Multi-Agent NLP Coordination Platform

My current project revolves around building a robust multi-agent system whose agents coordinate and plan actions in real time while relying on an NLP backbone for three key capabilities: text classification, sentiment analysis, and conversational interaction via a chatbot.

The core work will include designing the overall agent architecture, selecting or training language models, and wiring each agent so that they can seamlessly exchange state information, negotiate tasks, and surface insights through the chatbot interface. Python is the primary stack, and I’m already prototyping with PyTorch and Hugging Face transformers; however, I’m open to complementary frameworks that better serve agent orchestration or distributed messaging.

Deliverables
• System architecture diagram and succinct technical write-up
• Modular codebase implementing agent coordination logic, NLP pipelines, and chatbot interface
• Trained or fine-tuned models for classification and sentiment tasks, with evaluation metrics
• End-to-end demo (CLI or web) showing agents planning together, classifying incoming text, detecting sentiment, and responding conversationally

Acceptance criteria
– Agents must autonomously distribute and re-plan tasks when new data arrives.
– Text classification and sentiment modules should reach agreed-upon accuracy thresholds on a provided validation set.
– Chatbot responses need to reflect the collective agent state in real time.
– Code should run reproducibly in a Docker container or straightforward virtual environment.

If this outline matches your expertise, the next step is a quick technical discussion so I can share sample data and finalize performance targets.