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.

Enables AI agents to queue, commit, push, and track durable Git transactions through a shared daemon, preserving other agents' unstaged work and allowing recovery by request ID.

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.

Enables travelers and agents to route destination crowds in real time by dispersing visitors and reducing queue waits with queuing-theory and dynamic heatmap guidance.

Enables agents to optimize real-time attraction visitor dispersion and queueing wait times using queuing-theory models and dynamic heatmap routing.

Enables any MCP-compatible client to persist long-term memories for AI agents, with LLM-based fact extraction, semantic vector search, update/delete/list operations, and queue status checks across sessions.

Exposes tools that let an agent read a CV, search the Arbeitnow job board for Berlin AI/ML working student roles, score each vacancy against the CV, draft cover letters whose every claim cites CV facts, and queue them for human approval. The same tools can be driven from other MCP clients such as Claude Desktop, with scoring done deterministically in code and limits enforced outside the prompt.