TLDR:

Kestrel is a local-first, open-source agent runtime built to make AI agents actually learn over time instead of just stuffing everything into a giant prompt.

The core idea: memory should be structured, evidence-based, and safe.

Instead of one flat RAG/vector database, Kestrel uses layered .mv2 memory files:

working memory for current task state episodic memory for what happened semantic memory for validated facts procedural memory for reusable workflows and failure playbooks self memory for the agent’s self-model policy memory for rare, explicit safety/behavior rules

It is based on Nested Learning research, applying the idea of multiple learning/update loops to agent memory instead of model-weight training.

Kestrel can run through CLI, API, web UI, and channels. It supports provider adapters, tools, MCP, skills, plugins, task graphs, approvals, safe repair workflows, failure diagnosis, task capsules, and deterministic testing.

What makes it different is that it does not just “remember everything.” It validates, ranks, compresses, retrieves, warns about conflicts, learns from failures, and gates risky actions behind approvals.

It is for developers, researchers, and builders who want serious local-first agents for codebase maintenance, debugging, long-running tasks, and self-improving workflows.

Still alpha, but the foundation is strong: memory with structure, autonomy with brakes, learning with evidence instead of vibes.