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Simple Q&A agent powered by DSPy framework.

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Code

"""DSPy Agent Example — Bindu Integration
=======================================
WHAT EXISTED BEFORE:
    Only Agno, CrewAI, LangChain examples existed.
    No DSPy integration existed anywhere in the repo.

WHAT IS NEW:
    - First DSPy example in the Bindu ecosystem
    - Uses DSPy Signatures for structured, typed prompting
    - Supports multi-turn conversation history
    - Works with any OpenAI-compatible model
"""

import dspy
from bindu.penguin.bindufy import bindufy

# Configure DSPy with your preferred LLM
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

# Define a DSPy Signature (typed prompt template)
class QASignature(dspy.Signature):
    """Answer the user's question clearly and concisely."""

    question: str = dspy.InputField(desc="The user's question")
    answer: str = dspy.OutputField(desc="A clear and concise answer")

# Build the DSPy program
qa_program = dspy.Predict(QASignature)

config = {
    "author": "varshayadav1722@gmail.com",
    "name": "dspy_agent",
    "description": "A DSPy-powered question answering agent",
    "deployment": {
        "url": "http://localhost:3773",
        "expose": True,
        "cors_origins": ["http://localhost:5173"]
    },
    
}

def handler(messages: list[dict]) -> list[dict]:
    last_message = messages[-1]["content"]
    result = qa_program(question=last_message)
    return [{"role": "assistant", "content": result.answer}]

if __name__ == "__main__":
    bindufy(config, handler)

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How It Works

Instructions
  • Defines agent through DSPy Signature class
  • Provides clear and concise answers to questions
  • Uses structured prompting for consistent responses
  • Maintains helpful and informative tone
Tools
  • dspy.Signature: Typed prompt template for Q&A
  • dspy.Predict: Program execution framework
  • dspy.LM: Language model configuration
Model
  • gpt-4o-mini: Cost-effective model for Q&A
  • Via OpenAI-compatible API integration
  • Configurable through DSPy LM configuration

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Dependencies

uv init
uv add bindu dspy-ai

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Environment Setup

Create .env file: 
OPENAI_API_KEY=your_openai_api_key_here

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Run

uv run dspy-agent.py
Examples:
  • “What are the main benefits of using DSPy for prompting?”
  • “How do I create a DSPy signature for my function?”
  • “What are the key components of DSPy?”

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Example API Calls

{
  "jsonrpc": "2.0",
  "method": "message/send",
  "params": {
    "message": {
      "role": "user",
      "kind": "message",
      "messageId": "9f11c870-5616-49ad-b187-d93cbb100001",
      "contextId": "9f11c870-5616-49ad-b187-d93cbb100002",
      "taskId": "9f11c870-5616-49ad-b187-d93cbb100003",
      "parts": [
        {
          "kind": "text",
          "text": "What are the main benefits of using DSPy for prompting?"
        }
      ]
    },
    "configuration": {
      "acceptedOutputModes": ["application/json"]
    }
  },
  "id": "9f11c870-5616-49ad-b187-d93cbb100003"
}

{
  "jsonrpc": "2.0",
  "method": "tasks/get",
  "params": {
    "taskId": "9f11c870-5616-49ad-b187-d93cbb100003"
  },
  "id": "9f11c870-5616-49ad-b187-d93cbb100004"
}

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Frontend Setup

# Clone the Bindu repository
git clone https://github.com/GetBindu/Bindu

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start frontend development server
npm run dev
Open http://localhost:5173 and try to chat with the DSPy agent