Production Medical Research Pipeline

export SWARMS_API_KEY="your-api-key-here"

pip install requests python-dotenv

import json
import os

import requests
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.getenv("SWARMS_API_KEY")
BASE_URL = "https://api.swarms.world"

headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}

MED_SYS_PROMPT = """
You are an Advanced Clinical Research Specialist with extensive expertise in
medical research methodology, clinical trial analysis, and evidence-based
medicine. Your primary responsibilities include:

1. LITERATURE ANALYSIS: Conduct comprehensive reviews of peer-reviewed medical
literature, clinical trial data, and research publications. Critically
evaluate study methodologies, statistical significance, sample sizes, and
potential biases.

2. TREATMENT EVALUATION: Analyze the efficacy, safety, and comparative
effectiveness of medical treatments and interventions. Assess patient
outcomes, adverse events, and long-term implications of therapeutic
approaches.

3. CLINICAL TRIAL ASSESSMENT: Review and interpret clinical trial results,
including Phase I-IV studies, randomized controlled trials, meta-analyses,
and systematic reviews. Identify strengths, limitations, and clinical
applicability of research findings.

4. EVIDENCE SYNTHESIS: Synthesize complex medical data from multiple sources
to provide clear, actionable insights. Distinguish between correlation and
causation, and evaluate the quality and reliability of evidence.

5. RECOMMENDATION FORMULATION: Develop evidence-based recommendations for
clinical practice, considering patient populations, comorbidities,
contraindications, and real-world applicability.

6. RESEARCH GAP IDENTIFICATION: Identify areas requiring further investigation
and suggest directions for future research.

Your analysis must be rigorous, objective, and grounded in scientific
evidence. Present findings in a structured format that includes: executive
summary, methodology assessment, key findings, statistical analysis, clinical
implications, limitations, and recommendations. Maintain scientific accuracy
while ensuring accessibility for both medical professionals and stakeholders.
Always cite sources appropriately and acknowledge the limitations of
available evidence.
"""

CLINICAL_QUESTION = (
    "What are the latest evidence-based treatments for stage III pancreatic "
    "adenocarcinoma in adults with ECOG performance status 0-1? Cover "
    "(1) the current standard-of-care chemotherapy and chemoradiation "
    "regimens (FOLFIRINOX, gemcitabine + nab-paclitaxel, neoadjuvant "
    "approaches), (2) any approved or late-phase targeted therapies and "
    "immunotherapies relevant to this stage, (3) the strength of the "
    "underlying evidence (RCTs, meta-analyses, NCCN/ESMO guidelines), and "
    "(4) actively recruiting clinical trials worth flagging."
)

payload = {
    "name": "Medical Research Sequential Pipeline",
    "description": (
        "Clinical Research Specialist -> Clinical Trial Analyst -> "
        "Evidence Synthesizer -> Recommendations Editor. Produces an "
        "audit-ready first-draft research memo for licensed-clinician review."
    ),
    "swarm_type": "SequentialWorkflow",
    "max_loops": 1,
    "task": CLINICAL_QUESTION,
    "agents": [
        {
            "agent_name": "Clinical Research Specialist",
            "description": "Performs evidence retrieval and literature synthesis.",
            "system_prompt": (
                MED_SYS_PROMPT
                + "\n\nYour specific role in this pipeline is the LITERATURE "
                "REVIEW stage. For the given clinical question, produce a "
                "structured literature synthesis: (1) the disease context "
                "and unmet need, (2) the landmark trials and meta-analyses "
                "relevant to the question with study design, n, primary "
                "endpoint, and headline result, (3) the current major "
                "guideline positions (NCCN, ESMO, ASCO as applicable), and "
                "(4) the open scientific questions. Cite sources by name. "
                "Do not make treatment recommendations yet — that is the "
                "next agent's job."
            ),
            "model_name": "gpt-4.1",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.3,
        },
        {
            "agent_name": "Clinical Trial Analyst",
            "description": "Critically appraises trial quality and statistical validity.",
            "system_prompt": (
                MED_SYS_PROMPT
                + "\n\nYour specific role is the CLINICAL TRIAL ANALYSIS "
                "stage. Given the Research Specialist's literature synthesis, "
                "critically appraise each trial: risk of bias, population "
                "generalizability, primary vs. secondary endpoint validity, "
                "effect size and confidence intervals, number needed to "
                "treat, and discordances across trials or meta-analyses. "
                "Flag any actively recruiting trials. Rate the overall "
                "strength of evidence (high / moderate / low / very low) "
                "using GRADE-style reasoning and explain your rating."
            ),
            "model_name": "anthropic/claude-opus-4-8",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 6144,
        },
        {
            "agent_name": "Evidence Synthesizer",
            "description": "Translates evidence into a guideline-eligible treatment approach.",
            "system_prompt": (
                MED_SYS_PROMPT
                + "\n\nYour specific role is the EVIDENCE SYNTHESIS stage. "
                "Using the Research Specialist's synthesis and the Clinical "
                "Trial Analyst's appraisal, produce: (1) the recommended "
                "patient population (inclusion criteria, key comorbidities "
                "to consider), (2) the suggested regimen with sequencing "
                "and dose intensity considerations, (3) monitoring "
                "parameters and follow-up cadence, (4) anticipated benefit "
                "framed in median OS / PFS, absolute risk reduction, and "
                "NNT where reported, and (5) where the evidence is weakest "
                "and the recommendation is conditional. Be specific and "
                "clinical."
            ),
            "model_name": "gpt-4.1",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.3,
        },
        {
            "agent_name": "Recommendations Editor",
            "description": "Final editorial pass with explicit limitations and reviewer checklist.",
            "system_prompt": (
                MED_SYS_PROMPT
                + "\n\nYour specific role is the FINAL EDITORIAL stage. "
                "Convert the upstream synthesis into a clean, structured "
                "research memo with these sections: (1) Question, "
                "(2) Executive Summary (5-7 bullets), (3) Treatment "
                "Landscape with explicit evidence grades, (4) Key Trials "
                "Table, (5) Open Questions and Research Gaps, "
                "(6) Explicit Limitations of This Analysis, and (7) A "
                "Reviewer Checklist for the licensed physician who will "
                "sign off. Also audit the upstream draft for over-claiming, "
                "off-label statements, and populations excluded from the "
                "underlying trials. Conclude the memo with a single "
                "'Approved for physician review' or 'Revision required' "
                "verdict, plus an enumerated list of required revisions "
                "if any."
            ),
            "model_name": "anthropic/claude-opus-4-8",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 6144,
        },
    ],
}

response = requests.post(
    f"{BASE_URL}/v1/swarm/completions",
    headers=headers,
    json=payload,
    timeout=600,
)

response.raise_for_status()
result = response.json()

for output in result.get("output", []):
    print("=" * 70)
    print(output["role"])
    print("=" * 70)
    content = output["content"]
    if isinstance(content, list):
        content = " ".join(str(c) for c in content)
    print(str(content))
    print()

cost = result["usage"]["billing_info"]["total_cost"]
elapsed = result["execution_time"]
print(f"Total cost:    ${cost:.4f}")
print(f"Elapsed:       {elapsed:.1f}s")

final = result["output"][-1]
final_text = final["content"]
if isinstance(final_text, list):
    final_text = " ".join(str(c) for c in final_text)

if "Approved for physician review" in final_text:
    print("Memo cleared compliance. Routing to clinician inbox.")
else:
    print("Memo flagged. Do NOT route. Reviewer notes:")
    print(final_text)

import datetime

audit_record = {
    "job_id": result.get("job_id"),
    "timestamp_utc": datetime.datetime.utcnow().isoformat() + "Z",
    "question": CLINICAL_QUESTION,
    "pipeline": "Production Medical Research Pipeline",
    "per_agent_output": result.get("output", []),
    "usage": result.get("usage", {}),
    "execution_time_seconds": result.get("execution_time"),
}

filename = f"memo_{result.get('job_id', 'unknown')}.json"
with open(filename, "w") as f:
    json.dump(audit_record, f, indent=2)

print(f"Audit record saved to {filename}")

def build_payload(question: str) -> dict:
    """Clone the pipeline payload, swap in a new research question."""
    body = json.loads(json.dumps(payload))  # deep copy
    body["task"] = question
    return body


research_queue = [
    (
        "What are the latest evidence-based treatments for stage III "
        "pancreatic adenocarcinoma in adults with ECOG 0-1?"
    ),
    (
        "What does the current evidence say about GLP-1 receptor agonists "
        "for cardiovascular risk reduction in non-diabetic patients with "
        "obesity?"
    ),
    (
        "Summarize the evidence for CAR-T therapy in relapsed/refractory "
        "diffuse large B-cell lymphoma, including comparative effectiveness "
        "versus autologous stem cell transplant."
    ),
    (
        "What is the current standard of care and emerging evidence for "
        "minimal residual disease (MRD) monitoring in multiple myeloma?"
    ),
]

batch_payload = [build_payload(q) for q in research_queue]

batch_response = requests.post(
    f"{BASE_URL}/v1/swarm/batch/completions",
    headers=headers,
    json=batch_payload,
    timeout=1200,
)

batch_results = batch_response.json()

for idx, swarm_result in enumerate(batch_results):
    job_id = swarm_result.get("job_id", f"job-{idx}")
    cost = (
        swarm_result.get("usage", {})
        .get("billing_info", {})
        .get("total_cost", 0)
    )
    print(f"[{idx}] job_id={job_id}  cost=${cost:.4f}")
    with open(f"memo_{job_id}.json", "w") as f:
        json.dump(swarm_result, f, indent=2)