llm-app vs deep-searcher

Common questions

What is the difference between llm-app and deep-searcher?

llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data.. See the comparison table for live GitHub stats and shared categories.

When should I choose llm-app over deep-searcher?

Choose llm-app over deep-searcher when llm-app is primarily Jupyter Notebook; deep-searcher is Python; License: llm-app is MIT, deep-searcher is Apache-2.0; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; 'pathwaycom/llm-app' and 'deep-searcher' both aim at enabling private data searching with AI technologies. Their purposes overlap slightly, but the approach taken is different, making them related rather than alternatives; Tags unique to llm-app: chatbot, hugging-face, retrieval-augmented-generation; Also covers Data & Retrieval; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.

When should I choose deep-searcher over llm-app?

Choose deep-searcher over llm-app when deep-searcher is primarily Python; llm-app is Jupyter Notebook; License: deep-searcher is Apache-2.0, llm-app is MIT; 'pathwaycom/llm-app' and 'deep-searcher' both aim at enabling private data searching with AI technologies. Their purposes overlap slightly, but the approach taken is different, making them related rather than alternatives; Tags unique to deep-searcher: agent, agentic-rag, deep-research; Also covers AI Agents; deep-searcher ships Docker support for self-hosted deployment; When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3.

When should I avoid llm-app?

- You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.

When should I avoid deep-searcher?

Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily.

Is llm-app or deep-searcher more popular on GitHub?

llm-app has more GitHub stars (59,037 vs 8,060). Stars measure visibility, not whether either tool fits your constraints.

Are llm-app and deep-searcher open source?

Yes - both are open-source projects on GitHub (llm-app: MIT, deep-searcher: Apache-2.0).

Where can I find alternatives to llm-app or deep-searcher?

GraphCanon lists graph-backed alternatives at llm-app alternatives and deep-searcher alternatives (llm-app markdown twin, deep-searcher markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, llm-app or deep-searcher?

llm-app: Steady. deep-searcher: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for llm-app and deep-searcher?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-app trust report; deep-searcher trust report.