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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}
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