Agent Supervisor Example is an open-source Python framework that supervises and coordinates multiple AI agents. It provides dynamic task routing, error monitoring, logging, and modular integration with models like OpenAI, enabling developers to prototype scalable multi-agent workflows.
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Agent Supervisor Example
Agent Supervisor Example is an open-source Python framework that supervises and coordinates multiple AI agents. It provides dynamic task routing, error monitoring, logging, and modular integration with models like OpenAI, enabling developers to prototype scalable multi-agent workflows.
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What is Agent Supervisor Example?
The Agent Supervisor Example repository demonstrates how to orchestrate several autonomous AI agents in a coordinated workflow. Built in Python, it defines a Supervisor class to dispatch tasks, monitor agent status, handle failures, and aggregate responses. You can extend base agent classes, plug in different model APIs, and configure scheduling policies. It logs activities for auditing, supports parallel execution, and offers a modular design for easy customization and integration into larger AI systems.
Who will use Agent Supervisor Example?
- AI researchers experimenting with multi-agent systems
- Software developers prototyping agent orchestration
- Data engineers building parallel processing pipelines
- DevOps teams managing AI workflow automation
How to use the Agent Supervisor Example?
- Step1: Clone the GitHub repository: git clone https://github.com/viniciusfinger/agent-supervisor-example
- Step2: Install dependencies: pip install -r requirements.txt
- Step3: Configure agent tasks and API keys in config.yaml
- Step4: Extend BaseAgent to implement custom agent behaviors
- Step5: Run the supervisor script: python supervisor.py
- Step6: Monitor logs in ./logs for status, errors, and results
Platform
- Linux
- Mac
- Windows
Agent Supervisor Example's Core Features & Benefits
The Core Features
- Multi-agent orchestration
- Dynamic task scheduling
- Error monitoring and retry
- Centralized logging and auditing
- Modular agent integration
The Benefits
- Scalable coordination across AI agents
- Improved fault tolerance and recovery
- Flexible customization for various workflows
- Transparent activity tracking
- Plug-and-play model support
Agent Supervisor Example's Main Use Cases & Applications
- Coordinating NLP and vision agents in a unified pipeline
- Parallel data ingestion and processing workflows
- Automated question-answering systems with multiple sub-agents
- Research experiments on multi-modal agent collaboration
FAQs of Agent Supervisor Example
Agent Supervisor Example Company Information
Agent Supervisor Example Reviews
5/5
Agent Supervisor Example's Main Competitors and alternatives?
LangChain Agents
Microsoft Semantic Kernel
AutoGPT
BabyAGI
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