Your personal AI runtime, local-first. Patchwork OS gives any AI model a consistent set of tools, YAML recipes, a delegation policy with approval queue, and a durable trace memory — all on your machine, all under your policy.

A fast object-first data engine for applications and AI agents.

The open-source context layer for your AI. Catalog your tables, topics, queues and APIs then expose real metadata to your AI agents.

In-memory caching in Golang using samber/hot — eviction algorithms (LRU, LFU, TinyLFU, W-TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, FIFO), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Apply when using or adopting samber/hot, when the codebase imports

Your personal AI runtime, local-first. Patchwork OS gives any AI model a consistent set of tools, YAML recipes, a delegation policy with approval queue, and a durable trace memory — all on your machine, all under your policy.

A remote MCP server that lets an AI assistant session and a separate agent exchange queued messages via tools like send_to_chief and wait_for_reply, waking the agent with webhooks and long-polling for its replies.

All-in-one workflow plugin—loops, swarms, and teams on Claude Code's Task System. All enforce exit criteria—swarm is faster with parallel queue execution, teams add contract-first coordination. Plan your way, execute your way. Optional: Beads for persistent memory, Ralph TUI for dashboard.

Enables MCP clients to coordinate local AI model runs by monitoring RAM, system commit, and VRAM, queueing tasks across clients, performing admission checks before inference, renewing leases, completing and releasing runs, and reporting status, history, and health while preserving original generation quality parameters. It also supports controlled start/stop and optional idle application memory reclamation.