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This is an open source effort to create a similar experience to OpenAI's GPTs and Assistants API.It is powered byLangGraph - a framework for creating agent runtimes.It also builds uponLangChain,LangServe andLangSmith.OpenGPTs gives you more control, allowing you to configure:

• The LLM you use (choose between the 60+ that LangChain offers)
• The prompts you use (use LangSmith to debug those)
• The tools you give it (choose from LangChain's 100+ tools, or easily write your own)
• The vector database you use (choose from LangChain's 60+ vector database integrations)
• The retrieval algorithm you use
• The chat history database you use

Most importantly, it gives you full control over thecognitive architecture of your application.Currently, there are three different architectures implemented:

• Assistant
• RAG
• Chatbot

See below for more details on those.Because this is open source, if you do not like those architectures or want to modify them, you can easily do that!

Key Links

• GPTs: a simple hosted version
• Assistants API: a getting started guide
• Auth: a guide for production

This project supports a Docker-based setup, streamlining installation and execution. It automatically builds images forthe frontend and backend and sets up Postgres using docker-compose.

1. Prerequisites:
   Ensure you have Docker and docker-compose installed on your system.

2. Clone the Repository:
   Obtain the project files by cloning the repository.

   git clone https://github.com/langchain-ai/opengpts.gitcd opengpts

3. Set Up Environment Variables:
   Create a.env file in the root directory of the project by copying.env.example as a template, and add thefollowing environment variables:

   # At least one language model API key is requiredOPENAI_API_KEY=sk-...# LANGCHAIN_TRACING_V2=true# LANGCHAIN_API_KEY=...# Setup for Postgres. Docker compose will use these values to set up the database.POSTGRES_PORT=5432POSTGRES_DB=opengptsPOSTGRES_USER=postgresPOSTGRES_PASSWORD=...

   Replacesk-... with your OpenAI API key and... with your LangChain API key.

4. Run with Docker Compose:
   In the root directory of the project, execute:

   docker compose up

   This command builds the Docker images for the frontend and backend from their respective Dockerfiles and starts allnecessary services, including Postgres.

5. Access the Application:
   With the services running, access the frontend athttp://localhost:5173, substituting5173 with thedesignated port number.

6. Rebuilding After Changes:
   If you make changes to either the frontend or backend, rebuild the Docker images to reflect these changes. Run:

   docker compose up --build

   This command rebuilds the images with your latest changes and restarts the services.

PrerequisitesThe following instructions assume you have Python 3.11+ installed on your system. We strongly recommend using a virtualenvironment to manage dependencies.

For example, if you are usingpyenv, you can create a new virtual environment with:

pyenv install 3.11pyenv virtualenv 3.11 opengptspyenv activate opengpts

Once your Python environment is set up, you can install the project dependencies:

The backend service usespoetry to manage dependencies.

pip install poetrypip install langchain-community

Install Postgres and the Postgres Vector Extension

brew install postgresql pgvectorbrew services start postgresql

Configure persistence layer

The backend uses Postgres for saving agent configurations and chat message history.In order to use this, you need to set the following environment variables:

export POSTGRES_HOST=localhostexport POSTGRES_PORT=5432export POSTGRES_DB=opengptsexport POSTGRES_USER=postgresexport POSTGRES_PASSWORD=...

Create the database

createdb opengpts

Connect to the database and create thepostgres role

psql -d opengpts

CREATE ROLE postgres WITH LOGIN SUPERUSER CREATEDB CREATEROLE;

Install Golang Migrate

Database migrations are managed withgolang-migrate.

On MacOS, you can install it withbrew install golang-migrate. Instructions for other OSs or the Golang toolchain,can be foundhere.

Oncegolang-migrate is installed, you can run all the migrations with:

make migrate

This will enable the backend to use Postgres as a vector database and create the initial tables.

Install backend dependencies

cd backendpoetry install

Alternate vector databases

The instructions above use Postgres as a vector database,although you can easily switch this out to use any of the 50+ vector databases in LangChain.

Set up language models

By default, this uses OpenAI, but there are also options for Azure OpenAI and Anthropic.If you are using those, you may need to set different environment variables.

export OPENAI_API_KEY="sk-..."

Other language models can be used, and in order to use them you will need to set more environment variables.See the section below onLLMs for how to configure Azure OpenAI, Anthropic, and Amazon Bedrock.

Set up tools

By default this uses a lot of tools.Some of these require additional environment variables.You do not need to use any of these tools, and the environment variables are not required to spin up the app(they are only required if that tool is called).

For a full list of environment variables to enable, see theTools section below.

Set up monitoring

Set upLangSmith.This is optional, but it will help with debugging, logging, monitoring.Sign up at the link above and then set the relevant environment variables

export LANGCHAIN_TRACING_V2="true"export LANGCHAIN_API_KEY=...

Start the backend server

make start

cd frontendnpm installnpm run dev

Navigate tohttp://localhost:5173/ and enjoy!

Refer to thisguide for migrating data from Redis to Postgres.

Version 5 of the database migrations introduces a significant change to how thread checkpoints are managed:

• Transitions from a pickle-based checkpointing system to a new multi-table checkpoint management system (breaking change)
• Aligns with LangGraph's new checkpoint architecture for better state management and persistence
• Important: Historical threads/checkpoints (created before this migration) will not be accessible in the UI
• Previous checkpoint data is preserved in theold_checkpoints table but cannot be accessed by the new system
• This architectural change improves how thread state is stored and managed, enabling more reliable state persistence in LangGraph-based agents.

As much as possible, we are striving for feature parity with OpenAI.

• Sandbox - Provides an environment to import, test, and modify existing chatbots.
  • The chatbots used are all in code, so are easily editable
• Custom Actions - Define additional functionality for your chatbot using OpenAPI specifications
  • Supported by adding tools
• Knowledge Files - attach additional files that your chatbot can reference
  • Upload files from the UI or API, used by Retrieval tool
• Tools - Provides basic tools for web browsing, image creation, etc.
  • Basic DuckDuckGo and PythonREPL tools enabled by default
  • Image creation coming soon
• Analytics - View and analyze chatbot usage data
  • Use LangSmith for this
• Drafts - Save and share drafts of chatbots you're creating
  • Supports saving of configurations
• Publishing - publicly distribute your completed chatbot
  • Can do by deploying via LangServe
• Sharing - Set up and manage chatbot sharing
  • Can do by deploying via LangServe
• Marketplace - Search and deploy chatbots created by other users
  • Coming soon

• frontend: Code for the frontend
• backend: Code for the backend
  • app: LangServe code (for exposing APIs)
  • packages: Core logic
    • agent-executor: Runtime for the agent
    • gizmo-agent: Configuration for the agent

The big appeal of OpenGPTs as compared to using OpenAI directly is that it is more customizable.Specifically, you can choose which language models to use as well as more easily add custom tools.You can also use the underlying APIs directly and build a custom UI yourself should you choose.

This refers to the logic of how the GPT works.There are currently three different architectures supported, but because they are all written in LangGraph, it is veryeasy to modify them or add your own.

The three different architectures supported are assistants, RAG, and chatbots.

Assistants

Assistants can be equipped with arbitrary amount of tools and use an LLM to decide when to use them. This makes themthe most flexible choice, but they work well with fewer models and can be less reliable.

When creating an assistant, you specify a few things.

First, you choose the language model to use. Only a few language models can be used reliably well: GPT-3.5, GPT-4,Claude, and Gemini.

Second, you choose the tools to use. These can be predefined tools OR a retriever constructed from uploaded files. Youcan choose however many you want.

The cognitive architecture can then be thought of as a loop. First, the LLM is called to determine what (if any)actions to take. If it decides to take actions, then those actions are executed and it loops back. If no actions aredecided to take, then the response of the LLM is the final response, and it finishes the loop.

This can be a really powerful and flexible architecture. This is probably closest to how us humans operate. However,these also can be not super reliable, and generally only work with the more performant models (and even then they canmess up). Therefore, we introduced a few simpler architecures.

Assistants are implemented withLangGraphMessageGraph. AMessageGraph is a graph that models its state as alist of messages.

RAGBot

One of the big use cases of the GPT store is uploading files and giving the bot knowledge of those files. What would itmean to make an architecture more focused on that use case?

We added RAGBot - a retrieval-focused GPT with a straightforward architecture. First, a set of documents are retrieved.Then, those documents are passed in the system message to a separate call to the language model so it can respond.

Compared to assistants, it is more structured (but less powerful). It ALWAYS looks up something - which is good if youknow you want to look things up, but potentially wasteful if the user is just trying to have a normal conversation.Also importantly, this only looks up things once - so if it doesn't find the right results then it will yield a badresult (compared to an assistant, which could decide to look things up again).

Despite this being a more simple architecture, it is good for a few reasons. First, because it is simpler it can workpretty well with a wider variety of models (including lots of open source models). Second, if you have a use case whereyou don't NEED the flexibility of an assistant (eg you know users will be looking up information every time) then itcan be more focused. And third, compared to the final architecture below it can use external knowledge.

RAGBot is implemented withLangGraphStateGraph. AStateGraph is a generalized graph that can model arbitrary state (i.e.dict), not just alist of messages.

ChatBot

The final architecture is dead simple - just a call to a language model, parameterized by a system message. This allowsthe GPT to take on different personas and characters. This is clearly far less powerful than Assistants or RAGBots(which have access to external sources of data/computation) - but it's still valuable! A lot of popular GPTs are justsystem messages at the end of the day, and CharacterAI is crushing it despite largely just being system messages aswell.

ChatBot is implemented withLangGraphStateGraph. AStateGraph is a generalized graph that can model arbitrary state (i.e.dict), not just alist of messages.

You can choose between different LLMs to use.This takes advantage of LangChain's many integrations.It is important to note that depending on which LLM you use, you may need to change how you are prompting it.

We have exposed four agent types by default:

• "GPT 3.5 Turbo"
• "GPT 4"
• "Azure OpenAI"
• "Claude 2"

We will work to add more when we have confidence they can work well.

If you want to add your own LLM or agent configuration, or want to edit the existing ones, you can find them inbackend/app/agent_types

If using Claude 2, you will need to set the following environment variable:

export ANTHROPIC_API_KEY=sk-...

If using Azure OpenAI, you will need to set the following environment variables:

export AZURE_OPENAI_API_BASE=...export AZURE_OPENAI_API_VERSION=...export AZURE_OPENAI_API_KEY=...export AZURE_OPENAI_DEPLOYMENT_NAME=...

If using Amazon Bedrock, you either have valid credentials in~/.aws/credentials or set the following environmentvariables:

export AWS_ACCESS_KEY_ID=...export AWS_SECRET_ACCESS_KEY=...

One of the big benefits of having this be open source is that you can more easily add tools (directly in Python).

In practice, most teams we see define their own tools.This is easy to do within LangChain.Seethis guide for details on how to best dothis.

If you want to use some preconfigured tools, these include:

Sema4.ai Action Server

Run AI Python based actions withSema4.ai Action Server.Does not require a service API key, but it requires the credentials for a running Action Server instance to be defined.These you set while creating an assistant.

Connery Actions

Connect OpenGPTs to the real world withConnery.

Requires setting an environment variable, which you get during theConnery Runner setup:

CONNERY_RUNNER_URL=https://your-personal-connery-runner-urlCONNERY_RUNNER_API_KEY=...

DuckDuckGo Search

Search the web withDuckDuckGo. Does not require any API keys.

Tavily Search

Uses theTavily search engine. Requires setting an environment variable:

export TAVILY_API_KEY=tvly-...

Sign up for an API keyhere.

Tavily Search (Answer Only)

Uses theTavily search engine.This returns only the answer, no supporting evidence.Good when you need a short response (small context windows).Requires setting an environment variable:

export TAVILY_API_KEY=tvly-...

Sign up for an API keyhere.

You.com Search

UsesYou.com search, optimized responses for LLMs.Requires setting an environment variable:

export YDC_API_KEY=...

Sign up for an API keyhere

SEC Filings (Kay.ai)

Searches through SEC filings usingKay.ai.Requires setting an environment variable:

export KAY_API_KEY=...

Sign up for an API keyhere

Press Releases (Kay.ai)

Searches through press releases usingKay.ai.Requires setting an environment variable:

export KAY_API_KEY=...

Sign up for an API keyhere

Arxiv

SearchesArxiv. Does not require any API keys.

PubMed

SearchesPubMed. Does not require any API keys.

Wikipedia

SearchesWikipedia. Does not require any API keys.

1. Build the frontend

cd frontendyarnyarn build

2. Deploy to Google Cloud Run

You can deploy to GCP Cloud Run using the following command:

First create a.env.gcp.yaml file with the contents from.env.gcp.yaml.example and fill in the values. Then run:

gcloud run deploy opengpts --source. --port 8000 --env-vars-file .env.gcp.yaml --allow-unauthenticated \--region us-central1 --min-instances 1

We have a Helm chart for deploying the backend to Kubernetes. You can find more information here:README.md