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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.
Fully local web research and report writing assistant
| Date | Stars |
|---|---|
| 2026-07-31 | 9295 |
| 2026-08-01 | 9295 |
| 2026-08-02 | 9295 |
| 2026-08-03 | 9295 |
| 2026-08-04 | 9296 |
| 2026-08-05 | 9296 |
| 2026-08-06 | 9296 |
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# Local Deep Researcher Local Deep Researcher is a fully local web research assistant that uses any LLM hosted by [Ollama](https://ollama.com/search) or [LMStudio](https://lmstudio.ai/). Give it a topic and it will generate a web search query, gather web search results, summarize the results of web search, reflect on the summary to examine knowledge gaps, generate a new search query to address the gaps, and repeat for a user-defined number of cycles. It will provide the user a final markdown summary with all sources used to generate the summary.  Short summary video: <video src="https://github.com/user-attachments/assets/02084902-f067-4658-9683-ff312cab7944" controls></video> ## 🔥 Updates * 8/6/25: Added support for tool calling and [gpt-oss](https://openai.com/index/introducing-gpt-oss/). > ⚠️ **WARNING (8/6/25)**: The `gpt-oss` models do not support JSON mode in Ollama. Select `use_tool_calling` in the configuration to use tool calling instead of JSON mode. ## 📺 Video Tutorials See it in action or build it yourself? Check out these helpful video tutorials: - [Overview of Local Deep Researcher with R1](https://www.youtube.com/watch?v=sGUjmyfof4Q) - Load and test [DeepSeek R1](https://api-docs.deepseek.com/news/news250120) [distilled models](https://ollama.com/library/deepseek-r1). - [Building Local Deep Researcher from Scratch](https://www.youtube.com/watch?v=XGuTzHoqlj8) - Overview of how this is built. ## 🚀 Quickstart Clone the repository: ```shell git clone https://github.com/langchain-ai/local-deep-researcher.git cd local-deep-researcher ``` Then edit the `.env` file to customize the environment variables according to your needs. These environment variables control the model selection, search tools, and other configuration settings. When you run the application, these values will be automatically loaded via `python-dotenv` (because `langgraph.json` point to the "env" file). ```shell cp .env.example .env ``` ### Selecting local model with Ollama 1. Download the Ollama app for Mac [here](https://ollama.com/download). 2. Pull a local LLM from [Ollama](https://ollama.com/search). As an [example](https://ollama.com/library/deepseek-r1:8b): ```shell ollama pull deepseek-r1:8b ``` 3. Optionally, update the `.env` file with the following Ollama configuration settings. * If set, these values will take precedence over the defaults set in the `Configuration` class in `configuration.py`. ```shell LLM_PROVIDER=ollama OLLAMA_BASE_URL="http://localhost:11434" # Ollama service endpoint, defaults to `http://localhost:11434` LOCAL_LLM=model # the model to use, defaults to `llama3.2` if not set ``` ### Selecting local model with LMStudio 1. Download and install LMStudio from [here](https://lmstudio.ai/). 2. In LMStudio: - Download and load your preferred model (e.g., qwen_qwq-32b) - Go to the "Local Server" tab - Start the server with the OpenAI-compatible API - Note the server URL (default: http://localhost:1234/v1) 3. Optionally, update the `.env` file with the following LMStudio configuration settings. * If set, these values will take precedence over the defaults set in the `Configuration` class in `configuration.py`. ```shell LLM_PROVIDER=lmstudio LOCAL_LLM=qwen_qwq-32b # Use the exact model name as shown in LMStudio LMSTUDIO_BASE_URL=http://localhost:1234/v1 ``` ### Selecting search tool By default, it will use [DuckDuckGo](https://duckduckgo.com/) for web search, which does not require an API key. But you can also use [SearXNG](https://docs.searxng.org/), [Tavily](https://tavily.com/) or [Perplexity](https://www.perplexity.ai/hub/blog/introducing-the-sonar-pro-api) by adding their API keys to the environment file. Optionally, update the `.env` file with the following search tool configuration and API keys. If set, these values will take precedence over the defaults set in the `Configurat
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Lance Martin
78
Guido Schmutz · Accenture · Switzerland
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John Kennedy · @langchain-ai
23
14
4
2
Mason Daugherty · United States
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1
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1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1e00d8c5ea88b2a0, llm:Description: 'Fully local web research and report writing assistant' (repo: langchain-ai/local-deep-researcher). Language: Python. Likely uses LangChain for local research, retrieval, summarization, report generation.
matched fp:1e00d8c5ea88b2a0, llm:Description: 'Fully local web research and report writing assistant' (repo: langchain-ai/local-deep-researcher). Language: Python. Likely uses LangChain for local research, retrieval, summarization, report generation.
matched fp:1e00d8c5ea88b2a0, llm:Description: 'Fully local web research and report writing assistant' (repo: langchain-ai/local-deep-researcher). Language: Python. Likely uses LangChain for local research, retrieval, summarization, report generation.
matched fp:1e00d8c5ea88b2a0, llm:Description: 'Fully local web research and report writing assistant' (repo: langchain-ai/local-deep-researcher). Language: Python. Likely uses LangChain for local research, retrieval, summarization, report generation.