abstractframework 0.1.0

A unified ecosystem for AI-powered applications and intelligent systems

AbstractFramework

A unified ecosystem for AI-powered applications and intelligent systems.

AbstractFramework is an umbrella project that brings together a comprehensive suite of tools and libraries for building sophisticated AI applications. Each component is designed to work seamlessly together while maintaining independence and modularity.

🏗️ Framework Components

📚 AbstractCore ✅ Available

Unified Python library for interaction with multiple Large Language Model (LLM) providers.

Write once, run everywhere.

• Provider Agnostic: Works with OpenAI, Anthropic, Ollama, and more
• Tool Calling: Universal function calling across providers
• Structured Output: Type-safe Pydantic integration
• Embeddings & RAG: Built-in vector embeddings for semantic search
• Session Management: Persistent conversations and analytics
• Server Mode: Optional OpenAI-compatible API server

from abstractcore import create_llm

# Works with any provider
llm = create_llm("anthropic", model="claude-3-5-haiku-latest")
response = llm.generate("What is the capital of France?")
print(response.content)

🧠 AbstractMemory 🚧 Coming Soon

Advanced memory systems for AI agents and applications.

• Persistent Memory: Long-term storage and retrieval
• Contextual Memory: Semantic understanding and associations
• Memory Hierarchies: Short-term, working, and long-term memory
• Memory Compression: Efficient storage of large contexts
• Cross-Session Continuity: Maintain context across interactions

🤖 AbstractAgent 🚧 Coming Soon

Intelligent agent framework with reasoning and tool use capabilities.

• Autonomous Reasoning: Multi-step problem solving
• Tool Integration: Seamless integration with external tools
• Goal-Oriented Behavior: Task planning and execution
• Learning Capabilities: Adaptive behavior from experience
• Safety Mechanisms: Built-in guardrails and monitoring

🐝 AbstractSwarm 🚧 Coming Soon

Multi-agent coordination and swarm intelligence systems.

• Agent Orchestration: Coordinate multiple specialized agents
• Distributed Processing: Scale across multiple nodes
• Emergent Behavior: Complex behaviors from simple interactions
• Communication Protocols: Inter-agent messaging and coordination
• Collective Intelligence: Leverage swarm problem-solving

🚀 Quick Start

Installation

# Install the full framework (when all components are available)
pip install abstractframework[all]

# Or install individual components
pip install abstractcore[all]  # Available now
pip install abstractmemory     # Coming soon
pip install abstractagent      # Coming soon  
pip install abstractswarm      # Coming soon

Basic Usage

import abstractframework as af

# Create an intelligent agent with memory and LLM capabilities
agent = af.create_agent(
    llm_provider="openai",
    model="gpt-4o-mini",
    memory_type="persistent",
    tools=["web_search", "calculator", "file_system"]
)

# Have a conversation with persistent memory
response = agent.chat("Remember that I prefer Python over JavaScript")
print(response)

# The agent remembers across sessions
response = agent.chat("What programming language do I prefer?")
print(response)  # "You prefer Python over JavaScript"

🎯 Use Cases

1. Intelligent Applications

Build AI-powered applications with persistent memory, reasoning capabilities, and multi-provider LLM support.

2. Research & Development

Experiment with different AI architectures, memory systems, and agent behaviors in a unified framework.

3. Enterprise AI Systems

Deploy scalable AI solutions with swarm intelligence, distributed processing, and robust memory management.

4. Educational Projects

Learn AI concepts through hands-on experimentation with agents, memory systems, and LLM interactions.

🏛️ Architecture Philosophy

AbstractFramework follows key design principles:

• 🔧 Modularity: Each component works independently and together
• 🔄 Interoperability: Seamless integration between components
• 📈 Scalability: From single agents to distributed swarms
• 🛡️ Robustness: Production-ready with comprehensive error handling
• 🎨 Flexibility: Adapt to diverse use cases and requirements
• 📚 Simplicity: Clean APIs that hide complexity without limiting power

📊 Project Status

Component	Status	Version	Documentation
AbstractCore	✅ Available	2.4.1	Complete
AbstractMemory	🚧 In Development	-	Coming Soon
AbstractAgent	🚧 Planned	-	Coming Soon
AbstractSwarm	🚧 Planned	-	Coming Soon

🤝 Contributing

We welcome contributions to any component of the AbstractFramework ecosystem!

• AbstractCore: Contributing Guide
• Other Components: Contributing guides will be available as components are released

📄 License

MIT License - see LICENSE file for details.

All components of AbstractFramework are released under the MIT License to ensure maximum compatibility and adoption.

🔗 Links

• AbstractCore Repository: https://github.com/lpalbou/AbstractCore
• Documentation: Coming soon
• Community Discussions: Coming soon
• Issue Tracker: Coming soon

🌟 Vision

AbstractFramework aims to democratize AI development by providing:

1. Unified Interfaces: Consistent APIs across all AI capabilities
2. Production Ready: Enterprise-grade reliability and performance
3. Research Friendly: Easy experimentation and prototyping
4. Community Driven: Open source with active community involvement
5. Future Proof: Designed to evolve with AI advancements

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AbstractFramework - Building the future of AI applications, one component at a time.

Note: This is currently a placeholder project. AbstractCore is fully functional and available. Other components are in various stages of development. Star this repository to stay updated on releases!