k-p-archana/Multi-agent-LLM-based-text-classification-using-A2A-agent-to-agent-communication.
A multi-agent NLP system for handling diverse text classification tasks. Specialized agents perform sentiment analysis, offensive detection, and hate speech classification. An intelligent orchestrator routes inputs to the most suitable agent. Combines LLM and ML models to ensure accurate and reliable predictions.
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k-p-archana/Multi-agent-LLM-based-text-classification-using-A2A-agent-to-agent-communication. is a Jupyter Notebook A2A agent licensed under MIT. A multi-agent NLP system for handling diverse text classification tasks. Specialized agents perform sentiment analysis, offensive detection, and hate speech classification. An intelligent orchestrator routes inputs to the most suitable agent. Combines LLM and ML models to ensure accurate and reliable predictions.
Ranked #23 out of 110 indexed tools.
Actively maintained with commits in the last week.
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1 stars → early stage
Last commit today → actively maintained
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Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →
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# Multi-agent-LLM-based-text-classification-using-A2A-agent-to-agent-communication. This project presents a multi-agent LLM-based text classification system that leverages A2A (agent-to-agent) communication and an intelligent orchestrator to handle diverse natural language processing tasks efficiently. The system is designed to mimic a distributed AI architecture where multiple specialized agents collaborate to produce accurate and reliable predictions. At its core, the architecture consists of three independent LLM-powered agents, each dedicated to a specific classification task: sentiment analysis , offensive content detection, and hate speech classification. Each agent runs as a separate service on different ports (e.g., 8001, 8002, 8003), enabling modularity and scalability. These agents communicate through API calls, implementing an agent-to-agent (A2A) interaction paradigm, where multiple agents can be queried and their outputs compared when needed. Each agent primarily uses aRead full README on GitHub →
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