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‎.codespellignore‎

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‎.env.example‎

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TAVILY_API_KEY=...
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# To separate your traces from other application
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LANGSMITH_PROJECT=retrieval-agent
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# The following depend on your selected configuration
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## LLM choice:
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ANTHROPIC_API_KEY=....
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FIREWORKS_API_KEY=...
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OPENAI_API_KEY=...

‎.github/workflows/integration-tests.yml‎

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# This workflow will run integration tests for the current project once per day
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name: Integration Tests
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on:
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schedule:
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- cron: "37 14 * * *" # Run at 7:37 AM Pacific Time (14:37 UTC) every day
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workflow_dispatch: # Allows triggering the workflow manually in GitHub UI
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# If another scheduled run starts while this workflow is still running,
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# cancel the earlier run in favor of the next run.
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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integration-tests:
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name: Integration Tests
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strategy:
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matrix:
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os: [ubuntu-latest]
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python-version: ["3.11", "3.12"]
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runs-on: ${{ matrix.os }}
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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curl -LsSf https://astral.sh/uv/install.sh | sh
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uv venv
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uv pip install -r pyproject.toml
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uv pip install -U pytest-asyncio vcrpy
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- name: Run integration tests
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env:
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ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
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TAVILY_API_KEY: ${{ secrets.TAVILY_API_KEY }}
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LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
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LANGSMITH_TRACING: true
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LANGSMITH_TEST_CACHE: tests/casettes
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run: |
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uv run pytest tests/integration_tests

‎.github/workflows/unit-tests.yml‎

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# This workflow will run unit tests for the current project
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name: CI
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on:
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push:
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branches: ["main"]
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pull_request:
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workflow_dispatch: # Allows triggering the workflow manually in GitHub UI
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# If another push to the same PR or branch happens while this workflow is still running,
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# cancel the earlier run in favor of the next run.
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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unit-tests:
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name: Unit Tests
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strategy:
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matrix:
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os: [ubuntu-latest]
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python-version: ["3.11", "3.12"]
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runs-on: ${{ matrix.os }}
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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curl -LsSf https://astral.sh/uv/install.sh | sh
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uv venv
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uv pip install -r pyproject.toml
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- name: Lint with ruff
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run: |
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uv pip install ruff
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uv run ruff check .
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- name: Lint with mypy
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run: |
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uv pip install mypy
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uv run mypy --strict src/
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- name: Check README spelling
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uses: codespell-project/actions-codespell@v2
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with:
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ignore_words_file: .codespellignore
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path: README.md
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- name: Check code spelling
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uses: codespell-project/actions-codespell@v2
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with:
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ignore_words_file: .codespellignore
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path: src/
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- name: Run tests with pytest
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run: |
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uv pip install pytest
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uv run pytest tests/unit_tests

‎README.md‎

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11
# LangGraph ReAct Agent Template
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This LangGraph template implements a simple, extensible ReAct agent.
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[![CI](https://github.com/langchain-ai/react-agent/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/langchain-ai/react-agent/actions/workflows/unit-tests.yml)
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[![Integration Tests](https://github.com/langchain-ai/react-agent/actions/workflows/integration-tests.yml/badge.svg)](https://github.com/langchain-ai/react-agent/actions/workflows/integration-tests.yml)
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This template showcases a [ReAct agent](https://arxiv.org/abs/2210.03629) implemented using [LangGraph](https://github.com/langchain-ai/langgraph), designed for [LangGraph Studio](https://github.com/langchain-ai/langgraph-studio). ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools.
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![Graph view in LangGraph studio UI](./static/studio_ui.png)
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## Repo Structure
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```txt
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├── LICENSE
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├── README.md
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├── langgraph.json
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├── poetry.lock
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├── pyproject.toml
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├── react_agent
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│   ├── __init__.py
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│   ├── graph.py
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│   └── utils
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│   ├── __init__.py
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│   ├── configuration.py # Define the configurable variables
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│   ├── state.py # Define state variables and how they're updated
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│   ├── tools.py # Define the tools your agent can access
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│   └── utils.py # Other sundry utilities
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└── tests # Add whatever tests you'd like here
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├── integration_tests
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│   └── __init__.py
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└── unit_tests
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└── __init__.py
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The core logic, defined in `src/react_agent/graph.py`, demonstrates a flexible ReAct agent that iteratively reasons about user queries and executes actions, showcasing the power of this approach for complex problem-solving tasks.
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## What it does
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The ReAct agent:
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1. Takes a user **query** as input
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2. Reasons about the query and decides on an action
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3. Executes the chosen action using available tools
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4. Observes the result of the action
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5. Repeats steps 2-4 until it can provide a final answer
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By default, it's set up with a basic set of tools, but can be easily extended with custom tools to suit various use cases.
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## Getting Started
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Assuming you have already [installed LangGraph Studio](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download), to set up:
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1. Create a `.env` file.
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```bash
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cp .env.example .env
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```
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2. Define required API keys in your `.env` file.
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The primary [search tool](./src/react_agent/tools.py) [^1] used is [Tavily](https://tavily.com/). Create an API key [here](https://app.tavily.com/sign-in).
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<!--
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Setup instruction auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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-->
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<details>
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<summary>Setup for `model_name` and `scraper_tool_model_name`</summary>
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The `llm` configuration defaults are shown below:
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```yaml
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model_name: anthropic/claude-3-5-sonnet-20240620
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scraper_tool_model_name: accounts/fireworks/models/firefunction-v2
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```
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Follow the instructions below to get set up, or pick one of the additional options.
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### Anthropic Chat Models
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To use Anthropic's chat models:
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1. Sign up for an [Anthropic API key](https://console.anthropic.com/) if you haven't already.
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2. Once you have your API key, add it to your `.env` file:
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```
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ANTHROPIC_API_KEY=your-api-key
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```
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### Fireworks Chat Models
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To use Fireworks AI's chat models:
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1. Sign up for a [Fireworks AI account](https://app.fireworks.ai/signup) and obtain an API key.
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2. Add your Fireworks AI API key to your `.env` file:
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```
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FIREWORKS_API_KEY=your-api-key
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```
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#### OpenAI Chat Models
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To use OpenAI's chat models:
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1. Sign up for an [OpenAI API key](https://platform.openai.com/signup).
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2. Once you have your API key, add it to your `.env` file:
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```
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OPENAI_API_KEY=your-api-key
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```
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</details>
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<!--
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End setup instructions
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-->
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3. Customize whatever you'd like in the code.
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4. Open the folder LangGraph Studio!
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## How to customize
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1. **Add new tools**: Extend the agent's capabilities by adding new tools in [tools.py](./src/react_agent/tools.py). These can be any Python functions that perform specific tasks.
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2. **Select a different model**: We default to Anthropic's Claude 3 Sonnet. You can select a compatible chat model using `provider/model-name` via configuration. Example: `openai/gpt-4-turbo-preview`.
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3. **Customize the prompt**: We provide a default system prompt in [configuration.py](./src/react_agent/configuration.py). You can easily update this via configuration in the studio.
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You can also quickly extend this template by:
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- Modifying the agent's reasoning process in [graph.py](./src/react_agent/graph.py).
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- Adjusting the ReAct loop or adding additional steps to the agent's decision-making process.
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## Development
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While iterating on your graph, you can edit past state and rerun your app from past states to debug specific nodes. Local changes will be automatically applied via hot reload. Try adding an interrupt before the agent calls tools, updating the default system message in `src/react_agent/configuration.py` to take on a persona, or adding additional nodes and edges!
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Follow up requests will be appended to the same thread. You can create an entirely new thread, clearing previous history, using the `+` button in the top right.
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You can find the latest (under construction) docs on [LangGraph](https://github.com/langchain-ai/langgraph) here, including examples and other references. Using those guides can help you pick the right patterns to adapt here for your use case.
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LangGraph Studio also integrates with [LangSmith](https://smith.langchain.com/) for more in-depth tracing and collaboration with teammates.
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<!--
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Configuration auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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{
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"properties": {
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"system_prompt": {
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"type": "string",
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"default": "You are a helpful AI assistant.\nSystem time: {system_time}"
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"default": "You are a helpful AI assistant.\n\nSystem time: {system_time}"
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},
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"model_name": {
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"type": "string",
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"variables": "OPENAI_API_KEY"
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}
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]
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},
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"max_search_results": {
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"type": "integer",
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"default": 10
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}
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}
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}
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}
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}
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-->
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-->

‎pyproject.toml‎

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"langchain>=0.2.14",
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"langchain-fireworks>=0.1.7",
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"python-dotenv>=1.0.1",
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"langchain-community>=0.2.17",
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"tavily-python>=0.4.0",
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]
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"D417",
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"E501",
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]
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[tool.ruff.lint.per-file-ignores]
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"tests/*" = ["D", "UP"]
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[tool.ruff.lint.pydocstyle]
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convention = "google"

‎src/react_agent/configuration.py‎

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from __future__ import annotations
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from dataclasses import dataclass, fields
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from dataclasses import dataclass, field, fields
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from typing import Annotated, Optional
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from langchain_core.runnables import RunnableConfig, ensure_config
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from react_agent import prompts
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@dataclass(kw_only=True)
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class Configuration:
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"""The configuration for the agent."""
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system_prompt: str = "You are a helpful AI assistant.\nSystem time: {system_time}"
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system_prompt: str = field(default=prompts.SYSTEM_PROMPT)
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"""The system prompt to use for the agent's interactions.
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This prompt sets the context and behavior for the agent.
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"""
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model_name: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = (
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"anthropic/claude-3-5-sonnet-20240620"
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)
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"""The name of the language model to use for the agent's main interactions.
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Should be in the form: provider/model-name.
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"""
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scraper_tool_model_name: Annotated[
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str, {"__template_metadata__": {"kind": "llm"}}
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] = "accounts/fireworks/models/firefunction-v2"
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"""The name of the language model to use for the web scraping tool.
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This model is specifically used for summarizing and extracting information from web pages.
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"""
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max_search_results: int = 10
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"""The maximum number of search results to return for each search query."""
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@classmethod
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def from_runnable_config(

‎src/react_agent/graph.py‎

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"""Define a custom Reasoning and Action agent.
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"""Define a custom Reasoning and Action agent.
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Works with a chat model with tool calling support.
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"""

‎src/react_agent/prompts.py‎

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"""Default prompts used by the agent."""
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SYSTEM_PROMPT = """You are a helpful AI assistant.
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System time: {system_time}"""

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