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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.
| Date | Stars |
|---|---|
| 2026-07-31 | 19450 |
| 2026-08-01 | 19460 |
| 2026-08-06 | 19460 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Open Deep Research
An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models.
The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction over time and deep dive into a topic. Goal is to keep the repo size at <500 LoC so it is easy to understand and build on top of.
If you like this project, please consider starring it and giving me a follow on [X/Twitter](https://x.com/dzhng). This project is created by [Duet](https://duet.so).
## How It Works
```mermaid
flowchart TB
subgraph Input
Q[User Query]
B[Breadth Parameter]
D[Depth Parameter]
end
DR[Deep Research] -->
SQ[SERP Queries] -->
PR[Process Results]
subgraph Results[Results]
direction TB
NL((Learnings))
ND((Directions))
end
PR --> NL
PR --> ND
DP{depth > 0?}
RD["Next Direction:
- Prior Goals
- New Questions
- Learnings"]
MR[Markdown Report]
%% Main Flow
Q & B & D --> DR
%% Results to Decision
NL & ND --> DP
%% Circular Flow
DP -->|Yes| RD
RD -->|New Context| DR
%% Final Output
DP -->|No| MR
%% Styling
classDef input fill:#7bed9f,stroke:#2ed573,color:black
classDef process fill:#70a1ff,stroke:#1e90ff,color:black
classDef recursive fill:#ffa502,stroke:#ff7f50,color:black
classDef output fill:#ff4757,stroke:#ff6b81,color:black
classDef results fill:#a8e6cf,stroke:#3b7a57,color:black
class Q,B,D input
class DR,SQ,PR process
class DP,RD recursive
class MR output
class NL,ND results
```
## Features
- **Iterative Research**: Performs deep research by iteratively generating search queries, processing results, and diving deeper based on findings
- **Intelligent Query Generation**: Uses LLMs to generate targeted search queries based on research goals and previous findings
- **Depth & Breadth Control**: Configurable parameters to control how wide (breadth) and deep (depth) the research goes
- **Smart Follow-up**: Generates follow-up questions to better understand research needs
- **Comprehensive Reports**: Produces detailed markdown reports with findings and sources
- **Concurrent Processing**: Handles multiple searches and result processing in parallel for efficiency
## Requirements
- Node.js environment
- API keys for:
- Firecrawl API (for web search and content extraction)
- OpenAI API (for o3 mini model)
## Setup
### Node.js
1. Clone the repository
2. Install dependencies:
```bash
npm install
```
3. Set up environment variables in a `.env.local` file:
```bash
FIRECRAWL_KEY="your_firecrawl_key"
# If you want to use your self-hosted Firecrawl, add the following below:
# FIRECRAWL_BASE_URL="http://localhost:3002"
OPENAI_KEY="your_openai_key"
```
To use local LLM, comment out `OPENAI_KEY` and instead uncomment `OPENAI_ENDPOINT` and `OPENAI_MODEL`:
- Set `OPENAI_ENDPOINT` to the address of your local server (eg."http://localhost:1234/v1")
- Set `OPENAI_MODEL` to the name of the model loaded in your local server.
### Docker
1. Clone the repository
2. Rename `.env.example` to `.env.local` and set your API keys
3. Run `docker build -f Dockerfile`
4. Run the Docker image:
```bash
docker compose up -d
```
5. Execute `npm run docker` in the docker service:
```bash
docker exec -it deep-research npm run docker
```
## Usage
Run the research assistant:
```bash
npm start
```
You'll be prompted to:
1. Enter your research query
2. Specify research breadth (recommended: 3-10, default: 4)
3. Specify research depth (recommended: 1-5, default: 2)
4. Answer follow-up questions to refine the research direction
The system will then:
1. Generate and execute search queries
2. Process and analyze search results
3. Recursively explore deeper based on findings
4. Generate a comprehensive markdown repoExcerpt of 5,973 characters
Read on GitHub
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Mamy Ratsimbazafy · France
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1
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Ikko Eltociear Ashimine · Japan
1
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@voxel51
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Aakash Adesara · Consensus.app
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Michael Matloka · @PostHog
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:eefc7196bc744454, topic:gpt