Dear Huggingface community,
I was kind of experimenting with different models a few month ago, which ended up with this BETA version of AI TaskVector. Currently, it is a combination of both an AI Agent and factory that runs in local:
- Built-in low-code framework for automations
- Agentic runtime and orchestration
- Co-pilot mode and Long-polling mode (e.g. run this workflow to make the Agents talk over Telegram: config/workflow_examples/telegram_bot_gateway.json)
The idea was to create an open-source platform where anyone could be AI-assisted with building, running and training their agents without deep knowledge and skill involved. Anyways, these were just my thoughts looking forward, it may end up being something else, who knows…
I would be more than happy to have any useful feedback, comments. Feel free to create units, roles, play around with the repo and join my journey of the exploration, since I’m still learning.
DHEIVER 2
Orchestrating multiple agents locally introduces significant context management challenges. When agents converse over a loop like your Telegram gateway, the KV cache grows rapidly, especially with long-polling. Have you implemented a strategy to prune historical turns or summarize intermediate states to keep inference latency stable? Without aggressive context window management, the cost per successful task cycle can spike faster than the model’s raw token speed suggests.