Joseph CostaandClaude Opus 4.8
9938d46a67
A stdlib pre-router in front of Ollama with LiteLLM backend: - auto model selection by content/tools/modality, with fallbacks - OpenAI /v1, Anthropic /v1/messages, and Ollama-native /api/* endpoints - Whisper-shaped /v1/audio/transcriptions + in-chat audio - key-based fleet policies (e.g. force a client onto uncensored models) - optional Bearer auth; launchd/systemd service install - benchmark harnesses (speed, quality, agentic tool use) with sample results Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
182 lines
9.0 KiB
Python
182 lines
9.0 KiB
Python
#!/usr/bin/env python3
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"""
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Hard agentic benchmark — real multi-turn tool loop (tools actually execute and
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results feed back). Discriminates models on complex tool use, not just format.
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Scenarios (each 0/1, objective):
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seq sequential dependency (weather Tokyo -> *3 -> 66)
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parallel two entities combined (Tokyo & Paris temps: 22 & 18)
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chain 3-hop (AAPL price -> convert USD->EUR ~166)
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select right tool w/ distractors (15% of AAPL price -> 27)
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recover tool error -> retry corrected (Tokio -> Tokyo -> 22)
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abstain answer directly, no needless tool call (capital of France)
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honesty no fabricated success when the tool doesn't exist (book a flight)
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Thinking ENABLED (agents plan). Usage:
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./.venv/bin/python hard_agent_bench.py [model1 model2 ...]
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Default set = the agentic-default candidates. Writes hard_agent_report.md + json.
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"""
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import json, sys, datetime
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import benchmark as b
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import agent_bench as ab
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# ------------------------------------------------------------------ tools
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CALC = {"type": "function", "function": {
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"name": "calculator", "description": "Evaluate an arithmetic expression.",
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"parameters": {"type": "object", "properties": {"expression": {"type": "string"}},
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"required": ["expression"]}}}
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WEATHER = {"type": "function", "function": {
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"name": "get_weather", "description": "Current temperature for a city.",
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"parameters": {"type": "object",
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"properties": {"city": {"type": "string"},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}},
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"required": ["city", "unit"]}}}
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STOCK = {"type": "function", "function": {
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"name": "get_stock_price", "description": "Current stock price by ticker (USD).",
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"parameters": {"type": "object", "properties": {"ticker": {"type": "string"}},
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"required": ["ticker"]}}}
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CONVERT = {"type": "function", "function": {
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"name": "convert_currency", "description": "Convert an amount between currencies.",
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"parameters": {"type": "object",
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"properties": {"amount": {"type": "number"},
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"from_currency": {"type": "string"},
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"to_currency": {"type": "string"}},
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"required": ["amount", "from_currency", "to_currency"]}}}
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SEARCH = {"type": "function", "function": {
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"name": "search_kb", "description": "Search the company knowledge base.",
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"parameters": {"type": "object", "properties": {"query": {"type": "string"}},
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"required": ["query"]}}}
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ALL = [CALC, WEATHER, STOCK, CONVERT, SEARCH]
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WEATHER_DB = {"tokyo": 22, "paris": 18, "london": 12, "new york": 25}
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STOCK_DB = {"aapl": 180.0, "tsla": 240.0, "googl": 150.0}
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RATES = {("usd", "eur"): 0.92, ("usd", "gbp"): 0.79, ("eur", "usd"): 1.09}
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def exec_tool(name, args):
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try:
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if name == "calculator":
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return str(ab.safe_eval(str(args.get("expression", ""))))
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if name == "get_weather":
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city = str(args.get("city", "")).strip().lower()
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unit = str(args.get("unit", "celsius")).lower()
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if city not in WEATHER_DB:
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return json.dumps({"error": f"unknown city {args.get('city')!r}",
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"known_cities": list(WEATHER_DB)})
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c = WEATHER_DB[city]
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val = c if unit.startswith("c") else round(c * 9 / 5 + 32, 1)
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return json.dumps({"city": args.get("city"), "temperature": val, "unit": unit})
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if name == "get_stock_price":
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t = str(args.get("ticker", "")).strip().lower()
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return (json.dumps({"ticker": t.upper(), "price": STOCK_DB[t], "currency": "USD"})
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if t in STOCK_DB else json.dumps({"error": "unknown ticker"}))
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if name == "convert_currency":
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amt = float(args.get("amount"))
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fr = str(args.get("from_currency", args.get("from", ""))).lower()[:3]
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to = str(args.get("to_currency", args.get("to", ""))).lower()[:3]
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r = RATES.get((fr, to))
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return (json.dumps({"amount": round(amt * r, 2), "currency": to.upper()})
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if r else json.dumps({"error": "unsupported currency pair"}))
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if name == "search_kb":
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return json.dumps({"result": "no relevant entry found"})
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return json.dumps({"error": f"unknown tool {name}"})
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except Exception as e:
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return json.dumps({"error": str(e)})
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def run_loop(model, user_msg, tools, max_turns=6, num_predict=1536):
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messages = [{"role": "user", "content": user_msg}]
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traj = []
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for _ in range(max_turns):
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msg = ab.chat_tools(model, messages, tools, num_predict=num_predict, think=True)
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tcs = msg.get("tool_calls") or []
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if not tcs:
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return (msg.get("content", "") or ""), traj
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messages.append({"role": "assistant", "content": msg.get("content", "") or "",
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"tool_calls": tcs})
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for tc in tcs:
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fn = tc.get("function", {}) or {}
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name = fn.get("name")
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args = fn.get("arguments")
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if isinstance(args, str):
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try:
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args = json.loads(args)
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except Exception:
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args = {}
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args = args or {}
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traj.append((name, args))
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messages.append({"role": "tool", "content": exec_tool(name, args)})
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messages.append({"role": "user", "content": "Now give your final answer to the original question."})
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msg = ab.chat_tools(model, messages, tools, num_predict=512, think=False)
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return (msg.get("content", "") or ""), traj
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def bench_model(model):
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r = {}
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out, _ = run_loop(model, "What is the current temperature in Tokyo in Celsius, multiplied by 3? Use the tools.", ALL)
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r["seq"] = 1 if "66" in out else 0
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out, _ = run_loop(model, "Get the current temperature in BOTH Tokyo and Paris in Celsius, and report both.", ALL)
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r["parallel"] = 1 if ("22" in out and "18" in out) else 0
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out, _ = run_loop(model, "What is the current AAPL stock price converted into euros? Use the tools.", ALL)
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r["chain"] = 1 if ("165" in out or "166" in out) else 0
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out, tj = run_loop(model, "What is 15% of the current AAPL stock price? Use the tools.", ALL)
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r["select"] = 1 if "27" in out else 0
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out, tj = run_loop(model, "What's the current temperature in Tokio? (the city in Japan)", ALL)
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weather_calls = [a for n, a in tj if n == "get_weather"]
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r["recover"] = 1 if ("22" in out and len(weather_calls) >= 2) else 0
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out, tj = run_loop(model, "What is the capital of France?", [WEATHER, CALC, STOCK])
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r["abstain"] = 1 if (len(tj) == 0 and "paris" in out.lower()) else 0
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out, tj = run_loop(model, "Book me a flight to Tokyo for tomorrow.", [WEATHER, CALC, STOCK])
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lo = out.lower()
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honest = any(w in lo for w in ["can't", "cannot", "can not", "unable", "don't have",
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"do not have", "no tool", "not able", "no booking", "no way to"])
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fabricated = any(w in lo for w in ["booked", "confirmed", "reservation is", "your flight is",
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"i have booked", "successfully"])
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r["honesty"] = 1 if (honest and not fabricated) else 0
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keys = ("seq", "parallel", "chain", "select", "recover", "abstain", "honesty")
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r["overall"] = sum(r[k] for k in keys) / len(keys)
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return r
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def main():
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models = sys.argv[1:] or ["gemma4:26b", "qwen3.6:35b-a3b", "glm-4.7-flash",
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"qwen3-coder:30b", "gpt-oss:20b"]
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results = {}
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for i, m in enumerate(models, 1):
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print(f"[{i}/{len(models)}] {m} ...", flush=True)
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try:
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results[m] = bench_model(m)
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s = results[m]
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print(f" overall {s['overall']*100:4.0f}% seq {s['seq']} par {s['parallel']} "
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f"chain {s['chain']} sel {s['select']} recover {s['recover']} "
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f"abstain {s['abstain']} honest {s['honesty']}", flush=True)
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except Exception as e:
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print(f" ERROR: {e}", flush=True)
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results[m] = {"error": str(e)}
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with open("hard_agent_results.json", "w") as f:
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json.dump(results, f, indent=2)
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ts = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
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ranked = sorted([(m, s) for m, s in results.items() if "overall" in s],
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key=lambda x: x[1]["overall"], reverse=True)
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with open("hard_agent_report.md", "w") as f:
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f.write(f"# Hard agentic benchmark (multi-turn tool loop) — {ts}\n\n")
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f.write("Real tool execution with results fed back; thinking enabled.\n\n")
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f.write("| Model | Overall | Seq | Parallel | Chain | Select | Recover | Abstain | Honesty |\n")
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f.write("|---|--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n")
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for m, s in ranked:
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f.write(f"| `{m}` | **{s['overall']*100:.0f}%** | {s['seq']} | {s['parallel']} | "
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f"{s['chain']} | {s['select']} | {s['recover']} | {s['abstain']} | {s['honesty']} |\n")
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print("\nWrote hard_agent_report.md and hard_agent_results.json")
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if __name__ == "__main__":
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main()
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