clinicalagent 0.1.2

Add your description here

clinicalagent

AI-powered agent framework for clinical trials intelligence.

clinicalagent is a lightweight, flexible Python framework for building and orchestrating AI agents tailored for clinical trial workflows. It is designed to run locally, with a tool definition system closely aligned with the Model Context Protocol (MCP), making it simple to integrate clinical data sources, trial registries, and biomedical knowledge bases.

Features

• Clinical Trial Intelligence: Purpose-built for clinical trial analysis — eligibility screening, protocol summarization, adverse event detection, and more.
• Local-First: Run agents securely in your local environment, keeping sensitive patient and trial data under your control.
• MCP Integration: Seamlessly integrate with MCP servers to connect trial registries (e.g., ClinicalTrials.gov), EHR systems, and lab data tools.
• Thoughtful Agents: Optional "thought" process visibility for debugging, auditing, and regulatory transparency.
• Highly Configurable: Easy configuration via YAML or environment variables to adapt to different therapeutic areas and trial phases.
• Streaming: Built-in support for streaming agent responses in real time.
• Extensible: Create custom environments to control tool execution, clinical context management, and domain-specific logic.

Installation

You can install clinicalagent directly from PyPI:

pip install clinicalagent

Quick Start

Here is a simple example of how to create an agent that can look up clinical trial information and assess patient eligibility.

1. Define Tools & Environment

Create a file named main.py:

import asyncio
from fastmcp import Client, FastMCP
from clinicalagent import DefaultEnvironment, Agent
from clinicalagent.types import BaseToolModel, CallToolRequestParams
from clinicalagent.utils import mcp_tools

# 1. Define clinical trial tools using FastMCP
mcp = FastMCP()

@mcp.tool
def search_trials(condition: str, phase: str = "") -> str:
    """Search for active clinical trials by medical condition and optional trial phase (e.g., 'Phase 2')."""
    # In production, this would query ClinicalTrials.gov or an internal registry
    return f"Found 12 active trials for '{condition}'" + (f" in {phase}" if phase else "")

@mcp.tool
def check_eligibility(trial_id: str, patient_age: int, diagnosis: str) -> str:
    """Check whether a patient meets the eligibility criteria for a specific clinical trial."""
    # In production, this would evaluate inclusion/exclusion criteria
    return (
        f"Patient (age {patient_age}, diagnosis: {diagnosis}) "
        f"meets eligibility criteria for trial {trial_id}."
    )

@mcp.tool
def get_trial_summary(trial_id: str) -> str:
    """Retrieve a summary of a clinical trial including phase, sponsor, endpoints, and status."""
    return (
        f"Trial {trial_id}: Phase 3, Randomized, Double-Blind study "
        f"evaluating Drug X vs Placebo in Advanced NSCLC. "
        f"Primary endpoint: Progression-Free Survival. Status: Recruiting."
    )

# 2. Create an MCP client
client = Client(mcp)

# 3. Define the Environment
# The environment handles tool execution and clinical context management
class ClinicalTrialEnvironment[T: BaseToolModel](DefaultEnvironment[T]):
    async def call_tool(self, action: CallToolRequestParams[T]) -> str | None:
        async with client:
            result = await client.call_tool(
                name=action.tool_name,
                arguments=action.arguments.model_dump()
            )
        return "\n".join(res.text for res in result.content if res.type == "text")

# 4. Initialize Agent
async def main():
    # Initialize environment with clinical trial tools from the MCP client
    env = ClinicalTrialEnvironment(tools=mcp_tools(client))

    # Create the agent
    agent = Agent(
        environment=env,
        disable_thought=False  # Set to False to see the agent's reasoning process
    )

    # Add a clinical query to the history
    env.history.add_message(
        role="user",
        content="Find active Phase 3 clinical trials for non-small cell lung cancer and check if a 58-year-old patient with stage IIIB NSCLC is eligible."
    )

    # Run the agent
    async for event in agent.stream():
        print(event.model_dump_json(indent=2))
        print("------------")

if __name__ == "__main__":
    asyncio.run(main())

2. Configure LLM

Create a clinicalagent-config.yaml file in your project root to configure your LLM provider:

llm_model_name: "gpt-4o"
llm_api_key: "your-api-key-here"
# Optional: Base URL for other compatible providers
# llm_base_url: "https://api.openai.com/v1"

3. Run

python main.py

Configuration

clinicalagent uses pydantic-settings for configuration. You can configure it using a clinicalagent-config.yaml file, a .env file, or environment variables.

Setting	Description	Default
llm_model name	The name of the LLM model to use.	None
llm_api_key	API key for the LLM provider.	None
llm_base_url	Base URL for the LLM API.	None
max_agent_iterations	Maximum number of loops the agent can perform.	7
max_history_length	Maximum number of messages to keep in history.	11
llm_api_extra_kw	Extra keyword arguments for the LLM API call.	{}

Use Cases

clinicalagent can be applied across the clinical trial lifecycle:

• Trial Discovery & Matching: Search trial registries and match patients to eligible studies based on diagnosis, demographics, and biomarkers.
• Protocol Analysis: Summarize and compare trial protocols, identify inclusion/exclusion criteria, and flag potential issues.
• Adverse Event Monitoring: Analyze safety reports, detect signals, and classify adverse events by severity and causality.
• Regulatory Document Assistance: Draft or review sections of regulatory submissions (e.g., IND, CSR) with AI-assisted writing.
• Site Feasibility Assessment: Evaluate potential trial sites based on patient population, historical enrollment rates, and infrastructure.
• Literature Review: Search and synthesize relevant biomedical literature to support trial design and evidence generation.

Advanced Usage

Custom Environments

The Environment class is the heart of clinicalagent's extensibility. By subclassing DefaultEnvironment or implementing the Environment protocol, you can:

• Integrate Clinical Data Sources: Connect to EHR/EMR systems, CTMS platforms, lab data feeds, and trial registries.
• Customize Tool Execution: Handle tool calls locally, remotely, or via complex clinical data pipelines.
• Manage Clinical Context: Control how patient history, trial data, and conversation context are stored and presented to the LLM.
• Implement Termination Logic: Define custom criteria for when the agent should stop — e.g., after eligibility is confirmed or an adverse event is flagged.

Thought Process

clinicalagent can expose the agent's internal "thought" process. When disable_thought=False is passed to the Agent constructor, the agent will generate a thought trace before taking actions or answering. This is particularly valuable in clinical settings for:

• Audit Trails: Understanding why the agent made specific clinical recommendations.
• Debugging: Tracing the reasoning behind trial matching or eligibility decisions.
• Regulatory Transparency: Providing explainable AI outputs for compliance and review.

License

MIT