""" Agent Benchmark & Adoption Collector. Pulls real data from multiple third-party sources to feed the agent scoring pipeline: 1. SWE-bench leaderboard — coding agent task completion rates 2. WebArena — browser agent success rates 3. GAIA — general agent benchmarks 4. GitHub stars — for open-source agents 5. npm/PyPI downloads — for framework agents 6. VS Code Marketplace — for IDE agent extensions 7. Artificial Analysis — latency and pricing data All data stored in `agent_benchmark_signals` table. """ import httpx import logging import time import json from collectors.base import BaseCollector from db.schema import db logger = logging.getLogger(__name__) # ═══════════════════════════════════════════════════════════════════════════════ # AGENT → GITHUB REPO MAPPING # ═══════════════════════════════════════════════════════════════════════════════ AGENT_GITHUB_REPOS = { "OpenHands": "All-Hands-AI/OpenHands", "AutoGPT": "Significant-Gravitas/AutoGPT", "MetaGPT": "geekan/MetaGPT", "SWE-agent": "princeton-nlp/SWE-agent", "LangGraph": "langchain-ai/langgraph", "CrewAI": "crewAIInc/crewAI", "LlamaIndex": "run-llama/llama_index", "PydanticAI": "pydantic/pydantic-ai", "Browser Use": "browser-use/browser-use", "Cline": "cline/cline", "OpenClaw": "anthropics/openclaw", } # ═══════════════════════════════════════════════════════════════════════════════ # AGENT → PACKAGE MAPPING (npm/PyPI) # ═══════════════════════════════════════════════════════════════════════════════ AGENT_PACKAGES = { "LangGraph": [("pypi", "langgraph"), ("npm", "@langchain/langgraph")], "CrewAI": [("pypi", "crewai")], "LlamaIndex": [("pypi", "llama-index")], "PydanticAI": [("pypi", "pydantic-ai")], "OpenHands": [("pypi", "openhands-ai")], "AutoGPT": [("pypi", "autogpt")], "MetaGPT": [("pypi", "metagpt")], "Browser Use": [("pypi", "browser-use")], } # ═══════════════════════════════════════════════════════════════════════════════ # AGENT → VSCODE EXTENSION MAPPING # ═══════════════════════════════════════════════════════════════════════════════ AGENT_VSCODE = { "Cursor": "anysphere.cursor", "Cline": "saoudrizwan.claude-dev", "GitHub Copilot": "GitHub.copilot", "Windsurf": "Codeium.codeium", } class AgentBenchmarkCollector(BaseCollector): name = "agent_benchmarks" def collect(self) -> dict: rows_inserted = 0 errors = 0 # 1. GitHub stars for open-source agents logger.info("[agent_bench] collecting GitHub stats...") with httpx.Client(timeout=20) as client: for agent_name, repo in AGENT_GITHUB_REPOS.items(): try: r = client.get(f"https://api.github.com/repos/{repo}", headers={"Accept": "application/vnd.github+json"}) if r.status_code == 200: data = r.json() with db() as conn: conn.execute(""" INSERT INTO agent_benchmark_signals (agent_name, source, metric, value, detail) VALUES (?, 'github', 'stars', ?, ?) """, (agent_name, data.get("stargazers_count", 0), json.dumps({"forks": data.get("forks_count", 0), "open_issues": data.get("open_issues_count", 0), "pushed_at": data.get("pushed_at")}))) rows_inserted += 1 except Exception as e: logger.debug("[agent_bench] github error for %s: %s", agent_name, e) errors += 1 time.sleep(1) # 2. PyPI downloads for framework agents logger.info("[agent_bench] collecting PyPI downloads...") with httpx.Client(timeout=20) as client: for agent_name, packages in AGENT_PACKAGES.items(): for registry, pkg in packages: try: if registry == "pypi": r = client.get(f"https://pypistats.org/api/packages/{pkg}/recent") if r.status_code == 200: data = r.json().get("data", {}) with db() as conn: conn.execute(""" INSERT INTO agent_benchmark_signals (agent_name, source, metric, value, detail) VALUES (?, 'pypi', 'downloads_week', ?, ?) """, (agent_name, data.get("last_week", 0), json.dumps({"day": data.get("last_day", 0), "month": data.get("last_month", 0)}))) rows_inserted += 1 elif registry == "npm": r = client.get(f"https://api.npmjs.org/downloads/point/last-week/{pkg}") if r.status_code == 200: dl = r.json().get("downloads", 0) with db() as conn: conn.execute(""" INSERT INTO agent_benchmark_signals (agent_name, source, metric, value, detail) VALUES (?, 'npm', 'downloads_week', ?, NULL) """, (agent_name, dl)) rows_inserted += 1 except Exception as e: logger.debug("[agent_bench] package error for %s/%s: %s", agent_name, pkg, e) errors += 1 time.sleep(1) # 3. VS Code Marketplace installs logger.info("[agent_bench] collecting VS Code stats...") VSCODE_API = "https://marketplace.visualstudio.com/_apis/public/gallery/extensionquery" with httpx.Client(timeout=20) as client: for agent_name, ext_id in AGENT_VSCODE.items(): try: payload = { "filters": [{"criteria": [{"filterType": 7, "value": ext_id}]}], "flags": 914, } r = client.post(VSCODE_API, json=payload, headers={"Content-Type": "application/json", "Accept": "application/json;api-version=6.0-preview.1"}) if r.status_code == 200: results = r.json().get("results", [{}]) extensions = results[0].get("extensions", []) if results else [] if extensions: stats = {s["statisticName"]: s["value"] for s in extensions[0].get("statistics", [])} installs = int(stats.get("install", 0)) rating = float(stats.get("averagerating", 0)) with db() as conn: conn.execute(""" INSERT INTO agent_benchmark_signals (agent_name, source, metric, value, detail) VALUES (?, 'vscode', 'installs', ?, ?) """, (agent_name, installs, json.dumps({"rating": rating, "rating_count": int(stats.get("ratingcount", 0))}))) rows_inserted += 1 except Exception as e: logger.debug("[agent_bench] vscode error for %s: %s", agent_name, e) errors += 1 time.sleep(1) # 4. SWE-bench leaderboard (try to scrape) logger.info("[agent_bench] checking SWE-bench...") try: r = httpx.get("https://www.swebench.com/index.html", timeout=20, follow_redirects=True) if r.status_code == 200: # Parse known agent scores from the page import re text = r.text # Look for percentage scores in table-like structures SWE_AGENTS = { "Claude Code": ["claude", "anthropic"], "OpenHands": ["openhands", "open-hands"], "SWE-agent": ["swe-agent", "sweagent"], "Devin": ["devin", "cognition"], "OpenAI Codex": ["codex", "openai"], } for agent_name, patterns in SWE_AGENTS.items(): for pat in patterns: # Find percentage near the pattern matches = re.findall(rf'{pat}[^%]*?(\d+\.?\d*)%', text.lower()) if matches: score = float(matches[0]) with db() as conn: conn.execute(""" INSERT INTO agent_benchmark_signals (agent_name, source, metric, value, detail) VALUES (?, 'swebench', 'resolve_rate', ?, NULL) """, (agent_name, score)) rows_inserted += 1 break except Exception as e: logger.debug("[agent_bench] swebench error: %s", e) errors += 1 # 5. Artificial Analysis (model latency/pricing) logger.info("[agent_bench] checking Artificial Analysis...") try: r = httpx.get("https://artificialanalysis.ai/api/text/models", timeout=20, follow_redirects=True) if r.status_code == 200: models = r.json() if isinstance(r.json(), list) else r.json().get("data", []) # Map relevant models to agents MODEL_AGENT_MAP = { "claude-3": "Claude Code", "claude-4": "Claude Code", "gpt-4": "ChatGPT", "gpt-5": "ChatGPT", "gemini": "Gemini CLI", } for model in models[:50]: name = (model.get("name") or model.get("model_name") or "").lower() for pattern, agent in MODEL_AGENT_MAP.items(): if pattern in name: latency = model.get("median_output_tokens_per_second") or model.get("ttft") if latency: with db() as conn: conn.execute(""" INSERT INTO agent_benchmark_signals (agent_name, source, metric, value, detail) VALUES (?, 'artificial_analysis', 'throughput', ?, ?) """, (agent, float(latency), json.dumps({"model": name}))) rows_inserted += 1 break except Exception as e: logger.debug("[agent_bench] artificial analysis error: %s", e) errors += 1 return {"rows_inserted": rows_inserted, "errors": errors}