OneTwo

OneTwo is a python library for developing and deploying LLM agents in production. In just a few lines of code, you can define custom agents and combine them with an extensive library of pre-built components.

🔥 Features

• Swap models easily (Model-agnostic): Write code once using a uniform API and run it on any internal or external model without changing your logic.
• Write prompts your way (Convenient syntax): Build prompts flexibly using formatted strings, Jinja templates, or modular function calls.
• Run reproducible experiments: Automatically cache results to replay experiments instantly and save quota without making redundant requests.
• Equip models with tools: Seamlessly integrate external tools, including running model-generated Python code in a secure sandbox.
• Build and combine agents: Define and compose stateful agents to perform complex, step-by-step actions.
• Forget about execution details (Efficient & Flexible): Focus on your workflow while OneTwo automatically handles asynchronous execution, request batching, and smart caching.

Quick start

Installation

You may want to install the package in a virtual environment, in which case you will need to start a virtual environment with the following command:

python3 -m venv PATH_TO_DIRECTORY_FOR_VIRTUAL_ENV
# Activate it.
. PATH_TO_DIRECTORY_FOR_VIRTUAL_ENV/bin/activate

Once you no longer need it, this virtual environment can be deleted with the following command:

deactivate

Install the package:

pip install git+https://github.com/google-deepmind/onetwo

📝 Usage example

from onetwo import ot
from onetwo.builtins import llm
from onetwo.backends import google_genai_api

# Please provide the Gemini API key.
google_api_key = ""

# Select a model + the backend (e.g., Gemini API).
google_genai_api.GoogleGenAIAPI(
    vertexai=False,
    api_key=google_api_key,
    generate_model_name=f'models/gemini-2.5-flash',
    chat_model_name=f'models/gemini-2.5-flash',
).register()

# Define a prompting strategy with two LLM calls.
@ot.make_executable
async def invention_year(technology: str) -> int:
  """Get the year a technology was invented."""
  # First, get the inventor.
  inventor = await llm.instruct(f"Who is the primary inventor of {technology}? Answer with just the name.")
  # Then, get the year of invention.
  year = await llm.generate_object(f"In what year did {inventor} invent {technology}?", cls=int)
  return year


# Run the strategy for a single example.
year = ot.run(invention_year("World Wide Web"))
print(f"The World Wide Web was invented in: {year}")

Play with this snippet in the getting started tutorial.

🧠 OneTwo Agents

OneTwo makes it really easy to build stateful, autonomous agents that can reason, use tools, and solve complex problems. Compose agents naturally using Python control flow while OneTwo takes care of batching, streaming, and execution details behind the scenes.

Want to create and play with this agent? Check out our in-depth tutorial.

🔍 Retrieval Augmented Generation

Effortlessly ground your models in custom data. OneTwo provides a streamlined way to integrate retrieval systems into your workflows, allowing you to build powerful question-answering systems with minimal boilerplate.

Check out our RAG tutorial to easily create RAG and Question Answering workflows.

Running unit tests

In order to run the tests, first clone the repository:

git clone https://github.com/google-deepmind/onetwo

Then from the cloned directory you can invoke pytest:

pytest onetwo/core

However, doing pytest onetwo will not work as pytest collects all the test names without keeping their directory of origin so there may be name clashes, so you have to loop through the subdirectories.

Documentation

Some background on the basic concepts of the library can be found here: Basics.

Some of the frequently asked questions are discussed here: FAQ.

Citing OneTwo

To cite this repository:

@software{onetwo2026github,
  author = {Olivier Bousquet and Parth Kothari and Nathan Scales and Nathanael Sch{\"a}rli and Ilya Tolstikhin},
  title = {{O}ne{T}wo: {I}nteracting with {L}arge {M}odels},
  url = {https://github.com/google-deepmind/onetwo},
  version = {0.5.0},
  date = {2026-04-15},
  year = {2026}
}

In the above BibTeX entry, names are in alphabetical order, the version number is intended to be the one returned by ot.__version__ (i.e., the latest version mentioned in version.py and in the CHANGELOG, and the year and date correspond to the release of this specific version.

License

Copyright 2024 DeepMind Technologies Limited

This code is licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Disclaimer

This is not an official Google product.