In the rapidly evolving landscape of Artificial Intelligence (AI), research assistants offer a powerful tool for analyzing and synthesizing information from vast sources. "Local Deep Researcher" from langchain-ai is an application that promises to do just that, providing a fully local solution for web research and report generation. We tested the application to see if it lived up to its claims.
What is Local Deep Researcher?
Local Deep Researcher is a web research assistant that utilizes a local LLM (Large Language Model), hosted by either Ollama or LMStudio. The application carries out the following steps:
If functional, Local Deep Researcher would be a valuable tool for:
Once installed and configured, Local Deep Researcher should:
Unfortunately, after completing the installation and configuration steps, the application did not function as expected. Regardless of the chosen LLM model (including those recommended in the documentation), the application consistently failed to produce meaningful results. The same issues occurred even when using DuckDuckGo as the search method. The tool, however, seemed not to care about search API used.
As a concrete example of its failure, the application was tasked with summarizing a webpage containing entertainment news. Instead, the final output was a list of deceased US presidents. This demonstrates a fundamental disconnect between the inputs and the outputs, suggesting significant issues in the core processing pipeline. It consistently provides completely unrelated results.
Local Deep Researcher presents an intriguing concept – a localized, powerful web research tool. However, in its current implementation, the application is non-functional. While the idea is promising and the architecture intelligent, it encounters significant issues in execution, leading to incorrect results and an inability to perform its core function. The hope is that the developers will resolve these issues in the future, as the potential is clearly present.
What is Local Deep Researcher?
Local Deep Researcher is a web research assistant that utilizes a local LLM (Large Language Model), hosted by either Ollama or LMStudio. The application carries out the following steps:
- Search Query Generation: Receives a user-provided topic and generates a web search query.
- Web Search: Employs a search engine (DuckDuckGo by default, or SearXNG, Tavily, Perplexity) to find relevant results.
- Summarization: Utilizes the LLM to summarize the findings from the web search.
- Reflection: The LLM analyzes the summary, identifying knowledge gaps.
- New Search Query Generation: Creates a new search query to address the identified gaps.
- Iteration: Steps 2-5 are repeated for a configurable number of cycles.
- Output: Generates a final summary in markdown format, with citations to the sources used.
If functional, Local Deep Researcher would be a valuable tool for:
- Automated Research: Streamlining the often tedious process of web research.
- Summarization & Synthesis: Providing concise overviews of complex topics.
- Local Privacy: Allowing research without relying on cloud-based services and potentially compromising data privacy.
- Exploration of Diverse Topics: Offering a flexible platform for investigating any subject.
Once installed and configured, Local Deep Researcher should:
- Accept a research topic from the user.
- Generate relevant search queries for that topic.
- Conduct web searches and collect results.
- Summarize the findings.
- Analyze the summaries and identify knowledge gaps.
- Generate new search queries to address these gaps.
- Repeat this process for a specified number of iterations.
- Generate a final markdown summary with source citations.
Unfortunately, after completing the installation and configuration steps, the application did not function as expected. Regardless of the chosen LLM model (including those recommended in the documentation), the application consistently failed to produce meaningful results. The same issues occurred even when using DuckDuckGo as the search method. The tool, however, seemed not to care about search API used.
As a concrete example of its failure, the application was tasked with summarizing a webpage containing entertainment news. Instead, the final output was a list of deceased US presidents. This demonstrates a fundamental disconnect between the inputs and the outputs, suggesting significant issues in the core processing pipeline. It consistently provides completely unrelated results.
Local Deep Researcher presents an intriguing concept – a localized, powerful web research tool. However, in its current implementation, the application is non-functional. While the idea is promising and the architecture intelligent, it encounters significant issues in execution, leading to incorrect results and an inability to perform its core function. The hope is that the developers will resolve these issues in the future, as the potential is clearly present.