metacognitive-context-orchestrator

You can manage local context and memory by running an asynchronous daemon core that uses vector-store-backed memory to orchestrate state transitions between gateways and Python agents within WSL2.

Metacognitive reasoning for local RAG retrieval is an asynchronous, state-machine-based workflow that applies Bayesian confidence calibration and human-in-the-loop checkpoints to safely manage local vector store data.

Yes, you can build a background daemon in WSL2 using asyncio-enabled Python to handle asynchronous state transitions and orchestrate multi-agent flows while keeping all data local for sovereignty.

Yes, a local vector store is required to configure the memory path and enable RAG-style retrieval when you start the asynchronous metacognitive reasoning daemon.

Bayesian confidence calibration evaluates decision confidence to trigger human-in-the-loop support points, ensuring safe automation before the state machine executes multi-agent state transfers.

This approach is limited to WSL2 environments and requires asyncio-enabled Python, meaning it is bound by local vector store capacity and safe data residency constraints rather than distributed cloud scaling.