@arizeai/phoenix-mcp

# LangFlow Tracing ## Pull Langflow Repo Navigate to the Langflow GitHub repo and pull the project down {% @github-files/github-code-block url="https://github.com/langflow-ai/langflow" %} ## Create .env file Navigate to the repo and create a `.env` file with all the Arize Phoenix variables. You can use the `.env.example` as a template to create the `.env` file Add the following environment variable to the `.env` file ``` # Arize Phoenix Env Variables PHOENIX_API_KEY="YOUR_PHOENIX_KEY_HERE" ``` Note: This Langflow integration is for [Phoenix](https://app.phoenix.arize.com/login/sign-up)[ Cloud](https://app.phoenix.arize.com/login/sign-up) ## Start Docker Desktop Start Docker Desktop, build the images, and run the container (this will take around 10 minutes the first time)\ \ Go into your terminal into the Langflow directory and run the following commands <pre><code><strong>docker compose -f docker/dev.docker-compose.yml down || true </strong>docker compose -f docker/dev.docker-compose.yml up --remove-orphans </code></pre> ## Go to Hosted Langflow UI {% embed url="http://localhost:3000/" %} ## Create a Flow In this example, we'll use Simple Agent for this tutorial Add your OpenAI Key to the Agent component in Langflow Go into the Playground and run the Agent ## Go to Arize Phoenix Navigate to your project name (should match the name of of your Langflow Agent name) [https://app.phoenix.arize.com/](https://app.phoenix.arize.com/) ## Inspect Traces AgentExecutor Trace is Arize Phoenix instrumentation to capture what's happening with the LangChain being ran during the Langflow components The other UUID trace is the native Langflow tracing.