The Agent - VVG-web/aurora-studio GitHub Wiki

The agent

Wiki home ยท related: Trust ยท Asking the base ยท setup: INSTALL โ€” the built-in agent ยท design: architecture ยง4

The built-in agent does the work that needs meaning โ€” parsing sources into cards, writing theses, extracting definitions, answering questions, producing artifacts. Script first, model second: mass operations (lint, repair, dedupe, sync, trust, links) are deterministic scripts with a dry-run; the model only judges, and it writes only through whitelisted engine commands.

Without configured models everything that does not need them still works.

Roles and chains

A provider is any OpenAI-compatible API (a corporate gateway, cloud, local llama.cpp or vLLM). A backend is "provider + model" in a role. Each role has its own chain of backends: a call goes to the first; an unavailable or busy one is skipped, a recovered one is picked up on the next request.

flowchart LR
  CALL["role task"] --> R{"role chain<br>primary โ†’ fallback โ†’ โ€ฆ"}
  R -->|"answers"| OK["result"]
  R -->|"down or busy"| NEXT["next backend"] --> R
  R -->|"three failures in a row"| STOP["stop: it is the provider,<br>not the cards"]
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Role Does
worker routine steps: parsing, theses
planner boundaries and plans
critic checks a decision before it is written
qa Momus โ€” checks every claim of an answer against the source

Reasoning ("thinking") can be switched off per role, but never for qa and the critic.

Momus

A second model that reads the agent's output and the source and marks every claim either supported (with a quote) or "no support". Claims without support are listed for a human (ops:todo) and are not carried into documents. agent:distill --recheck checks them again.

One setup per kit

Models are set up once per kit โ€” the panel's "Models" section, the file <kit>/local/models.json; every project uses it, none has its own. First the providers (URL, key, width), then in the LLM, OCR and embeddings tabs the roles, each with a chain of "provider + model" backends: the first is the primary, "+" adds a fallback, the order changes by dragging, a fallback can be switched off. A provider can be added right from a role. Details โ€” INSTALL.

OCR and embeddings โ€” the same pattern

Semantic search (kb:embed) and scan recognition (kb:ingest-office) have the same roles and chains. An embeddings fallback uses only the same model: vectors of different models do not compare. All texts go to the providers of the setup: if your perimeter forbids sending materials out, do not switch on semantics and scans.

Checking the chain

Command Shows
agent:ping every backend of the LLM chains by a live request: roles, speed; an empty answer counts as a refusal
agent:probe "no connection", "wrong key" or "no such model"; the model list; --why โ€” which layer of the request is rejected
agent:width how many simultaneous requests each provider holds
agent:pydantic what goes to the gateway through Pydantic AI and whether the installed version is compatible

Safety properties

  • One writer per project. A writing agent run takes a lock in the project; a second one is refused with the holder's pid and start time. A run killed with Ctrl+C does not lock the base.
  • A checkpoint first. Before a writing run the agent commits the current state; no git โ†’ it refuses to write.
  • A failure on one card does not stop the run; three in a row do.

This wiki is generated from the wiki/ folder of the repository โ€” edit the pages there, changes made here are overwritten. ยท RU-Home

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