Open Deep Research - An open-source replica of Deep Research's intelligent agent, supporting switching between multiple language models.
Open Deep Research is an open-source AI agent, a replica of the Deep Research open-source project. It performs complex, multi-step research tasks based on inference of large amounts of network data. Open Deep Research uses Firecrawl...
What is Open Deep Research?
Open Deep Research is an open-source AI agent, a reproducible open-source project of Deep Research, capable of performing complex, multi-step research tasks based on inference of large amounts of network data. Open Deep Research utilizes Firecrawl's search and extraction capabilities, without relying on OpenAI's o3 fine-tuning model. It supports multiple language models (such as OpenAI, Anthropic, and Cohere), provides a unified API and Next.js application framework, and features real-time data input, structured data extraction, and server-side rendering.
Main functions of Open Deep Research
- Data extraction and search
- Firecrawl integrationData is retrieved from multiple websites in real time, structured, and used to provide foundational information for subsequent reasoning.
- Multi-source data integrationExtract key information from different types of web pages, support multiple data formats, and ensure data diversity and richness.
- Reasoning and Analysis
- AI Inference EngineBased on powerful inference models (such as OpenAI's GPT-4o or other LLMs), the extracted data is deeply analyzed and inferred to generate comprehensive conclusions.
- Multidimensional analysis: To conduct multi-dimensional analysis of data, including text content understanding, data correlation analysis, trend prediction, etc.
The technical principles of Open Deep Research
- Data extraction and search technologies:
- FirecrawlFirecrawl is used to quickly crawl data from multiple websites, simulate browser behavior, access target websites and extract web page content, and then process it into JSON or other formats.
- Real-time data streamThe extracted data is transmitted to the AI model in real time via API to ensure the timeliness of the analysis.
- AI inference model:
- Language ModelIt uses advanced language models (such as OpenAI's GPT series) as its core inference engine.
- Model callBased on the AI SDK, users can flexibly call different language models and choose the appropriate model for inference according to their needs.
- Front-end and back-end integration:
- Next.js frameworkNext.js, as a front-end framework, combines React Server Components and Server Actions to achieve efficient server-side rendering and dynamic user interfaces.
- API InterfaceBased on the unified API interface provided by the AI SDK, the front end can easily call the AI models and services of the back end.
- Data persistence and storage:
- Vercel PostgresIt uses Vercel Postgres as its database, supporting the storage and querying of structured data.
- Vercel BlobUsed for storing file data, supporting efficient storage and access of large files.
Open Deep Research project address
- GitHub repository:https://github.com/nickscamara/open-deep-research
Application scenarios of Open Deep Research
- literature reviewIt helps researchers quickly collect and organize literature in relevant fields and generate literature review reports.
- Industry AnalysisIt can quickly collect and analyze information such as market dynamics, competitive landscape, and development trends of specific industries to generate industry analysis reports.
- Investment ResearchAnalyze company financial statements, industry trends, market sentiment, and other information to provide data support for investment decisions.
- Policy ResearchCollect and analyze relevant domestic and international policies, regulations, and cases to provide reference for policy formulation.
- News reportIt can quickly collect and organize relevant information about news events and generate initial drafts of news reports.