{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-gpt-researcher","slug":"gpt-researcher","name":"GPT Researcher","type":"agent","url":"https://github.com/assafelovic/gpt-researcher","page_url":"https://unfragile.ai/gpt-researcher","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-gpt-researcher__cap_0","uri":"capability://search.retrieval.multi.source.web.research.orchestration.with.llm.guided.query.generation","name":"multi-source web research orchestration with llm-guided query generation","description":"Orchestrates parallel web searches across multiple sources (Google, Bing, DuckDuckGo, Tavily API) by using an LLM to decompose research topics into targeted sub-queries, then aggregates and deduplicates results. Implements a query expansion loop where the LLM analyzes initial results to identify information gaps and generates follow-up searches, creating a depth-first research graph rather than simple keyword matching.","intents":["I need to research a complex topic and get comprehensive coverage without manually crafting 20 different search queries","I want to automatically discover related subtopics and follow research threads without human intervention","I need to compare information across multiple search engines to validate facts and find consensus"],"best_for":["researchers building automated intelligence gathering systems","teams needing fact-checked summaries without manual research","developers building AI agents that require real-time information"],"limitations":["Search quality depends on LLM's ability to formulate queries — poor query generation leads to irrelevant results","Rate limiting on free search APIs (Google, Bing) may throttle parallel requests; Tavily API requires paid tier for high volume","No built-in deduplication of semantically similar content across sources — requires post-processing","Search results are point-in-time snapshots; no continuous monitoring or update tracking"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, or local model)","Internet connectivity for web search APIs","Optional: Tavily API key for enhanced search quality"],"input_types":["text (research topic/query)"],"output_types":["structured research report (markdown or JSON)","list of source URLs with relevance scores","extracted facts with citations"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_1","uri":"capability://text.generation.language.context.aware.research.report.synthesis.with.source.attribution","name":"context-aware research report synthesis with source attribution","description":"Aggregates raw search results into a structured research report by using an LLM to synthesize information across sources, organize findings by topic hierarchy, and maintain inline citations linking each claim to its source URL. Implements a two-pass approach: first pass clusters results by semantic similarity, second pass generates report sections with citation metadata embedded in the output structure.","intents":["I want a well-organized research report that reads like a human-written summary, not a list of search snippets","I need to verify every claim in the report by clicking through to its source","I want the report structured by topic (e.g., history, current state, future outlook) not by search result order"],"best_for":["knowledge workers creating research documents for stakeholders","teams building fact-checked knowledge bases with audit trails","developers integrating research into RAG pipelines where source attribution is critical"],"limitations":["Synthesis quality depends on LLM's ability to reconcile conflicting sources — no built-in conflict detection or consensus scoring","Report structure is LLM-generated; no user control over section hierarchy or organization schema","Citation accuracy relies on LLM correctly mapping claims to source URLs — hallucination of citations is possible","Long reports (>10k tokens) may lose coherence due to context window constraints"],"requires":["Python 3.9+","LLM API with sufficient context window (8k+ tokens recommended)","Completed web search results from prior research phase"],"input_types":["structured search results (URLs + snippets)","research topic/query for context"],"output_types":["markdown report with inline citations","JSON report with structured sections and source metadata","plain text report"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.fallback.and.cost.optimization","name":"multi-provider llm abstraction with fallback and cost optimization","description":"Provides a unified interface to multiple LLM providers (OpenAI, Anthropic, Ollama, local models, Azure OpenAI) with automatic provider selection based on cost, latency, or capability requirements. Implements a provider registry pattern where each provider exposes a standardized interface, and the orchestrator selects the optimal provider for each task (e.g., cheap model for query generation, expensive model for synthesis).","intents":["I want to use different LLM providers for different steps without rewriting code for each API","I need to minimize research costs by using cheaper models for simple tasks and premium models only when necessary","I want to fall back to a local model if API calls fail or hit rate limits"],"best_for":["cost-conscious teams running research at scale","developers building multi-provider AI systems","organizations with existing LLM infrastructure (Ollama, local models) wanting to integrate cloud APIs"],"limitations":["Provider abstraction adds ~50-100ms overhead per LLM call due to interface translation","Not all providers support identical features (e.g., function calling, streaming) — feature parity gaps require conditional logic","Fallback logic is sequential, not parallel — if primary provider fails, latency increases by full retry time","Cost optimization requires manual configuration of provider preferences; no automatic cost-benefit analysis"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, Azure, etc.)","Optional: Ollama running locally for fallback or cost reduction"],"input_types":["text prompts","system instructions"],"output_types":["text completions","structured JSON (if provider supports function calling)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_3","uri":"capability://automation.workflow.research.task.decomposition.with.dependency.graph.execution","name":"research task decomposition with dependency graph execution","description":"Breaks down a research request into subtasks (query generation, search execution, result aggregation, synthesis) and executes them in dependency order using an async task graph. Each task is a node with input/output contracts, and the executor resolves dependencies and parallelizes independent tasks. Implements a DAG (directed acyclic graph) pattern where task outputs feed into downstream tasks, enabling efficient resource utilization and resumable execution.","intents":["I want to research a topic that requires multiple sequential steps without manually orchestrating each step","I need to resume a failed research job without re-running completed steps","I want to parallelize independent searches while maintaining dependencies (e.g., run multiple searches in parallel, then synthesize results)"],"best_for":["teams building complex research pipelines with multiple stages","developers needing resumable/checkpointable research workflows","systems requiring transparent task execution logging and debugging"],"limitations":["Task graph construction is implicit in code flow — no visual DAG editor or explicit graph definition","No built-in checkpointing; if a task fails mid-execution, all downstream tasks are invalidated","Async execution adds complexity for error handling and debugging — stack traces may be difficult to trace","Task dependencies are hardcoded; no dynamic task generation based on intermediate results"],"requires":["Python 3.9+","asyncio event loop (Python standard library)","All upstream task dependencies resolved before execution"],"input_types":["research topic/query","task configuration (which steps to run)"],"output_types":["task execution logs","intermediate results (search results, synthesis output)","final research report"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_4","uri":"capability://planning.reasoning.configurable.research.scope.and.depth.control","name":"configurable research scope and depth control","description":"Allows users to specify research parameters (number of search iterations, result limit per query, report length, focus areas) that control the breadth and depth of investigation. Implements a configuration object that propagates through the task graph, affecting query generation (how many follow-up queries), search execution (how many results to fetch), and synthesis (report length and detail level).","intents":["I want a quick 5-minute research summary for a status update, not a comprehensive 2-hour deep dive","I need to focus research on specific subtopics (e.g., 'recent developments' vs 'historical context')","I want to control research cost by limiting the number of LLM calls and search queries"],"best_for":["teams with varying research needs (quick summaries vs deep dives)","cost-conscious users wanting to control research spend","developers building research APIs with user-configurable parameters"],"limitations":["Configuration options are not auto-discovered; users must know which parameters to tune","No intelligent defaults based on topic complexity — users must manually adjust depth for different topics","Scope constraints are soft (e.g., 'max 5 iterations') not hard — LLM may ignore them if it deems more research necessary","No feedback loop to suggest optimal depth based on information gain"],"requires":["Python 3.9+","Configuration object passed to research orchestrator"],"input_types":["configuration parameters (depth, scope, focus areas)","research topic"],"output_types":["research report (length/detail determined by config)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_5","uri":"capability://data.processing.analysis.web.scraping.and.content.extraction.from.search.results","name":"web scraping and content extraction from search results","description":"Fetches full HTML content from search result URLs and extracts relevant text using HTML parsing and optional LLM-based content filtering. Implements a scraper that handles common web page structures (articles, blog posts, documentation) and filters out boilerplate (navigation, ads, comments) to extract the core content. Uses BeautifulSoup or similar for parsing, with optional LLM post-processing to identify relevant sections.","intents":["I want to extract the actual article content from search results, not just the snippet","I need to filter out ads and navigation to get the core information","I want to handle different website layouts (news sites, blogs, documentation) without manual rules"],"best_for":["research systems needing full-text content, not just search snippets","teams building knowledge extraction pipelines","developers integrating web content into RAG systems"],"limitations":["Web scraping is slow (~1-2 seconds per URL) and blocks parallel search execution","Many websites block automated scraping (robots.txt, rate limiting, JavaScript rendering); no built-in handling for JavaScript-heavy sites","Content extraction heuristics are brittle — unusual page layouts may produce poor results","LLM-based filtering adds latency and cost; may over-filter or under-filter depending on prompt quality"],"requires":["Python 3.9+","HTTP client library (requests, httpx)","HTML parsing library (BeautifulSoup4)","Optional: Selenium or Playwright for JavaScript rendering"],"input_types":["list of URLs from search results"],"output_types":["extracted text content","structured content (title, body, metadata)","relevance scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_6","uri":"capability://memory.knowledge.research.memory.and.context.caching.across.sessions","name":"research memory and context caching across sessions","description":"Caches research results and intermediate outputs (search results, synthesis) to avoid redundant API calls and LLM invocations when the same topic is researched multiple times. Implements a simple file-based or database cache keyed by research topic hash, with optional TTL (time-to-live) to refresh stale results. Enables resumable research where a failed job can pick up from the last completed task.","intents":["I want to research the same topic multiple times without re-running expensive searches and LLM calls","I need to resume a failed research job without starting from scratch","I want to share research results across team members without duplicating work"],"best_for":["teams researching overlapping topics repeatedly","systems with unreliable network connections needing resumable workflows","cost-conscious users wanting to minimize API calls"],"limitations":["Cache invalidation is manual or TTL-based; no automatic detection of stale information","File-based cache doesn't scale to large deployments; requires external cache store (Redis, database) for production","Cache key is topic-based; similar but distinct topics may incorrectly hit the cache","No cache versioning; if research logic changes, old cached results may be incompatible"],"requires":["Python 3.9+","File system or external cache store (Redis, database)","Optional: cache invalidation strategy (TTL, manual refresh)"],"input_types":["research topic","cache configuration (TTL, storage backend)"],"output_types":["cached research results","cache hit/miss metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_7","uri":"capability://text.generation.language.structured.output.formatting.with.multiple.report.templates","name":"structured output formatting with multiple report templates","description":"Generates research reports in multiple formats (markdown, JSON, HTML, plain text) using template-based rendering. Implements a template system where each format has a corresponding template that defines structure, styling, and citation formatting. Supports custom templates for domain-specific report structures (e.g., competitive analysis, market research, technical documentation).","intents":["I want the research report in markdown for my wiki, JSON for my API, and HTML for my website","I need a custom report structure for competitive analysis that includes SWOT, market size, and key players","I want to embed research reports directly into my application with consistent formatting"],"best_for":["teams integrating research into multiple downstream systems","developers building research APIs with flexible output formats","organizations with custom report requirements"],"limitations":["Template system is basic; complex custom formats may require code changes","No built-in template validation; malformed templates produce broken output","Format conversion may lose information (e.g., markdown to plain text loses formatting)","Custom templates require domain knowledge of template syntax"],"requires":["Python 3.9+","Template engine (Jinja2 or similar)","Research results in structured format (JSON)"],"input_types":["structured research results","template selection or custom template"],"output_types":["markdown report","JSON report","HTML report","plain text report","custom format (user-defined)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_8","uri":"capability://data.processing.analysis.research.quality.assessment.and.confidence.scoring","name":"research quality assessment and confidence scoring","description":"Evaluates research quality by analyzing source diversity, information consensus, and claim support. Implements heuristics that score research based on number of independent sources per claim, agreement between sources, and recency of information. Produces a confidence score (0-100) for the overall research and per-section confidence metrics.","intents":["I want to know how confident I should be in the research results","I need to identify sections that need more research because sources disagree","I want to flag claims that are only supported by a single source"],"best_for":["teams making decisions based on research and needing confidence metrics","systems requiring quality gates before publishing research","developers building research APIs with quality indicators"],"limitations":["Confidence scoring is heuristic-based, not statistically rigorous; no formal uncertainty quantification","Source diversity is measured by URL count, not semantic diversity — multiple articles from the same domain count as diverse","No detection of coordinated misinformation or echo chambers","Consensus scoring assumes sources are independent; doesn't account for sources citing each other"],"requires":["Python 3.9+","Structured research results with source attribution"],"input_types":["research results with source metadata"],"output_types":["confidence score (0-100)","per-section confidence metrics","quality assessment report"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-gpt-researcher__cap_9","uri":"capability://planning.reasoning.research.topic.expansion.and.related.topic.discovery","name":"research topic expansion and related topic discovery","description":"Automatically discovers related topics and subtopics by analyzing search results and using LLM to identify conceptual relationships. Implements a topic graph where nodes are topics and edges represent relationships (e.g., 'is-a', 'related-to', 'causes'). Enables users to expand research scope by following topic relationships or narrow scope by focusing on specific subtopics.","intents":["I'm researching AI and want to automatically discover related topics like machine learning, neural networks, and ethics","I want to understand how my research topic relates to other domains","I need to expand my research scope without manually brainstorming related topics"],"best_for":["exploratory research where scope is not pre-defined","teams discovering new market segments or competitive threats","developers building knowledge discovery systems"],"limitations":["Topic discovery is LLM-driven; quality depends on LLM's ability to identify relationships from text","Topic graph is acyclic but may contain spurious relationships (false positives)","No weighting of relationship strength; all related topics are treated equally","Scope explosion risk — following all related topics may lead to unbounded research"],"requires":["Python 3.9+","LLM API for topic relationship extraction","Search results from initial research"],"input_types":["research topic","search results"],"output_types":["list of related topics","topic relationship graph","expanded research scope"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, or local model)","Internet connectivity for web search APIs","Optional: Tavily API key for enhanced search quality","LLM API with sufficient context window (8k+ tokens recommended)","Completed web search results from prior research phase","API keys for at least one LLM provider (OpenAI, Anthropic, Azure, etc.)","Optional: Ollama running locally for fallback or cost reduction","asyncio event loop (Python standard library)","All upstream task dependencies resolved before execution"],"failure_modes":["Search quality depends on LLM's ability to formulate queries — poor query generation leads to irrelevant results","Rate limiting on free search APIs (Google, Bing) may throttle parallel requests; Tavily API requires paid tier for high volume","No built-in deduplication of semantically similar content across sources — requires post-processing","Search results are point-in-time snapshots; no continuous monitoring or update tracking","Synthesis quality depends on LLM's ability to reconcile conflicting sources — no built-in conflict detection or consensus scoring","Report structure is LLM-generated; no user control over section hierarchy or organization schema","Citation accuracy relies on LLM correctly mapping claims to source URLs — hallucination of citations is possible","Long reports (>10k tokens) may lose coherence due to context window constraints","Provider abstraction adds ~50-100ms overhead per LLM call due to interface translation","Not all providers support identical features (e.g., function calling, streaming) — feature parity gaps require conditional logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=gpt-researcher","compare_url":"https://unfragile.ai/compare?artifact=gpt-researcher"}},"signature":"KIUTSfoX+YyeSuX2/DpEPqwycSzIjkpllpEDO35DLT/IvMqtw+2git/RPta/svLWOgstGPmotOAXBFjb+CZ2DA==","signedAt":"2026-06-23T13:31:53.038Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gpt-researcher","artifact":"https://unfragile.ai/gpt-researcher","verify":"https://unfragile.ai/api/v1/verify?slug=gpt-researcher","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}