BODHI (Bridging, Open, Discerning, Humble, Inquiring) is a lightweight Python library that wraps any LLM to produce responses that are genuinely curious, appropriately humble, and clinically specific.

pip install bodhi-llm

The Problem

LLMs tend to be overconfident, give vague advice, and assume rather than ask.

The Solution

BODHI uses a two-pass prompting strategy that teaches LLMs to:

Quick Start

from bodhi import BODHI
from openai import OpenAI

client = OpenAI()
bodhi = BODHI.from_openai(client, model="gpt-4o-mini")

response = bodhi.complete("I have chest pain")
print(response.content)

Output characteristics:

• Asks: "Are you experiencing shortness of breath right now?"
• Specific: "Take aspirin 325mg if not allergic"
• Clear: "Call 911 immediately if pain spreads to arm or jaw"

Installation

# Basic
pip install bodhi-llm

# With OpenAI
pip install bodhi-llm[openai]

# All providers
pip install bodhi-llm[all]

How It Works

%%{init: {
  'theme': 'base',
  'themeVariables': {
    'primaryColor': '#4A90A4',
    'primaryTextColor': '#ffffff',
    'primaryBorderColor': '#2D5A6B',
    'lineColor': '#666666',
    'secondaryColor': '#7B68EE',
    'tertiaryColor': '#20B2AA'
  }
}}%%
flowchart TD
    A[User Prompt<br/>'I have chest pain'] --> B[Pass 1: Analysis]

    B --> B1[Task Type]
    B --> B2[Audience Detection]
    B --> B3[Uncertainties & Red Flags]

    B1 --> C[Pass 2: Response]
    B2 --> C
    B3 --> C

    C --> C1[BE SPECIFIC<br/>dosages, timeframes]
    C --> C2[ACTIVELY ASK<br/>direct questions]
    C --> C3[INCLUDE ALTERNATIVES<br/>multiple options]

    classDef input fill:#5D6D7E,stroke:#2C3E50,color:#fff,stroke-width:2px
    classDef analysis fill:#4A90A4,stroke:#2D5A6B,color:#fff,stroke-width:2px
    classDef detail fill:#7B68EE,stroke:#5B4ACE,color:#fff,stroke-width:2px
    classDef response fill:#20B2AA,stroke:#178B82,color:#fff,stroke-width:2px
    classDef output fill:#3CB371,stroke:#2E8B57,color:#fff,stroke-width:2px

    class A input
    class B analysis
    class B1,B2,B3 detail
    class C response
    class C1,C2,C3 output

Task Routing

Usage

With Any LLM

from bodhi import BODHI

def my_llm(messages):
    # Your LLM call here
    return "response"

bodhi = BODHI(my_llm)
response = bodhi.complete("I have a headache")

Configuration

from bodhi import BODHI, BODHIConfig

config = BODHIConfig(
    domain="medical",  # or "general"
)

bodhi = BODHI(chat, config=config)

Analysis Only

# Inspect the reasoning without generating response
analysis = bodhi.analyze("I have chest pain")
print(analysis)

API

BODHI

BODHI(chat_function, config=None)
BODHI.from_openai(client, model="gpt-4o-mini")

Methods:

• complete(prompt) → BODHIResponse
• analyze(prompt) → str

BODHIResponse

response.content   # Final response text
response.analysis  # Pass 1 reasoning
response.metadata  # Timing, domain info

Citation

@article{cajas2026beyond,
  title={Beyond overconfidence: Embedding curiosity and humility for ethical medical AI},
  author={Cajas Ordóñez, Sebastián Andrés and Ocampo Osorio, Felipe and Lange, Maximin and Celi, Leo Anthony and others},
  journal={PLOS Digital Health},
  year={2026},
  doi={10.1371/journal.pdig.0001013}
}

@article{ordonez2025humility,
  title={Humility and curiosity in human--AI systems for health care},
  author={Ordoñez, Sebastián Andrés Cajas and Lange, Maximin and Lunde, Torleif Markussen and Meni, Mackenzie J and Premo, Anna E},
  journal={The Lancet},
  volume={406},
  number={10505},
  year={2025}
}

Contributing

BODHI is built by an interdisciplinary team of philosophers, clinicians, engineers, and scientists spanning generations and continents. We welcome contributions from anyone passionate about making AI more humble and curious.

Future Directions

• Anti-sycophancy — Resist agreeing just to please
• Critical thinking — Challenge assumptions constructively
• Cultural sensitivity — Adapt to cultural contexts
• Epistemic justice — Recognize diverse ways of knowing

Interested? Reach out: sebasmos@mit.edu

Links

PyPI · PLOS Digital Health · The Lancet · HumbleAILLMs