Open Deep Research: Inside LangChain's Production Deep Research Agent

The Deep Research Revolution

When OpenAI and Google launched their deep research capabilities in 2025, they demonstrated that AI could conduct comprehensive research rivaling human analysts. But how do these systems actually work? While commercial offerings remain proprietary, the open-source community has produced implementations that reveal the architectural patterns behind effective deep research.

Open Deep Research, built on LangChain's LangGraph framework, represents the most complete open-source implementation of production deep research capabilities. It achieves competitive results on the Deep Research Bench—a collection of one hundred PhD-level research tasks—while remaining fully transparent and customizable.

This deep dive explores every layer of Open Deep Research's architecture, from the initial query clarification through parallel investigation to final report synthesis. Understanding this implementation provides insight into how modern AI research systems transform vague questions into comprehensive, well-cited reports.

The Five-Phase Architecture

Open Deep Research implements a hierarchical workflow that mirrors how skilled human researchers approach complex questions. Rather than attempting to answer queries directly, the system decomposes research into manageable phases, each building on the previous.

Phase Overview

The journey from question to report traverses five distinct phases. The clarification phase determines whether the query needs refinement before research begins. The research brief phase transforms the query into a structured specification that guides subsequent investigation. The supervisor phase strategically delegates research tasks to parallel agents. The researcher phase conducts focused investigation on delegated topics. The synthesis phase aggregates findings into a coherent, well-cited report.

This phased approach provides several advantages over monolithic research attempts. Each phase has clear inputs and outputs, enabling debugging and quality assessment at intermediate points. The hierarchical structure naturally supports parallel execution where independent work can proceed simultaneously. The explicit planning creates transparency into how the system interpreted and approached the query.

The Clarification Phase

Research quality depends heavily on query clarity. A question like "compare AI companies" could generate a brief overview or a comprehensive market analysis depending on unstated assumptions about scope, depth, and focus. The clarification phase makes these implicit decisions explicit.

When enabled, the system evaluates whether the query contains ambiguities that could lead research astray. It looks for undefined acronyms or abbreviations that might have multiple meanings. It assesses whether the scope is clear—does the query want global analysis or specific regional focus? It considers whether the dimensions of comparison or analysis are specified.

If clarification would improve research quality, the system generates a focused question and awaits user response. If the query is already sufficiently clear, it proceeds directly to research brief generation. This optional phase can be disabled for scenarios where queries are known to be well-formed or where user interaction isn't possible.

The clarification mechanism reflects a broader principle: investing time in understanding the question prevents wasted effort pursuing the wrong answers. Professional researchers spend significant effort scoping projects before diving into investigation, and effective AI systems mirror this practice.

The Research Brief Phase

With a clear query established, the system generates a research brief—a detailed specification that guides all subsequent investigation. This brief transforms the user's natural language question into structured instructions that downstream components can execute.

The research brief serves multiple purposes. It defines the specific aspects the research should cover, ensuring comprehensive treatment of the topic. It establishes constraints like geographic scope, time horizon, and depth expectations. It provides context that helps researchers understand why they're investigating particular subtopics. It creates a standard against which the final report can be evaluated.

Brief generation uses structured output to ensure consistent format. The resulting specification includes the core research question, dimensions to investigate, constraints to respect, and guidance for how findings should be organized. This structured approach prevents the ambiguity that would result from free-form planning.

The research brief becomes the primary input to the supervisor phase, framing all delegation decisions. A well-constructed brief enables focused, efficient research; a vague brief leads to scattered, inefficient investigation.

The Supervisor-Researcher Pattern

At the heart of Open Deep Research lies a two-level hierarchy where a supervisor agent coordinates multiple researcher agents. This pattern enables sophisticated decomposition of complex queries while maintaining coherent direction throughout the research process.

Supervisor Responsibilities

The supervisor receives the research brief and makes strategic decisions about how to investigate it. Rather than conducting research directly, the supervisor thinks about what information is needed and delegates focused tasks to researcher agents.

The supervisor operates through a loop of reflection and delegation. In each iteration, it assesses what information has been gathered so far, identifies gaps in coverage, and decides whether to delegate additional research or conclude that sufficient information exists.

Three tools enable supervisor decision-making. The think tool allows strategic reflection without taking action—the supervisor can reason through its approach before committing to delegation. The conduct research tool spawns a researcher agent with a specific topic to investigate. The research complete tool signals that the supervisor believes sufficient information exists to produce the final report.

This tool-based design creates transparency into supervisor reasoning. Every delegation decision is explicit, logged, and reviewable. The system never silently decides to investigate something; every research direction results from a deliberate tool invocation.

Delegation Strategy

How the supervisor decomposes queries into research tasks significantly impacts research quality and efficiency. Different query types warrant different decomposition strategies.

Simple queries that ask for specific information—like listing the top coffee shops in a city—typically warrant a single researcher. Spawning multiple agents to investigate the same focused question creates redundancy without improving coverage. The supervisor recognizes these cases and avoids over-parallelization.

Comparison queries that ask about multiple entities naturally decompose along entity boundaries. A query comparing three companies' AI safety approaches becomes three parallel research tasks, one per company, followed potentially by a synthesizing task that compares findings. Each researcher handles a distinct, non-overlapping subtopic.

Complex queries with multiple dimensions require strategic planning before delegation. The supervisor uses the think tool to reason through the decomposition approach, considering what aspects need investigation and how they relate to each other. This reflection prevents haphazard delegation that might miss important angles or create redundant coverage.

The supervisor maintains awareness of iteration limits. Hard limits prevent runaway research: a maximum number of concurrent researchers caps parallelism, and a maximum number of supervisor iterations bounds total reflection cycles. These limits ensure research completes in bounded time while providing flexibility for appropriately complex queries.

Parallel Researcher Execution

When the supervisor delegates research tasks, it can spawn multiple researcher agents simultaneously. Each researcher operates independently, investigating its assigned topic through a focused tool-calling loop.

Parallel execution dramatically accelerates research. If each researcher requires thirty seconds to investigate a topic, running three researchers serially takes ninety seconds while running them in parallel takes just thirty. For comprehensive research involving many subtopics, parallelization is the difference between minutes and hours.

Researchers don't communicate with each other during execution. Each receives its topic from the supervisor, investigates independently, and returns compressed findings. The supervisor aggregates these findings and reasons about what additional investigation is needed. This isolation simplifies coordination while the supervisor provides global coherence.

The degree of parallelism is configurable. Conservative settings might limit to two or three concurrent researchers, reducing load on search APIs and LLM providers. Aggressive settings might allow five or more concurrent researchers, maximizing speed at the cost of higher resource consumption. The appropriate setting depends on infrastructure capacity and time requirements.

The Researcher Loop

Each researcher agent implements a focused investigation loop that gathers information through tool calling, reflects on what's been found, and synthesizes findings into a compressed summary.

Tool-Based Investigation

Researchers investigate through structured tool calls rather than free-form generation. This tool-based approach ensures that all information gathering happens through verifiable channels and produces traceable results.

The primary investigation tool performs web searches, accepting one or more queries and returning results from across the web. Multiple queries can execute in parallel within a single tool call, enabling efficient information gathering. Results include not just summaries but raw content that enables detailed analysis.

The think tool enables researcher reflection between searches. After gathering initial information, the researcher can reason about what gaps remain and what additional searches would fill them. This reflection prevents blind iteration that might miss obvious gaps or redundantly search for already-found information.

The search complete tool signals that the researcher believes it has gathered sufficient information on its assigned topic. Explicit completion signaling ensures researchers don't continue searching indefinitely or stop prematurely without reasoning about coverage.

Search Strategy

Effective research follows a progression from broad to narrow searches. Initial searches cast a wide net, gathering general information about the topic. Subsequent searches target specific gaps identified through reflection.

Researchers receive guidance on search limits appropriate to query complexity. Simple queries that seek specific facts might need only two or three searches. Complex queries requiring comprehensive coverage might warrant up to five searches. These limits prevent over-investigation while ensuring adequate coverage.

Stopping criteria help researchers recognize when to conclude. Finding three or more relevant sources suggests adequate coverage for most topics. Observing that the last two searches returned similar information indicates diminishing returns from additional searching. Filling all identified information gaps signals comprehensive investigation.

The search strategy adapts to what's found. If early searches return rich, relevant information, fewer subsequent searches are needed. If early searches return sparse or tangential results, additional searches with refined queries become necessary. This adaptive approach matches research effort to topic difficulty.

Information Processing

Raw search results undergo processing before reaching researchers. This processing extracts relevant information while managing the volume that would otherwise overwhelm context windows.

Web page content is summarized to extract the key information relevant to the research topic. A dedicated summarization model processes each page, generating a concise summary of relevant content plus key excerpts that might warrant direct quotation. This summarization preserves important details while dramatically reducing token consumption.

Deduplication ensures that the same source doesn't appear multiple times across different search queries. When the same URL appears in results from multiple queries, it's processed once and included once. This deduplication prevents both redundant processing and citation confusion.

Results are formatted to facilitate researcher reasoning. Each source receives a clear identifier, URL, summary, and key excerpts. This structured format enables researchers to reason about sources individually while maintaining clear attribution for synthesis.

Finding Compression

After completing investigation, each researcher compresses its findings into a clean summary suitable for supervisor aggregation and final report generation.

Compression serves several purposes. It removes duplicative information that appeared across multiple sources. It structures findings into coherent narrative rather than disjointed search results. It preserves attribution information enabling proper citation. It reduces volume to manageable size for downstream processing.

The compression process maintains fidelity to sources. Findings should reflect what sources actually said, not creative interpretation. Key quotes and specific data points preserve source authority. The goal is synthesis that organizes and connects information, not generation that invents it.

Compressed findings return to the supervisor along with raw notes that enable later verification. The supervisor sees clean summaries for reasoning about coverage gaps while the final report generation phase can access detailed source information for accurate citation.

Search and Retrieval Integration

Open Deep Research supports multiple search backends, enabling flexibility in how web information is gathered. This modularity allows deployment in various contexts with different available services.

Tavily Search Integration

Tavily provides the default search backend, offering web search optimized for AI applications. The integration handles query execution, result processing, and content extraction through Tavily's API.

Search queries execute asynchronously, enabling parallel execution of multiple queries. Results return with both summaries and raw content, providing flexibility in how much detail to process. Topic filtering can focus searches on general content, news, or financial information depending on research needs.

The Tavily integration includes sophisticated result processing. Raw content from each result page is extracted and passed to the summarization model. This two-stage approach—search then summarize—ensures that researchers receive relevant, digestible information rather than raw web pages.

Native Provider Search

Modern LLM providers increasingly offer native search capabilities that integrate directly with model inference. Open Deep Research detects and supports these native search features when available.

OpenAI's native web search integrates search directly into the completion API. When enabled, the model can execute searches as part of generation, returning results alongside generated content. The system detects when native search was used by examining response metadata and handles results appropriately.

Anthropic's native search similarly integrates web search into completion requests. Server-side tool execution handles the search, with results flowing back through the standard response format. Detection examines usage metadata to identify when server-side search occurred.

Native search integration provides convenience but reduces control compared to explicit search tools. The system supports both approaches, allowing configuration based on requirements for control versus simplicity.

Model Context Protocol Integration

The Model Context Protocol enables integration with arbitrary external tools beyond built-in search capabilities. This extensibility allows Open Deep Research to access specialized data sources, internal databases, or custom APIs.

MCP servers expose tools through a standardized protocol. When configured, Open Deep Research connects to specified MCP servers and loads their available tools into the researcher's toolkit. These tools appear alongside built-in tools, enabling researchers to invoke them as needed.

Tool filtering ensures that only relevant MCP tools are exposed to researchers. Configuring specific tool names prevents confusion from exposing large tool catalogs and focuses researchers on capabilities relevant to the research task.

Authentication support enables MCP tools that require credentials. The system handles OAuth token exchange for tools that need authenticated access, managing the complexity of credential flow transparently.

Context and State Management

Effective research requires sophisticated management of accumulated context. As investigation proceeds, the system must track what's been found, what's been tried, and what remains to investigate.

State Structure

Open Deep Research uses typed state objects that flow through the workflow graph. Different phases access different portions of state, maintaining separation of concerns while enabling information flow.

The main agent state tracks the overall conversation, research brief, accumulated notes, and final report. This top-level state provides continuity across the entire research process.

Supervisor state tracks supervisor-specific context including the supervisor's message history, research iteration count, and aggregated findings from researchers. This state enables the supervisor to maintain awareness across multiple delegation cycles.

Researcher state tracks individual researcher context including the researcher's message history, tool call count, assigned topic, and compressed findings. This state is scoped to a single researcher's investigation and doesn't persist beyond that researcher's execution.

State Flow

State flows through the workflow graph following defined patterns. Input state provides the initial query. Each phase reads relevant state, performs its work, and writes results to state. Subsequent phases read accumulated state and add their contributions.

Reducer functions control how state accumulates. Some fields override previous values—a new research brief replaces any previous brief. Other fields append—each researcher's findings add to the accumulated notes. These reducer semantics ensure predictable state evolution.

State scoping prevents inappropriate access. Researchers cannot directly access other researchers' findings; they see only their assigned topic. The supervisor aggregates researcher findings into its own state. This scoping maintains clean boundaries between components.

Token Limit Handling

Large language models have finite context windows, and comprehensive research can easily generate more context than models can process. Open Deep Research implements sophisticated token limit detection and recovery.

Token limit detection recognizes when API requests fail due to context length. Different providers signal this condition differently—OpenAI returns specific error codes, Anthropic returns error messages with characteristic patterns, Google returns resource exhaustion errors. The system recognizes all these patterns and responds appropriately.

Recovery strategies progressively reduce context when limits are hit. Initial failures trigger truncation to a multiple of the model's token limit. Subsequent failures reduce context further. This progressive reduction finds the largest context that fits while preserving as much information as possible.

Critical information receives protection during truncation. The research brief and most recent findings take priority over older context. This prioritization ensures that truncation doesn't discard the information most relevant to current processing.

Report Generation

The final phase synthesizes all accumulated findings into a comprehensive, well-structured report. This synthesis transforms scattered research notes into coherent narrative with proper attribution.

Synthesis Process

Report generation receives the complete context accumulated through research: the original query, research brief, all researcher findings, and raw source information. From this context, it must produce a report that comprehensively addresses the query.

The generation model—potentially different from the research model—produces the report in a single generation pass. This single-pass approach ensures coherent narrative flow, avoiding the fragmentation that can result from piecewise generation.

Prompt engineering guides report structure and style. Instructions specify that reports should be well-organized with proper heading structure, include specific facts with sources, provide balanced analysis, and match the language of the original query. These instructions shape output quality without constraining content.

Citation Management

Proper citation distinguishes rigorous research from unreliable generation. Open Deep Research implements citation practices that ensure claims trace to sources.

Inline citations use sequential numbering—[1], [2], [3]—without gaps. Each URL receives a single citation number regardless of how many times it's referenced. The sources section at the report's end lists all citations with their corresponding numbers.

Citation density reflects research quality. Reports should cite sources for factual claims, statistics, and specific assertions. General knowledge or logical inferences may not require citation, but controversial or specific claims should trace to sources.

The raw notes preserved through research enable citation verification. If questions arise about whether a citation supports its claim, the original source content is available for review. This traceability supports both automated verification and human auditing.

Structural Flexibility

Different queries warrant different report structures. The system adapts structure to content rather than forcing queries into rigid templates.

Comparison queries naturally structure around the entities being compared. Introduction establishes the comparison context. Sections cover each entity. A comparison section synthesizes differences and similarities. Conclusion draws implications.

List queries may need minimal structure. A simple numbered list might fully address the query without introduction, conclusion, or extensive narrative. Over-structuring simple queries wastes both tokens and reader attention.

Complex analytical queries warrant sophisticated structure with multiple sections, subsections, and narrative threads. These reports might include background context, methodology notes, detailed analysis, and forward-looking implications.

The generation prompt provides structural examples for different query types while emphasizing flexibility. The goal is appropriate structure that serves the content, not rigid templates that constrain it.

Configuration System

Open Deep Research provides extensive configuration options enabling adaptation to different requirements, resources, and use cases. Configuration spans model selection, search configuration, execution limits, and behavioral settings.

Model Configuration

Four distinct model roles enable optimization for different tasks. The summarization model processes raw web content, generating concise summaries. The research model powers supervisor reasoning and researcher investigation. The compression model synthesizes researcher findings. The final report model generates the output report.

Each role can use a different model. A fast, cheap model might handle summarization where quality requirements are lower. A capable, expensive model might handle final report generation where output quality matters most. This role separation enables cost-quality optimization.

Token limits configure how much output each model can generate. Summarization might be limited to eight thousand tokens—enough for thorough summaries without excessive length. Report generation might allow ten thousand tokens—enough for comprehensive reports without runaway generation.

Execution Limits

Configurable limits bound execution across multiple dimensions. Maximum concurrent research units limits parallelism—how many researchers can run simultaneously. Maximum researcher iterations limits supervisor cycles—how many times the supervisor can reflect and delegate. Maximum tool calls limits individual researcher depth—how many searches a single researcher can perform.

These limits serve multiple purposes. They bound execution time, ensuring research completes in predictable duration. They bound resource consumption, preventing runaway API costs. They force prioritization, requiring the system to focus on most important research rather than exhaustively investigating everything.

Appropriate limits depend on use case. Quick research for simple queries might use tight limits—two concurrent researchers, three iterations, five tool calls. Comprehensive research for complex queries might use loose limits—five concurrent researchers, six iterations, ten tool calls.

Behavioral Configuration

Beyond resource limits, configuration controls behavioral aspects. Clarification can be enabled or disabled depending on whether user interaction is available. Search API selection chooses between Tavily, native provider search, or no search capability.

Content length limits control how much raw web page content is processed. Longer limits capture more detail at higher cost. Shorter limits reduce cost but might miss important information. The default of fifty thousand characters balances these concerns for typical use cases.

Retry limits control resilience to transient failures. Structured output parsing might fail occasionally; configurable retries provide resilience without requiring manual intervention.

Evaluation and Benchmarking

Rigorous evaluation enables measurement of research quality and comparison across configurations. Open Deep Research integrates with the Deep Research Bench, a standardized evaluation suite for deep research systems.

Deep Research Bench

The benchmark comprises one hundred research tasks drawn from PhD-level academic work. Fifty tasks use English; fifty use Chinese. Twenty-two domains span science, technology, business, finance, and more. Expert-compiled golden reports provide reference standards.

This benchmark tests real research capability, not synthetic tasks. Questions require genuine investigation, synthesis, and analysis. Simple retrieval or generation cannot achieve high scores; systems must actually research.

The benchmark enables meaningful comparison across systems and configurations. Does a more expensive model produce better research? Does additional parallelism improve coverage? Does native search outperform Tavily? Benchmark scores answer these questions quantitatively.

Evaluation Metrics

Six metrics assess different quality dimensions. Overall quality evaluates research depth, source quality, analytical rigor, practical value, balance, and writing quality—each scored on a five-point scale. Relevance measures whether the report addresses the user's actual question. Structure assesses logical organization and flow.

Correctness compares claims against golden reference answers. This metric catches factual errors that might otherwise go unnoticed. Groundedness verifies that claims are supported by cited sources—checking that the system isn't hallucinating information it attributes to sources. Completeness measures coverage of important aspects identified in golden references.

Together, these metrics provide comprehensive quality assessment. A system might score well on some metrics while struggling on others. Understanding the metric profile helps identify improvement opportunities.

Performance Benchmarks

Benchmark results demonstrate the effectiveness of different configurations. GPT-5 achieves the highest scores, reflecting its advanced capabilities. Claude Sonnet 4 achieves competitive scores with different cost-performance characteristics. The default GPT-4.1 configuration achieves solid scores while balancing cost and capability.

These benchmarks inform configuration decisions. Users can choose configurations based on their quality requirements and resource constraints, with benchmark data indicating expected performance levels.

Design Patterns and Principles

Several design patterns recur throughout Open Deep Research, reflecting principles that enable effective agent systems.

Hierarchical Decomposition

The supervisor-researcher pattern exemplifies hierarchical decomposition. Rather than a single agent attempting complex research directly, the system decomposes work across levels. The supervisor handles strategic planning and coordination. Researchers handle focused investigation. The final report generator handles synthesis.

This hierarchy enables each component to focus on its specialty. The supervisor doesn't need to be good at searching—that's the researchers' job. Researchers don't need to understand the overall research strategy—the supervisor handles that. Specialization enables optimization at each level.

Hierarchical decomposition also provides natural parallelism opportunities. Researcher tasks are independent once delegated, enabling parallel execution. Without decomposition, the entire research process would be sequential.

Explicit Tool Use

All agent actions happen through explicit tool calls rather than implicit behavior. Searching requires invoking the search tool. Reflecting requires invoking the think tool. Completing research requires invoking the complete tool.

This explicitness provides traceability. Every action is logged, reviewable, and debuggable. When something goes wrong, the tool call history reveals exactly what the agent attempted. Implicit behavior would hide these details.

Explicitness also enables control. Tool availability can be configured—disable search to force reliance on existing knowledge. Tool behavior can be customized—modify the search tool to use different backends. This control would be impossible with implicit behavior.

Structured Outputs

Throughout the system, structured outputs ensure predictable formats. The research brief uses a defined schema. Researcher findings use a defined schema. Summarizations use a defined schema. This structure enables reliable downstream processing.

Structured outputs also provide validation. Schema violations are caught immediately rather than causing subtle downstream failures. A malformed research brief won't propagate confusion through subsequent phases—it will be rejected at generation time.

The structured output approach leverages modern LLM capabilities for reliable JSON generation. Retry logic handles occasional generation failures, providing resilience without sacrificing structure benefits.

Progressive Degradation

When problems occur, the system degrades progressively rather than failing completely. Token limit exceeded? Truncate context and retry. Summarization failed? Return original content. Search returned no results? Continue with available information.

This progressive degradation ensures that partial results are produced rather than complete failures. A research report with some sections might be more valuable than no report at all. The system communicates degradation clearly while continuing to provide value.

Practical Considerations

Deploying deep research systems involves considerations beyond the core architecture. Cost, latency, reliability, and observability all matter for production use.

Cost Management

Deep research consumes significant resources. Each supervisor iteration requires LLM inference. Each researcher performs multiple searches and summarizations. Final report generation requires a lengthy generation pass. Costs accumulate quickly for comprehensive research.

Several strategies manage costs. Model selection trades capability for cost—using GPT-4.1-mini for summarization costs less than GPT-4.1 while maintaining adequate quality. Execution limits cap maximum resource consumption regardless of query complexity. Caching prevents redundant processing when the same content is accessed multiple times.

Cost visibility enables informed decisions. Tracking token usage, search API calls, and inference time reveals where resources go. This visibility enables optimization and appropriate pricing for downstream users.

Latency Expectations

Comprehensive research takes time. Even with parallelization, multiple search-summarize cycles, supervisor iterations, and final report generation require minutes rather than seconds. User expectations must align with this reality.

Progress indication maintains engagement during lengthy research. Showing which phase is executing, how many researchers are active, and what has been found so far demonstrates that work is happening even when final results aren't yet available.

Tiered offerings can address different latency requirements. Quick summaries with tight limits might complete in one to two minutes. Comprehensive research with loose limits might require ten to fifteen minutes. Matching offering to need prevents unnecessary waiting or inadequate research.

Reliability Patterns

Production systems require reliability beyond what works in development. Retry logic handles transient failures from API rate limits, network issues, and service hiccups. Timeout handling prevents hung requests from blocking indefinitely.

Fallback strategies provide resilience when primary approaches fail. If Tavily search is unavailable, fallback to alternative search providers. If summarization fails repeatedly, return truncated original content. These fallbacks maintain progress despite component failures.

Clear failure communication distinguishes recoverable issues from fatal errors. "Search API rate limited, retrying in 10 seconds" indicates transient issues. "Unable to access any search providers" indicates more serious problems requiring intervention.

Observability

Production deep research requires observability into system behavior. LangSmith integration provides tracing of the entire workflow graph, showing how each node executed and what state flowed between them.

Token usage tracking reveals resource consumption patterns. Which phases consume the most tokens? Which queries require the most research? This data informs both optimization and pricing decisions.

Quality monitoring catches degradation before users notice. Tracking evaluation metrics over time reveals whether system changes improve or harm research quality. Automated alerts can flag significant quality drops for investigation.

Conclusion

Open Deep Research demonstrates how modern AI systems can conduct comprehensive research that previously required hours of human effort. The key is orchestration: decomposing complex queries into manageable tasks, executing investigation in parallel, synthesizing findings intelligently, and producing well-structured reports with proper attribution.

The supervisor-researcher pattern provides the architectural foundation. Strategic delegation enables flexible decomposition while maintaining coherent direction. Parallel execution maximizes throughput while bounded limits ensure completion. Progressive synthesis transforms scattered findings into unified narrative.

For developers seeking to understand how deep research systems work, Open Deep Research provides a complete, transparent implementation. For teams building research capabilities, it offers a production-ready foundation that can be customized for specific needs. For the broader AI community, it represents the open-source commitment to making advanced AI capabilities accessible to all.

The ability to conduct thorough research on demand—answering complex questions with comprehensive, well-sourced reports—represents a fundamental capability for AI assistants. Open Deep Research shows how this capability can be built with transparency, flexibility, and quality that matches commercial offerings.