feat: Add readme and nits for production

Author: bracesproul

.env.example

@@ -3,12 +3,14 @@ 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
+# LangGraph Deployment, or local development server via LangGraph Studio.
 LANGGRAPH_API_URL=
-NEXT_PUBLIC_LANGGRAPH_GRAPH_ID="agent"

README.md

@@ -1 +1,64 @@
-# Open Canvas
+# Open Canvas
+
+![Diagram of the Open Canvas graph](./public//lg_studio_graph_diagram.png)
+
+Open Canvas is an open source web application with built in memory powered by dual agents. It is inspired by [OpenAI's "Canvas"](https://openai.com/index/introducing-canvas/), but with a few key differences.
+
+1. Open Canvas is open source and free to use. 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.
+
+## 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 recomended.)
+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!

package.json

@@ -1,6 +1,8 @@
 {
-  "name": "streaming_chat_frontend",
-  "version": "0.1.0",
+  "name": "open_canvas",
+  "author": "Brace Sproul",
+  "homepage": "https://open-canvas-lc.vercel.app",
+  "repository": "https://github.com/langchain-ai/open-canvas",
   "private": true,
   "scripts": {
     "dev": "next dev",