https://github.com/beamlit/template-deepresearch
A template implementation of a deep research agent using LangGraph and GPT-4. This agent performs comprehensive research on any given topic by: Generating an initial research plan and outline, breaking down the topic into logical sections and performing targeted web searches using Tavily.
https://github.com/beamlit/template-deepresearch
agent deepresearch langgraph python
Last synced: 3 months ago
JSON representation
A template implementation of a deep research agent using LangGraph and GPT-4. This agent performs comprehensive research on any given topic by: Generating an initial research plan and outline, breaking down the topic into logical sections and performing targeted web searches using Tavily.
- Host: GitHub
- URL: https://github.com/beamlit/template-deepresearch
- Owner: beamlit
- License: mit
- Created: 2025-02-19T17:33:44.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-28T18:07:30.000Z (4 months ago)
- Last Synced: 2025-02-28T21:52:08.303Z (4 months ago)
- Topics: agent, deepresearch, langgraph, python
- Language: Python
- Homepage: https://blaxel.ai
- Size: 112 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Template DeepResearch
A template implementation of a deep research agent using LangGraph and GPT-4. This agent performs comprehensive research on any given topic by:
1. Generating an initial research plan and outline
2. Breaking down the topic into logical sections
3. Performing targeted web searches using Tavily for each section
4. Writing detailed section content based on search results
5. Compiling and refining the final report
The implementation uses LangGraph for orchestrating the research workflow, with parallel processing of sections for improved efficiency. It leverages GPT-4 for content generation and the Tavily search API for gathering relevant, up-to-date information from the web.
Key features:
- Automated research planning and execution
- Parallel processing of research sections
- Web search integration via Tavily
- Structured report generation with citations
- Configurable search depth and report formatting
Special thanks to:
- [MG](https://www.analyticsvidhya.com/blog/2025/02/build-your-own-deep-research-agent): Creating this agent and showing an amazing tutorial
- [Langchain](https://github.com/langchain-ai/open_deep_research/tree/main): Creating an awesome implementation of DeepResearch with langgraph
- [OpenAI](https://openai.com/index/introducing-deep-research/): Showing everyone this feature
## Prerequisites
- **Python:** 3.12 or later.
- **[UV](https://github.com/astral-sh/uv):** An extremely fast Python package and project manager, written in Rust.
- **[Blaxel CLI](https://docs.blaxel.ai/Get-started):** Ensure you have the Blaxel CLI installed. If not, install it globally:
```bash
curl -fsSL https://raw.githubusercontent.com/beamlit/toolkit/main/install.sh | BINDIR=$HOME/.local/bin sh
```
- **Blaxel login:** Login to Blaxel platform
```bash
bl login YOUR-WORKSPACE
```
## Installation
- **Clone the repository and install the dependencies**:
```bash
git clone https://github.com/beamlit/template-deepresearch.git
cd template-deepresearch
uv sync
```
- **Environment Variables:** Create a `.env` file with your configuration. You can begin by copying the sample file:
```bash
cp .env-sample .env
```
Then, update the following values with your own credentials:
- [Tavily Api Key](https://app.tavily.com/home): `TAVILY_API_KEY`
- [OpenAI api key](https://platform.openai.com/api-keys): `OPENAI_API_KEY`
- **Blaxel apply:** register your integration connection / functions / models on blaxel.ai
```bash
bl apply -R -f .blaxel
```
## Running the Server Locally
Start the development server with hot reloading using the Blaxel CLI command:
```bash
bl serve --hotrealod
```
_Note:_ This command starts the server and enables hot reload so that changes to the source code are automatically reflected.
## Testing your agent
```bash
bl chat --local template-deepresearch
```
_Note:_ This command starts a chat interface. Example question: Do a report of annual revenu for the last 10 years of NVIDIA
or
```
bl run agent template-deepresearch --local --data '{"input": "Do a report of annual revenu for the last 10 years of NVIDIA", "report_plan_depth": 20, "recursion_limit": 100 }'
```
## Deploying to Blaxel
When you are ready to deploy your application, run:
```bash
bl deploy
```
This command uses your code and the configuration files under the `.blaxel` directory to deploy your application.
## Project Structure
- **src/**
- `agent.py` - Configures the chat agent, streams HTTP responses, and integrates conversational context.
- `llmlogic.py` - Where the magic happens
- `prompts.py` - List of prompts given to agents
- `functions` - Functions to search the web with Tavily
- **.blaxel/** - Contains configuration files for Blaxel functions and models.
- **pyproject.toml** -UV package manager file.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.