deep-research
An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models.
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Decision brief
Deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics.
Good fit when
- When you need a tool that can refine its topic focus over time through repeated iterations.
- If your research project requires integration with both Firecrawl (for search/content extraction) and OpenAI APIs for enhanced capabilities.
Avoid when
- When you prefer a language other than TypeScript, as deep-research specifically requires a Node.js environment.
- If your use case does not necessitate the use of both Firecrawl and OpenAI APIs, preferring instead solutions with more API flexibility or that do not require API keys.
- Requirements:
- Requires Docker
Observed Jul 11, 2026 · Source: enrich:decision_facts
Verify the decision
Adoption
Package downloads where a registry match exists. GitHub stars (19,571) are secondary evidence.
- npm downloads (30d)
- 61·npm downloads API·today
Maintenance and security
Full trust report- Maintenance
- Slowing (129d since push)
- As of today
- Provenance
- Not a fork · Personal account
- As of today
- Security (OSV)
- 39 low (39 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
npm install deep-researchHow it fits your stack(13)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Integrates
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
dzhng/deep-research offers a deep research agent implementation combining search functions with AI technologies for iterative and in-depth exploration of topics. It operates within a TypeScript environment, relying on Node.js to run and involving specific API keys (Firecrawl and OpenAI) for functionality via an easily adjustable Docker setup.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 19, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 19, 2026
- CLI
- CLI entrypoint
Source: package.json:bin|scripts · Aug 19, 2026
- MCP server
- No MCP server detected
Source: repo_scan · Aug 19, 2026
- Languages
- typescript, javascript
Source: github.language+package.json · Aug 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 19, 2026)
- Node.js environmentSource link
Source: README excerpt (regex_v1, Aug 19, 2026)
- OpenAI API (for o3 mini model)Source link
Tags
README
Requirements
- Node.js environment
- API keys for:
- Firecrawl API (for web search and content extraction)
- OpenAI API (for o3 mini model)
Docker
-
Clone the repository
-
Rename
.env.exampleto.env.localand set your API keys -
Run
docker build -f Dockerfile -
Run the Docker image:
docker compose up -d
- Execute
npm run dockerin the docker service:
docker exec -it deep-research npm run docker
License
MIT License - feel free to use and modify as needed.
For agents
This page has a .md twin and JSON over the API.