Quick Start Guide

Installation

pip install fluxgraph

For development:

git clone https://github.com/yourusername/fluxgraph.git
cd fluxgraph
pip install -e .

Basic Agent Example

Create a simple conversational agent:

from fluxgraph import FluxApp

app = FluxApp(
    title="Customer Support Agent",
    version="1.0.0",
    enable_advanced_memory=True
)

@app.agent(name="support_agent")
async def support_agent(
    message: str,
    session_id: str = None,
    advanced_memory=None
):
    # Store conversation in memory
    if advanced_memory:
        await advanced_memory.store(
            message,
            memory_type="episodic",
            metadata={"session_id": session_id}
        )

    return {
        "response": "I'm here to help! How can I assist you?",
        "status": "success"
    }

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8000)

Running the Agent

Start your agent server:

python app.py

Access the API:

curl -X POST http://localhost:8000/ask/support_agent \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello!", "session_id": "user123"}'

Using LLM Providers

Integrate TinyLlama:

import aiohttp

class TinyLlamaLLM:
    async def generate(self, prompt: str):
        async with aiohttp.ClientSession() as session:
            resp = await session.post(
                "https://tinyllm.alphanetwork.com.pk/chat",
                headers={"Authorization": f"Bearer {token}"},
                json={"prompt": prompt, "max_tokens": 200}
            )
            data = await resp.json()
            return data["text"]

llm = TinyLlamaLLM()

@app.agent(name="ai_agent")
async def ai_agent(message: str):
    response = await llm.generate(f"User: {message}\nAssistant:")
    return {"response": response}

Next Steps

• Learn about Agent Development and orchestration

• Explore memory systems

• Build workflows

• Deploy to deployment