Research agent
Local Deep Research
Open-source iterative deep research agent that runs on local or cloud LLMs -- achieves near-frontier research quality using Ollama, llama.cpp, or commercial APIs with 10+ search backend integrations.
Intermediate · Python package -- local CLI and API mode; Docker container available. Connects to Ollama on localhost or any OpenAI-compatible endpoint.
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About Local Deep Research
Open-source iterative deep research agent that runs on local or cloud LLMs -- achieves near-frontier research quality using Ollama, llama.cpp, or commercial APIs with 10+ search backend integrations.
Best for: Developers and researchers who want GPT-Researcher-class deep research capability running against local models (Ollama, llama.cpp) or any OpenAI-compatible endpoint -- offline, private, and without per-query API costs.
Deployment: Python package -- local CLI and API mode; Docker container available. Connects to Ollama on localhost or any OpenAI-compatible endpoint.
Skill level: Intermediate
Tradeoffs
Research quality scales with model size -- 7B models produce weaker synthesis than 27B+ on complex multi-hop questions. Multi-round search loops take several minutes per query on consumer hardware. Requires search API key management if using paid backends like Brave or Tavily.
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Best for
Developers and researchers who want GPT-Researcher-class deep research capability running against local models (Ollama, llama.cpp) or any OpenAI-compatible endpoint -- offline, private, and without per-query API costs.
Why use it
Local Deep Research brings the iterative multi-step research loop (search, read, synthesize, follow-up) to locally hosted models. It integrates with Brave, ArXiv, Wikipedia, and 10+ other sources, supports multi-modal search, and can operate fully offline. A 27B model on a 3090 achieves ~95% on SimpleQA -- competitive with cloud research agents at zero per-query cost.
Key features
- Iterative research loop: decomposes a question into sub-queries, searches, reads, synthesizes, and follows up across multiple rounds until confident
- Ollama and llama.cpp integration: runs the full research pipeline against locally hosted models with no API costs or data egress
- 10+ search backend support: Brave, SearXNG, ArXiv, Wikipedia, DuckDuckGo, Tavily, and more -- configurable per research query
- OpenAI-compatible API mode: exposes a REST endpoint so any tool can trigger deep research programmatically
Common AI use cases
- Run private multi-step research on proprietary documents or competitive intelligence without data egress
- Build an offline research agent for air-gapped or regulated environments
- Replace paid cloud research API calls with a local Ollama-backed pipeline
Who should use it
- Teams who want research agent capability without per-query cloud API costs
- Privacy-sensitive workflows requiring research on sensitive documents without cloud data exposure
- Builders integrating deep research into local RAG or knowledge management pipelines
Who should not use it
- Users who need instant results -- iterative research loops take minutes per query on consumer hardware
- Beginners without experience running Ollama and local models -- setup requires Python and model configuration
Tradeoffs
Research quality scales with model size -- 7B models produce weaker synthesis than 27B+ on complex multi-hop questions. Multi-round search loops take several minutes per query on consumer hardware. Requires search API key management if using paid backends like Brave or Tavily.
Alternatives
- Ollama
- Dify
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