Agentic AI MVP Template

A lightweight, framework-agnostic template for building agentic AI applications quickly. Perfect for MVPs and proof-of-concept projects.

🔗 Repository: https://github.com/kython220282/AgenticAI-MVP-Project-Template

⚠️ SETUP REQUIRED: This template needs configuration before use. See SETUP_GUIDE.md for required steps.

📌 How to Use This Template:

1. Click "Use this template" button on GitHub OR clone directly:

   git clone https://github.com/kython220282/AgenticAI-MVP-Project-Template.git
   cd AgenticAI-MVP-Project-Template
   

2. Follow the Setup Guide to configure API keys and LLM integration
3. Run the Quick Start Guide
4. Customize for your specific use case

🎯 Design Philosophy

• Modular: Clean separation of agents, tools, and config
• Framework-Agnostic: Works with any LLM provider or agent framework
• Minimal: Only essential components - no bloat
• Production-Ready: Scale up when validated, not before
• Developer-Friendly: Simple to understand and customize

✨ Features

• 🤖 Framework-agnostic agents - Works with OpenAI, Anthropic, Azure, or any LLM
• 🛠️ Extensible tool system - Easy-to-add custom tools with registry
• 🎨 Interactive web dashboard - Showcase agent capabilities in real-time
• 📡 REST API + SSE - Full API with live updates via Server-Sent Events
• 📚 Comprehensive docs - Quick start, examples, and architecture guides
• 🔧 Production patterns - Config management, logging, error handling
• ⚡ Quick setup - Running in 5 minutes with minimal dependencies

📚 Documentation

• Quick Start Guide - Get running in 5 minutes
• Dashboard Guide - Web interface for showcasing agents
• Project Structure - Architecture and design patterns

🎬 What You Get

This template includes:

• ✅ Base Agent Classes - Inherit and customize for your domain
• ✅ Memory Systems - Short-term, long-term, and hybrid memory for agents
• ✅ RAG Support - Document retrieval and context-augmented generation
• ✅ Caching Layer - Cache expensive LLM calls and tool results
• ✅ Tool System - Add custom tools easily with a registry pattern
• ✅ Web Dashboard - Live demo interface for stakeholders
• ✅ REST API - FastAPI backend with auto-generated docs
• ✅ Real-time Updates - Server-Sent Events for live monitoring
• ✅ Config Management - Multi-provider LLM support (OpenAI, Anthropic, Azure)
• ✅ Working Examples - 6 runnable examples to learn from
• ✅ Production Patterns - Logging, error handling, validation

Perfect for: Proof-of-concepts, hackathons, MVP validation, client demos

� Framework Compatibility

This template is framework-agnostic and works with any agent/LLM framework:

✅ Compatible LLM Providers

• OpenAI - GPT-4, GPT-3.5, GPT-4 Turbo
• Anthropic - Claude 3 (Opus, Sonnet, Haiku)
• Azure OpenAI - Enterprise deployments
• Google - Gemini, PaLM
• Cohere - Command models
• Open Source - Llama, Mistral, etc. via Ollama/vLLM
• Custom APIs - Any HTTP-based LLM service

✅ Compatible Agent Frameworks

• LangChain - Wrap agents in SimpleLLMAgent class
• CrewAI - Adapt crew agents to BaseAgent interface
• AutoGen - Use as orchestration layer
• Haystack - Integrate pipeline components
• Custom - Build from scratch with provided base classes

✅ Compatible Vector Databases (Optional)

• Pinecone - Managed vector search
• Weaviate - Open-source vector database
• ChromaDB - Embedded vector store
• Qdrant - Performance-focused vector search
• FAISS - Facebook's similarity search

How to Integrate

Example: Using with LangChain

from langchain.chat_models import ChatOpenAI
from agents import SimpleLLMAgent

class LangChainAgent(SimpleLLMAgent):
    def __init__(self, name, role, goal):
        llm = ChatOpenAI(model="gpt-4")
        super().__init__(name, role, goal, llm_client=llm)
    
    def execute(self, task, context=None):
        response = self.llm_client.invoke(task)
        return {"output": response.content, "status": "success"}

Example: Using with Anthropic

import anthropic
from agents import SimpleLLMAgent

class ClaudeAgent(SimpleLLMAgent):
    def __init__(self, name, role, goal):
        client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
        super().__init__(name, role, goal, llm_client=client)
    
    def execute(self, task, context=None):
        response = self.llm_client.messages.create(
            model="claude-3-sonnet-20240229",
            messages=[{"role": "user", "content": task}]
        )
        return {"output": response.content[0].text, "status": "success"}

See examples/ for more integration patterns.

⚖️ Strengths & Limitations

✅ Strengths

Speed & Simplicity

• ✅ Running in 5 minutes vs hours/days from scratch
• ✅ Minimal dependencies - only 3 required packages
• ✅ Clear, readable code - easy to understand and modify
• ✅ No complex build processes or configurations

Flexibility

• ✅ Framework-agnostic - not locked into any vendor
• ✅ LLM-agnostic - swap providers without refactoring
• ✅ Modular design - use only what you need
• ✅ Easy to extend - add agents, tools, features incrementally

Production-Ready Patterns

• ✅ Config management with environment variables
• ✅ Logging and error handling built-in
• ✅ REST API with auto-generated documentation
• ✅ Real-time updates via Server-Sent Events
• ✅ Clean separation of concerns

Showcase & Demo

• ✅ Web dashboard included - impress stakeholders
• ✅ Interactive API docs - test endpoints in browser
• ✅ Working examples - demonstrate capabilities immediately
• ✅ Professional UI - dark theme, responsive design

⚠️ Limitations

What This Template Doesn't Include

• ❌ No pre-built LLM integrations - You implement LLM calls (examples provided)
• ❌ No database - Add PostgreSQL/MongoDB when needed (SQLite for dev included)
• ❌ No authentication - Implement JWT/OAuth for production
• ❌ No production cache - Uses in-memory cache (add Redis when scaling)
• ❌ No vector embeddings - Keyword search included (add OpenAI/Cohere embeddings for production RAG)
• ❌ No deployment configs - Add Docker/K8s for production
• ❌ No monitoring - Add Prometheus/Grafana for production

Not Ideal For

• ❌ Enterprise production apps (use full enterprise template)
• ❌ Complex multi-model pipelines (consider LangChain/Haystack)
• ❌ Heavy reinforcement learning (needs training infrastructure)
• ❌ Real-time streaming at scale (needs different architecture)

When to Graduate Move to a full framework or enterprise template when you:

• Have validated your MVP and need production scale
• Need advanced features (RAG, agents with memory, etc.)
• Require enterprise security and compliance
• Have 10,000+ users or complex workflows
• Need dedicated DevOps/monitoring infrastructure

Migration Path: Start here for MVP → Validate → Add features → Scale to production

🎯 Best Use Cases

Perfect For:

• ✅ Building MVPs in days, not weeks
• ✅ Proof-of-concept demonstrations
• ✅ Hackathon projects
• ✅ Client demos and showcases
• ✅ Learning agentic AI patterns
• ✅ Testing different LLM providers
• ✅ Rapid prototyping and experimentation

Good For:

• ✅ Small to medium agent systems (1-10 agents)
• ✅ Internal tools and automation
• ✅ Research and experimentation
• ✅ Educational projects

Not Recommended For:

• ❌ Production apps with 1000s of concurrent users
• ❌ Mission-critical systems requiring 99.99% uptime
• ❌ Complex enterprise workflows without customization
• ❌ Systems requiring extensive audit trails and compliance

�🚀 Quick Start

1. Setup Environment

# Create virtual environment
python -m venv venv

# Activate (Windows)
venv\Scripts\activate

# Activate (Linux/Mac)
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

2. Configure

# Copy environment template
cp .env.example .env

# Edit .env and add your API keys
# At minimum, add one LLM provider API key

3. Run

Option A: Command Line Usage

# Run the main application
python main.py

# Or run specific examples
python examples/basic_agent.py
python examples/multi_agent.py
python examples/tools_usage.py

Option B: Web Dashboard (Recommended for Demos)

# Start the web server
python run_server.py

# Open browser to:
# http://localhost:8000 - Interactive Dashboard
# http://localhost:8000/docs - API Documentation

📁 Project Structure

AgenticAI_MVP_Project_Template/
├── agents/                 # Agent definitions
│   ├── __init__.py
│   └── base.py            # Base agent classes
├── tools/                  # Tool implementations
│   ├── __init__.py
│   ├── base.py            # Base tool classes
│   └── implementations.py # Example tools
├── api/                    # Web API backend
│   ├── __init__.py
│   └── app.py             # FastAPI application
├── frontend/              # Web dashboard UI
│   ├── index.html         # Main dashboard
│   ├── styles.css         # Styling
│   └── app.js             # Frontend logic
├── examples/              # Usage examples
│   ├── basic_agent.py     # Single agent example
│   ├── multi_agent.py     # Multi-agent workflow
│   ├── tools_usage.py     # Tools integration
│   ├── memory_agent.py    # Memory system demo
│   ├── rag_example.py     # RAG demonstration
│   └── caching_example.py # Caching demo
├── docs/                  # Documentation
│   ├── QUICKSTART.md      # Quick start guide
│   ├── SETUP_GUIDE.md     # Setup instructions
│   ├── DASHBOARD.md       # Dashboard guide
│   ├── STRUCTURE.md       # Architecture details
│   └── CONTRIBUTING.md    # Customization guide
├── data/                  # Data storage (gitignored)
│   └── README.md          # Data directory info
├── utils/                 # Utility modules
│   ├── helpers.py         # Logging, formatting, validation
│   ├── cache.py           # Caching system
│   └── rag.py             # RAG utilities
├── config.py              # Configuration management
├── main.py                # Main entry point
├── run_server.py          # Web server launcher
├── requirements.txt       # Python dependencies
├── .env.example          # Environment template
├── .gitignore            # Git ignore rules
├── LICENSE               # MIT License
└── README.md             # This file

🛠️ Core Components

Agents

Framework-agnostic agent base classes that work with any LLM:

• BaseAgent: Abstract base for all agents
• SimpleLLMAgent: Basic LLM-powered agent
• AgentOrchestrator: Coordinate multiple agents

Tools

Extensible tool system compatible with any framework:

• BaseTool: Abstract base for tools
• FunctionTool: Wrap Python functions as tools
• ToolRegistry: Manage and discover tools

Memory & RAG

Context-aware agents with memory and document retrieval:

• ShortTermMemory: Conversation history and recent context
• LongTermMemory: Persistent knowledge with disk storage
• HybridMemory: Combined short and long-term memory
• SimpleRAG: Document chunking and retrieval
• VectorRAG: Placeholder for vector database integration

Caching

Performance optimization for expensive operations:

• SimpleCache: In-memory cache with TTL
• @cached: Decorator for function caching
• @cache_agent_response: Cache agent outputs
• @cache_tool_result: Cache tool results

Web Dashboard

Interactive UI for showcasing agent capabilities:

• Create Agents: Define agents with custom roles and goals
• Execute Tasks: Run single-agent tasks with real-time results
• Multi-Agent Workflows: Chain multiple agents together
• Live Monitoring: Real-time updates via Server-Sent Events
• History Tracking: View all past executions

Configuration

Web Dashboard (Best for Showcasing)

# Start the server
python run_server.py

# Open http://localhost:8000 in your browser
# 1. Create agents with custom roles
# 2. Execute tasks and see results in real-time
# 3. Build multi-agent workflows visually
# 4. View execution history

Single Agent (Code)

Centralized config supporting multiple LLM providers:

• OpenAI
• Anthropic (Claude)
• Azure OpenAI
• Easy to add more

📝 Usage Examples

Single Agent

from agents import SimpleLLMAgent

agent = SimpleLLMAgent(
    name="ResearchAgent",
    role="Research Assistant",
    goal="Find and analyze information"
)

result = agent.execute("Research topic X")

Multi-Agent Workflow

from agents import SimpleLLMAgent, AgentOrchestrator

# Create agents
researcher = SimpleLLMAgent(name="Researcher", ...)
writer = SimpleLLMAgent(name="Writer", ...)

# Orchestrate
orchestrator = AgentOrchestrator([researcher, writer])

workflow = [
    {"agent": "Researcher", "task": "Research topic"},
    {"agent": "Writer", "task": "Write summary"}
]

results = orchestrator.execute_workflow(workflow)

Agent with Memory

from agents import SimpleLLMAgent, HybridMemory

agent = SimpleLLMAgent(name="Assistant", ...)
memory = HybridMemory(storage_path="data/memory.json")

# Add to memory
memory.add_to_short_term("user_message", "Hello!")

# Get context for next interaction
context = memory.get_context(query="greeting", recent_count=5)
result = agent.execute("Respond to user", context=context)

RAG (Retrieval-Augmented Generation)

from utils import SimpleRAG

rag = SimpleRAG(chunk_size=500)
rag.add_document(content="Your document text...", metadata={"source": "doc1"})

# Retrieve context for query
context = rag.get_context("What is Python?", top_k=3)
result = agent.execute(f"Context: {context}\n\nQuestion: What is Python?")

Caching

from utils import cached, cache_agent_response

# Cache function results
@cached(ttl=300)
def expensive_api_call(query):
    return call_external_api(query)

# Cache agent responses
class CachedAgent(SimpleLLMAgent):
    @cache_agent_response(ttl=600)
    def execute(self, task, context=None):
        return super().execute(task, context)

Custom Tools

from tools import BaseTool, tool_registry

class MyTool(BaseTool):
    def __init__(self):
        super().__init__("my_tool", "Does something useful")
    
    def execute(self, input_data):
        # Your logic here
        return result

# Register
tool_registry.register(MyTool())

🔌 Integration Examples

With LangChain

from langchain.chat_models import ChatOpenAI
from agents import SimpleLLMAgent

llm = ChatOpenAI(model="gpt-4")
agent = SimpleLLMAgent(name="Agent", llm_client=llm)

With Anthropic

import anthropic
from agents import SimpleLLMAgent

client = anthropic.Anthropic(api_key="your-key")
agent = SimpleLLMAgent(name="Agent", llm_client=client)

With CrewAI/AutoGen

Adapt the base classes to wrap CrewAI or AutoGen agents - the interface remains consistent.

🎨 Customization Guide

Adding a New Agent

1. Inherit from BaseAgent
2. Implement the execute() method
3. Add any custom behavior

Adding a New Tool

1. Inherit from BaseTool
2. Implement the execute() method
3. Register with tool_registry

Changing LLM Provider

1. Update .env with new provider credentials
2. Modify agent's LLM client initialization
3. Adapt the execute() method if needed

📦 Scaling to Production

When your MVP is validated:

1. Add Testing: Integrate pytest, add unit/integration tests
2. Add API Layer: Use FastAPI for API endpoints
3. Add Monitoring: Logging, metrics, error tracking
4. Add Database: PostgreSQL, MongoDB, etc.
5. Add CI/CD: GitHub Actions, deployment automation
6. Add Security: Auth, rate limiting, input validation
7. Add Documentation: API docs, architecture diagrams

Migrate to the full enterprise template when ready! � Customization Checklist

After cloning this template, customize these key areas:

• [ ] Update .env - Add your LLM API keys
• [ ] Customize Agents - Modify agents/base.py for your domain
• [ ] Add Custom Tools - Create tools in tools/implementations.py
• [ ] Update Frontend - Change branding in frontend/styles.css
• [ ] Implement LLM Calls - Replace placeholders in agent execute() methods
• [ ] Add Your Logic - Customize main.py for your use case
• [ ] Update README - Replace this README with your project details

See CONTRIBUTING.md for detailed customization guide.

🆚 Why This Template?

| Feature | This Template | From Scratch | Enterprise Template | |---------|--------------|--------------|---------------------| | Setup Time | 5 minutes | Hours/Days | 30-60 minutes | | Framework Lock-in | None | Depends | Often Yes | | Dashboard Included | ✅ Yes | ❌ No | ✅ Yes | | Production Patterns | ✅ Core ones | ❌ No | ✅✅ Extensive | | Complexity | Low | Varies | High | | Best For | MVPs, Demos | Custom builds | Production apps |

🛠️ Tech Stack

Backend:

• Python 3.8+
• FastAPI (API framework)
• Pydantic (Data validation)

Frontend:

• Vanilla JavaScript (no framework dependencies)
• HTML5/CSS3 with modern dark theme
• Server-Sent Events for real-time updates

Optional Integrations:

• Any LLM provider (OpenAI, Anthropic, Azure, etc.)
• Any agent framework (LangChain, CrewAI, AutoGen, etc.)
• Any vector database (Pinecone, Weaviate, ChromaDB, etc.)

🤝 Contributing

This is a template repository designed to be forked and customized for your needs.

Using this template:

• Fork it and make it yours - no attribution required
• Customize everything - it's designed for that
• Share your improvements via PRs if you'd like

See CONTRIBUTING.md for customization guidelines.

🌟 Show Your Support

If this template helped you ship faster:

• ⭐ Star this repository
• 🐛 Report issues or suggest features
• 🔀 Share your use cases (open an issue with "Showcase" label)
• 📣 Tell others about it

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

TL;DR: Use it commercially, modify it, distribute it. Just include the license

• Focus on rapid iteration
• Document assumptions

📄 License

This template is provided as-is for your use. Modify freely!

🆘 Troubleshooting

No LLM API key configured

• Add API key to .env file

Module not found errors

• Ensure you're in the virtual environment: venv\Scripts\activate
• Install dependencies: pip install -r requirements.txt

Want to use framework X?

• This template is agnostic - adapt the agent/tool classes to wrap your framework

Dashboard not loading?

• See Dashboard Guide for detailed troubleshooting

📚 Next Steps

1. Get Started: Follow the Quick Start Guide
2. Explore Dashboard: Check out the Dashboard Guide for showcasing
3. Understand Structure: Read Project Structure for architecture details
4. Configure your LLM provider in .env
5. Add domain-specific agents and tools
6. Build your MVP!

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Ready to start?

• 📥 Clone: git clone https://github.com/kython220282/AgenticAI-MVP-Project-Template.git
• 📖 Follow: Quick Start Guide
• 🚀 Build your AI MVP!

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

This is a template project designed to be forked and customized. See CONTRIBUTING.md for guidelines on how to adapt it for your needs.

Found this helpful? ⭐ Star the repo: https://github.com/kython220282/AgenticAI-MVP-Project-Template

🙏 Acknowledgments

Built with the philosophy that perfect is the enemy of done - ship fast, learn, iterate!

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Star ⭐ this repo if it helped you build your MVP faster!