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Hugging Face built an open-source research agent roughly 24 hours after OpenAI announced Deep Research—but it did not copy OpenAI’s model or reproduce the complete proprietary system. It recreated the visible product pattern: an AI model that plans research, searches the web, reads documents, uses tools, and produces a cited report.
The result was impressive but incomplete. Hugging Face reported a 55.15% score on the GAIA validation benchmark, compared with 67.36% for OpenAI’s Deep Research. That makes it a strong proof of concept, not a demonstrated one-for-one replacement.
The 24-hour race began with OpenAI’s Deep Research
OpenAI announced Deep Research on February 2, 2025. Unlike a conventional chatbot response, the feature was designed to perform multi-step research: it searches the internet, analyzes sources, works with text, images, PDFs, uploaded files and spreadsheets, and produces a report with citations.
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OpenAI said the original version was powered by a version of its forthcoming o3 model optimized for web browsing and data analysis. A research task could take approximately 5 to 30 minutes, because the system was intended to plan, browse, inspect information and revise its work rather than answer from a single completion.
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Two days later, on February 4, Hugging Face published Open Deep Research. The team described the effort as a 24-hour reproduction mission. In practice, that meant assembling an agentic workflow from existing models, open-source software and web tools—not training a frontier model overnight.
What Hugging Face actually reproduced
Hugging Face did not obtain or clone OpenAI’s model weights. It also could not duplicate OpenAI’s undisclosed internal prompts, browsing infrastructure, safety systems, ranking methods or production environment.
Instead, the project recreated the architecture around a language model. Its components included:
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- An agent framework for planning and executing multiple steps.
- A text-based web browser.
- A tool for inspecting documents and other text.
- A code-generating agent that could express several actions programmatically.
The implementation was built around Hugging Face’s smolagents framework. The important lesson was that a large part of a research product’s visible behavior comes from orchestration: deciding what to search, choosing which tools to use, retaining intermediate results and turning the collected evidence into a report.
That is very different from saying that the underlying model is interchangeable. The framework could work with different models, including commercial APIs and open models. The quality, speed and cost of the resulting system therefore depended heavily on the selected model and tools.
How close was it to OpenAI’s result?
Hugging Face reported the following scores on the GAIA validation benchmark:
| System | Reported GAIA validation score |
|---|---|
| OpenAI Deep Research | 67.36% |
| Hugging Face Open Deep Research | 55.15% |
| Hugging Face setup using conventional JSON actions | Approximately 33% |
The gap between the two headline results was 12.21 percentage points. That is close enough to show that an open system could become competitive quickly, but not close enough to claim parity.
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The numbers should also be treated as an attributed comparison. The systems did not necessarily use identical models, tools, prompts, browsing environments or evaluation conditions. Hugging Face reported its own score, while the OpenAI result was reported in the context of OpenAI’s system. The comparison is useful evidence of capability, not a controlled claim that the two products were technically equivalent.
Why code-based actions performed better than JSON tool calls
One of the project’s most notable findings was that the agent performed substantially better when it could write code to operate tools instead of emitting one rigid JSON action at a time.
A conventional tool-calling agent might produce an instruction like this:
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{
"tool": "search",
"query": "topic"
}A code agent can represent a longer sequence of operations in one program:
results = search("topic")
pages = [open_page(item.url) for item in results[:5]]
summary = summarize(pages)Code gives the agent a natural way to use loops, branches, variables and intermediate results. It can search several sources, retain the outputs and pass them into later operations without repeatedly reconstructing the state in separate tool-call messages.
Hugging Face said the code-based setup required fewer steps and reached 55.15%, while the same general setup using conventional JSON actions fell to roughly 33%. That does not mean generated code is automatically safer or more reliable. It means that, for these tasks, a programmable action space gave the agent a more expressive way to coordinate its tools.
The security trade-off
Code execution also creates additional risks. A production system would need a sandbox, strict permissions, network controls, resource limits, secret isolation, filesystem restrictions and detailed logging. It would also need to handle failed or partially completed programs without allowing the agent to repeat dangerous actions indefinitely.
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What the 24-hour claim really means
“Built in 24 hours” describes a rapid public sprint and proof of concept. It does not mean that Hugging Face engineered a production-ready alternative, trained a model comparable to OpenAI’s or solved the reliability problems of autonomous research in one day.
The team could move quickly because it relied on existing open-source infrastructure, already available models and established web and document tools. Turning that prototype into a dependable service would require additional work on evaluation, browser interaction, file handling, multimodal inputs, prompt-injection defenses, monitoring, deployment, maintenance and user support.
The project therefore demonstrates a distinction that is easy to miss in headline coverage: feature-level replication can be fast even when frontier-model development is not. Recreating a product workflow is much easier when the underlying models and tools are accessible than reproducing the model, data pipeline and infrastructure that power a proprietary service.
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What was still missing?
Hugging Face described Open Deep Research as an early work in progress. Several differences limited its parity with OpenAI’s product:
- Browser capability: The project used a simpler text-based browser rather than a full visual browser capable of interacting with complex web interfaces.
- Web-page interaction: Dynamic pages, logins, paywalls, region restrictions, robots controls and JavaScript-heavy sites can prevent an agent from reaching or interpreting the information a user expects.
- File support: Handling PDFs, spreadsheets, scans, tables and unusual file formats reliably requires more than basic text extraction.
- Multimodal ability: Charts, images, scanned documents and visual layouts can introduce errors that are not visible in plain text.
- Model dependence: The agent framework does not guarantee the same reasoning quality across all supported models.
- Safety and reliability: OpenAI’s full browsing stack, internal safeguards, source-selection methods and post-processing were not available for independent inspection.
Hugging Face pointed to more advanced browser interaction, including capabilities similar to OpenAI’s Operator-style visual browsing, as an area needed for fuller parity.
What the result proves—and what it does not
What it supports
- Open agentic research workflows can be assembled rapidly from public components.
- The orchestration layer is a major part of the user-visible research experience.
- An open system can perform strongly without reproducing a frontier model from scratch.
- Code-native agents may outperform rigid tool-calling designs on some complex tasks.
- Proprietary AI products can face fast feature-level competition.
What it does not prove
- Hugging Face stole, reverse-engineered or duplicated OpenAI’s model.
- Open Deep Research achieved production-level parity.
- Open-source agents are equally accurate, safe, fast or reliable.
- Running an open framework is free.
- A business can reproduce the complete product, infrastructure, safety layer and user experience in one day.
- GAIA performance transfers directly to legal, medical, financial or scientific research.
Where research agents can fail
Whether the system is hosted or self-managed, an automated research report still needs review. Common failure modes include:
- Citation laundering: A report cites a real page, but the page does not support the exact claim made.
- Poor source selection: SEO pages, forums, copied summaries or rumors are treated as authoritative.
- Search loops: The agent repeatedly searches similar queries without improving the evidence.
- Tool hallucinations: The model claims to have opened a page, used a tool or inspected a file when it did not.
- Stale information: Current and obsolete sources are combined without clearly marking their dates.
- Access barriers: Paywalls, logins, dynamic pages and regional restrictions distort the research set.
- Multimodal errors: Charts, scanned PDFs and tables are misread.
- Prompt injection: A malicious web page or document attempts to give the agent instructions.
- Unsafe execution: A code agent performs unintended operations without adequate sandboxing.
- Benchmark overconfidence: A strong general benchmark score is mistaken for professional-grade accuracy.
OpenAI itself warned that Deep Research could struggle to distinguish authoritative information from rumors and could misrepresent uncertainty. The same general warning applies to open implementations.
Could a business use an open alternative?
That depends on what the organization values most.
| Need | More suitable direction | Main trade-off |
|---|---|---|
| Immediate access and minimal setup | Hosted research agent | Less control over models, prompts, data handling and updates |
| Inspectable and customizable workflows | Open Deep Research or smolagents |
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| Highly specialized internal research | Custom agent using an LLM API, search tools and document parsers | Maximum control, but also maximum maintenance and integration work |
| Strict data-control requirements | Local or private deployment | Hardware, model quality, browsing and operational complexity can limit results |
An open-source framework can be attractive when data must stay inside a controlled environment, when developers need to change the agent loop or when the organization wants to select its own model provider. It is less attractive to a nontechnical user who wants a polished interface, predictable billing and vendor-managed updates.
Hosted options such as ChatGPT Deep Research, Google Gemini and Perplexity trade transparency and control for convenience. They should be compared on citation precision, source controls, freshness, file support, browser capability, privacy, usage limits, exports, API access and human-review features—not simply on whether they advertise “deep research.”
Open-source does not mean cost-free. A deployment may still require model APIs, search services, hosting, GPUs, storage, monitoring and engineering time. It also requires the operator to handle prompt injection, permissions, audit logs and incident response.
Why this mattered for AI competition
The project highlighted a growing separation between model capability and product orchestration. A frontier model remains difficult and expensive to train, but a useful agent can sometimes be assembled by combining an available model with tools, prompts and an execution loop.
That does not make the underlying model irrelevant. Better models may plan more effectively, recover from errors, interpret difficult documents and produce more accurate conclusions. But the model is only one part of the final product. Browsing quality, source ranking, context management, code execution, citation generation, safety controls and interface design all affect what the user experiences.
As of the supplied research date, August 16, 2026, this should be understood as a historical account of a February 2025 sprint. OpenAI’s Deep Research product has since evolved with features including broader access, a lightweight version, MCP and app connections, trusted-site restrictions, progress tracking and agent-mode integration. Those later changes are product evolution, not features that should be retroactively attributed to the original launch or assumed to exist in Hugging Face’s first reproduction.
Bottom line
Hugging Face showed that an impressive Deep Research-like workflow could be assembled from open components in roughly a day. Its 55.15% GAIA result was strong evidence that agent design and tool orchestration can close much of the gap with a proprietary system.
But the project did not duplicate OpenAI’s model or complete product, and it did not beat OpenAI’s reported 67.36% result. The practical takeaway is more nuanced: open frameworks can give technical teams control and a fast starting point, while hosted products still offer a more complete and managed experience. For consequential work, either approach requires source checking and human review.
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