A modular platform for building decentralized, scalable AI systems powered by DeepSeek-R1, a state-of-the-art language model trained through large-scale reinforcement learning. The framework integrates blockchain, distributed storage, swarm intelligence, and reinforcement learning capabilities.
The Zeno Framework demonstrates how DeepSeek-R1's advanced reasoning capabilities can be leveraged alongside decentralized technologies to create sophisticated AI solutions. By combining DeepSeek-R1's multi-stage training and natural reasoning abilities with distributed systems architecture, the framework enables developers and researchers to prototype cutting-edge AI applications with enhanced decision-making capabilities and scalable deployment options.
- Multi-modal capabilities for handling text, image, and audio data processing.
- Agents autonomously make decisions using reinforcement learning, improving over time.
- Inter-agent messaging and task delegation with minimal configuration.
- IPFS Integration: Store and retrieve data in a decentralized environment to enhance redundancy, transparency, and resilience.
- Solana: Create and manage wallets, execute transactions, and interact with smart contracts.
- Ethereum: Retrieve balances, transfer tokens, and log immutable data on the Ethereum network.
- Redis-based mechanism for group decision-making and voting.
- Lua scripting enables high-performance distributed consensus and task delegation at scale.
- Agents leverage Q-learning (or similar algorithms) to optimize task execution dynamically.
- Facilitate experimental or research-oriented environments for adaptive AI behaviors.
Clone the repository and install dependencies:
git clone https://github.com/yourusername/zeno-framework.git
cd zeno-framework
pip install -r requirements.txt- Python 3.8+
- Redis (for distributed message queues and swarm coordination)
- IPFS daemon (for decentralized file storage)
- Ethereum RPC endpoint (e.g., Infura)
- Solana CLI and relevant local network or mainnet configuration
# Basic Agent Example
python examples/basic_agent_example.py
# Reinforcement Learning Optimization
python examples/agent_self_optimization.py
# Collaboration Framework Example
python examples/agent_collaboration_example.py-
AI Agent
Each agent has a configurable pipeline for handling specific tasks (e.g., NLP, image recognition). Agents can broadcast requests and delegate workload, ensuring modularity and scalability. -
Knowledge Graph
A graph-based structure to store and query relationships among various entities, tasks, and learned behaviors. -
Swarm Consensus
Coordination and task assignment among multiple agents via Redis-based messaging. Agents vote on proposals, automating decision-making with built-in consensus rules. -
Blockchain Manager
Unified interface for interacting with different blockchains, minimizing the need for specialized code. Examples include checking wallet balances, sending tokens, or storing activity logs. -
IPFS Utility
Manages file operations over IPFS, leveraging content-addressable storage to reduce duplication and enhance reliability.
Combining AI, blockchain, IPFS, and distributed consensus within a single framework enables exploration of decentralized approaches to complex problems:
- Reliability: Decentralized storage reduces single points of failure.
- Auditability: Blockchains provide transparent, tamper-resistant logs.
- Scalability: Swarm intelligence distributes workload among multiple agents.
- Adaptability: Reinforcement learning allows for continuous improvement and self-optimization.
The Zeno Framework leverages DeepSeek-R1, a state-of-the-art language model trained through large-scale reinforcement learning. This choice brings several key advantages:
- Enhanced Reasoning: DeepSeek-R1's architecture is specifically optimized for complex reasoning tasks, making it ideal for autonomous agent decision-making.
- Multi-Stage Training: The model incorporates sophisticated training techniques that improve readability and consistency in outputs.
- Scale-Ready: Available in various sizes (1.5B to 70B parameters) to suit different deployment needs.
-
Improved Decision Making
- Agents can handle complex, multi-step reasoning tasks more effectively
- Better handling of edge cases and unexpected scenarios
- More reliable autonomous operations
-
Resource Considerations
- Different model sizes allow for flexible deployment based on computational resources
- Smaller models (1.5B-14B) suitable for edge devices
- Larger models (32B-70B) optimal for central coordination nodes
-
Development Approach
- Framework design leverages DeepSeek-R1's natural reasoning capabilities
- Reduced need for complex prompt engineering
- More intuitive agent-to-agent communication patterns
-
Limitations
- Performance depends on model size and available computational resources
- May require fine-tuning for specific use cases
- Regular updates needed to maintain alignment with latest model versions
This repository is published under the MIT License. However, certain components may have different licensing terms or restrictions not publicly disclosed here. For specific licensing inquiries, please contact the maintainers.
- This framework is intended as an illustrative starting point for research and development.
- It is not production-hardened and comes with no guarantees of reliability, security, or performance.
- Users should rigorously test and audit the code before deploying in mission-critical or sensitive environments.
- The goal is to demonstrate possible integrations and encourage open experimentation, not to provide a universal solution.