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https://github.com/langchain-ai/open-canvas


https://github.com/langchain-ai/open-canvas

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README 

        
# Open Canvas



[TRY IT OUT HERE](https://open-canvas-lc.vercel.app)



![Screenshot of app](./public/screenshot.jpg)



Open Canvas is an open source web application for collaborating with agents to better write documents. It is inspired by [OpenAI's "Canvas"](https://openai.com/index/introducing-canvas/), but with a few key differences.



1. **Open Source**: All the code, from the frontend, to the content generation agent, to the reflection agent is open source and MIT licensed.

2. **Built in memory**: Open Canvas ships out of the box with a [reflection agent](https://langchain-ai.github.io/langgraphjs/tutorials/reflection/reflection/) which stores style rules and user insights in a [shared memory store](https://langchain-ai.github.io/langgraphjs/concepts/memory/). This allows Open Canvas to remember facts about you across sessions.

3. **Start from existing documents**: Open Canvas allows users to start with a blank text, or code editor in the language of their choice, allowing you to start the session with your existing content, instead of being forced to start with a chat interaction. We believe this is an ideal UX because many times you will already have some content to start with, and want to iterate on-top of it.



## How to use



You can use our deployed version for free by visiting [open-canvas-lc.vercel.app](https://open-canvas-lc.vercel.app/)



or



You can clone this repository and run locally/deploy to your own cloud. See the next section for steps on how to do this.



![Diagram of the Open Canvas graph](./public/lg_studio_graph_diagram.png)



## Development



Running or developing Open Canvas is easy. Start by cloning this repository and navigating into the directory.



```bash

git clone https://github.com/langchain-ai/open-canvas.git



cd open-canvas

```



Next, install the dependencies via Yarn:



```bash

yarn install

```



Then [install LangGraph Studio](https://studio.langchain.com/) which is required to run the graphs locally, or [create a LangSmith account](https://smith.langchain.com/) to deploy to production on LangGraph Cloud.



After that, copy the `.env.example` file contents into `.env` and set the required values:



```bash

# LangSmith tracing (optional, but recommended.)

LANGCHAIN_TRACING_V2=true

LANGCHAIN_API_KEY=



# LLM API keys

# Anthropic used for reflection

ANTHROPIC_API_KEY=

# OpenAI used for content generation

OPENAI_API_KEY=



# Vercel KV stores. Used for system prompt storage.

KV_REST_API_URL=

KV_REST_API_TOKEN=



# LangGraph Deployment, or local development server via LangGraph Studio.

LANGGRAPH_API_URL=

```



Finally, start the development server:



```bash

yarn dev

```



Then, open [localhost:3000](http://localhost:3000) with your browser and start interacting!



## Roadmap



We'd like to continue developing and improving Open Canvas, and need your help!



To start, there are a handful of GitHub issues with feature requests outlining improvements and additions to make the app's UX even better.

There are three main labels:



- `frontend`: This label is added to issues which are UI focused, and do not require much if any work on the agent(s).

- `ai`: This label is added to issues which are focused on improving the LLM agent(s).

- `fullstack`: This label is added to issues which require touching both the frontend and agent code.



If you have questions about contributing, please reach out to me via email: `brace(at)langchain(dot)dev`. For general bugs/issues with the code, please [open an issue on GitHub](https://github.com/langchain-ai/open-canvas/issues/new).