{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-learningcircuit--local-deep-research","slug":"learningcircuit--local-deep-research","name":"local-deep-research","type":"benchmark","url":"https://github.com/LearningCircuit/local-deep-research","page_url":"https://unfragile.ai/learningcircuit--local-deep-research","categories":["research-search","testing-quality"],"tags":["academia","anthropic","arxiv","brave","deep-research","encryption","home-automation","homeserver","local","local-deep-research","local-llm","mistral","ollama","openai","pubmed","research","research-tool","retrieval-augmented-generation","searxng","self-hosted"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-learningcircuit--local-deep-research__cap_0","uri":"capability://planning.reasoning.multi.source.iterative.research.with.llm.driven.query.refinement","name":"multi-source iterative research with llm-driven query refinement","description":"Executes deep, multi-turn research workflows that iteratively refine queries based on LLM analysis of intermediate results. The system searches 10+ sources (arXiv, PubMed, web via Brave/SearXNG, private documents) in a coordinated loop, with each iteration using LLM reasoning to identify gaps and reformulate queries. Research execution is managed through a service-oriented architecture with thread-safe settings context, enabling parallel research tasks while maintaining isolation per user and per research session.","intents":["Run comprehensive literature reviews that automatically discover related papers and refine search strategies","Perform fact-checking queries that validate claims across academic and web sources","Build research reports with proper citations that trace back to original sources","Execute multi-step research workflows without manual query reformulation between steps"],"best_for":["researchers and academics building automated literature review pipelines","teams deploying privacy-critical research infrastructure on-premise","developers integrating deep research capabilities into larger AI agent systems"],"limitations":["Research execution latency scales with number of sources and LLM response time; typical multi-turn research takes 30-120 seconds depending on query complexity","Query refinement relies on LLM reasoning quality; weaker models may produce suboptimal follow-up queries","Private document search requires pre-indexing via RAG pipeline; real-time document addition has ~5-10 second indexing latency per document","No built-in deduplication across sources; duplicate results may appear in final research output"],"requires":["Python 3.9+","At least one LLM provider configured (local Ollama, OpenAI, Anthropic, Google, Mistral, etc.)","At least one search engine configured (Brave API key, or self-hosted SearXNG instance)","SQLCipher for encrypted database (included in Docker, requires compilation on bare metal)"],"input_types":["natural language research query (string)","optional research configuration (max iterations, source preferences, citation style)"],"output_types":["structured research report with citations","markdown or HTML formatted output","JSON with source metadata and evidence chains"],"categories":["planning-reasoning","search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_1","uri":"capability://safety.moderation.per.user.encrypted.database.with.pbkdf2.derived.key.derivation","name":"per-user encrypted database with pbkdf2-derived key derivation","description":"Provides per-user data isolation through SQLCipher databases encrypted with AES-256-CBC, where each user's password is derived via PBKDF2-HMAC-SHA512 with 256,000 iterations and a per-user random salt. The database architecture separates user data (research history, collections, settings) from system configuration, with automatic encryption key management and password-based access control. Database encryption check utilities verify SQLCipher compatibility at startup.","intents":["Deploy multi-user research systems where each user's data is cryptographically isolated","Ensure research data remains encrypted at rest without external key management infrastructure","Implement password-based access control where user credentials directly derive encryption keys","Migrate from plaintext databases to encrypted storage without data loss"],"best_for":["teams deploying on shared infrastructure or untrusted cloud environments","organizations with data residency or encryption-at-rest compliance requirements","developers building multi-tenant research platforms with strong privacy guarantees"],"limitations":["SQLCipher compilation required on bare metal; Docker images include pre-compiled binaries but custom builds need SQLCipher development headers","Key derivation with 256,000 PBKDF2 iterations adds ~100-200ms latency to database initialization per user session","Password changes require re-encryption of entire database; operation is blocking and can take 10-30 seconds for large databases","No built-in key rotation or escrow mechanisms; lost passwords result in permanent data loss"],"requires":["SQLCipher 4.5+ (included in Docker, requires libsqlcipher-dev on Linux for bare metal)","Python 3.9+","User password with minimum entropy (enforced at registration)"],"input_types":["user credentials (username, password)","database path (file system location)"],"output_types":["encrypted SQLite database file","decrypted data accessible through ORM (SQLAlchemy)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_10","uri":"capability://automation.workflow.flask.web.application.with.real.time.research.ui.and.result.streaming","name":"flask web application with real-time research ui and result streaming","description":"Provides a web-based user interface built with Flask backend and modern frontend (likely React or Vue.js based on build system references). The web UI enables real-time research execution with streaming result updates, research history management, and collection/library organization. Frontend communicates with Flask backend via REST API, with WebSocket support for real-time status updates during long-running research.","intents":["Execute research queries through an interactive web interface without CLI knowledge","Monitor research progress in real-time as results are discovered and refined","Organize research results into collections and libraries for later reference","Share research results with other users through web interface"],"best_for":["non-technical users who need research capabilities without CLI","teams collaborating on research with shared web interface","organizations deploying research as internal service"],"limitations":["Web UI requires modern browser (Chrome 90+, Firefox 88+, Safari 14+)","Real-time streaming requires WebSocket support; some proxies/firewalls may block WebSockets","Large research reports may have slow rendering in browser; pagination recommended for 1000+ results","No offline mode; web UI requires active connection to Flask server"],"requires":["Flask 2.0+","Modern web browser","Network access to Flask server (localhost or remote)"],"input_types":["web form input: research query, source selection, configuration options","file upload for private documents"],"output_types":["HTML-rendered research report with citations","downloadable report in multiple formats (markdown, PDF, HTML)","real-time status updates via WebSocket"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_11","uri":"capability://automation.workflow.thread.safe.settings.and.context.management.for.concurrent.research.execution","name":"thread-safe settings and context management for concurrent research execution","description":"Implements thread-safe settings management through context variables that enable concurrent research tasks to maintain isolated configuration and state. Each research execution gets its own context (LLM provider, search sources, user credentials) that is thread-local, preventing cross-contamination between concurrent requests. Settings are loaded from environment variables and configuration files with runtime override capability.","intents":["Execute multiple research tasks concurrently without configuration conflicts","Switch LLM providers or search sources per-task without global configuration changes","Implement per-request configuration overrides in multi-user deployments","Maintain isolated state for each research execution in async/concurrent environments"],"best_for":["multi-user deployments with concurrent research requests","async/await-based applications requiring thread-safe configuration","teams implementing per-request configuration overrides"],"limitations":["Context variables add ~5-10ms overhead per research execution","Configuration changes require context variable updates; global configuration changes are not automatically propagated","Thread-local storage may leak memory if contexts are not properly cleaned up","Debugging concurrent execution with context variables requires specialized tools"],"requires":["Python 3.7+ (contextvars module)","async/await support for concurrent execution"],"input_types":["configuration parameters (LLM provider, search sources, etc.)","context variable updates"],"output_types":["thread-safe configuration accessible within research execution context"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_12","uri":"capability://automation.workflow.benchmarking.system.with.simpleqa.evaluation.and.accuracy.metrics","name":"benchmarking system with simpleqa evaluation and accuracy metrics","description":"Includes built-in benchmarking infrastructure that evaluates research quality against the SimpleQA benchmark, measuring accuracy, citation correctness, and source attribution. The benchmarking system executes research on benchmark queries, compares results against ground truth, and generates accuracy reports. This enables quantitative evaluation of research quality across different LLM providers and configurations.","intents":["Measure research accuracy against standardized benchmarks (SimpleQA)","Compare quality across different LLM providers (GPT-4, Claude, Mistral, etc.)","Evaluate impact of configuration changes (max iterations, source selection) on accuracy","Generate accuracy reports for documentation and performance tracking"],"best_for":["researchers evaluating LLM-based research systems","teams optimizing research quality and accuracy","organizations benchmarking research tools for procurement decisions"],"limitations":["SimpleQA benchmark is limited to factual questions; may not reflect quality on other research types","Benchmarking requires running research on 100+ queries; full benchmark takes 1-2 hours","Accuracy metrics depend on ground truth data; SimpleQA ground truth may be incomplete or outdated","Benchmark results are specific to configured LLM and search sources; results may not generalize"],"requires":["SimpleQA benchmark dataset (included in repository)","At least one LLM provider configured","At least one search engine configured"],"input_types":["benchmark configuration (number of queries, LLM provider, search sources)","optional: custom ground truth data"],"output_types":["accuracy metrics (overall accuracy, citation accuracy, source attribution accuracy)","per-query results with ground truth comparison","JSON report with detailed metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_13","uri":"capability://data.processing.analysis.document.download.and.management.with.automatic.metadata.extraction","name":"document download and management with automatic metadata extraction","description":"Automatically downloads and manages research documents (PDFs, web pages) discovered during research, with automatic metadata extraction (title, authors, publication date). Downloaded documents are stored in encrypted database with full-text indexing for later search. Metadata extraction uses heuristics and optional OCR for PDFs, enabling documents to be cited and referenced in future research.","intents":["Build local archives of research documents discovered during research","Extract metadata from downloaded documents for citation generation","Search downloaded documents in future research without re-downloading","Maintain document provenance by tracking download source and date"],"best_for":["researchers building personal research libraries","teams maintaining institutional document archives","organizations implementing document governance with download tracking"],"limitations":["PDF metadata extraction is heuristic-based; complex PDFs may have incorrect metadata","OCR is optional and adds 5-10 seconds per PDF; accuracy depends on PDF quality","Downloaded documents are stored in encrypted database; storage overhead is 1-2MB per document","No automatic deduplication; duplicate documents may be downloaded multiple times","Copyright restrictions may apply to downloaded documents; users are responsible for compliance"],"requires":["Document download enabled in configuration","Storage space for encrypted database (1-2MB per document)","Optional: OCR library (pytesseract) for PDF text extraction"],"input_types":["document URL or file path","optional metadata (title, authors, publication date)"],"output_types":["downloaded document stored in encrypted database","extracted metadata (title, authors, publication date, DOI)","full-text index for search"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_14","uri":"capability://automation.workflow.news.and.subscription.management.for.continuous.research.updates","name":"news and subscription management for continuous research updates","description":"Enables subscription to research topics with automatic periodic research execution and result delivery. The system maintains topic subscriptions in encrypted database, executes research on subscribed topics at configured intervals (daily, weekly, monthly), and delivers results via email or web UI notifications. Subscription management includes filtering, deduplication, and archival of subscription results.","intents":["Monitor research topics continuously without manual query execution","Receive periodic research updates on topics of interest via email","Build research archives of subscription results for historical analysis","Implement research alerts that notify users when new relevant sources are discovered"],"best_for":["researchers monitoring specific topics for new developments","teams tracking competitive intelligence or market research","organizations implementing continuous research monitoring"],"limitations":["Subscription execution requires background task scheduler (Celery, APScheduler); single-process deployment will not execute subscriptions","Email delivery requires SMTP configuration; email may be filtered as spam","Subscription results are deduplicated heuristically; similar results may still appear in multiple deliveries","No built-in result summarization; subscription emails may be very long for active topics"],"requires":["Background task scheduler configured (Celery, APScheduler, etc.)","SMTP server configured for email delivery (optional; web UI notifications work without email)","At least one LLM provider and search engine configured"],"input_types":["subscription configuration: topic, research frequency (daily/weekly/monthly), delivery method (email/web)","optional: result filters (source types, date range)"],"output_types":["periodic research results delivered via email or web UI","subscription result archive in encrypted database","notification of new relevant sources"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_15","uri":"capability://text.generation.language.report.generation.and.export.in.multiple.formats","name":"report generation and export in multiple formats","description":"Generates research reports from research results with support for multiple export formats (markdown, HTML, PDF, JSON). Report generation includes automatic formatting, citation insertion, table of contents generation, and optional styling. Exported reports can be shared externally while maintaining citation metadata for verification.","intents":["Export research results in formats suitable for publication or sharing","Generate formatted reports with proper citations for academic use","Create PDF reports for printing or archival","Export research metadata in JSON for programmatic processing"],"best_for":["researchers publishing research results","teams generating reports for stakeholders","organizations archiving research in multiple formats"],"limitations":["PDF generation requires additional dependencies (weasyprint, wkhtmltopdf); adds ~2-5 seconds per report","HTML styling is basic; complex formatting may require manual CSS customization","Large reports (1000+ pages) may have slow PDF generation; pagination recommended","Citation format conversion requires manual configuration per format"],"requires":["Report generation library (Jinja2 for templating, optional: weasyprint for PDF)","Research results with citations"],"input_types":["research results object","export format (markdown, html, pdf, json)","optional: styling configuration, citation format"],"output_types":["formatted report file (markdown, HTML, PDF, or JSON)","report metadata (title, authors, generation date)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Abstracts multiple LLM providers (OpenAI, Anthropic, Google, Mistral, Ollama) behind a unified Python interface, enabling runtime provider switching without code changes. Configuration is managed through environment variables and thread-safe settings context, with provider-specific parameters (temperature, max_tokens, system prompts) normalized across APIs. The abstraction handles provider-specific response formats, streaming behavior, and error handling transparently.","intents":["Switch between local (Ollama) and cloud LLMs (OpenAI, Anthropic) without changing research code","Compare research quality across different LLM providers using identical workflows","Implement fallback logic where research automatically retries with alternative providers on failure","Configure provider-specific parameters (model size, temperature) per research task"],"best_for":["developers building LLM-agnostic research tools that support multiple backends","teams evaluating LLM providers for cost/quality tradeoffs","organizations requiring local-first deployment with cloud fallback capability"],"limitations":["Provider-specific features (vision, function calling, streaming) are not uniformly exposed; some providers lack certain capabilities","Response latency varies significantly by provider (Ollama local: 1-5s, OpenAI: 2-10s, Anthropic: 3-15s depending on model)","Token counting differs across providers; cost estimation may be inaccurate for non-OpenAI providers","Rate limiting and quota management are provider-specific; no unified rate limiter across providers"],"requires":["Python 3.9+","API keys for cloud providers (OpenAI, Anthropic, Google, Mistral) OR local Ollama instance","Environment variables configured for selected provider (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)"],"input_types":["provider name (string: 'openai', 'anthropic', 'ollama', 'google', 'mistral')","model identifier (string: 'gpt-4', 'claude-3-sonnet', 'mistral-7b', etc.)","prompt and optional system message (strings)","optional parameters (temperature, max_tokens, top_p)"],"output_types":["LLM response text (string)","token usage metadata (prompt_tokens, completion_tokens)","streaming response iterator (optional)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_3","uri":"capability://search.retrieval.search.engine.integration.layer.with.10.source.coordination","name":"search engine integration layer with 10+ source coordination","description":"Coordinates searches across 10+ sources (arXiv, PubMed, Brave web search, SearXNG, Google Scholar, private document collections) through a unified search interface. The Search Engine Integration Layer abstracts provider-specific APIs, query syntax, and result formats into a common result schema with source attribution. Search execution is parallelized where possible, with configurable timeouts and fallback behavior when sources are unavailable.","intents":["Execute parallel searches across academic (arXiv, PubMed) and web sources simultaneously","Search private document collections indexed via RAG alongside public sources","Handle source-specific query syntax (arXiv filters, PubMed MeSH terms) transparently","Implement source-specific ranking and deduplication across heterogeneous results"],"best_for":["researchers needing comprehensive coverage across academic and web sources","organizations with proprietary document collections that must be searched alongside public sources","teams deploying on-premise search infrastructure (SearXNG) to avoid external API dependencies"],"limitations":["Search latency is bottlenecked by slowest source; typical multi-source search takes 5-15 seconds","Brave API requires paid subscription for high-volume searches; free tier limited to 100 queries/month","SearXNG self-hosting requires separate infrastructure; public instances may have rate limits or unreliable uptime","Private document search requires pre-indexing; adding new documents to collections has 5-10 second latency","Result deduplication across sources is heuristic-based (URL/title matching); semantic duplicates may not be detected"],"requires":["At least one search engine configured (Brave API key, SearXNG instance URL, or local Ollama for embedding-based search)","For private document search: RAG system initialized with document collection","Network access to configured search sources"],"input_types":["search query (string)","optional source filter (list of source names to include)","optional search parameters (date range, language, result limit)"],"output_types":["list of search results with schema: {title, url, snippet, source, relevance_score, publication_date}","source metadata (availability status, query execution time)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_4","uri":"capability://memory.knowledge.rag.based.private.document.indexing.and.retrieval","name":"rag-based private document indexing and retrieval","description":"Indexes private documents (PDFs, markdown, text) into vector embeddings using local or cloud embedding models, enabling semantic search across document collections. Documents are stored in per-user encrypted databases with metadata (source, date, collection), and retrieval uses cosine similarity search on embeddings to find relevant passages. The RAG system integrates with the research workflow to supplement search results from public sources with organization-specific knowledge.","intents":["Search internal documentation, research papers, or proprietary datasets alongside public sources","Build research reports that cite both public sources and private documents with proper attribution","Implement document-level access control where users can only search collections they have permission to access","Maintain searchable archives of downloaded papers with automatic metadata extraction"],"best_for":["enterprises with proprietary research or documentation that must remain private","research teams maintaining institutional knowledge bases alongside public literature","organizations implementing document governance with per-user access control"],"limitations":["Document indexing latency is 5-10 seconds per document; bulk indexing of 1000+ documents requires 1-2 hours","Embedding quality depends on model choice; smaller models (384-dim) have lower semantic accuracy than larger models (1536-dim)","Vector search is approximate; semantic similarity may miss relevant documents with different terminology","No built-in document update detection; modified documents require manual re-indexing","Storage overhead is ~1-2MB per document for embeddings plus original content"],"requires":["Embedding model configured (local: Ollama with embedding model, cloud: OpenAI, Anthropic, Google)","Document collection initialized in database","Supported document formats: PDF, markdown, plain text (other formats require custom parsers)"],"input_types":["document file (PDF, markdown, text)","optional metadata (title, author, date, collection name)","search query (string)"],"output_types":["indexed document with embeddings stored in encrypted database","search results with schema: {document_id, passage_text, relevance_score, source_metadata}","document metadata for citation generation"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_5","uri":"capability://data.processing.analysis.citation.tracking.and.source.attribution.with.evidence.chains","name":"citation tracking and source attribution with evidence chains","description":"Automatically tracks citations throughout the research process, maintaining evidence chains that link claims in the final report back to original sources. The citation handler (citation_handler.py) extracts source metadata (URL, publication date, authors) from search results and embeds citations in generated content. Reports can be exported with full citation metadata in multiple formats (markdown with footnotes, HTML with hyperlinks, JSON with source provenance).","intents":["Generate research reports with proper citations that can be verified by readers","Trace claims back to original sources to verify accuracy and identify potential hallucinations","Export research in academic citation formats (APA, MLA, Chicago) for publication","Audit research provenance by examining which sources contributed to each claim"],"best_for":["academic researchers requiring proper citation for publications","fact-checking and verification workflows that need source traceability","compliance teams auditing AI-generated content for source accuracy"],"limitations":["Citation accuracy depends on source metadata quality; some sources (web pages) may have incomplete or incorrect metadata","LLM-generated text may paraphrase sources in ways that obscure original attribution","No automatic detection of hallucinations; citations are only as accurate as the sources they reference","Citation format conversion (APA to MLA) requires manual configuration per format","Private document citations may expose sensitive information if reports are shared externally"],"requires":["Search results with source metadata (URL, publication date, authors)","LLM-generated content that references sources","Citation format configuration (optional; defaults to markdown footnotes)"],"input_types":["search results with metadata","LLM-generated research content (string)","optional citation format specification"],"output_types":["research report with embedded citations","citation metadata JSON with source provenance","formatted bibliography in specified citation style"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_6","uri":"capability://tool.use.integration.rest.api.with.async.request.handling.and.long.running.research.tasks","name":"rest api with async request handling and long-running research tasks","description":"Exposes research capabilities through a REST API built on Flask with async task execution for long-running research workflows. API endpoints support both synchronous queries (for quick searches) and asynchronous research execution (for deep research that may take 30+ seconds). Task status can be polled via job IDs, with results cached in encrypted database. API authentication uses per-user tokens derived from encrypted credentials.","intents":["Integrate Local Deep Research into web applications or microservices via REST API","Execute long-running research tasks asynchronously without blocking client connections","Build research dashboards that poll task status and display results as they complete","Implement API-based access control with per-user authentication and rate limiting"],"best_for":["developers building web applications that need research capabilities","teams deploying research as a microservice in larger systems","organizations implementing API-first research infrastructure"],"limitations":["Async task execution requires background worker process; single-process deployment will block on long-running research","Task results are cached in database; no built-in result streaming for very large reports","API rate limiting is per-user; no global rate limiting across all users","Authentication tokens are stored in encrypted database; token revocation requires database update"],"requires":["Flask 2.0+","Python 3.9+","Background task worker (Celery, APScheduler, or similar) for async execution","User credentials for API authentication"],"input_types":["HTTP POST request with JSON body: {query, sources, max_iterations, llm_provider}","optional: task_id for status polling"],"output_types":["JSON response with schema: {task_id, status, result (when complete), error (if failed)}","HTTP status codes: 202 (accepted), 200 (complete), 400 (invalid request), 401 (unauthorized)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_7","uri":"capability://tool.use.integration.python.programmatic.api.ldrclient.for.direct.integration","name":"python programmatic api (ldrclient) for direct integration","description":"Provides a Python client library (LDRClient) that enables direct programmatic access to research capabilities without REST API overhead. The client handles user authentication, database access, and research execution through a simple Python interface. Supports both synchronous and asynchronous research execution, with context managers for automatic resource cleanup.","intents":["Integrate research capabilities directly into Python applications without HTTP overhead","Build research pipelines that chain multiple research tasks with intermediate processing","Access research results programmatically for further analysis or transformation","Implement custom research workflows that extend built-in capabilities"],"best_for":["Python developers building research tools or data pipelines","data scientists integrating research into analysis workflows","teams building research agents that need direct library access"],"limitations":["Python-only; no support for other languages without additional bindings","Requires local database access; cannot be used with remote deployments","Async support requires Python 3.7+ with asyncio knowledge","No built-in connection pooling; multiple concurrent clients may contend for database locks"],"requires":["Python 3.9+","local-deep-research package installed (pip install local-deep-research)","User credentials for authentication"],"input_types":["research query (string)","optional configuration (sources, max_iterations, llm_provider)"],"output_types":["ResearchResult object with attributes: query, report, sources, citations, execution_time","async iterator for streaming results (optional)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_8","uri":"capability://automation.workflow.cli.tools.ldr.ldr.web.for.command.line.research.execution","name":"cli tools (ldr, ldr-web) for command-line research execution","description":"Provides command-line interfaces for research execution and web server management. The `ldr` command enables direct research queries from shell scripts, while `ldr-web` manages the Flask web application lifecycle. CLI tools support configuration via environment variables and command-line flags, with output formatting options (JSON, markdown, plain text).","intents":["Execute research queries from shell scripts or CI/CD pipelines","Automate research tasks as part of larger workflows (e.g., fact-checking in content pipelines)","Manage Local Deep Research deployment (start/stop web server, configure settings)","Export research results in formats suitable for further processing"],"best_for":["DevOps teams automating research as part of CI/CD pipelines","system administrators managing Local Deep Research deployments","shell script developers integrating research into automation workflows"],"limitations":["CLI output is text-based; complex result structures require JSON parsing","No interactive mode; each invocation requires full authentication","Configuration via environment variables can be verbose for complex setups","Error handling is basic; detailed error messages may require log file inspection"],"requires":["local-deep-research package installed with CLI entry points","Python 3.9+","User credentials configured via environment variables or config file"],"input_types":["command-line arguments: query string, optional flags (--sources, --format, --max-iterations)","environment variables for configuration"],"output_types":["stdout with formatted research results (JSON, markdown, or plain text)","exit code indicating success/failure"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-learningcircuit--local-deep-research__cap_9","uri":"capability://tool.use.integration.mcp.server.ldr.mcp.for.claude.desktop.and.ai.assistant.integration","name":"mcp server (ldr-mcp) for claude desktop and ai assistant integration","description":"Implements Model Context Protocol (MCP) server that enables integration with Claude Desktop and other AI assistants. The MCP server exposes research capabilities as tools that Claude can invoke directly, with automatic result formatting and context injection. 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