Deep Research API

A Deep Research API is a deep research service that is an automated research API.

AKA: AI Deep Research Interface, Automated Deep Research API, Research Synthesis API, Multi-Step Research API, Autonomous Research API.
Context:
- It can typically decompose Deep Research Queryes into deep research sub-questions through deep research planning algorithms.
- It can typically orchestrate Deep Research Tools for deep research data gathering via deep research tool coordination.
- It can typically synthesize Deep Research Findings into deep research reports using deep research synthesis models.
- It can typically manage Deep Research Citations with deep research source tracking and deep research provenance chains.
- It can typically execute Deep Research Workflows through deep research model orchestration with deep research step monitoring.
- It can typically support Deep Research Iterations via deep research refinement loops.
- It can typically generate Deep Research Plans through deep research strategy formulation.
- It can typically evaluate Deep Research Sources for deep research credibility assessment.
- It can typically implement Deep Research Web Search through deep research search engine integration.
- It can typically produce Deep Research Documents with deep research structured output formats.
- ...
- It can often integrate Deep Research Knowledge Sources via deep research protocols and deep research connectors.
- It can often maintain Deep Research Context State across deep research sessions through deep research memory systems.
- It can often parallelize Deep Research Subtasks for deep research efficiency using deep research task schedulers.
- It can often validate Deep Research Results through deep research verification and deep research fact-checking.
- It can often implement Deep Research Reasoning Chains via deep research thought processes.
- It can often provide Deep Research Progress Updates through deep research streaming interfaces.
- It can often apply Deep Research Quality Control via deep research accuracy metrics.
- It can often support Deep Research Export Formats including deep research PDF generation and deep research document formats.
- It can often execute Deep Research Code Analysis through deep research code interpreters.
- It can often leverage Deep Research Multi-Agent Architectures for deep research task distribution.
- It can often implement Deep Research MCP Integrations via deep research Model Context Protocol.
- It can often provide Deep Research Webhook Notifications for deep research event handling.
- ...
- It can range from being a Simple Deep Research API to being a Complex Deep Research API, depending on its deep research capability scope.
- It can range from being a Synchronous Deep Research API to being an Asynchronous Deep Research API, depending on its deep research execution model.
- It can range from being a Single-Domain Deep Research API to being a Multi-Domain Deep Research API, depending on its deep research knowledge breadth.
- It can range from being a Web-Only Deep Research API to being a Multi-Source Deep Research API, depending on its deep research data access.
- It can range from being a Text-Only Deep Research API to being a Multimodal Deep Research API, depending on its deep research content type support.
- It can range from being a Quick Deep Research API to being an Exhaustive Deep Research API, depending on its deep research depth parameter.
- It can range from being a Guided Deep Research API to being an Autonomous Deep Research API, depending on its deep research decision-making model.
- It can range from being a Basic-Verification Deep Research API to being a Comprehensive-Verification Deep Research API, depending on its deep research fact-checking rigor.
- It can range from being a Public-Source Deep Research API to being a Private-Source Deep Research API, depending on its deep research data repository access.
- It can range from being a Stateless Deep Research API to being a Stateful Deep Research API, depending on its deep research session persistence.
- ...
- It can interface with Deep Research Client Applications through deep research endpoints and deep research SDKs.
- It can connect to Deep Research Models via deep research model adapters and deep research model registrys.
- It can access Deep Research Tool Registrys for deep research tool discovery and deep research capability expansion.
- It can utilize Deep Research Cache Systems for deep research performance optimization and deep research result reuse.
- It can implement Deep Research Security Protocols for deep research data protection and deep research access control.
- It can leverage Deep Research Compute Infrastructure for deep research scalability and deep research resource management.
- It can integrate Deep Research MCP Servers for deep research tool extensions and deep research data sources.
- It can employ Deep Research Rate Limiters for deep research request throttling and deep research cost control.
- It can utilize Deep Research Monitoring Systems for deep research performance tracking and deep research error logging.
- ...
Example(s):
- Commercial Deep Research APIs, such as:
  - OpenAI Deep Research APIs, such as:
    - OpenAI Deep Research API (2025-06), implementing deep research models like o3-deep-research-2025-06-26 and o4-mini-deep-research-2025-06-26 with deep research web search and deep research code execution.
    - OpenAI Deep Research Background Mode (2025-06), supporting deep research asynchronous processing with deep research background flag.
  - Perplexity Deep Research APIs, such as:
    - Perplexity Sonar Deep Research API (2025-03), utilizing DeepSeek R1 model for deep research multi-step retrieval and deep research synthesis.
    - Perplexity Deep Research MCP Server (2025), enabling deep research Model Context Protocol integration.
  - Google Deep Research APIs, such as:
    - Google Gemini Deep Research API (2025), enabling deep research analysis with deep research Google Search integration.
    - Google Gemini 2.0 Deep Research (2024-12), providing deep research agentic exploration with deep research multi-step research.
  - Anthropic Deep Research API (2025), supporting deep research conversations with deep research Claude models.
  - Firecrawl Deep Research API (2025), implementing deep research web crawling with deep research activity logging.
- Deep Research API Implementation Patterns, such as:
- Domain-Specific Deep Research APIs, such as:
- Open-Source Deep Research Implementations, such as:
  - LangChain Deep Research Implementations, such as:
    - LangChain Open Deep Research (2025), providing deep research graph-based implementation and deep research multi-agent system.
    - LangChain Deep Research Chain for deep research workflow orchestration and deep research tool integration.
  - AutoGPT Deep Research Module for deep research autonomous agent implementation.
  - CrewAI Deep Research Agent for deep research multi-agent collaboration.
- Enterprise Deep Research Platforms, such as:
- ...
Counter-Example(s):
- Simple Search API, which lacks deep research synthesis capability and deep research multi-step reasoning.
- Question-Answering API, which lacks deep research iterative exploration and deep research comprehensive analysis.
- Document Retrieval API, which lacks deep research analytical processing and deep research report generation.
- RAG API, which performs single-step retrieval without deep research planning or deep research iteration.
- Chat API, which provides conversational responses without deep research methodology or deep research citation.
- Web Scraping API, which collects raw web content without deep research analysis or deep research synthesis.
See: AI Research System, Research Automation Platform, API Endpoint, LLM Function Calling, Research Task, Multi-Agent System, Model Context Protocol, AI-Powered Research Tool, Automated Research Workflow, Research Synthesis System, Deep Learning Algorithm, Natural Language Processing API, Knowledge Graph API, Scientific Research Automation, Research Report Generation, Citation Management System.

References

2025-01-10

Deep Research https://chatgpt.com/s/dr_687043a1b09081919b3f09d30c25bd34
- Question: What is a Deep Research API and how does it work?
- Answer: A Deep Research API is an AI-driven research system interface that automates complex, multi-step web research tasks. It acts as an agentic tool for turning high-level questions into structured, citation-rich reports. Unlike a simple search or standard single-turn Q&A, a Deep Research API can independently plan and execute a research strategy – breaking down queries, gathering information from numerous sources, and synthesizing findings into comprehensive answers.
  - Key Capabilities:
    - Automated Query Decomposition: The API can break down complex queries into sub-questions or topics and formulate a step-by-step research plan. For example, given a broad question like "What are the latest developments in quantum computing?", a deep research agent might split it into subtopics (recent breakthroughs, key players, challenges, etc.) and tackle each in turn.
    - Tool Orchestration & Web Exploration: It orchestrates multiple tools and resources to gather data. Typically, it performs iterative web searches (to find relevant information) and may use other tools like web browsers or code interpreters to analyze data. For instance, OpenAI's Deep Research agent can not only search the web but also run Python code for data analysis during the research. The agent adapts its strategy based on intermediate results – iteratively searching and reading sources, then refining its plan as it learns more (much like a human researcher adjusting queries upon discovering new clues).
    - Information Synthesis into Reports: The gathered information is analyzed and synthesized into a coherent report or answer. A Deep Research API doesn't just return raw search results; it produces well-structured, in-depth reports that compile the findings. These outputs are often organized with headings, summaries, and key insights supported by evidence, approaching the quality of a human research analyst's work. Every claim is typically backed by references – e.g., the final report will cite its sources for transparency.
    - Citation Management and Traceability: Every insight is attributed to its source in the output. Deep Research APIs are designed to provide traceable evidence for verification. For example, OpenAI's deep research reports are "fully documented, with clear citations" to original webpages or documents. This allows users to verify facts and follow the provenance of each piece of information, addressing one of the key trust issues with AI-generated content.
    - Workflow Execution & Monitoring: The API typically executes a multi-step workflow autonomously, with the ability to monitor and report its progress. It may generate a research plan outline at the start and then carry out steps like searching, reading, and note-taking. Google's Deep Research agent, for instance, "generates a plan outlining the steps it will take," streams updates as it researches, and then compiles a report of findings. Advanced implementations provide a timeline or log of actions (queries made, sources accessed) for transparency. This structured workflow ensures complex tasks are tackled systematically rather than in one giant leap.
    - Iterative Reasoning & Refinement: Deep Research systems use iterative reasoning loops – they can reconsider and refine their approach as new information emerges. If an initial search doesn't answer the question, the agent can formulate new queries or pivot its strategy. Perplexity's Deep Research mode explicitly "iteratively searches, reads documents, and reasons about what to do next, refining its research plan as it learns". This helps uncover non-obvious, multi-hop insights that wouldn't surface from a single search. It's especially crucial for complex questions where the answer is scattered across multiple sources or hidden behind layers of inference.
    - Source Evaluation and Credibility: A Deep Research API typically prioritizes reliable sources and evaluates information quality. In practice, this might mean favoring peer-reviewed literature, authoritative databases, or official reports over random blog posts. While not perfect, the agent is often guided (via its prompts or training) to check for source credibility and cross-verify facts. For example, developers using OpenAI's API can instruct the agent to "prioritize reliable, up-to-date sources: peer-reviewed research, health organizations, etc." This built-in skepticism helps reduce the chance of accepting false or low-quality information, although final judgment may still require human review.
    - Support for Code and Data Analysis: Many Deep Research APIs integrate a code interpreter or data analysis tool to handle quantitative data or run computations during research. For instance, if the research question involves analyzing a dataset (like economic indicators or experimental results), the agent can invoke a Python tool to crunch numbers and extract statistics. OpenAI's deep research agent, powered by models like o3-deep-research, indeed supports a Python sandbox to perform calculations or parse complex data as part of its workflow. This extends the agent's capabilities beyond text retrieval – enabling it to produce data-driven insights (e.g. summarizing a CSV file or visualizing trends) as part of the research output.
    - Long Context and Memory: To handle extensive research, these APIs often have a large context window and can maintain state within a single task. OpenAI's implementation, for example, allows tens of thousands of tokens in context to accumulate information and intermediate conclusions. Throughout one deep research session, the agent "remembers" what it has found so far and uses it to inform subsequent steps. Some systems can even accept user-provided documents as context (e.g. you might attach PDFs or spreadsheets for the agent to analyze along with web data). However, by default, each deep research call is self-contained; persistence of state across separate sessions (true long-term memory) is not common unless the developer builds that on top.
    - Progress Updates and Streaming Results: Deep Research APIs usually handle long-running tasks (5–30+ minutes), so they provide interim progress updates. Rather than remaining silent until the final report, the agent can stream its intermediate steps or partial findings. In ChatGPT's interface, for instance, a sidebar updates with the summary of steps being taken during a deep research run. Google's Gemini Deep Research even allows the user to monitor the research in real-time, showing which topics are being researched and the questions being asked at each step. This transparency keeps users informed and engaged during the process and also allows intervention if needed.
    - Quality Control and Verification: To ensure accuracy, advanced deep research systems may implement verification steps. This could include cross-checking multiple sources for the same fact or flagging statements that lack sufficient support. In practice, current implementations still have limitations – for example, Firecrawl's Deep Research API (an independent service) notes that "manual verification of critical information is recommended," acknowledging that the AI might not catch every mistake. However, the trend is toward comprehensive verification, where the agent might use multiple independent sources or even external fact-checking tools to confirm important findings before finalizing its report.
    - Multi-Modal Integration: While primarily focused on text-based research, some Deep Research APIs can handle multimodal content. This means they can read and analyze images, PDFs, or other media if relevant to the query. OpenAI's Deep Research is powered by a model variant optimized for web browsing and data analysis, capable of interpreting text in web pages and documents, and even analyzing images to an extent. For example, it could pull data from a chart image or extract text from a PDF report during its investigation. This broadens the scope of research, allowing the agent to use information from diverse formats (not just HTML text).
  - Variations and Configuration:
    - Simple vs. Complex: Simple Deep Research APIs handle straightforward research tasks with a limited toolset or scope, whereas complex implementations integrate many advanced features and cover broad, intricate investigations. For example, a simple version might answer a well-defined question by searching a few sources and summarizing them. In contrast, a complex deep research system (like an enterprise research assistant) could coordinate numerous tools (web search, databases, code execution, etc.), manage very large contexts, and perform extensive analysis across domains. The complexity often scales with capability: Firecrawl's service lets developers "control cost and speed with tunable depth, time, and URL limits," essentially dialing research complexity up or down as needed.
    - Synchronous vs. Asynchronous: Some deep research APIs operate synchronously (the client waits for the final report in one response), but given the potentially long run time, asynchronous operation is common. For instance, OpenAI's Deep Research API tasks can take several minutes, so they recommend running the request in the background or via a job system. In practice, an asynchronous deep research API might provide a job ID or stream partial results, allowing the client to poll or receive a callback/webhook when the research is complete. This ensures that long analyses don't time out and that clients can handle the results when ready.
    - Single-Domain vs. Multi-Domain: A single-domain deep research API is specialized for a particular field or data source (e.g., a legal research AI that only combs law databases). In contrast, a multi-domain deep research API has a broad knowledge scope and can tackle questions across finance, science, history, engineering, etc., by pulling from a wide array of online sources. OpenAI's Deep Research was benchmarked on 100+ subjects ranging from mathematics to literature, demonstrating a multi-domain approach. Still, organizations might deploy specialized versions; for example, a medical deep research agent could be tuned to only use medical journals and databases, trading breadth for depth in one field.
    - Web-Only vs. Multi-Source: Early deep research tools focused on open web data, but many systems can integrate multiple data sources. A web-only deep research API strictly uses internet searches and public websites. A multi-source deep research API can also tap into private or proprietary databases, enterprise knowledge bases, or other internal data. Google's Deep Research agent exemplifies this: it uses data indexed in a company's app (internal documents) and can use web results if enabled. In other words, it can combine internal knowledge (e.g., corporate documents, intranet content) with external research from the web. This versatility is crucial for business use-cases where not all relevant info is publicly available.
    - Text-Only vs. Multimodal: A text-only deep research API limits itself to textual information and outputs. A multimodal deep research API can handle various content types – reading images, charts, or even videos if necessary, and possibly producing non-textual outputs. OpenAI's agent already reads images/PDFs during research, and Google's Gemini can provide an audio summary of the report in addition to text. Multimodal deep research might also entail generating visualizations (charts/graphs) as part of the report. For instance, an advanced system could return a chart comparing competitors if that helps answer a market analysis query. This category is evolving as AI models gain more multimodal capabilities.
    - Quick vs. Exhaustive: Depending on user needs, a Deep Research API might run in a "quick mode" for faster, lighter answers versus an "exhaustive mode" for thorough deep dives. Quick research might stop after finding just a few high-quality sources and provide a shorter summary (useful for less critical questions or when time is limited). Exhaustive research will spend more time and examine dozens of sources to maximize thoroughness. For example, Perplexity AI notes that its Deep Research typically spends 2–4 minutes to do what a human might do in many hours – this is an exhaustive process compared to a normal 20-second query. In contrast, Google's Gemini suggests users start with simple questions and only invoke Deep Research for truly complex needs. The difference mirrors what DataCamp observed: standard ChatGPT browsing provides quick answers, whereas Deep Research does comprehensive, multi-step investigations. Users can choose between speed and depth.
    - Guided vs. Autonomous: Some systems allow a guided approach, where the user (or developer) can steer or adjust the research plan, while others run fully autonomously once initiated. Google's Gemini Deep Research feature actually creates a multi-step plan and then lets the user "either revise or approve" that plan before execution. This means you can inject domain expertise or preferences (guidance) into the process – for example, instruct it to focus on certain subtopics or skip known irrelevant areas. An autonomous deep research API, on the other hand, decides the plan entirely on its own given the query and built-in logic, without user intervention during the run. Both modes have value: guided research can leverage human expertise in the loop, whereas autonomous research maximizes convenience and objectivity by not requiring any manual adjustments.
    - Basic Verification vs. Comprehensive Verification: All deep research agents try to cite sources, but the rigor of fact-checking can vary. A basic-verification approach might just retrieve information and trust the sources at face value (aside from providing citations). A comprehensive-verification approach will cross-verify facts among multiple sources and possibly flag discrepancies or uncertainties. For example, a basic system might include a statistic it found on one website, while an advanced system might verify that number against an official dataset or at least find a second source to corroborate it. The goal of comprehensive verification is to minimize the chances of inaccuracies or hallucinations in the final report. As an illustration, less robust systems might warn that users should double-check critical info (Firecrawl's Deep Research in alpha states that critical information should be manually verified), whereas a more robust system would attempt to do that validation itself. This dimension is crucial for high-stakes applications like legal or medical research, where incorrect information can be harmful.
    - Public-Source vs. Private-Source: A public-source deep research API only uses publicly available information (e.g., open web pages, Wikipedia, news articles). A private-source capable system can also incorporate private data provided by the user or organization. Many enterprise-oriented research APIs allow uploading documents or have connectors to databases, so the research can combine public knowledge with confidential reports or internal data. For instance, OpenAI's tool can accept user-uploaded files to consider in the research, and Google's version can index a company's documents to answer queries within that context. This flexibility means the AI can answer questions that require integrating proprietary information (like internal sales figures or client reports) with external market research – something purely public systems cannot do.
    - Stateless vs. Stateful: Stateless deep research APIs treat each query independently – once a report is delivered, the system does not retain context unless the client explicitly provides it again. Stateful deep research would maintain context over a session or allow follow-up queries that reference the previous results. In practice, many deep research implementations are session-based within a chat interface. For example, after a deep research report is generated in ChatGPT or Gemini, you can often ask follow-up questions like "Can you explain more about point X in that report?" and the assistant will use the prior context to answer. This indicates a degree of statefulness (the agent remembers its findings). However, via a pure API call, one might need to supply the content of the report as context for a follow-up query, unless the API has an ongoing session mechanism. Stateful operation is useful for iterative exploration – you review the report and then ask the agent to dig deeper or clarify something without starting from scratch.
  - Examples:
    - OpenAI Deep Research API (ChatGPT Advanced): OpenAI's Deep Research is available through ChatGPT (for certain user tiers) and via their API/SDK for developers. It uses the o3-deep-research family of models, which are optimized for high-quality reasoning and web-based analysis. Example: A financial analyst could use OpenAI's Deep Research API to investigate "the economic impact of new diabetes medications" – the agent would search for reports from WHO, CDC, recent studies on healthcare costs, possibly run calculations on epidemiological data, and return a structured report with statistics and citations. The Apidog developer guide describes how this API can be invoked with tools like web_search_preview and code_interpreter enabled, producing a detailed analysis over several minutes. The output is a comprehensive answer with source references for each key fact.
    - Google Gemini Deep Research (Agentspace): Google's Gemini AI (the successor to Bard) includes Deep Research as a feature for advanced users. It acts as a personal research assistant that can handle internal company data and web information together. Example: A product manager could ask Gemini's Deep Research, "Compare our product's user experience with competitors." The system will generate a multi-step plan, ask clarifying sub-questions if needed, search both the company's user feedback database and the web for competitor reviews, and then produce a report with its findings (complete with citations and even an audio summary). Google's implementation highlights the interactive element: it creates a plan that the user can review or modify before execution, and results can be obtained through an API (streamAssist method in Agentspace) for integration into apps.
    - Perplexity.ai Deep Research Mode: Perplexity, an AI search engine, offers a Deep Research mode accessible on their website and apps. This isn't exposed as a public API, but it's a notable example of the capability. Example: A user might enable Deep Research on Perplexity to ask, "What are the long-term effects of climate change on coral reefs?" Perplexity's agent will spend a few minutes performing "dozens of searches, reading hundreds of sources, and reasoning through the material" to deliver a thorough answer. The final output is a comprehensive report with sections summarizing the evidence, all fully cited. This showcases how deep research can be delivered in a consumer-facing product – the user only sees the final answer and references, but under the hood the agent did intensive multi-hop research (with search and even code if needed).
    - Firecrawl Deep Research API: Firecrawl (a smaller AI tools provider) introduced their own Deep Research API in 2025. It's designed for developers to embed research capabilities into applications. Example: A news aggregation app could use Firecrawl's API to automatically investigate trending topics. By sending a query like "Explain the recent developments in AI regulation in the EU," the app receives a structured output that includes a Final Analysis (summary of findings), a list of Sources with URLs, and an Activities log of what steps the agent took. Notably, Firecrawl's API allows results in JSON format for easy consumption by software, and supports customization of depth (how many iterations) and breadth (how many URLs to explore). This flexibility shows how developers can fine-tune a deep research process for their specific use cases (e.g., quick briefings vs. exhaustive reports).
    - Open-Source Deep Research Agents: There are community-driven projects aiming to replicate deep research functionality. For instance, DeepSeek-R1 (by Jina AI) and other GitHub projects provide frameworks for an AI agent to perform multi-step web research. Example: An open-source implementation might use a pipeline where one component generates search queries, another fetches documents, and an LLM synthesizes the content. Developers have demonstrated such agents answering complex questions (20+ page reports) using combinations of local models and search APIs. While these DIY solutions may not yet match the polish of OpenAI or Google's systems, they illustrate the concept's accessibility – one can build a "deep research" workflow using Python libraries, browser automation, and an LLM, to serve specialized needs or to avoid proprietary APIs.
  - Counter-Examples (Near-Related Concepts):
    - Standard Web Search APIs: Traditional search engine APIs (e.g. Google Custom Search or Bing Web Search) are not deep research APIs. They retrieve a list of relevant documents for a query but do no further reasoning or synthesis. The burden is on the user to read and connect those results. In contrast, a deep research API "explores the web to find answers and synthesizes key insights" autonomously. A simple query like "market share of electric cars in 2023" on a search API might return some links; a deep research agent would actually read those links, pull out statistics and trends, and produce a summary with citations. The search API itself doesn't produce a report or verify facts – it's essentially a single-step retrieval tool.
    - Basic QA or Chatbot Models: A common AI service is a question-answering model (like the standard GPT-4 or ChatGPT without browsing) that answers using its trained knowledge. These are not deep research APIs because they don't perform live multi-step research. They give quick responses based on static knowledge (up to a cutoff date) and may not cite sources. For example, asking vanilla ChatGPT "What is the latest research on X?" might yield a fluent answer, but it's drawing from its training data (which could be outdated) and it won't provide citations for where each fact came from. As noted by observers, "unlike standard ChatGPT browsing capabilities, which provide quick responses," deep research goes further to find and analyze fresh information from hundreds of sources. Thus, a single-turn Q&A model is a faster but shallower approach – it won't reliably perform the extensive fact-finding that a Deep Research API would.
    - Browsing or Retrieval-Augmented Generation (RAG) without Multi-step Reasoning: Some AI systems use a single round of retrieval (RAG) to answer a question – for instance, Bing Chat or earlier ChatGPT web-browsing plugins. These might fetch a few pages and then generate an answer. While useful, they often stop at the first layer of search. If those initial pages don't have the answer, the system might fail or hallucinate. Deep Research, by contrast, entails multi-hop reasoning and creative search reformulation when initial strategies fail. A counter-example is the difference between asking Bing Chat a very complex query versus using OpenAI's Deep Research: Bing might give a partial answer from one or two sources, whereas Deep Research would systematically dig through many sources and pivot searches as needed. The latter's iterative depth is what sets it apart. A single-turn retrieval-augmented answer is "quick and shallow"; a deep research answer is "slow but thorough."
    - Domain-Specific Research Tools without Web Integration: There are tools like academic search engines (Semantic Scholar, Google Scholar API) or domain-specific QA systems which retrieve information from a limited corpus (like a patent database or an internal knowledge base). These can be powerful, but if they don't perform multi-step reasoning or cross-reference multiple databases, they aren't full Deep Research APIs. For instance, a legal discovery AI that only searches an internal repository of documents in one go might miss context from the broader web or fail to plan an approach beyond keyword matching. A true deep research system would be able to, say, search case law, then also search news archives for commentary, and combine insights. The key distinction is the breadth of sources and the autonomous synthesis. A narrow-scope QA system may be a near cousin but not as autonomous or wide-ranging as a Deep Research API.
  - Summary: Deep Research APIs represent a significant evolution in AI's ability to conduct research. They blend search engine capabilities with advanced AI reasoning and synthesis. By coordinating multiple steps – planning, searching, evaluating, coding, and writing – they can deliver comprehensive, well-sourced answers to complex questions. This makes them invaluable for professionals and anyone who needs to quickly gather and trust information from the vast resources of the web. As the technology matures (with improvements in accuracy, speed, and multi-modality), we can expect deep research agents to become even more adept, possibly working in teams (multi-agent systems) or integrating real-time data streams, all towards the goal of automating and enhancing the research process. The current state-of-the-art already shows that such AI agents can outperform humans in certain research benchmarks, solving problems that stump conventional search tools – pointing toward a future where tedious information digging is largely handled by AI, guided by human curiosity and expertise.
- Citations:

[1] Introducing Deep Research API - https://www.firecrawl.dev/blog/deep-research-api
[2] OpenAI's Deep Research: A Guide With Practical Examples | DataCamp - https://www.datacamp.com/blog/deep-research-openai
[3] Introducing deep research | OpenAI - https://openai.com/index/introducing-deep-research/
[4] A Developer's Guide to the OpenAI Deep Research API - https://apidog.com/blog/openai-deep-research-api/
[5] Introducing Perplexity Deep Research - https://www.perplexity.ai/hub/blog/introducing-perplexity-deep-research
[6] Get reports with Deep Research | Google Agentspace | Google Cloud - https://cloud.google.com/agentspace/agentspace-enterprise/docs/research-assistant
[7] State of the Art Deep Research APIs | Parallel Web Systems | Query the web - https://parallel.ai/blog/deep-research
[8] 6 tips to get the most out of Gemini Deep Research - Google Blog - https://blog.google/products/gemini/tips-how-to-use-deep-research/
[9] Gemini: Try Deep Research and Gemini 2.0 Flash Experimental - https://blog.google/products/gemini/google-gemini-deep-research/
[10] Deep research is now available in the OpenAI API. - LinkedIn - https://www.linkedin.com/posts/openai-for-business_deep-research-is-now-available-in-the-activity-7344055416386961410-QQ9g
[11] OpenAI Releases New Model for Deep Research API: o3/o4-mini ... - https://www.aibase.com/news/19300
[12] I built Open Source Deep Research - here's how it works : r/LLMDevs - https://www.reddit.com/r/LLMDevs/comments/1jpfa8f/i_built_open_source_deep_research_heres_how_it/
[13] Google's new Deep Research is impressive : r/perplexity_ai - https://www.reddit.com/r/perplexity_ai/comments/1hd86x8/googles_new_deep_research_is_impressive/

Deep Research API

References

2025-01-10

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