{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tavily-agent","slug":"tavily-agent","name":"Tavily Agent","type":"agent","url":"https://tavily.com","page_url":"https://unfragile.ai/tavily-agent","categories":["ai-agents","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tavily-agent__cap_0","uri":"capability://search.retrieval.real.time.web.search.with.llm.optimized.result.formatting","name":"real-time web search with llm-optimized result formatting","description":"Executes live web searches and returns results pre-processed into structured, LLM-consumable format with extracted snippets, source metadata, and relevance scoring. Implements intelligent caching and indexing to maintain sub-200ms p50 latency at scale (100M+ monthly requests). Results are chunked and formatted specifically for RAG pipeline ingestion rather than human-readable search engine output.","intents":["Ground my LLM application with current web data without building my own search infrastructure","Retrieve fresh factual information to augment LLM knowledge cutoffs in real-time","Get search results pre-formatted for direct consumption by vector databases and RAG systems","Reduce latency of web-grounded LLM responses from seconds to sub-200ms"],"best_for":["LLM application developers building grounded QA systems","RAG pipeline builders needing fresh web retrieval components","Teams building research assistants or fact-checking agents","Developers migrating from basic web search APIs to LLM-optimized retrieval"],"limitations":["Credit-based pricing model with unclear per-query cost (documentation states 'API credit' definition but specifics not provided)","Free tier limited to 1,000 credits/month (insufficient for production applications with high query volume)","Web-only access — cannot retrieve from private databases, internal APIs, or non-public sources","No documented SLA on data freshness or index update frequency","Maximum number of results per query and crawl depth/scope not documented"],"requires":["Tavily API key (obtained via free registration at tavily.com)","HTTP client capable of making REST API calls","Understanding of API credit consumption model (exact formula not publicly documented)","Network connectivity to Tavily's cloud infrastructure"],"input_types":["text (search query string)","optional parameters: max_results (integer), include_answer (boolean), include_raw_content (boolean)"],"output_types":["JSON object with array of search results","Each result contains: title, url, content (extracted snippet), score (relevance), raw_content (optional full page text)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_1","uri":"capability://data.processing.analysis.intelligent.content.extraction.and.summarization.from.web.pages","name":"intelligent content extraction and summarization from web pages","description":"Extracts relevant content from web pages and automatically summarizes it into concise, LLM-ready format. Handles both static HTML and JavaScript-rendered content (mechanism for JS rendering not documented). Implements content validation to filter out PII, malicious sources, and prompt injection attempts before returning to consuming LLM. Output is structured as extracted text with optional raw HTML for downstream processing.","intents":["Extract key information from search results without sending raw HTML to my LLM","Automatically summarize web pages for inclusion in RAG context","Prevent prompt injection attacks embedded in web content from reaching my LLM","Get clean, structured text from pages without boilerplate, ads, or navigation elements"],"best_for":["RAG systems requiring clean content extraction before vector embedding","LLM applications that need to cite specific web sources with extracted quotes","Security-conscious teams building grounded LLMs with untrusted web content","Developers building research tools that aggregate content from multiple sources"],"limitations":["Mechanism for handling JavaScript-rendered content not documented (may fail on heavily JS-dependent sites)","Security layer implementation details unknown — false positive/negative rates for PII detection and prompt injection filtering not published","No control over summarization style, length, or emphasis — fully automated with no customization","Raw content option may return full page HTML, increasing token consumption and latency","No documented handling of paywalled, authenticated, or rate-limited content"],"requires":["Tavily API key with sufficient credits for extraction operations","Valid, publicly accessible URL to extract from","Understanding that extraction consumes API credits (exact cost per page not documented)"],"input_types":["text (URL string)","optional parameters: include_raw_content (boolean), max_tokens (integer, if supported)"],"output_types":["JSON object containing: extracted_text (string), summary (string, if requested), raw_content (HTML string, if requested), metadata (source, timestamp, etc.)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_10","uri":"capability://tool.use.integration.agent.framework.integration.via.mcp.and.native.sdks","name":"agent framework integration via mcp and native sdks","description":"Provides native SDKs for popular agent frameworks (LangChain, CrewAI, AutoGen) and exposes Tavily capabilities via Model Context Protocol (MCP) for seamless integration into agent systems. Handles authentication, parameter marshaling, and response formatting automatically, reducing boilerplate code. Enables agents to call Tavily search/extract/crawl as first-class tools without custom wrapper code.","intents":["I want to add web search to my LangChain agent without writing custom integration code","I need Tavily to work with my CrewAI multi-agent system","I want to use Tavily via MCP in my Claude agent","I need a native SDK for my preferred agent framework"],"best_for":["Developers using LangChain, CrewAI, AutoGen, or other supported frameworks","Teams building agents with MCP support","Organizations standardizing on specific agent frameworks","Developers who want minimal integration boilerplate"],"limitations":["SDK support limited to documented frameworks — custom frameworks require manual integration","MCP integration requires MCP-compatible agent framework — not all frameworks support MCP","SDK versions may lag behind Tavily API updates — feature parity not guaranteed","Integration quality varies by framework — some frameworks may have incomplete Tavily support","No built-in error handling or retry logic beyond framework defaults"],"requires":["Tavily API key","Supported agent framework (LangChain, CrewAI, AutoGen, etc.)","Native SDK or MCP client for your framework","Framework version compatibility with Tavily SDK"],"input_types":["agent framework tool definitions","Tavily API parameters"],"output_types":["framework-native tool results","formatted for agent consumption"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_11","uri":"capability://automation.workflow.scalable.infrastructure.with.99.99.uptime.sla.and.100m.monthly.requests","name":"scalable infrastructure with 99.99% uptime sla and 100m+ monthly requests","description":"Operates cloud-hosted infrastructure designed to handle 100M+ monthly API requests with 99.99% uptime SLA (Enterprise tier). Implements automatic scaling, load balancing, and redundancy to maintain performance under high load. P50 latency of 180ms per search request enables real-time agent interactions, with geographic distribution to minimize latency for global users.","intents":["I need a search service that can handle high-volume agent traffic without downtime","I want predictable latency for real-time agent interactions","I need SLA guarantees for production applications","I want global availability with low latency for international users"],"best_for":["Production LLM applications with high traffic requirements","SaaS platforms offering agent features to many users","Organizations requiring SLA guarantees","Global applications needing low-latency search"],"limitations":["99.99% SLA only available on Enterprise tier — Free and Project tiers have no published SLA","P50 latency of 180ms adds cumulative delay in multi-step agent loops (e.g., 5 searches = 900ms overhead)","No control over geographic routing or latency optimization","Scaling is automatic but not transparent — no visibility into infrastructure utilization","SLA credits or remedies for downtime not publicly specified"],"requires":["Tavily API key","Enterprise tier subscription for SLA guarantees","Network connectivity to Tavily cloud service","Acceptance of 180ms P50 latency per request"],"input_types":["API requests (search, extract, crawl)"],"output_types":["API responses","implicit uptime guarantees (Enterprise tier only)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_2","uri":"capability://search.retrieval.web.crawling.with.configurable.depth.and.scope","name":"web crawling with configurable depth and scope","description":"Crawls web pages starting from a given URL and follows links to retrieve content from multiple pages. Scope and maximum crawl depth not documented in available materials. Returns structured content from all crawled pages suitable for RAG ingestion. Implements rate limiting and respects robots.txt to avoid overwhelming target servers. Crawl results are cached to reduce redundant requests.","intents":["Index an entire website or documentation site for RAG without building my own crawler","Retrieve related content across multiple pages for comprehensive context in LLM responses","Build a knowledge base from a website without manual page-by-page extraction","Keep website content fresh in my RAG system through periodic re-crawls"],"best_for":["Teams building documentation-grounded LLM assistants","RAG systems that need to index entire websites or knowledge bases","Developers building competitive intelligence or market research tools","Organizations migrating from manual content curation to automated web crawling"],"limitations":["Maximum crawl depth and scope not documented — unclear if suitable for large websites or limited to shallow crawls","No documented control over crawl speed, concurrency, or resource consumption","Crawl results subject to same credit-based pricing as search (cost per page crawled not specified)","No documented handling of authentication, cookies, or session state during crawls","Robots.txt compliance may prevent crawling of some sites or sections","No documented support for crawling behind paywalls or authenticated content"],"requires":["Tavily API key with sufficient credits for crawl operations","Valid, publicly accessible starting URL","Understanding that crawls consume credits proportional to number of pages crawled (exact formula not documented)"],"input_types":["text (starting URL string)","optional parameters: max_depth (integer, if supported), max_pages (integer, if supported), follow_external_links (boolean, if supported)"],"output_types":["JSON array of crawled pages, each containing: url, extracted_content, title, metadata (crawl_depth, timestamp, etc.)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_3","uri":"capability://planning.reasoning.research.focused.multi.step.web.investigation.with.synthesis","name":"research-focused multi-step web investigation with synthesis","description":"Performs multi-step web research by iteratively searching, extracting, and synthesizing information across multiple sources to answer complex research questions. Implements internal reasoning loop to determine follow-up searches based on initial results (mechanism not documented). Returns synthesized answer with source attribution and confidence scoring. Claimed as 'state-of-the-art' research capability but specific methodology and performance metrics not published.","intents":["Answer complex research questions that require information from multiple sources","Get synthesized research summaries with source attribution for fact-checking","Perform competitive analysis or market research without manual source aggregation","Build research-grade answers with confidence scores and source transparency"],"best_for":["Researchers and analysts building automated research assistants","LLM applications requiring multi-source fact verification","Teams building competitive intelligence or due diligence tools","Organizations needing transparent, source-attributed answers for compliance or audit purposes"],"limitations":["Internal reasoning mechanism not documented — unclear how follow-up searches are determined or when research terminates","Confidence scoring methodology not published — unclear how reliability is assessed","No documented control over research depth, number of sources, or iteration count","Research endpoint consumes significantly more credits than single search (exact cost not specified)","Performance on benchmark tasks (SimpleQA, GAIA, DeepResearch Bench) claimed but actual scores not published","No documented support for research on specialized domains or proprietary information"],"requires":["Tavily API key with sufficient credits for multi-step research operations","Research question or topic as text input","Understanding that research operations consume multiple credits per query (exact formula not documented)"],"input_types":["text (research question or topic string)","optional parameters: max_sources (integer, if supported), research_depth (enum, if supported)"],"output_types":["JSON object containing: synthesized_answer (string), sources (array of {url, title, relevance_score}), confidence_score (float 0-1), research_steps (array of intermediate searches, if available)"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_4","uri":"capability://tool.use.integration.drop.in.integration.with.major.llm.providers.via.native.function.calling","name":"drop-in integration with major llm providers via native function calling","description":"Provides pre-built function calling schemas compatible with OpenAI, Anthropic, and Groq function-calling APIs, enabling LLM applications to call Tavily search/extract/crawl/research endpoints directly without custom integration code. Schemas define input parameters, output types, and descriptions for automatic tool discovery and invocation by LLMs. Integration is stateless — each function call is independent with no session or conversation context maintained.","intents":["Enable my LLM to call web search directly without writing custom function calling code","Integrate Tavily into existing LLM applications using OpenAI Assistants or Anthropic tool use","Reduce integration time from hours (custom code) to minutes (pre-built schemas)","Support multi-turn conversations where LLM decides when to search for fresh information"],"best_for":["Developers building LLM applications with OpenAI, Anthropic, or Groq models","Teams using LLM frameworks (LangChain, LlamaIndex, etc.) that support function calling","Rapid prototyping and MVP development requiring minimal integration overhead","Organizations standardizing on specific LLM providers with native Tavily support"],"limitations":["Limited to OpenAI, Anthropic, and Groq — no documented support for other LLM providers or open-source models","Stateless integration — no conversation history or context persistence across API calls","LLM must explicitly decide to call Tavily (no automatic grounding) — requires prompt engineering to encourage web search","Function calling overhead adds latency compared to direct API calls (exact overhead not documented)","No documented support for streaming results or progressive content delivery"],"requires":["API key for OpenAI, Anthropic, or Groq (whichever LLM provider is used)","Tavily API key","LLM framework or SDK that supports function calling (e.g., OpenAI Python SDK, Anthropic SDK, LangChain)","Understanding of function calling semantics for your chosen LLM provider"],"input_types":["LLM function calling request with Tavily schema","Parameters: query (string), max_results (integer), include_answer (boolean), etc."],"output_types":["LLM function calling response with search results in provider-specific format"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_5","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.ide.and.tool.ecosystem.access","name":"model context protocol (mcp) integration for ide and tool ecosystem access","description":"Exposes Tavily search and extraction capabilities via Model Context Protocol (MCP) standard, enabling integration with MCP-compatible tools, IDEs, and LLM applications. Partnership with Databricks enables distribution via MCP Marketplace. MCP integration allows Tavily to be discovered and invoked by any MCP-compatible client without custom integration code. Supports both request-response and streaming patterns (streaming support not confirmed).","intents":["Use Tavily search in any MCP-compatible IDE or tool without custom plugins","Integrate Tavily into MCP-based LLM applications and frameworks","Enable real-time web search in development environments (e.g., JetBrains IDEs)","Build MCP servers that compose Tavily with other tools and services"],"best_for":["Developers using MCP-compatible IDEs (JetBrains, VS Code with MCP extensions)","Teams building MCP servers that need web search capabilities","Organizations standardizing on MCP for tool integration","LLM application developers using MCP-based frameworks"],"limitations":["MCP support is relatively new — ecosystem maturity and tool compatibility not fully established","JetBrains integration mentioned but specific IDE versions and feature support not documented","No documented support for custom MCP server configuration or parameter tuning","MCP discovery and invocation depends on client implementation — behavior may vary across tools","Streaming support for large result sets not confirmed"],"requires":["MCP-compatible client or IDE (e.g., JetBrains IDE with MCP support, Claude Desktop, etc.)","Tavily API key configured in MCP client","Understanding of MCP protocol and client-specific configuration"],"input_types":["MCP tool invocation with parameters: query (string), max_results (integer), etc."],"output_types":["MCP tool result with search results in standard MCP format"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_6","uri":"capability://automation.workflow.api.credit.based.usage.metering.and.cost.control","name":"api credit-based usage metering and cost control","description":"Implements credit-based pricing model where each API operation (search, extract, crawl, research) consumes a variable number of credits. Free tier provides 1,000 credits/month; pay-as-you-go costs $0.008 per credit; project tier offers 4,000 credits/month with variable pricing. Exact credit consumption per operation type not documented. Pricing slider available but formula not published. No documented usage tracking, quota alerts, or cost estimation tools.","intents":["Control costs of web search operations in production LLM applications","Estimate budget for Tavily integration before deployment","Monitor and optimize API usage to stay within budget constraints","Choose appropriate pricing tier based on expected query volume"],"best_for":["Cost-conscious teams evaluating Tavily for production use","Developers building applications with variable or unpredictable search volume","Organizations with strict budget constraints or cost allocation requirements","Teams needing transparent cost tracking for chargeback or billing"],"limitations":["API credit definition vague — documentation states 'What is an API credit and how is usage calculated?' but answer not provided","No published cost per operation type (search vs. extract vs. crawl vs. research) — makes budget estimation impossible","No documented usage tracking dashboard or cost estimation tools","No documented quota alerts or spending limits to prevent surprise bills","Free tier (1,000 credits/month) insufficient for production applications with moderate query volume","Project tier pricing uses variable slider but formula not published — makes tier selection difficult"],"requires":["Tavily account with payment method on file (for paid tiers)","Understanding of credit consumption model (not publicly documented)","Ability to estimate query volume and operation mix for budget planning"],"input_types":["Account configuration: pricing tier selection, optional spending limit (if supported)"],"output_types":["Usage metrics: credits consumed per operation, remaining credits, billing history (if available)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_7","uri":"capability://safety.moderation.security.layer.with.prompt.injection.detection.and.pii.filtering","name":"security layer with prompt injection detection and pii filtering","description":"Implements built-in security layer that blocks prompt injection attacks embedded in web content and filters personally identifiable information (PII) before returning results to consuming LLM. Specific detection mechanisms, false positive/negative rates, and bypass vectors not documented. Security filtering is applied automatically to all extracted content without configuration options.","intents":["Prevent adversarial web content from poisoning my LLM's behavior through prompt injection","Ensure extracted web content doesn't leak PII that could violate privacy regulations","Reduce security review overhead for grounded LLM applications","Meet compliance requirements for PII handling in regulated industries"],"best_for":["Security-conscious teams building production LLM applications","Organizations in regulated industries (healthcare, finance) with PII handling requirements","Applications that consume untrusted web content from arbitrary sources","Teams without dedicated security infrastructure for content validation"],"limitations":["Security mechanism implementation details not documented — unclear what detection methods are used","False positive/negative rates not published — unclear if legitimate content is incorrectly filtered","No documented way to customize or disable security filtering for specific use cases","PII filtering scope not documented — unclear which data types are detected (SSN, email, phone, etc.)","No documented audit trail or visibility into what content was filtered and why","Security layer may introduce latency (overhead not quantified)","No documented handling of obfuscated or encoded PII"],"requires":["Tavily API key (security filtering applied automatically)","Trust in Tavily's security implementation without ability to audit or customize"],"input_types":["Web content (automatically filtered during extraction)"],"output_types":["Filtered content with PII removed and prompt injection attempts blocked"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_8","uri":"capability://automation.workflow.intelligent.result.caching.and.indexing.for.sub.200ms.latency","name":"intelligent result caching and indexing for sub-200ms latency","description":"Implements proprietary intelligent caching and indexing layer that maintains sub-200ms p50 latency for search queries at scale (100M+ monthly requests). Caching strategy is optimized for LLM query patterns rather than generic web search patterns. Index is continuously updated to maintain data freshness (update frequency not documented). Caching is transparent to API consumers — no configuration or cache invalidation required.","intents":["Achieve sub-200ms response times for web search in LLM applications","Scale to millions of queries per month without performance degradation","Reduce infrastructure costs by offloading caching to Tavily","Provide consistent, predictable latency for real-time LLM applications"],"best_for":["Production LLM applications with strict latency requirements (<500ms end-to-end)","High-volume applications serving millions of queries per month","Real-time research assistants and fact-checking systems","Teams building conversational AI with web grounding"],"limitations":["Caching strategy and index update frequency not documented — unclear how fresh results are","No documented control over cache behavior (TTL, invalidation, etc.)","Latency claim (180ms p50) not independently verified — based on Tavily's own benchmarks","Latency may vary based on query complexity, result set size, and network conditions","No documented support for cache warming or pre-population","Cache hit rate and performance characteristics not published"],"requires":["Tavily API key (caching applied automatically)","Network connectivity to Tavily's cloud infrastructure","Acceptance of Tavily's caching strategy without customization"],"input_types":["Search query (string)"],"output_types":["Search results with sub-200ms p50 latency (claimed)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__cap_9","uri":"capability://planning.reasoning.benchmark.based.performance.validation.on.research.and.qa.tasks","name":"benchmark-based performance validation on research and qa tasks","description":"Publishes performance claims on multiple research and QA benchmarks including SimpleQA (OpenAI's factual QA benchmark), GAIA, DeepResearch Bench, Leetcode 75, and Document Relevance. SimpleQA methodology documented: GPT-4.1 grounded by Tavily results with max 10 documents per query. Other benchmark methodologies and actual performance scores not published. Benchmarks used to validate research endpoint quality and search result relevance.","intents":["Evaluate Tavily's suitability for my specific use case by comparing benchmark performance","Verify that Tavily results improve LLM accuracy on factual QA tasks","Compare Tavily performance against competitors using standardized benchmarks","Understand Tavily's strengths and weaknesses on different task types"],"best_for":["Teams evaluating Tavily for production deployment","Researchers comparing web search APIs for LLM grounding","Organizations with strict accuracy requirements for QA or research tasks","Developers building applications on specific benchmark tasks (e.g., GAIA, Leetcode)"],"limitations":["Actual benchmark scores not published — only claims that Tavily performs well","SimpleQA methodology partially documented but other benchmarks (GAIA, DeepResearch Bench, Leetcode 75) have no published methodology","No documented comparison against competitors on same benchmarks","Benchmark performance may not generalize to production use cases outside benchmark scope","No documented performance on domain-specific tasks or specialized knowledge areas","Benchmark results may be outdated — publication dates not provided"],"requires":["Access to benchmark datasets (publicly available for SimpleQA, GAIA, Leetcode; others may be proprietary)","Ability to run Tavily against benchmarks and evaluate results","Understanding of benchmark methodology and limitations"],"input_types":["Benchmark task (question, research topic, code problem, etc.)"],"output_types":["Benchmark performance metrics (accuracy, precision, recall, etc.) — exact metrics not documented"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tavily-agent__headline","uri":"capability://tool.use.integration.ai.optimized.search.agent.for.llm.applications","name":"ai-optimized search agent for llm applications","description":"Tavily Agent is an AI-optimized search agent that provides real-time web search results and summarized content specifically designed for LLM applications and RAG pipelines.","intents":["best AI search agent","AI agent for real-time web search","RAG framework for LLM integration","AI data extraction tool","best agent for web crawling"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Tavily API key (obtained via free registration at tavily.com)","HTTP client capable of making REST API calls","Understanding of API credit consumption model (exact formula not publicly documented)","Network connectivity to Tavily's cloud infrastructure","Tavily API key with sufficient credits for extraction operations","Valid, publicly accessible URL to extract from","Understanding that extraction consumes API credits (exact cost per page not documented)","Tavily API key","Supported agent framework (LangChain, CrewAI, AutoGen, etc.)","Native SDK or MCP client for your framework"],"failure_modes":["Credit-based pricing model with unclear per-query cost (documentation states 'API credit' definition but specifics not provided)","Free tier limited to 1,000 credits/month (insufficient for production applications with high query volume)","Web-only access — cannot retrieve from private databases, internal APIs, or non-public sources","No documented SLA on data freshness or index update frequency","Maximum number of results per query and crawl depth/scope not documented","Mechanism for handling JavaScript-rendered content not documented (may fail on heavily JS-dependent sites)","Security layer implementation details unknown — false positive/negative rates for PII detection and prompt injection filtering not published","No control over summarization style, length, or emphasis — fully automated with no customization","Raw content option may return full page HTML, increasing token consumption and latency","No documented handling of paywalled, authenticated, or rate-limited content","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:28.696Z","last_scraped_at":null,"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=tavily-agent","compare_url":"https://unfragile.ai/compare?artifact=tavily-agent"}},"signature":"ZaxzwYA+HH+b+NpUcTBa7rvncMogy+xP/DHLV5qKOiepMsVsOiRQLTql1RWsehtFI7JNAv4KxVItcZwIG9XwDQ==","signedAt":"2026-06-22T03:57:11.011Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tavily-agent","artifact":"https://unfragile.ai/tavily-agent","verify":"https://unfragile.ai/api/v1/verify?slug=tavily-agent","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"}}