txagent 0.1.1.1

TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools

TxAgent: An AI agent for therapeutic reasoning across a universe of tools

Shanghua Gao, Richard Zhu, Zhenglun Kong, Ayush Noori, Xiaorui Su, Curtis Ginder, Theodoros Tsiligkaridis, Marinka Zitnik^,

Overview

Precision therapeutics require multimodal adaptive models that generate personalized treatment recommendations. We introduce TxAgent, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge retrieval across a toolbox of 211 tools to analyze drug interactions, contraindications, and patient-specific treatment strategies. TxAgent evaluates how drugs interact at molecular, pharmacokinetic, and clinical levels, identifies contraindications based on patient comorbidities and concurrent medications, and tailors treatment strategies to individual patient characteristics, including age, genetic factors, and disease progression. TxAgent retrieves and synthesizes evidence from multiple biomedical sources, assesses interactions between drugs and patient conditions, and refines treatment recommendations through iterative reasoning. It selects tools based on task objectives and executes structured function calls to solve therapeutic tasks that require clinical reasoning and cross-source validation. The ToolUniverse consolidates 211 tools from trusted sources, including all US FDA-approved drugs since 1939 and validated clinical insights from Open Targets. TxAgent outperforms leading LLMs, tool-use models, and reasoning agents across five new benchmarks: DrugPC, BrandPC, GenericPC, TreatmentPC, and DescriptionPC, covering 3,168 drug reasoning tasks and 456 personalized treatment scenarios. It achieves 92.1% accuracy in open-ended drug reasoning tasks, surpassing GPT-4o by up to 25.8% and outperforming DeepSeek-R1 (671B) in structured multi-step reasoning. TxAgent generalizes across drug name variants and descriptions, maintaining a variance of < 0.01 between brand, generic, and description-based drug references, exceeding existing tool-use LLMs by over 55%. By integrating multi-step inference, real-time knowledge grounding, and tool- assisted decision-making, TxAgent ensures that treatment recommendations align with established clinical guidelines and real-world evidence, reducing the risk of adverse events and improving therapeutic decision-making.

Setups

Dependency:

- An H100 GPU with more than 80GB of memory is recommended when running TxAgent. 
- ToolUniverse requires a device with an internet connection.

Install ToolUniverse:

# Install from source code:
git clone https://github.com/mims-harvard/ToolUniverse.git
cd ToolUniverse
python -m pip install . --no-cache-dir

# Install from pip:
pip install tooluniverse

Install TxAgent:

cd ../
python -m pip install . --no-cache-dir

Run the example:

python run_example.py

Run the gradio demo:

python run_txagent_app.py

Pretrained models

Pretrained model weights are available in HuggingFace.

Model	Description
TxAgent-T1-Llama-3.1-8B	TxAgent LLM
ToolRAG-T1-GTE-Qwen2-1.5B	Tool RAG embedding model

Demo cases

Please visit project page for more details.

Citation