Domain Pipelines

These packages give AI agents a long-term memory layer that persists information across sessions, not just within a single conversation. You'd adopt them to solve the problem of agents forgetting user preferences, contradicting themselves, or accumulating irrelevant information over time. The most distinctive thing is that memory is treated as a managed asset with an explicit lifecycle—extraction, decay, forgetting, and contradiction resolution—rather than a fire-and-forget vector store.

These packages give you a set of MCP tools for generating and processing images, audio, video, documents, and 3D models, then chaining those operations into pipelines with artifact passing between steps. You would adopt them to avoid wiring together multiple provider SDKs, retry logic, cost tracking, and caching yourself when building AI agents that produce media as output. The most distinctive thing is that every operation is a composable pipeline step with built-in quality gates, budget enforcement, content-addressed caching, and multi-provider routing, so you can define a multi-step workflow once and have it handle provider fallback, cost caps, and resumable execution automatically.

These packages give you the full pipeline to turn microphone or phone audio into an AI agent response and back — speech-to-text, MCP tool calls, text-to-speech, and telephony or WebRTC transport, all orchestrated with per-stage latency budgets. You'd adopt them to build a production voice agent without writing the audio plumbing, provider switching, or session management yourself. The most distinctive thing is that the agent logic lives entirely in an external MCP server, so the pipeline is a pure transport layer that can be swapped between Twilio, WebRTC, or a local simulator without changing the agent.