Codemni 1.1.1

Production-Ready Python Toolkit for AI Development - LLM wrappers, AI agents, and conversation memory

Codemni

🚀 The Complete AI Agent Framework for Python

The most powerful framework for building autonomous AI agents - featuring intelligent tool execution, multi-LLM orchestration, and advanced conversational memory

Features • Installation • Modules • Quick Start • Documentation

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📖 About

Codemni is a powerful Python framework for building production-ready AI agents and LLM applications. Unlike simple wrappers, Codemni provides a complete ecosystem with intelligent tool-calling agents, multi-provider LLM integrations, and sophisticated memory systems. Whether you're building chatbots, automation systems, or complex AI workflows, Codemni gives you the foundation to create robust, scalable solutions.

Why Choose Codemni?

• 🤖 Complete Agent Framework: Not just an LLM wrapper - build agents that can think, decide, and execute tools
• ✨ Production-Ready: Battle-tested with built-in error handling, retries, and intelligent fallbacks
• 🎯 Multi-Provider Support: Seamlessly switch between OpenAI, Google, Anthropic, Groq, and Ollama
• 🧠 Advanced Memory: 4 memory strategies to maintain context and conversation history
• 🔧 Developer-Friendly: Intuitive APIs, comprehensive documentation, and consistent interfaces
• 🚀 Performance-Optimized: Designed for speed, efficiency, and reliability at scale
• 🛡️ Enterprise-Grade: Robust error handling, logging, and production-ready code

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🧩 Modules

🤖 ToolCalling Agent - AI Agent Framework

Powerful and flexible AI agent framework that enables LLMs to intelligently select and execute tools.

Key Features:

• 🔧 Dynamic tool execution based on LLM decisions
• 💾 Optional conversation memory (4 different strategies)
• 🎨 Custom agent personality/role support
• 📊 Verbose mode for debugging
• 🔌 Multi-LLM support (OpenAI, Google Gemini, Anthropic, Groq, Ollama)
• ⚠️ Designed for standard models (reasoning models like o1, o3 not supported)

📚 Full Agent Documentation →

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💾 Memory Module - Conversation History Management

Flexible conversation memory system for maintaining context in multi-turn interactions.

Available Memory Types:

• 📝 ConversationalBufferMemory - Store all messages
• 🪟 ConversationalWindowMemory - Keep last N exchanges
• 🎫 ConversationalTokenBufferMemory - Limit by token count
• 📋 ConversationalSummaryMemory - Summarize old conversations

Key Features:

• Common API across all memory types
• Easy serialization (save/load)
• Lightweight and efficient
• Integrates seamlessly with ToolCalling Agent

📚 Full Memory Documentation →

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📡 LLM Module - Large Language Model Wrappers

Production-ready wrappers for popular LLM providers with unified interface.

Supported Providers:

• 🔷 Google Gemini (gemini-pro, gemini-2.0-flash-exp)
• 🟢 OpenAI (gpt-4, gpt-3.5-turbo, gpt-4-turbo)
• 🟣 Anthropic Claude (claude-3-opus, claude-3-sonnet, claude-3-haiku)
• ⚡ Groq (llama3-70b, mixtral-8x7b)
• 🦙 Ollama (Local models: llama2, mistral, codellama)

Key Features:

• Automatic retries with exponential backoff
• Configurable timeouts
• Consistent API across all providers
• Both function and class-based interfaces
• Silent operation (no logging)
• Minimal dependencies

📚 Full LLM Documentation →

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📦 Installation

Install from PyPI (Recommended)

# Install the base package
pip install Codemni

# Install with specific LLM providers
pip install Codemni[openai]        # OpenAI support
pip install Codemni[anthropic]     # Anthropic Claude support
pip install Codemni[groq]          # Groq support
pip install Codemni[google]        # Google Gemini support
pip install Codemni[ollama]        # Ollama (local) support

# Install with all LLM providers
pip install Codemni[all]

Install from Source

# Clone the repository
git clone https://github.com/CodexJitin/Codemni.git
cd Codemni

# Install in development mode
pip install -e .

# Or install with all dependencies
pip install -e .[all]

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🚀 Quick Start

Installation

pip install Codemni[all]  # Install with all LLM providers

ToolCalling Agent - Basic Usage

from Codemni.TOOL_CALLING_AGENT.agent import Create_ToolCalling_Agent
from Codemni.llm.Google_llm import GoogleLLM

# Initialize LLM
llm = GoogleLLM(
    model="gemini-2.0-flash-exp",
    api_key="YOUR_API_KEY"  # or set GOOGLE_API_KEY env var
)

# Create agent
agent = Create_ToolCalling_Agent(llm=llm, verbose=True)

# Define a tool
def calculator(expression):
    return str(eval(expression))

# Add tool to agent
agent.add_tool("calculator", "Evaluate mathematical expressions", calculator)

# Use the agent
response = agent.invoke("What is 125 * 48?")
print(response)  # Agent will use the calculator tool

ToolCalling Agent with Memory

from Codemni.TOOL_CALLING_AGENT.agent import Create_ToolCalling_Agent
from Codemni.llm.Google_llm import GoogleLLM
from Codemni.memory.conversational_buffer_memory import ConversationalBufferMemory

# Initialize LLM and memory
llm = GoogleLLM(model="gemini-2.0-flash-exp", api_key="YOUR_API_KEY")
memory = ConversationalBufferMemory()

# Create agent with memory
agent = Create_ToolCalling_Agent(llm=llm, memory=memory, verbose=True)
agent.add_tool("calculator", "Evaluate math", calculator)

# Multi-turn conversation with context
response1 = agent.invoke("Calculate 50 + 25")  # Returns: 75
response2 = agent.invoke("Now multiply that by 2")  # Returns: 150 (remembers 75!)

LLM Module - Basic Usage

from Codemni.llm import google_llm, openai_llm, anthropic_llm

# Google Gemini
response = google_llm(
    prompt="Explain quantum computing in simple terms",
    model="gemini-pro",
    api_key="your-api-key"  # or set GOOGLE_API_KEY env var
)
print(response)

# OpenAI GPT
response = openai_llm(
    prompt="Write a Python function to calculate fibonacci",
    model="gpt-4",
    temperature=0.7,
    max_tokens=500
)
print(response)

# Anthropic Claude
response = anthropic_llm(
    prompt="Explain the concept of recursion",
    model="claude-3-sonnet-20240229",
    max_tokens=300
)
print(response)

Error Handling

from Codemni.llm import google_llm, GoogleLLMError, GoogleLLMAPIError

try:
    response = google_llm(
        prompt="Hello, world!",
        model="gemini-pro"
    )
    print(response)
except GoogleLLMAPIError as e:
    print(f"API Error: {e}")
except GoogleLLMError as e:
    print(f"General Error: {e}")

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🔐 Configuration

Environment Variables

Set these to avoid hardcoding API keys:

# Linux/Mac
export GOOGLE_API_KEY="your-google-key"
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"
export GROQ_API_KEY="your-groq-key"
export OLLAMA_BASE_URL="http://localhost:11434"  # Optional

# Windows PowerShell
$env:GOOGLE_API_KEY="your-google-key"
$env:OPENAI_API_KEY="your-openai-key"

Using .env File

# Install python-dotenv
pip install python-dotenv

from dotenv import load_dotenv
load_dotenv()

# Now your environment variables are loaded
from Codemni.llm import google_llm

response = google_llm(prompt="Hello", model="gemini-pro")

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🏗️ Project Structure

Codemni/
├── 📄 README.md              # This file - Main documentation
├── 📄 LICENSE                # License information
├── 📄 requirements.txt       # Base dependencies
├── 📄 __init__.py            # Package initialization
│
├── � TOOL_CALLING_AGENT/    # AI Agent Module
│   ├── __init__.py
│   ├── README.md             # Agent documentation
│   ├── agent.py              # Main agent implementation
│   └── prompt.py             # Prompt templates
│
├── � memory/                # Memory Module
│   ├── __init__.py
│   ├── README.md             # Memory documentation
│   ├── conversational_buffer_memory.py
│   ├── conversational_window_memory.py
│   ├── conversational_token_buffer_memory.py
│   └── conversational_summary_memory.py
│
├── 📁 llm/                   # LLM Module
│   ├── __init__.py
│   ├── README.md             # LLM module documentation
│   ├── Google_llm.py         # Google Gemini wrapper
│   ├── OpenAI_llm.py         # OpenAI wrapper
│   ├── Anthropic_llm.py      # Anthropic wrapper
│   ├── Groq_llm.py           # Groq wrapper
│   └── Ollama_llm.py         # Ollama wrapper
│
├── 📁 core/                  # Core utilities
│   └── adapter.py            # Tool execution adapter
│
└── 📁 assets/                # Assets and media
    └── codemni-logo.jpg

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📚 Documentation

Module Documentation

• ToolCalling Agent - AI agent framework guide

  • Complete API reference for all methods
  • Memory integration guide
  • Tool definition best practices
  • Custom prompt guidelines
  • Troubleshooting and examples

• Memory Module - Conversation memory guide

  • Memory type comparison
  • Usage examples for each type
  • Serialization and persistence
  • Integration with agents

• LLM Module - Comprehensive guide to LLM wrappers

  • API reference for all providers
  • Advanced usage examples
  • Exception handling guide
  • Provider-specific notes

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✨ Features by Module

ToolCalling Agent

Feature	Description
🤖 Multi-LLM Support	Works with OpenAI, Google Gemini, Anthropic, Groq, Ollama
🔧 Dynamic Tools	Automatically selects and executes appropriate tools
💾 Optional Memory	4 memory strategies for conversation context
🎨 Custom Prompts	Customize agent personality and role
📊 Verbose Mode	Detailed logging for debugging
⚠️ Standard Models	Optimized for instruction-following models (not reasoning models)

Memory Module

Feature	Description
📝 Buffer Memory	Store all conversation messages
🪟 Window Memory	Keep only recent N exchanges
🎫 Token Buffer	Limit memory by token count
📋 Summary Memory	Summarize old conversations
💾 Serialization	Save/load conversation history
🔌 Easy Integration	Works seamlessly with agents

LLM Module

Feature	Description
🔄 Auto Retry	Exponential backoff for transient failures
⏱️ Timeouts	Configurable request timeouts (30-60s)
🛡️ Error Handling	Clear exception hierarchy per provider
🔇 Silent Mode	No logging - clean operation
📦 Minimal Deps	Install only what you need
🎯 Unified API	Same interface across all providers
✅ Validation	Input validation and defensive coding
🔧 Dual Interface	Both function and class-based APIs

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. 🍴 Fork the repository
2. 🌿 Create a feature branch (git checkout -b feature/AmazingFeature)
3. 💾 Commit your changes (git commit -m 'Add some AmazingFeature')
4. 📤 Push to the branch (git push origin feature/AmazingFeature)
5. 🔀 Open a Pull Request

Development Guidelines

• Follow existing code style and patterns
• Add tests for new features
• Update documentation
• Keep dependencies minimal
• Maintain backward compatibility

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📋 Requirements

• Python 3.8 or higher
• Optional dependencies (install as needed):
  • openai>=1.0.0 - For OpenAI support
  • anthropic>=0.18.0 - For Anthropic support
  • groq>=0.4.0 - For Groq support
  • google-generativeai>=0.3.0 - For Google Gemini support
  • ollama>=0.1.0 - For Ollama support

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📄 License

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

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👤 Author

CodexJitin

• GitHub: @CodexJitin
• Repository: Codemni

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🔖 Version

Current Version: 1.1.0

Changelog

v1.1.0 (2025-10-25)

• 🎉 Added ToolCalling Agent module
• 💾 Added Memory module with 4 memory strategies
• 🔧 LLM module now supports both function and class-based interfaces
• 📚 Comprehensive documentation for all modules
• ⚠️ Added warnings about reasoning model compatibility

v1.0.0 (2025-10-24)

• 🎉 Initial release
• ✅ LLM module with 5 provider support
• ✅ Production-ready error handling
• ✅ Comprehensive documentation

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🌟 Show Your Support

If you find Codemni useful, please consider:

• ⭐ Starring the repository
• 🐛 Reporting bugs
• 💡 Suggesting new features
• 🔀 Contributing code

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📞 Support

• 📧 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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Made with ❤️ by CodexJitin

Empowering developers to build better AI applications