# { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" } """ NewsOracle — a GenLayer Intelligent Contract that produces multi-validator AI consensus analyses of crypto / Web3 news articles. Each `analyze()` call: 1. Has every validator independently run an LLM over the same article. 2. Reaches consensus via the equivalence principle (sentiment label and category must match exactly; the score must be within a tolerance). 3. Persists the consensus verdict on-chain forever, keyed by article id. """ from genlayer import * import json import hashlib import typing SYSTEM_PROMPT = """You are a professional Web3 / crypto market analyst with deep expertise in blockchain, DeFi, NFTs, and crypto markets. Analyse the provided news article and respond with ONLY a valid JSON object — no markdown, no explanation, no preamble: { "sentiment": "bullish" | "bearish" | "neutral", "sentiment_score": , "summary": "<2-3 sentence plain English summary of what happened and why it matters>", "key_points": ["", "", "", ""], "market_impact": "<1-2 sentences on potential short-term market impact>", "risk_level": "low" | "medium" | "high", "category": "DeFi" | "NFT" | "Layer1" | "Layer2" | "Regulation" | "Macro" | "Exchange" | "AI" | "Infrastructure" | "Other", "entities": ["", ...], "tldr": "" }""" # Validators reach consensus when these conditions are jointly satisfied. # Loose enough to absorb LLM variance, tight enough that bad-faith leaders # can't slip distorted analyses past honest validators. EQUIVALENCE_PRINCIPLE = ( "`sentiment`, `risk_level`, and `category` fields MUST match exactly. " "`sentiment_score` values must be within 20 points of each other. " "`key_points` and `entities` lists must cover substantially the same " "themes / names (order and wording may differ). " "`summary`, `market_impact`, and `tldr` may be phrased differently but " "must convey the same overall conclusion." ) def _article_id(title: str, content: str) -> str: """Stable id from the article text — lets clients dedupe cheaply.""" h = hashlib.sha256() h.update(title.strip().encode("utf-8")) h.update(b"\x00") h.update(content.strip().encode("utf-8")) return h.hexdigest()[:24] class NewsOracle(gl.Contract): # article_id -> JSON-encoded consensus analysis analyses: TreeMap[str, str] # rolling log of analyzed article ids, newest last history: DynArray[str] def __init__(self): pass # -- Reads -------------------------------------------------------------- @gl.public.view def get_analysis(self, article_id: str) -> str: if article_id in self.analyses: return self.analyses[article_id] return "" @gl.public.view def has_analysis(self, title: str, content: str) -> bool: return _article_id(title, content) in self.analyses @gl.public.view def get_recent(self) -> list[str]: items = list(self.history) items.reverse() return items[:50] # -- Writes ------------------------------------------------------------- @gl.public.write def analyze(self, title: str, content: str) -> typing.Any: """ Reach multi-validator consensus on the analysis of a single article and persist the verdict on-chain. """ if not title: raise Exception("title is required") article_id = _article_id(title, content) # Idempotent: serve cached consensus if we've already analysed this. if article_id in self.analyses: cached = json.loads(self.analyses[article_id]) cached["cached"] = True return cached # Trim aggressively — every byte the leader returns has to be agreed # upon and stored on-chain. body = (content or title)[:3000] text = f"Title: {title}\n\nContent: {body}" def leader_and_validator() -> str: # Each validator independently asks its own LLM the same question. prompt = f"{SYSTEM_PROMPT}\n\n=== Article ===\n{text}\n=== End ===" raw = gl.nondet.exec_prompt(prompt) cleaned = raw.replace("```json", "").replace("```", "").strip() # Tolerate a model that wraps JSON in extra prose. start = cleaned.find("{") end = cleaned.rfind("}") if start == -1 or end == -1 or end <= start: raise Exception(f"LLM did not return JSON: {raw[:200]}") parsed = json.loads(cleaned[start : end + 1]) # Schema validation — reject obvious garbage early so a bad # leader can't poison state. for field in ( "sentiment", "sentiment_score", "summary", "key_points", "market_impact", "risk_level", "category", "entities", "tldr", ): if field not in parsed: raise Exception(f"missing field in LLM response: {field}") score = int(parsed["sentiment_score"]) if score < 0 or score > 100: raise Exception(f"sentiment_score out of range: {score}") parsed["sentiment_score"] = score parsed["sentiment"] = str(parsed["sentiment"]).lower() parsed["risk_level"] = str(parsed["risk_level"]).lower() # Canonical ordering so validator string-comparison is stable. return json.dumps(parsed, sort_keys=True) verdict_json = gl.eq_principle.prompt_comparative( leader_and_validator, principle=EQUIVALENCE_PRINCIPLE, ) verdict = json.loads(verdict_json) verdict["article_id"] = article_id verdict["title"] = title verdict["cached"] = False stored = json.dumps(verdict, sort_keys=True) self.analyses[article_id] = stored self.history.append(article_id) return verdict

Hash	Type	Status		From	To	Method	GenVM Result	Consensus Result	Time
0xb4ee577b...791daca2	Call	FINALIZED	IN	0xDcb56A...290ed0	0x713300...1826ae	analyze	SUCCESS	Accepted	26 days ago
0x200db780...9689ec8d	Deploy	FINALIZED	IN	0xDcb56A...290ed0	0x713300...1826ae	(constructor)	SUCCESS	Accepted	29 days ago