🤖 AI Agents for Enhanced Chatbots

Enterprise-grade AI agents that work together to create smarter, safer, and more efficient chatbots with conversation memory and advanced features.

🎯 Quick Start

from agentic_chatbot import create_openai_agent

# Basic agent with tools
agent = create_openai_agent(enable_tools=True)
response = agent.invoke("user_123", "What's the latest news about AI?")

# Full-featured agent with RAG
agent = create_openai_agent(enable_rag=True, enable_tools=True)
response = agent.invoke("user_123", "Tell me about quantum computing")

✨ Key Features

🛡️ Security Agent - Detects malicious content and security threats
🧠 Context Agent - Analyzes query relevance and conversation flow
🎯 Model Selection Agent - Intelligently selects optimal LLM models
💬 Advanced Conversation Agent - Multi-user memory with RAG, tools, and monitoring

🚀 Core Capabilities

Multi-LLM Support: OpenAI, Anthropic, Google, Ollama
RAG Integration: Document retrieval with ChromaDB and FAISS
Tool Integration: Google Search, Wikipedia, custom tools
Multi-User Sessions: Separate conversation history per user
Enterprise Storage: Redis, MongoDB, PostgreSQL backends
Comprehensive Monitoring: Token tracking, cost estimation, analytics

📦 Installation

pip install agentic-chatbot

🔧 Optional Features

# RAG capabilities
pip install agentic-chatbot[rag]

# Tool integration  
pip install agentic-chatbot[tools]

# Enterprise features
pip install agentic-chatbot[enterprise]

# Development tools
pip install agentic-chatbot[dev]

📚 Documentation

Full Documentation: GitHub Repository
Examples: See examples/ directory
Advanced Guide: COMPREHENSIVE_AGENT_GUIDE.md

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

MIT License - see LICENSE file for details.

Ready to build enterprise-grade AI chatbots? 🚀

Made with ❤️ by the AI Agents Team

ai agents chatbot security context model-selection openai gpt llm machine-learning nlp conversational-ai rag retrieval-augmented-generation enterprise multi-user conversation-memory vector-store chromadb redis postgresql anthropic claude google-gemini ollama langchain streaming async tools function-calling monitoring analytics

Release files for agentic-chatbot 2.0.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for agentic-chatbot 2.0.3
File	Interpreter	ABI	Platform	Reset
agentic_chatbot-2.0.3-py3-none-any.whl 50.3 kB Sep 16, 2025	Python 3	none	any	Details

Release files / agentic_chatbot-2.0.3-py3-none-any.whl

Download URL	agentic_chatbot-2.0.3-py3-none-any.whl
Size	50.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`09792b0a31856cd1ea4ff0a9592ba2108443fbcb8d1d7e60e823c3db8a2b2ada`
BLAKE2b-256 checksum How to use checksums	`f0d7f5992920de28064a7bf075b2257ddc7ef99fd449187a59b8d7e2fb629047`
Upload date	Sep 16, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.12.11`