahara,Right-sized clear-thinking flexibility,2001,Luxury Goods / Jewelry,1746
47,CbBbFcdd0eaE2cF,Bartlett-Arroyo,https://cruz.com/,Northern Mariana Islands,Realigned didactic function,1976,Civic / Social Organization,3987
48,49aECbDaE6aBD53,"Wallace, Madden and Morris",http://www.blevins-fernandez.biz/,Germany,Persistent real-time customer loyalty,2016,Pharmaceuticals,9443
49,7b3fe6e7E72bFa4,Berg-Sparks,https://cisneros-love.com/,Canada,Stand-alone static implementation,1974,Arts / Crafts,2073
50,c6DedA82A8aef7E,Gonzales Ltd,http://bird.com/,Tonga,Managed human-resource policy,1988,Consumer Goods,9069
51,7D9FBF85cdC3871,Lawson and Sons,https://www.wong.com/,French Southern Territories,Compatible analyzing intranet,2021,Arts / Crafts,3527
52,7dd18Fb7cB07b65,"Mcguire, Mcconnell and Olsen",https://melton-briggs.com/,Korea,Profound client-server frame,1988,Printing,8445
53,EF5B55FadccB8Fe,Charles-Phillips,https://bowman.com/,Cote d'Ivoire,Monitored client-server implementation,2012,Mental Health Care,3450
54,f8D4B99e11fAF5D,Odom Ltd,https://www.humphrey-hess.com/,Cote d'Ivoire,Advanced static process improvement,2012,Management Consulting,1825
55,e24D21BFd3bF1E5,Richard PLC,https://holden-coleman.net/,Mayotte,Object-based optimizing model,1971,Broadcast Media,4942
56,B9BdfEB6D3Ca44E,Sampson Ltd,https://blevins.com/,Cayman Islands,Intuitive local adapter,2005,Farming,1418
57,2a74D6f3D3B268e,"Cherry, Le and Callahan",https://waller-delacruz.biz/,Nigeria,Universal human-resource collaboration,2017,Entertainment / Movie Production,7202
58,Bf3F3f62c8aBC33,Cherry PLC,https://www.avila.info/,Marshall Islands,Persistent tertiary website,1980,Plastics,8245
59,aeBe26B80a7a23c,Melton-Nichols,https://kennedy.com/,Palau,User-friendly clear-thinking productivity,2021,Legislative Office,8741
60,aAeb29ad43886C6,Potter-Walsh,http://thomas-french.org/,Turkey,Optional non-volatile open system,2008,Human Resources / HR,6923
61,bD1bc6bB6d1FeD3,Freeman-Chen,https://mathis.com/,Timor-Leste,Phased next generation adapter,1973,International Trade / Development,346
62,EB9f456e8b7022a,Soto Group,https://norris.info/,Vietnam,Enterprise-wide executive installation,1988,Business Supplies / Equipment,9097
63,Dfef38C51D8DAe3,"Poole, Cruz and Whitney",https://reed.info/,Reunion,Balanced analyzing groupware,1978,Marketing / Advertising / Sales,2992
64,055ffEfB2Dd95B0,Riley Ltd,http://wiley.com/,Brazil,Optional exuding superstructure,1986,Textiles,9315
65,cBfe4dbAE1699da,"Erickson, Andrews and Bailey",https://www.hobbs-grant.com/,Eritrea,Vision-oriented secondary project,2014,Consumer Electronics,7829
66,fdFbecbadcdCdf1,"Wilkinson, Charles and Arroyo",http://hunter-mcfarland.com/,United States Virgin Islands,Assimilated 24/7 archive,1996,Building Materials,602
67,5DCb8A5a5ca03c0,Floyd Ltd,http://www.whitney.com/,Falkland Islands (Malvinas),Function-based fault-tolerant concept,2017,Public Relations / PR,2911
68,ce57DCbcFD6d618,Newman-Galloway,https://www.scott.com/,Luxembourg,Enhanced foreground collaboration,1987,Information Technology / IT,3934
69,5aaD187dc929371,Frazier-Butler,https://www.daugherty-farley.info/,Northern Mariana Islands,Persistent interactive circuit,1972,Outsourcing / Offshoring,5130
70,902D7Ac8b6d476b,Newton Inc,https://www.richmond-manning.info/,Netherlands Antilles,Fundamental stable info-mediaries,1976,Military Industry,563
71,32BB9Ff4d939788,Duffy-Levy,https://www.potter.com/,Guernsey,Diverse exuding installation,1982,Wireless,6146
72,adcB0afbE58bAe3,Wagner LLC,https://decker-esparza.com/,Uruguay,Reactive attitude-oriented toolset,1987,International Affairs,6874
73,dfcA1c84AdB61Ac,Mccall-Holmes,http://www.dean.com/,Benin,Object-based value-added database,2009,Legal Services,696
74,208044AC2fe52F3,Massey LLC,https://frazier.biz/,Suriname,Configurable zero administration Graphical User Interface,1986,Accounting,5004
75,f3C365f0c1A0623,Hicks LLC,http://alvarez.biz/,Pakistan,Quality-focused client-server Graphical User Interface,1970,Computer Software / Engineering,8480
76,ec5Bdd3CBAfaB93,"Cole, Russell and Avery",http://www.blankenship.com/,Mongolia,De-engineered fault-tolerant challenge,2000,Law Enforcement,7012
77,DDB19Be7eeB56B4,Cummings-Rojas,https://simon-pearson.com/,Svalbard & Jan Mayen Islands,User-centric modular customer loyalty,2012,Financial Services,7529
78,dd6CA3d0bc3cAfc,"Beasley, Greene and Mahoney",http://www.petersen-lawrence.com/,Togo,Extended content-based methodology,1976,Religious Institutions,869
79,A0B9d56e61070e3,"Beasley, Sims and Allison",http://burke.info/,Latvia,Secured zero tolerance hub,1972,Facilities Services,6182
80,cBa7EFe5D05Adaf,Crawford-Rivera,https://black-ramirez.org/,Cuba,Persevering exuding budgetary management,1999,Online Publishing,7805
81,Ea3f6D52Ec73563,Montes-Hensley,https://krueger.org/,Liechtenstein,Multi-tiered secondary productivity,2009,Printing,8433
82,bC0CEd48A8000E0,Velazquez-Odom,https://stokes.com/,Djibouti,Streamlined 6thgeneration function,2002,Alternative Dispute Resolution,4044
83,c89b9b59BC4baa1,Eaton-Morales,https://www.reeves-graham.com/,Micronesia,Customer-focused explicit frame,1990,Capital Markets / Hedge Fund / Private Equity,7013
84,FEC51bce8421a7b,"Roberson, Pennington and Palmer",http://www.keith-fisher.com/,Cameroon,Adaptive bi-directional hierarchy,1993,Telecommunications,5571
85,e0E8e27eAc9CAd5,"George, Russo and Guerra",https://drake.com/,Sweden,Centralized non-volatile capability,1989,Military Industry,2880
86,B97a6CF9bf5983C,Davila Inc,https://mcconnell.info/,Cocos (Keeling) Islands,Profit-focused dedicated frame,2017,Consumer Electronics,2215
87,a0a6f9b3DbcBEb5,Mays-Preston,http://www.browning-key.com/,Mali,User-centric heuristic focus group,2006,Military Industry,5786
88,8cC1bDa330a5871,Pineda-Morton,https://www.carr.com/,United States Virgin Islands,Grass-roots methodical info-mediaries,1991,Printing,6168
89,ED889CB2FE9cbd3,Huang and Sons,https://www.bolton.com/,Eritrea,Re-contextualized dynamic hierarchy,1981,Semiconductors,7484
90,F4Dc1417BC6cb8f,Gilbert-Simon,https://www.bradford.biz/,Burundi,Grass-roots radical parallelism,1973,Newspapers / Journalism,1927
91,7ABc3c7ecA03B34,Sampson-Griffith,http://hendricks.org/,Benin,Multi-layered composite paradigm,1972,Textiles,3881
92,4e0719FBE38e0aB,Miles-Dominguez,http://www.turner.com/,Gibraltar,Organized empowering forecast,1996,Civic / Social Organization,897
93,dEbDAAeDfaed00A,Rowe and Sons,https://www.simpson.org/,El Salvador,Balanced multimedia knowledgebase,1978,Facilities Services,8172
94,61BDeCfeFD0cEF5,"Valenzuela, Holmes and Rowland",https://www.dorsey.net/,Taiwan,Persistent tertiary focus group,1999,Transportation,1483
95,4e91eD25f486110,"Best, Wade and Shepard",https://zimmerman.com/,Zimbabwe,Innovative background definition,1991,Gambling / Casinos,4873
96,0a0bfFbBbB8eC7c,Holmes Group,https://mcdowell.org/,Ethiopia,Right-sized zero tolerance focus group,1975,Photography,2988
97,BA6Cd9Dae2Efd62,Good Ltd,http://duffy.com/,Anguilla,Reverse-engineered composite moratorium,1971,Consumer Services,4292
98,E7df80C60Abd7f9,Clements-Espinoza,http://www.flowers.net/,Falkland Islands (Malvinas),Progressive modular hub,1991,Broadcast Media,236
99,AFc285dbE2fEd24,Mendez Inc,https://www.burke.net/,Kyrgyz Republic,User-friendly exuding migration,1993,Education Management,339
100,e9eB5A60Cef8354,Watkins-Kaiser,http://www.herring.com/,Togo,Synergistic background access,2009,Financial Services,2785
1 Index Organization Id Name Website Country Description Founded Industry Number of employees
2 1 FAB0d41d5b5d22c Ferrell LLC https://price.net/ Papua New Guinea Horizontal empowering knowledgebase 1990 Plastics 3498
3 2 6A7EdDEA9FaDC52 Mckinney, Riley and Day http://www.hall-buchanan.info/ Finland User-centric system-worthy leverage 2015 Glass / Ceramics / Concrete 4952
4 3 0bFED1ADAE4bcC1 Hester Ltd http://sullivan-reed.com/ China Switchable scalable moratorium 1971 Public Safety 5287
5 4 2bFC1Be8a4ce42f Holder-Sellers https://becker.com/ Turkmenistan De-engineered systemic artificial intelligence 2004 Automotive 921
6 5 9eE8A6a4Eb96C24 Mayer Group http://www.brewer.com/ Mauritius Synchronized needs-based challenge 1991 Transportation 7870
7 6 cC757116fe1C085 Henry-Thompson http://morse.net/ Bahamas Face-to-face well-modulated customer loyalty 1992 Primary / Secondary Education 4914
8 7 219233e8aFF1BC3 Hansen-Everett https://www.kidd.org/ Pakistan Seamless disintermediate collaboration 2018 Publishing Industry 7832
9 8 ccc93DCF81a31CD Mcintosh-Mora https://www.brooks.com/ Heard Island and McDonald Islands Centralized attitude-oriented capability 1970 Import / Export 4389
10 9 0B4F93aA06ED03e Carr Inc http://ross.com/ Kuwait Distributed impactful customer loyalty 1996 Plastics 8167
11 10 738b5aDe6B1C6A5 Gaines Inc http://sandoval-hooper.com/ Uzbekistan Multi-lateral scalable protocol 1997 Outsourcing / Offshoring 9698
12 11 AE61b8Ffebbc476 Kidd Group http://www.lyons.com/ Bouvet Island (Bouvetoya) Proactive foreground paradigm 2001 Primary / Secondary Education 7473
13 12 eb3B7D06cCdD609 Crane-Clarke https://www.sandoval.com/ Denmark Front-line clear-thinking encryption 2014 Food / Beverages 9011
14 13 8D0c29189C9798B Keller, Campos and Black https://www.garner.info/ Liberia Ameliorated directional emulation 2020 Museums / Institutions 2862
15 14 D2c91cc03CA394c Glover-Pope http://www.silva.biz/ United Arab Emirates Persevering contextually-based approach 2013 Medical Practice 9079
16 15 C8AC1eaf9C036F4 Pacheco-Spears https://aguilar.com/ Sweden Secured logistical synergy 1984 Maritime 769
17 16 b5D10A14f7a8AfE Hodge-Ayers http://www.archer-elliott.com/ Honduras Future-proofed radical implementation 1990 Facilities Services 8508
18 17 68139b5C4De03B4 Bowers, Guerra and Krause http://www.carrillo-nicholson.com/ Uganda De-engineered transitional strategy 1972 Primary / Secondary Education 6986
19 18 5c2EffEfdba2BdF Mckenzie-Melton http://montoya-thompson.com/ Hong Kong Reverse-engineered heuristic alliance 1998 Investment Management / Hedge Fund / Private Equity 4589
20 19 ba179F19F7925f5 Branch-Mann http://www.lozano.com/ Botswana Adaptive intangible frame 1999 Architecture / Planning 7961
21 20 c1Ce9B350BAc66b Weiss and Sons https://barrett.com/ Korea Sharable optimal functionalities 2011 Plastics 5984
22 21 8de40AC4e6EaCa4 Velez, Payne and Coffey http://burton.com/ Luxembourg Mandatory coherent synergy 1986 Wholesale 5010
23 22 Aad86a4F0385F2d Harrell LLC http://www.frey-rosario.com/ Guadeloupe Reverse-engineered mission-critical moratorium 2018 Construction 2185
24 23 22aC3FFd64fD703 Eaton, Reynolds and Vargas http://www.freeman.biz/ Monaco Self-enabling multi-tasking process improvement 2014 Luxury Goods / Jewelry 8987
25 24 5Ec4C272bCf085c Robbins-Cummings http://donaldson-wilkins.com/ Belgium Organic non-volatile hierarchy 1991 Pharmaceuticals 5038
26 25 5fDBeA8BB91a000 Jenkins Inc http://www.kirk.biz/ South Africa Front-line systematic help-desk 2002 Insurance 1215
27 26 dFfD6a6F9AC2d9C Greene, Benjamin and Novak http://www.kent.net/ Romania Centralized leadingedge moratorium 2012 Museums / Institutions 4941
28 27 4B217cC5a0674C5 Dickson, Richmond and Clay http://everett.com/ Czech Republic Team-oriented tangible complexity 1980 Real Estate / Mortgage 3122
29 28 88b1f1cDcf59a37 Prince-David http://thompson.com/ Christmas Island Virtual holistic methodology 1970 Banking / Mortgage 1046
30 29 f9F7bBCAEeC360F Ayala LLC http://www.zhang.com/ Philippines Open-source zero administration hierarchy 2021 Legal Services 7664
31 30 7Cb3AeFcE4Ba31e Rivas Group https://hebert.org/ Australia Open-architected well-modulated capacity 1998 Logistics / Procurement 4155
32 31 ccBcC32adcbc530 Sloan, Mays and Whitehead http://lawson.com/ Chad Face-to-face high-level conglomeration 1997 Civil Engineering 365
33 32 f5afd686b3d05F5 Durham, Allen and Barnes http://chan-stafford.org/ Zimbabwe Synergistic web-enabled framework 1993 Mechanical or Industrial Engineering 6135
34 33 38C6cfC5074Fa5e Fritz-Franklin http://www.lambert.com/ Nepal Automated 4thgeneration website 1972 Hospitality 4516
35 34 5Cd7efccCcba38f Burch-Ewing http://cline.net/ Taiwan User-centric 4thgeneration system engine 1981 Venture Capital / VC 7443
36 35 9E6Acb51e3F9d6F Glass, Barrera and Turner https://dunlap.com/ Kyrgyz Republic Multi-channeled 3rdgeneration open system 2020 Utilities 2610
37 36 4D4d7E18321eaeC Pineda-Cox http://aguilar.org/ Bolivia Fundamental asynchronous capability 2010 Human Resources / HR 1312
38 37 485f5d06B938F2b Baker, Mccann and Macdonald http://www.anderson-barker.com/ Kenya Cross-group user-facing focus group 2013 Legislative Office 1638
39 38 19E3a5Bf6dBDc4F Cuevas-Moss https://dodson-castaneda.net/ Guatemala Extended human-resource intranet 1994 Music 9995
40 39 6883A965c7b68F7 Hahn PLC http://newman.com/ Belarus Organic logistical leverage 2012 Electrical / Electronic Manufacturing 3715
41 40 AC5B7AA74Aa4A2E Valentine, Ferguson and Kramer http://stuart.net/ Jersey Centralized secondary time-frame 1997 Non - Profit / Volunteering 3585
42 41 decab0D5027CA6a Arroyo Inc https://www.turner.com/ Grenada Managed demand-driven website 2006 Writing / Editing 9067
43 42 dF084FbBb613eea Walls LLC http://www.reese-vasquez.biz/ Cape Verde Self-enabling fresh-thinking installation 1989 Investment Management / Hedge Fund / Private Equity 1678
44 43 A2D89Ab9bCcAd4e Mitchell, Warren and Schneider https://fox.biz/ Trinidad and Tobago Enhanced intangible time-frame 2021 Capital Markets / Hedge Fund / Private Equity 3816
45 44 77aDc905434a49f Prince PLC https://www.watts.com/ Sweden Profit-focused coherent installation 2016 Individual / Family Services 7645
46 45 235fdEFE2cfDa5F Brock-Blackwell http://www.small.com/ Benin Secured foreground emulation 1986 Online Publishing 7034
47 46 1eD64cFe986BBbE Walton-Barnett https://ashley-schaefer.com/ Western Sahara Right-sized clear-thinking flexibility 2001 Luxury Goods / Jewelry 1746
48 47 CbBbFcdd0eaE2cF Bartlett-Arroyo https://cruz.com/ Northern Mariana Islands Realigned didactic function 1976 Civic / Social Organization 3987
49 48 49aECbDaE6aBD53 Wallace, Madden and Morris http://www.blevins-fernandez.biz/ Germany Persistent real-time customer loyalty 2016 Pharmaceuticals 9443
50 49 7b3fe6e7E72bFa4 Berg-Sparks https://cisneros-love.com/ Canada Stand-alone static implementation 1974 Arts / Crafts 2073
51 50 c6DedA82A8aef7E Gonzales Ltd http://bird.com/ Tonga Managed human-resource policy 1988 Consumer Goods 9069
52 51 7D9FBF85cdC3871 Lawson and Sons https://www.wong.com/ French Southern Territories Compatible analyzing intranet 2021 Arts / Crafts 3527
53 52 7dd18Fb7cB07b65 Mcguire, Mcconnell and Olsen https://melton-briggs.com/ Korea Profound client-server frame 1988 Printing 8445
54 53 EF5B55FadccB8Fe Charles-Phillips https://bowman.com/ Cote d'Ivoire Monitored client-server implementation 2012 Mental Health Care 3450
55 54 f8D4B99e11fAF5D Odom Ltd https://www.humphrey-hess.com/ Cote d'Ivoire Advanced static process improvement 2012 Management Consulting 1825
56 55 e24D21BFd3bF1E5 Richard PLC https://holden-coleman.net/ Mayotte Object-based optimizing model 1971 Broadcast Media 4942
57 56 B9BdfEB6D3Ca44E Sampson Ltd https://blevins.com/ Cayman Islands Intuitive local adapter 2005 Farming 1418
58 57 2a74D6f3D3B268e Cherry, Le and Callahan https://waller-delacruz.biz/ Nigeria Universal human-resource collaboration 2017 Entertainment / Movie Production 7202
59 58 Bf3F3f62c8aBC33 Cherry PLC https://www.avila.info/ Marshall Islands Persistent tertiary website 1980 Plastics 8245
60 59 aeBe26B80a7a23c Melton-Nichols https://kennedy.com/ Palau User-friendly clear-thinking productivity 2021 Legislative Office 8741
61 60 aAeb29ad43886C6 Potter-Walsh http://thomas-french.org/ Turkey Optional non-volatile open system 2008 Human Resources / HR 6923
62 61 bD1bc6bB6d1FeD3 Freeman-Chen https://mathis.com/ Timor-Leste Phased next generation adapter 1973 International Trade / Development 346
63 62 EB9f456e8b7022a Soto Group https://norris.info/ Vietnam Enterprise-wide executive installation 1988 Business Supplies / Equipment 9097
64 63 Dfef38C51D8DAe3 Poole, Cruz and Whitney https://reed.info/ Reunion Balanced analyzing groupware 1978 Marketing / Advertising / Sales 2992
65 64 055ffEfB2Dd95B0 Riley Ltd http://wiley.com/ Brazil Optional exuding superstructure 1986 Textiles 9315
66 65 cBfe4dbAE1699da Erickson, Andrews and Bailey https://www.hobbs-grant.com/ Eritrea Vision-oriented secondary project 2014 Consumer Electronics 7829
67 66 fdFbecbadcdCdf1 Wilkinson, Charles and Arroyo http://hunter-mcfarland.com/ United States Virgin Islands Assimilated 24/7 archive 1996 Building Materials 602
68 67 5DCb8A5a5ca03c0 Floyd Ltd http://www.whitney.com/ Falkland Islands (Malvinas) Function-based fault-tolerant concept 2017 Public Relations / PR 2911
69 68 ce57DCbcFD6d618 Newman-Galloway https://www.scott.com/ Luxembourg Enhanced foreground collaboration 1987 Information Technology / IT 3934
70 69 5aaD187dc929371 Frazier-Butler https://www.daugherty-farley.info/ Northern Mariana Islands Persistent interactive circuit 1972 Outsourcing / Offshoring 5130
71 70 902D7Ac8b6d476b Newton Inc https://www.richmond-manning.info/ Netherlands Antilles Fundamental stable info-mediaries 1976 Military Industry 563
72 71 32BB9Ff4d939788 Duffy-Levy https://www.potter.com/ Guernsey Diverse exuding installation 1982 Wireless 6146
73 72 adcB0afbE58bAe3 Wagner LLC https://decker-esparza.com/ Uruguay Reactive attitude-oriented toolset 1987 International Affairs 6874
74 73 dfcA1c84AdB61Ac Mccall-Holmes http://www.dean.com/ Benin Object-based value-added database 2009 Legal Services 696
75 74 208044AC2fe52F3 Massey LLC https://frazier.biz/ Suriname Configurable zero administration Graphical User Interface 1986 Accounting 5004
76 75 f3C365f0c1A0623 Hicks LLC http://alvarez.biz/ Pakistan Quality-focused client-server Graphical User Interface 1970 Computer Software / Engineering 8480
77 76 ec5Bdd3CBAfaB93 Cole, Russell and Avery http://www.blankenship.com/ Mongolia De-engineered fault-tolerant challenge 2000 Law Enforcement 7012
78 77 DDB19Be7eeB56B4 Cummings-Rojas https://simon-pearson.com/ Svalbard & Jan Mayen Islands User-centric modular customer loyalty 2012 Financial Services 7529
79 78 dd6CA3d0bc3cAfc Beasley, Greene and Mahoney http://www.petersen-lawrence.com/ Togo Extended content-based methodology 1976 Religious Institutions 869
80 79 A0B9d56e61070e3 Beasley, Sims and Allison http://burke.info/ Latvia Secured zero tolerance hub 1972 Facilities Services 6182
81 80 cBa7EFe5D05Adaf Crawford-Rivera https://black-ramirez.org/ Cuba Persevering exuding budgetary management 1999 Online Publishing 7805
82 81 Ea3f6D52Ec73563 Montes-Hensley https://krueger.org/ Liechtenstein Multi-tiered secondary productivity 2009 Printing 8433
83 82 bC0CEd48A8000E0 Velazquez-Odom https://stokes.com/ Djibouti Streamlined 6thgeneration function 2002 Alternative Dispute Resolution 4044
84 83 c89b9b59BC4baa1 Eaton-Morales https://www.reeves-graham.com/ Micronesia Customer-focused explicit frame 1990 Capital Markets / Hedge Fund / Private Equity 7013
85 84 FEC51bce8421a7b Roberson, Pennington and Palmer http://www.keith-fisher.com/ Cameroon Adaptive bi-directional hierarchy 1993 Telecommunications 5571
86 85 e0E8e27eAc9CAd5 George, Russo and Guerra https://drake.com/ Sweden Centralized non-volatile capability 1989 Military Industry 2880
87 86 B97a6CF9bf5983C Davila Inc https://mcconnell.info/ Cocos (Keeling) Islands Profit-focused dedicated frame 2017 Consumer Electronics 2215
88 87 a0a6f9b3DbcBEb5 Mays-Preston http://www.browning-key.com/ Mali User-centric heuristic focus group 2006 Military Industry 5786
89 88 8cC1bDa330a5871 Pineda-Morton https://www.carr.com/ United States Virgin Islands Grass-roots methodical info-mediaries 1991 Printing 6168
90 89 ED889CB2FE9cbd3 Huang and Sons https://www.bolton.com/ Eritrea Re-contextualized dynamic hierarchy 1981 Semiconductors 7484
91 90 F4Dc1417BC6cb8f Gilbert-Simon https://www.bradford.biz/ Burundi Grass-roots radical parallelism 1973 Newspapers / Journalism 1927
92 91 7ABc3c7ecA03B34 Sampson-Griffith http://hendricks.org/ Benin Multi-layered composite paradigm 1972 Textiles 3881
93 92 4e0719FBE38e0aB Miles-Dominguez http://www.turner.com/ Gibraltar Organized empowering forecast 1996 Civic / Social Organization 897
94 93 dEbDAAeDfaed00A Rowe and Sons https://www.simpson.org/ El Salvador Balanced multimedia knowledgebase 1978 Facilities Services 8172
95 94 61BDeCfeFD0cEF5 Valenzuela, Holmes and Rowland https://www.dorsey.net/ Taiwan Persistent tertiary focus group 1999 Transportation 1483
96 95 4e91eD25f486110 Best, Wade and Shepard https://zimmerman.com/ Zimbabwe Innovative background definition 1991 Gambling / Casinos 4873
97 96 0a0bfFbBbB8eC7c Holmes Group https://mcdowell.org/ Ethiopia Right-sized zero tolerance focus group 1975 Photography 2988
98 97 BA6Cd9Dae2Efd62 Good Ltd http://duffy.com/ Anguilla Reverse-engineered composite moratorium 1971 Consumer Services 4292
99 98 E7df80C60Abd7f9 Clements-Espinoza http://www.flowers.net/ Falkland Islands (Malvinas) Progressive modular hub 1991 Broadcast Media 236
100 99 AFc285dbE2fEd24 Mendez Inc https://www.burke.net/ Kyrgyz Republic User-friendly exuding migration 1993 Education Management 339
101 100 e9eB5A60Cef8354 Watkins-Kaiser http://www.herring.com/ Togo Synergistic background access 2009 Financial Services 2785

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