{"uid":"cap_frB1BAnxK1bTX7wWzxJum","slug":"gedx402-cited-research-pack-e7daceb4","name":"GEDX402 Cited Research Pack","description":"x402 workers ai. pay with usdc on base, polygon, arbitrum, world, or solana. no api keys.","url":"https://browser.gedx402.com/v1/recipes/cited-research-pack","method":"GET","headers":{},"bodySchema":{"type":"object","properties":{"url":{"type":"string","description":"HTTPS URL to research."},"poll":{"type":"boolean","description":"Poll index upload until ready (default false)."},"safe":{"type":"boolean","description":"Run Llama Guard safe-extract first (default true). Set false to skip guard."},"top_k":{"type":"integer","description":"Retrieval top_k for answer step (1-20)."},"budget":{"type":"object","properties":{"max_queries":{"type":"integer","maximum":64,"minimum":1},"max_index_ops":{"type":"integer","maximum":32,"minimum":1}},"description":"Optional RAG session budget forwarded to outcome/answer step."},"question":{"type":"string","description":"Natural-language question answered with citations."}}},"responseSchema":{"type":"json","example":{"answer":"The article argues … [1]","recipe":"cited-research-pack","artifacts":{"persist":{"key":"extract/uuid.md","get_url":"https://storage.gedx402.com/v1/objects/extract/uuid.md"}},"citations":[{"url":"https://example.com/blog/post","index":1,"snippet":"…"}],"session_token":"abc123…","completed_steps":["safe-extract","persist","outcome/answer"]}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"1.105","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"down","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$1.105/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"1.105","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"1.105","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm__xWCigBbEYxbwOCIwKWaz","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"1.105","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Fetches a URL, extracts its content with optional safety filtering, indexes it via RAG, and returns a cited answer to a natural-language question about it","exampleAgentPrompt":"Look up https://arxiv.org/abs/2310.06825 and answer the question 'What are the main contributions of this paper?' with citations — use top_k of 5 and keep safety filtering on.","exampleUseCases":null,"resultDescription":"A JSON object containing a cited natural-language answer to the question, an array of citation objects (each with URL, index number, and a text snippet), a session token for the RAG session, a reference to the persisted markdown artifact stored at a retrievable URL, and a list of completed pipeline steps (safe-extract, persist, outcome/answer).","failureModes":["URL is unreachable or returns non-200 — extraction step fails with an error","Content flagged by Llama Guard safe-extract — pipeline halts early with safety rejection","RAG index upload times out or fails — answer step cannot proceed without index","question is empty or too vague — answer may be low quality or generic","Payment rejected or insufficient USDC balance — 402 response before processing begins","top_k out of range (must be 1-20) — validation error","budget.max_queries or max_index_ops out of allowed range — validation error"],"whenToPreferThis":"Choose this endpoint when you need to ask a specific question about the content of a known URL and get back a cited, grounded answer — not just a summary. It is especially suited for research tasks where traceability to source snippets matters. It combines web fetching, safety filtering, RAG indexing, and LLM Q&A in a single call, saving orchestration overhead. Prefer it over generic web scrapers when you need answer + citations rather than raw text, and over general search APIs when you already know the source URL.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-08-24T04:01:08.043Z","isFirstParty":false}