How To Use “Open Deep Research”: A Budget-Friendly Alternative to OpenAI’s Deep Research AI Agent
OpenAI’s Deep Research tool is renowned for its multi-step internet research capabilities, completing complex analysis in mere minutes—tasks that might otherwise take an entire team hours or even days. However, this elite feature is accessible only through a $200 monthly Pro plan, which might make perfect sense for those in high-level academia, hedge fund analysis, or deeply technical fields. Yet, someone who just needs solid research—like a blogger, marketer, or content creator—may not require such an intense deep-dive or the associated steep cost.
Thankfully, there is a solution: an open-source alternative called Open Deep Research. This project aims to replicate the most critical functionality of OpenAI’s Deep Research without the premium price tag, providing an accessible way to perform robust, iterative research on just about any topic.
What is Open Deep Research?
Open Deep Research is an AI-driven assistant capable of leveraging search engines, web scraping, and large language models to conduct step-by-step, in-depth research. It was developed by David Zhang—co-founder and CEO of Aomni—with the aim of creating a minimal implementation of a deep research agent. The code, kept under 500 lines, is intentionally streamlined so developers can easily understand and extend its capabilities. 
Essentially, Open Deep Research positions itself as the open-source counterpart to OpenAI’s Deep Research feature, eliminating the need to shell out $200 a month for advanced functionality. Instead, it provides a simpler yet effective means of refining a research focus over time, diving deeply into a subject matter, and assembling a comprehensive final report.
How Open Deep Research Works
Under the hood, Open Deep Research operates by breaking the user’s query into multiple sub-research pathways. It explores them in parallel, continuously iterates based on new findings, spawns additional research threads, and collects information until it achieves the required breadth and depth. Here is a quick overview of its process:
- Initial Setup
- Receives the user’s query plus parameters for how broad and deep the research should go.
- Generates follow-up questions to clarify the research scope.
- Deep Research Process
- Forms multiple SERP (Search Engine Results Page) queries tailored to the research goals.
- Analyzes search results to extract vital insights.
- Proposes new directions for further exploration.
- Recursive Exploration
- If the chosen depth is greater than zero, it continues digging into new research angles iteratively.
- Each cycle builds upon previous discoveries.
- Retains the context of overarching research objectives.
- Report Generation
- Compiles all findings into a well-organized Markdown document.
- Includes references, sources, and any relevant data.
- Structures the final output in a clear, reader-friendly format.
Key Features of Open Deep Research
- Iterative Research
The AI systematically refines its exploration by generating new queries and delving deeper based on each round of results. - Intelligent Query Generation
By drawing on large language models, it crafts precise queries aligned with research goals. - Depth & Breadth Control
Users define how widely and deeply to investigate a topic, balancing thoroughness with efficiency. - Smart Follow-up
Additional questions help guide the AI to gather more targeted information. - Comprehensive Markdown Reports
All findings are stored in an easily digestible format, replete with credible sources. - Concurrent Processing
Multiple search and analysis tasks can run in parallel, enhancing speed and productivity.
Setting Up the Open Deep Research Project
To get started, a user would typically visit the project’s GitHub repository and clone it to a local machine. Once inside the project folder (for instance, opened in VS Code), running npm install installs the necessary dependencies.
Next, rename .env.example to .env and configure both FIRECRAWL_KEY and OPENAI_KEY. The Firecrawl API key can be obtained through Firecrawl’s developer website. Meanwhile, the OpenAI key can be generated at OpenAI’s developer portal—both keys must be placed into the environment file for Open Deep Research to function properly.
(Graphic Credit: Jim Clyde Monge)
In some cases, the package.json might require adjustments if the user prefers not to deploy the app in Docker. The script section below offers an example configuration:
json
{ "name": "open-deep-research", "version": "0.0.1", "main": "index.ts", "scripts": { "format": "prettier --write \"src/**/*.{ts,tsx}\"", "dev": "next dev --turbopack", "build": "next build", "start": "tsx --env-file=.env src/run.ts" }, "author": "", "license": "ISC", "description": "", "devDependencies": { "@ianvs/prettier-plugin-sort-imports": "^4.4.1", "@types/lodash-es": "^4.17.12", "@types/node": "^22.13.0", "prettier": "^3.4.2", "tsx": "^4.19.2", "typescript": "^5.7.3" }, "dependencies": { "@ai-sdk/openai": "^1.1.9", "@mendable/firecrawl-js": "^1.16.0", "ai": "^4.1.17", "js-tiktoken": "^1.0.17", "lodash-es": "^4.17.21", "next": "^14.1.0", "p-limit": "^6.2.0", "zod": "^3.24.1" }, "engines": { "node": "22.x" }}With no errors present, the app can be launched.
Running Open Deep Research
Executing npm start prompts a few questions:
- “What would you like to research?”
- “Enter research breadth (recommended 2–10, default 4)”
- “Enter research depth (recommended 1–5, default 2)”
The size of the output depends on these parameters. A higher depth might produce more detailed information but can also incur longer runtimes and higher API usage charges. The tool may ask follow-up questions—for instance, clarifying whether someone wants details on a specific publication or simply an overview—so it can narrow its focus.
During the research process, Open Deep Research reveals the agent’s “internal monologue” (the AI’s reasoning) and displays progress in real-time. Ultimately, it compiles a Markdown report stored in the project’s root directory. This report can be quite comprehensive, though users on a free tier might occasionally face rate-limit issues. Reducing the search parameters typically resolves that snag, though it slows down the entire process (akin to sipping coffee through a tiny straw rather than guzzling it from a tall mug).
Important Observations
After examining Open Deep Research, several strengths and limitations become apparent. Below is a summary of the noteworthy pros and cons:
Pros
- Real-time Internal Monologue and Prompts
The AI’s transparent reasoning process helps users understand each pivot in the research approach. - Visual Progress Monitor
A handy progress bar or indicator lets one see how far along the research has gone—reassuring for those who worry about “Where did that data vanish?” - Decent Response Quality
The final Markdown report is typically well-structured, with clear references and credible sources—comparable to well-managed manual research.
Cons
- Not an Exact Match for ChatGPT’s Deep Research
Proprietary optimizations and advanced features in OpenAI’s premium model might not have direct equivalents in Open Deep Research. - Firecrawl Restrictions
Firecrawl’s free usage tier can be limited, prompting the need for a paid subscription—an unwelcome layer of complication for some. - Local-Only Output
Reports save to a local output.md file, lacking any integrated cloud backup or multi-user versioning. - Limited Model Compatibility
The tool uses OpenAI’s o3-mini model, as opposed to the upcoming, more robust o3. This might reduce the depth or accuracy for complex or highly specialized inquiries.
Note that the model setting can be changed in the deep-research.ts file (under the generateObject function), and system prompts are tweakable in the prompt.ts file for those craving custom instructions or disclaimers.
ts
export const systemPrompt = () => { const now = new Date().toISOString(); return `You are an expert researcher. Today is ${now}... [Additional instructions go here] `; };Final Thoughts
Open Deep Research provides an economical alternative to OpenAI’s $200 Deep Research plan but demands some technical knowledge—especially during the environment setup phase. The command-line interface may appear intimidating to individuals unfamiliar with terminal operations. Meanwhile, it can be tricky to track API usage costs since this approach doesn’t offer an obvious usage dashboard.
In general, Open Deep Research delivers robust functionality for a fraction of the premium price, though less-technical users might prefer spending more for the convenience of a polished UI and easy maintenance. In many cases, comfort and reliability win out over cost alone—especially when research time is money. After all, if someone rummages around the living room couch for spare change instead of focusing on actual research, the entire point of saving money is moot!
Overall, it’s an innovative project that can empower those who want thorough, iterative research without emptying their wallets. Has this open-source option caught the eye of the curious? Perhaps it’s time to dive in and see what new knowledge can be uncovered. For anyone encountering setup difficulties or seeking clarifications, a friendly comment in the community discussions is usually a good way to find help.
