{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-assafelovic--gpt-researcher","slug":"assafelovic--gpt-researcher","name":"gpt-researcher","type":"agent","url":"https://gptr.dev","page_url":"https://unfragile.ai/assafelovic--gpt-researcher","categories":["ai-agents"],"tags":["agent","ai","automation","deepresearch","llms","mcp","mcp-server","python","research","search","webscraping"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-assafelovic--gpt-researcher__cap_0","uri":"capability://planning.reasoning.autonomous.multi.step.research.orchestration.with.plan.and.solve.decomposition","name":"autonomous multi-step research orchestration with plan-and-solve decomposition","description":"Orchestrates end-to-end research workflows by decomposing user queries into parallel sub-queries, executing them concurrently across multiple LLM providers, and synthesizing results into structured reports. Uses a planner-executor agent pattern where the planner decomposes tasks and the executor conducts parallel research, inspired by Plan-and-Solve and RAG papers. The ResearchConductor class manages the workflow state, skill invocation sequencing, and context compression across research phases.","intents":["I need to research a complex topic and get a comprehensive report with citations without manually searching multiple sources","I want to parallelize research tasks to reduce total execution time compared to sequential searching","I need to decompose a broad research question into focused sub-queries that can be researched independently"],"best_for":["teams building autonomous research agents","developers integrating deep research capabilities into larger applications","non-technical users needing factual, cited research reports on demand"],"limitations":["Parallel sub-query execution adds orchestration overhead (~500ms-2s per research cycle depending on provider latency)","Context compression may lose nuance in highly specialized domains requiring deep technical knowledge","No built-in fact-checking beyond source curation — relies on LLM provider accuracy","Report quality depends heavily on query decomposition quality, which varies by LLM provider tier"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Internet connectivity for web retrieval (unless using local-only document sources)","Minimum 2GB RAM for concurrent agent execution"],"input_types":["text query (natural language research question)","optional: research mode specification (standard/detailed/deep)","optional: domain filters and source constraints"],"output_types":["structured research report (markdown/JSON)","source citations with URLs","research metadata (execution time, sources used, sub-queries executed)"],"categories":["planning-reasoning","automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.three.tier.strategy.and.model.specific.handling","name":"multi-provider llm abstraction with three-tier strategy and model-specific handling","description":"Abstracts 25+ LLM providers (OpenAI, Anthropic, Ollama, Groq, etc.) behind a unified interface using a three-tier strategy: planner LLM (query decomposition), executor LLM (research execution), and writer LLM (report generation). Implements provider-specific prompt formatting, token limits, and capability detection. The Config class manages provider selection, fallback chains, and model-specific parameters like temperature and max_tokens, enabling seamless provider swapping without code changes.","intents":["I want to use different LLM providers for different research phases (e.g., fast planner, powerful executor, specialized writer)","I need to switch between providers based on cost, latency, or capability without rewriting my research pipeline","I want to run research locally using Ollama while maintaining the same API as cloud providers"],"best_for":["developers building provider-agnostic research agents","teams with multi-provider LLM strategies (cost optimization, latency reduction)","organizations needing on-premise LLM execution via Ollama"],"limitations":["Provider-specific prompt formatting adds ~50-100ms overhead per LLM call","Model-specific handling requires manual configuration for new providers; no auto-detection of capabilities","Fallback chains not automatically optimized — requires manual tuning of provider order","Token limit enforcement is approximate; some providers may reject requests near limits"],"requires":["Python 3.9+","API keys for selected providers (or Ollama running locally on port 11434)","Network access to provider endpoints (or local Ollama instance)","Provider-specific SDK or HTTP client (handled by gpt-researcher internally)"],"input_types":["provider name (string, e.g., 'openai', 'anthropic', 'ollama')","model identifier (string, e.g., 'gpt-4', 'claude-3-opus')","optional: provider-specific config (temperature, max_tokens, api_base)"],"output_types":["LLM response (text completion)","provider metadata (model used, tokens consumed, latency)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_10","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.tool.agnostic.research.access","name":"model context protocol (mcp) server integration for tool-agnostic research access","description":"Exposes GPT Researcher as an MCP server, enabling integration with any MCP-compatible client (Claude, other AI assistants, custom tools). Implements MCP protocol for resource discovery, tool invocation, and streaming responses. Allows AI assistants to invoke research tasks as native tools without custom integrations. MCP server configuration is declarative through environment variables and config files.","intents":["I want to use GPT Researcher as a tool in Claude or other AI assistants","I need to integrate research capabilities into MCP-compatible applications","I want to enable AI assistants to autonomously invoke research tasks"],"best_for":["teams using Claude or other MCP-compatible AI assistants","developers building MCP-compatible applications","organizations wanting tool-agnostic research access"],"limitations":["MCP protocol adds serialization overhead (~50-100ms per call)","Tool discovery and invocation is limited by MCP protocol capabilities","Error handling through MCP is less detailed than direct API calls","Streaming responses through MCP may have latency overhead","MCP server requires separate process/port; adds deployment complexity"],"requires":["Python 3.9+","MCP-compatible client (Claude, custom tool, etc.)","MCP server running and accessible to client","Network connectivity between client and MCP server"],"input_types":["MCP tool invocation (JSON-RPC format)","research query and configuration"],"output_types":["MCP response (JSON-RPC format)","research report","streaming progress updates (if supported by client)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_11","uri":"capability://safety.moderation.domain.filtering.and.source.validation.for.research.credibility","name":"domain filtering and source validation for research credibility","description":"Filters research sources by domain whitelist/blacklist and validates source credibility using heuristics (domain reputation, HTTPS, content freshness). The Curator skill evaluates sources before inclusion in research context, removing low-credibility sources and prioritizing authoritative domains. Supports custom domain filters and source validation rules. Domain filtering is applied during retrieval and curation phases.","intents":["I want to exclude unreliable sources (misinformation sites, low-quality blogs) from my research","I need to prioritize authoritative sources (academic papers, official documentation) in results","I want to customize source validation rules for my domain (e.g., only medical journals for health research)"],"best_for":["teams researching sensitive topics (health, finance, law) requiring credible sources","organizations with specific source requirements or compliance needs","applications needing to reduce misinformation in research results"],"limitations":["Domain reputation heuristics are approximate; no real-time reputation scoring","Whitelist/blacklist maintenance is manual; requires ongoing curation","Source validation may be too aggressive, filtering out legitimate niche sources","No support for source-specific credibility (e.g., trusting one author on a low-reputation site)","Filtering adds latency (~100-200ms per source evaluation)"],"requires":["Python 3.9+","Optional: domain reputation database or API (for enhanced validation)","Domain whitelist/blacklist configuration"],"input_types":["list of retrieved sources (URLs, domains)","optional: domain whitelist/blacklist","optional: custom validation rules"],"output_types":["filtered source list (credible sources only)","source credibility scores","filtering metadata (sources removed, reasons)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_12","uri":"capability://image.visual.image.generation.for.visual.research.reports","name":"image generation for visual research reports","description":"Generates images for research reports using DALL-E, Stable Diffusion, or other image generation APIs. Images are generated based on research content and can be embedded in reports. Image generation is optional and triggered based on report type or explicit configuration. Generated images are cached to avoid duplicate generation for similar queries.","intents":["I want to include visual elements (charts, diagrams, illustrations) in research reports","I need to generate images that illustrate research findings","I want to create more engaging reports with visual content"],"best_for":["teams creating visual research reports for presentations","applications needing to generate illustrations from research content","organizations wanting to enhance report engagement with images"],"limitations":["Image generation adds 5-30s latency per image and increases API costs significantly","Generated images may not accurately represent research content; requires human review","Image generation is non-deterministic; same prompt may produce different images","Caching is simple (hash-based); may miss semantically similar queries","No support for custom image styles or branding"],"requires":["Python 3.9+","Image generation API key (DALL-E, Stable Diffusion, etc.)","Image generation model configuration"],"input_types":["research content (text)","optional: image generation prompts","optional: image style preferences"],"output_types":["generated image URLs or base64-encoded images","image metadata (generation time, model used, prompt)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_13","uri":"capability://automation.workflow.docker.deployment.with.containerized.research.infrastructure","name":"docker deployment with containerized research infrastructure","description":"Provides Docker and Docker Compose configurations for containerized deployment of GPT Researcher with FastAPI backend, NextJS frontend, and optional services (Redis for caching, PostgreSQL for history). Enables one-command deployment to cloud platforms (AWS, GCP, Azure, Heroku). Includes environment variable configuration for provider selection and API keys. Supports scaling through container orchestration (Kubernetes, Docker Swarm).","intents":["I want to deploy GPT Researcher to production with minimal configuration","I need to scale research infrastructure across multiple containers","I want to use Docker Compose for local development matching production environment"],"best_for":["teams deploying to cloud platforms with Docker support","organizations needing containerized research infrastructure","developers wanting production-parity local development"],"limitations":["Docker adds deployment complexity; requires Docker knowledge","Container resource limits may throttle research execution for large tasks","Networking between containers adds latency (~10-50ms per inter-container call)","Persistent storage requires external services (S3, PostgreSQL); no built-in local persistence","Scaling is manual; no auto-scaling based on research queue depth"],"requires":["Docker 20.10+","Docker Compose 2.0+ (for local development)","Cloud platform account (AWS, GCP, Azure, etc.) for production deployment","Environment variables for API keys and configuration"],"input_types":["Dockerfile and docker-compose.yml","environment variables (.env file)","optional: Kubernetes manifests for orchestration"],"output_types":["containerized application (running in Docker)","deployment logs and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_14","uri":"capability://automation.workflow.configuration.system.with.environment.variable.and.file.based.settings","name":"configuration system with environment variable and file-based settings","description":"Centralizes all configuration through a Config class supporting environment variables, YAML/JSON files, and programmatic overrides. Configuration includes LLM provider selection, research modes, retriever settings, vector store backends, and deployment options. Supports configuration inheritance and defaults, enabling easy switching between development/staging/production environments. Configuration validation ensures required parameters are set before research execution.","intents":["I want to configure GPT Researcher through environment variables without code changes","I need to support multiple deployment environments (dev/staging/prod) with different configurations","I want to validate configuration before research execution to catch errors early"],"best_for":["teams managing multiple deployment environments","organizations needing configuration-driven research setup","developers wanting to avoid hardcoding API keys and settings"],"limitations":["Configuration validation is basic; doesn't catch all invalid combinations","No runtime configuration updates; requires restart to apply changes","Configuration inheritance can be confusing with multiple override levels","No built-in configuration encryption; API keys stored in plaintext in env files","Limited configuration UI; mostly command-line and file-based"],"requires":["Python 3.9+","Environment variables or configuration files","Optional: YAML/JSON parser (included in dependencies)"],"input_types":["environment variables (KEY=VALUE)","optional: YAML/JSON configuration file","optional: programmatic Config object"],"output_types":["validated Config object","configuration metadata (source, overrides applied)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_2","uri":"capability://search.retrieval.parallel.web.scraping.and.document.retrieval.with.multi.source.aggregation","name":"parallel web scraping and document retrieval with multi-source aggregation","description":"Executes parallel web scraping and document retrieval across multiple sources (web search, local documents, cloud storage) using a pluggable Retriever system. The web scraping module uses browser automation (Playwright/Selenium) to handle JavaScript-heavy sites, while document loaders support PDF, DOCX, TXT, and other formats. Sources are deduplicated, ranked by relevance, and filtered by domain constraints before being passed to the research pipeline. The system supports cloud storage integration (S3, GCS) for document sources.","intents":["I need to gather information from multiple web sources and documents in parallel without blocking on individual requests","I want to research using both public web sources and private documents (PDFs, internal docs) in a single research task","I need to filter sources by domain, exclude unreliable sources, and deduplicate results"],"best_for":["teams researching across heterogeneous data sources (web + documents + cloud storage)","organizations with private document repositories needing integration with research workflows","developers building research tools that need to handle JavaScript-heavy websites"],"limitations":["Browser automation (Playwright) adds 2-5s latency per page vs. simple HTTP requests","Document parsing quality varies by format; complex PDFs with images may lose content","Web scraping may be blocked by rate limiting or robots.txt; no built-in retry logic with exponential backoff","Cloud storage integration requires pre-configured credentials; no automatic credential discovery","Parallel retrieval is bounded by system resources (file handles, memory) — no configurable concurrency limits"],"requires":["Python 3.9+","Playwright or Selenium for browser automation (optional, for JS-heavy sites)","Document parsing libraries (PyPDF2, python-docx, etc.)","Cloud storage credentials if using S3/GCS (AWS_ACCESS_KEY_ID, GCS_PROJECT_ID, etc.)","Internet connectivity for web retrieval"],"input_types":["search query (text)","optional: list of document paths or cloud storage URIs","optional: domain filters (whitelist/blacklist)","optional: retriever type selection (web, documents, hybrid)"],"output_types":["list of retrieved documents (text content + metadata)","source URLs and document paths","relevance scores (if using semantic ranking)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_3","uri":"capability://memory.knowledge.context.compression.and.semantic.deduplication.for.token.efficiency","name":"context compression and semantic deduplication for token efficiency","description":"Compresses research context using semantic deduplication and relevance-based filtering to stay within LLM token limits while preserving critical information. The ContextManager skill analyzes retrieved sources, removes redundant content, and prioritizes high-relevance passages. Uses embeddings-based similarity detection to identify duplicate information across sources. Compression is adaptive based on research mode (standard uses ~2k tokens, detailed uses ~4k, deep uses ~8k+) and available context window of the selected LLM.","intents":["I need to research a topic with many sources but stay within my LLM's token limits without losing critical information","I want to avoid redundant context when multiple sources cover the same information","I need adaptive context compression that scales with research depth and LLM capabilities"],"best_for":["teams using smaller/cheaper LLM models with limited context windows","research workflows with hundreds of sources that need intelligent filtering","cost-conscious applications needing to minimize token consumption"],"limitations":["Semantic deduplication requires embedding computation (~100-200ms per source), adding latency","Compression may lose domain-specific nuance or edge cases in highly specialized research","Adaptive token limits are heuristic-based; may over-compress for some research types","No user control over compression strategy — fully automated with limited configurability","Embeddings quality depends on embedding model; poor embeddings lead to incorrect deduplication"],"requires":["Python 3.9+","Embeddings model (OpenAI, Ollama, or local embeddings)","Retrieved sources with content (from retriever system)","LLM context window information (automatically detected from provider)"],"input_types":["list of retrieved documents with content","research mode (standard/detailed/deep)","optional: target token limit (overrides mode-based default)"],"output_types":["compressed context (text)","compression metadata (original vs. compressed token count, sources retained/dropped)","relevance scores for retained passages"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_4","uri":"capability://text.generation.language.structured.report.generation.with.source.attribution.and.formatting","name":"structured report generation with source attribution and formatting","description":"Generates formatted research reports (markdown, JSON, HTML) with automatic source attribution, citations, and metadata. The Writer skill uses LLM-based synthesis to create coherent narratives from compressed research context, with configurable report structure (sections, subsections, summary). Supports multiple output formats and includes optional image generation for visual reports. Source tracking is maintained throughout the pipeline, enabling full citation trails and source validation.","intents":["I need research reports with proper citations and source attribution for credibility","I want to generate reports in multiple formats (markdown for docs, JSON for APIs, HTML for web)","I need to include visual elements (charts, images) in research reports"],"best_for":["teams generating research reports for external stakeholders","applications needing structured, cited research output","organizations requiring audit trails of research sources"],"limitations":["Report quality depends on Writer LLM capability; weaker models may produce incoherent narratives","Image generation adds 5-30s latency per image and requires additional API calls","Citation formatting is basic; no support for BibTeX, APA, or other academic formats","No built-in fact-checking of generated claims against sources","Report structure is template-based; limited customization of section organization"],"requires":["Python 3.9+","Writer LLM (configured separately from planner/executor)","Compressed research context from ContextManager","Optional: image generation API (DALL-E, Stable Diffusion) for visual reports"],"input_types":["compressed research context (text)","source list with URLs/paths","report format (markdown/JSON/HTML)","optional: report structure template","optional: image generation prompts"],"output_types":["formatted report (markdown/JSON/HTML string)","source citations with URLs","report metadata (generation time, sources used, word count)","optional: generated images"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_5","uri":"capability://planning.reasoning.multi.agent.research.coordination.with.chiefeditoragent.orchestration","name":"multi-agent research coordination with chiefeditoragent orchestration","description":"Coordinates multiple specialized research agents (Planner, Researcher, Writer, Curator) through a ChiefEditorAgent that manages task distribution, review-revision workflows, and consensus building. Each agent has specific responsibilities: Planner decomposes queries, Researcher executes searches, Curator validates sources, Writer synthesizes reports. The ChiefEditorAgent monitors agent outputs, triggers revisions when quality thresholds aren't met, and manages inter-agent communication. Supports AG2 framework integration for advanced multi-agent patterns.","intents":["I need multiple specialized agents working together on research tasks with quality control and revision loops","I want to automate the review-revision process where agents critique each other's work","I need to coordinate agents with different expertise (planning, searching, curation, writing)"],"best_for":["teams building sophisticated multi-agent research systems","applications requiring quality control and revision workflows","organizations needing specialized agent roles with clear responsibilities"],"limitations":["Multi-agent coordination adds 30-50% latency overhead vs. single-agent execution","Quality thresholds for triggering revisions are heuristic-based; may over-revise or under-revise","Inter-agent communication requires careful prompt engineering; poor prompts lead to ineffective collaboration","AG2 integration is optional; adds complexity without guaranteed quality improvements","No built-in conflict resolution when agents disagree on research direction"],"requires":["Python 3.9+","Multiple LLM instances (one per agent, or time-shared)","AG2 library (optional, for advanced patterns)","Sufficient API quota for multiple concurrent LLM calls"],"input_types":["research task (text query)","agent configuration (which agents to use, their roles)","optional: quality thresholds for revision triggers","optional: AG2 task configuration"],"output_types":["research report (from Writer agent)","agent execution logs (planning steps, search results, curation decisions)","revision history (if revisions were triggered)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_6","uri":"capability://automation.workflow.research.mode.adaptation.with.standard.detailed.deep.configurations","name":"research mode adaptation with standard/detailed/deep configurations","description":"Adapts research depth and execution strategy based on three configurable modes: Standard (fast, ~2-5 sources, basic synthesis), Detailed (balanced, ~5-10 sources, comprehensive analysis), and Deep (thorough, ~10-20+ sources, multi-round research with follow-up queries). Each mode adjusts context compression limits, number of sub-queries, retrieval depth, and revision cycles. Configuration is declarative through the Config class, enabling mode switching without code changes.","intents":["I need quick research summaries for some topics but deep analysis for others, with a single API","I want to control research depth without changing my code — just switch a mode parameter","I need to balance research quality with execution time and cost based on task importance"],"best_for":["applications with variable research requirements (quick summaries vs. deep dives)","teams needing cost-quality tradeoffs without code changes","users wanting to control research depth through simple configuration"],"limitations":["Mode definitions are fixed; no custom intermediate modes between standard/detailed/deep","Deep mode can take 5-15 minutes for complex queries; no progress indication or cancellation","Mode selection is manual; no automatic mode selection based on query complexity","Cost scaling is not transparent; users must estimate cost per mode","Quality improvement from deeper modes is not guaranteed; depends on source quality"],"requires":["Python 3.9+","Config object with research_mode parameter set to 'standard', 'detailed', or 'deep'","Sufficient API quota for selected mode (deep mode requires more calls)"],"input_types":["research_mode parameter (string: 'standard', 'detailed', or 'deep')","research query (text)"],"output_types":["research report (format varies by mode)","execution metadata (sources used, sub-queries executed, total time)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_7","uri":"capability://memory.knowledge.vector.store.integration.for.semantic.search.and.embeddings.based.retrieval","name":"vector store integration for semantic search and embeddings-based retrieval","description":"Integrates with vector stores (Pinecone, Weaviate, Milvus, local FAISS) for semantic search and embeddings-based document retrieval. Documents are embedded using configurable embedding models (OpenAI, Ollama, local), stored in vector databases, and retrieved using similarity search. Enables hybrid search combining keyword and semantic matching. Vector store configuration is abstracted through the Config class, supporting multiple backends without code changes.","intents":["I want to search documents semantically rather than by keyword matching for better relevance","I need to maintain a persistent vector database of research documents for fast retrieval","I want to use different embedding models and vector stores without changing my code"],"best_for":["teams with large document collections needing semantic search","applications requiring persistent vector databases across research sessions","organizations using specialized vector stores (Pinecone, Weaviate) for compliance/performance"],"limitations":["Embedding computation adds 100-500ms per document depending on embedding model","Vector store setup and maintenance adds operational complexity","Semantic search quality depends on embedding model quality; poor embeddings lead to irrelevant results","Hybrid search (keyword + semantic) requires tuning of weighting parameters","Vector store costs can be significant for large document collections (Pinecone pricing)"],"requires":["Python 3.9+","Vector store instance (Pinecone, Weaviate, Milvus, or local FAISS)","Embedding model API key or local model (OpenAI, Ollama, etc.)","Vector store credentials and connection details"],"input_types":["search query (text)","optional: embedding model selection","optional: vector store configuration (backend, credentials)","optional: hybrid search weights"],"output_types":["list of semantically similar documents","similarity scores","document metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_8","uri":"capability://tool.use.integration.fastapi.websocket.server.with.real.time.research.streaming.and.state.management","name":"fastapi websocket server with real-time research streaming and state management","description":"Exposes research capabilities through a FastAPI backend with WebSocket support for real-time streaming of research progress. Clients receive live updates on query decomposition, source retrieval, and report generation as they happen. The server manages research session state, maintains history, and supports concurrent research tasks. WebSocket protocol includes heartbeat/keepalive, error handling, and graceful disconnection. Frontend state is synchronized with backend through event-driven updates.","intents":["I want to show users real-time progress as research is being conducted","I need to support concurrent research tasks from multiple clients with isolated state","I want to maintain research history and enable session resumption"],"best_for":["teams building web-based research UIs with real-time feedback","applications needing concurrent research task management","organizations requiring research session persistence and history"],"limitations":["WebSocket connections require persistent network; mobile clients may experience disconnections","State management adds memory overhead; long-running research sessions consume resources","Real-time streaming adds latency to research execution (event serialization/transmission)","No built-in authentication/authorization; requires external security layer","Concurrent task limits depend on server resources; no automatic load balancing"],"requires":["Python 3.9+","FastAPI 0.95+","WebSocket client (browser WebSocket API or Python websockets library)","Server infrastructure with sufficient memory for concurrent sessions"],"input_types":["WebSocket connection (from client)","research task JSON (query, mode, configuration)","optional: session ID for resuming previous research"],"output_types":["WebSocket events (JSON): query_decomposed, sources_retrieved, report_generated, etc.","final research report","session metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-assafelovic--gpt-researcher__cap_9","uri":"capability://tool.use.integration.nextjs.production.frontend.with.research.ui.and.history.management","name":"nextjs production frontend with research ui and history management","description":"Provides a production-ready NextJS frontend for research task submission, real-time progress visualization, and result browsing. Features include research history with filtering/search, result caching, and responsive design. Frontend communicates with FastAPI backend via WebSocket for real-time updates and REST API for historical data retrieval. State management uses React hooks with optional Redux for complex scenarios. 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