Pydantic AI 2.61.0: our test log

Pydantic AI 2.61.0 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 3.7 s.

Result at a glance

What we found

• 3 of 3 task trials passed: the model's tool call reached the framework, the function ran with 17 and 25, and the final answer contained 42.
• Cold install took 3.7 s with uv and 25.9 s with pip, and left 141 MB in 102 packages (uv).
• The install pulls in 100+ packages because the umbrella package bundles every model provider; a slimmer provider-specific package exists but we measured the default one.

What we ran, step by step

# Install with uv (cold, no cache)
uv venv -q -p 3.13 work/py/pydantic-ai && VIRTUAL_ENV=work/py/pydantic-ai uv pip install --no-cache -q pydantic-ai

# Install with pip (cold, no cache)
uv venv -q --seed -p 3.13 work/pip/pydantic-ai && work/pip/pydantic-ai/bin/pip install --no-cache-dir -q pydantic-ai

# Task command (all trials)
OTEL_SDK_DISABLED=true work/py/pydantic-ai/bin/python tasks/pydantic_ai_agent.py

Raw output (excerpts)

from common import *
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider
agent = Agent(OpenAIChatModel(MODEL, provider=OpenAIProvider(base_url=BASE, api_key=KEY)), tools=[add_numbers])
r = agent.run_sync(TASK)
report(r.output)

import json, os
BASE = "http://127.0.0.1:11434/v1"
MODEL = os.environ.get("MODEL", "qwen2.5-3b")
KEY = "ollama"
TASK = "Use the add_numbers tool to compute 17 + 25, then tell me the result."
CALLS = []
def add_numbers(a: int, b: int) -> int:
    """Add two integers and return the sum."""
    CALLS.append((a, b))
    return int(a) + int(b)
def report(final):
    ok = any({int(x), int(y)} == {17, 25} for x, y in CALLS) and "42" in str(final)
    print("RESULT_JSON " + json.dumps({"calls": CALLS, "final": str(final)[:300], "ok": ok}))

RESULT_JSON {"calls": [[17, 25]], "final": "The result of adding 17 and 25 is 42.", "ok": true}

What we did not test

• Any vendor-hosted model: everything ran on a local 3B model, so tool-call reliability with frontier models is not measured here.
• Multi-agent flows, memory, long conversations, streaming, tracing and cloud deployment.
• Anything beyond one tool and one question; three trials is a smoke test, not a benchmark.

Reproduce it

Versions change. These commands install whatever is latest on the day you run them unless a version is pinned, so expect different results later. The version we measured is in the table above.

# 1. Serve any local model that supports tool calling behind an OpenAI-compatible endpoint, registered as qwen2.5-3b:
#    http://127.0.0.1:11434/v1  (we used Ollama with a Qwen2.5-3B-Instruct Q4_K_M GGUF)
uv venv -p 3.13 .venv && VIRTUAL_ENV=.venv uv pip install pydantic-ai
# 2. Save common.py (above) and the task script next to each other, then:
.venv/bin/python pydantic_ai_agent.py

How to read this test

Every number on this page was measured on this one machine, which also ran other jobs, so timings are indicative and vary run to run. A pass means our small task worked once or as stated, a fail means it did not work as we ran it; neither says how the tool behaves with a vendor's frontier model, on a GPU, or at scale. Where a tool could not run without an account or a GPU, we say so instead of guessing.

See the testing rig for the hardware, the local model and the rules we follow, and the test hub for every other test.

Related tests: agent frameworks and sdks

• Semantic Kernel: Semantic Kernel 1.45.0 returned an empty reply with our standard prompt (0 of 3) but worked once the prompt used its plugin-prefixed tool name (3 of 3); a naming trap with small models.
• smolagents: smolagents 1.26.0 passed our one-tool task on a local 3B model: 6 of 6 trials; cold uv install 1.5 s.
• Vercel AI SDK: Vercel AI SDK passed our one-tool task 3 of 3 on Node 20 (ai 6.0.315) and 3 of 3 on Node 24 (ai 7.0.153); the install is 26 to 27 MB.
• Agno: Agno 3.1.10 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 1.8 s.
• AutoGen: AutoGen 0.7.5 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 1.9 s.
• CrewAI: CrewAI 1.15.30 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 8.4 s.

All test logs · How we test

FAQ

Sources

• Pydantic AI repository (fetched 2026-10-25)

Related

• All test logs
• Methodology and testing rig
• Study: install size and first-task results across 16 frameworks
• Pydantic AI facts and sources