# DESIGN — Agap Agent Platform (A2A, model queues, agents)

Status: **draft for review** · Owner: alvis · Drafted 2026-07-21

This is the overall design for turning the Agap homelab from "one Adolf carrying every
tool + a few background LLM calls" into a **multi-agent platform**: agents as
personas, models as queued compute, and A2A as the way work moves between them.

---

## 1. Why

Current pain, all observed on the live stack:

- **Duplicated LLM spend.** Every Adolf turn costs two Kimi calls: the reply
  (~32.8K tokens in) and a *separate* background Hindsight retain/extraction
  (~22.8K). Only ~425 tokens of that is the actual conversation.
- **Tool bloat.** One Adolf carries ~84 MCP tool schemas (~12K tokens) + ~26
  built-in Kimi tools (~10K) on **every** turn, whether relevant or not.
- **Quota cliffs.** Kimi is a flat, window-limited subscription (~60 messages per
  5h, ~300/week measured). When the window is spent, Adolf goes dark. There is no
  graceful degradation and no way to park work until the window resets.
- **Background work is hardcoded to a model.** Hindsight's reflect/consolidate
  call a fixed LLM directly. There is no scheduling, no priority, no quota
  awareness, no way to say "do this on the big model when it's free".
- **No room to grow.** The ambition is autonomous research agents, remote llama
  nodes, more GPUs, more agents. None of that fits a single hardcoded assistant.

## 2. Core concepts (and the distinctions that matter)

The central insight: **an agent is not a queue, and a model is not an agent.**

### Task
The unit of work. Durable, addressable, and **context-by-reference**: a task
carries *pointers* (memory bank id, git ref, board task id, file path), never
pasted context. Fields: id, intent, required capability/tier, target model queue,
priority, status, context refs, result ref, submitter, deadline.

### Model (backbone) — the scarce resource
A concrete LLM endpoint reached through the LiteLLM gateway. Examples today:
`kimi` (flat quota), `claude-haiku` (paid, already wired), local ollama
(`qwen3.5:4b`, `qwen3:8b`, `gemma3:4b`), later a remote llama box or a second GPU.

**Each model has its own queue and its own worker**, because the model is what is
actually scarce (quota, VRAM, cost, rate limit).

### Agent — the persona
An agent is a **combination of personality + system prompt + memory + tool scope**
(e.g. Adolf the proactive auditor; Torgash the marketplace analyst; a research
agent; the Claude coding loop). An agent is a *configuration*, not a runtime
resource. Critically:

> **An agent may change its backbone LLM.** Adolf on Kimi today, on a local model
> tomorrow, on Claude for a hard task. Therefore **queues are keyed by model, not
> by agent.** An agent *submits into* and *consumes from* model queues.

### Queue — per model, async, with a lifecycle
Queues are asynchronous by design and differ in how they drain:

| Lifecycle | Behaviour | Example |
|---|---|---|
| **always-on** | worker drains continuously in the background | local ollama models |
| **quota-gated** | drains until the window is exhausted, then parks and resumes on reset | Kimi |
| **cost-gated** | drains under a budget ceiling; stops/falls back when spent | paid Haiku/Flash |
| **on-demand** | node is woken/attached when work exists | future remote llama / extra GPU |

A task parked on a quota-gated queue is not lost — it waits for the window, or is
re-routed if it is urgent and another queue can satisfy the required capability.

## 3. Architecture

Four planes. Keeping them separate is the whole point.

```
┌─ Coordination plane ──────────────────────────────────────────┐
│  Task registry + lifecycle (Kanboard as blackboard today)      │
│  context-by-reference; claim/status; audit trail               │
└───────────────────────────────────────────────────────────────┘
┌─ Agent plane ─────────────────────────────────────────────────┐
│  Agent registry: persona + system prompt + memory bank +       │
│  tool scope + preferred capability tier                        │
│  (Adolf, Torgash, research-agent, claude-coder, …)             │
└───────────────────────────────────────────────────────────────┘
┌─ Scheduling plane ────────────────────────────────────────────┐
│  Per-MODEL queues + workers; lifecycle policy (always-on /     │
│  quota-gated / cost-gated / on-demand); priority; claiming     │
└───────────────────────────────────────────────────────────────┘
┌─ Model plane ─────────────────────────────────────────────────┐
│  LiteLLM gateway: kimi | claude-haiku | local ollama | remote  │
│  routing, fallback on 429/quota, per-agent virtual keys+budget │
└───────────────────────────────────────────────────────────────┘
```

**Shared context stores** (what task references point at): Hindsight (memory
banks), git/gitea (code + docs), Kanboard (task context), files.

### A2A on top
A2A gives the vocabulary we otherwise have to invent: **agent cards**
(capability advertisement), **task lifecycle states**, structured task
submission/tracking, and — most importantly — the **context-by-reference**
pattern (send a `contextId`, let the worker read the shared store). We adopt the
*patterns* first; the wire protocol can follow once more than one runtime needs
to interoperate.

## 4. Worked examples (the required minimal set)

**(1) Hindsight `reflect` becomes an A2A task.** Reflect is async by design.
Instead of Hindsight calling a fixed LLM inline, it **submits a task** — intent
`reflect`, context ref = bank + query, required tier = *large* — onto the
large-model queue. A worker runs it when that model has capacity/quota; the
result is written back to the bank. Same for consolidation. This removes the
hardcoded background call and makes memory work schedulable, priced, and
quota-aware. (See also the "in-loop extraction" option, which is the cheaper
counterpart for the *retain* path.)

**(2) The Claude Code CLI loop is just an agent.** `claude-coder` = an agent
whose persona is "implementer", whose backbone is a Claude model, and whose
consumption rule is *pull complex/coding tasks*. It is a **special case of a
queue consumer**, not a privileged component. This is why it already works:
Adolf files tasks, the Claude loop pulls them. We are formalising what exists.

**(3) Model queues ≠ agent queues.** Adolf may run on Kimi now and something else
later; Torgash may be cheap-tier normally and escalate to a large model for a
tricky comparison. So a task is queued against **the capability/model it needs**,
and the agent identity travels *with the task* (persona + memory refs), not with
the queue.

**(4) Queues drain differently.** The local queue works all night; the Kimi queue
stops at 100% of the 5h window and resumes after reset; a paid queue stops at its
budget. Submitters therefore must state urgency, and the router must be able to
re-route or park.

## 5. Growing the lab

- **More GPUs / remote llama** → new model entries + their own queues and
  workers; `on-demand` lifecycle for nodes that are not always up. Nothing else
  changes.
- **More agents** (research, finance, home) → new agent registry entries with
  scoped tools + their own memory banks. They inherit queues and A2A for free.
- **Autonomous research agents** → long-running, low-priority tasks on always-on
  local queues, escalating to the large model only for synthesis. This is exactly
  what per-model queues + priorities make affordable.

## 6. Migration (phased, smallest useful step first)

1. **Registries + schemas** — model registry (endpoint, capability, lifecycle,
   quota), agent registry (persona/prompt/memory/tools), task schema.
2. **One queue + one worker** — always-on local model, end-to-end.
3. **Quota-aware worker** — Kimi: park on exhaustion, resume on reset.
4. **A2A submission/tracking** with context-by-reference.
5. **Cut over the examples** — Hindsight reflect → queue; Claude loop → declared
   agent/consumer; Adolf → declared agent with scoped tools.
6. **Scale** — remote/extra models, more agents.

## 7. Open questions

- Is Kanboard the queue itself, or does it stay the *human-facing* board while
  workers use a dedicated queue store (and the two are synced)?
- Where does the routing decision live — submitter picks the tier, or a central
  policy re-routes based on live quota/budget?
- How much A2A do we actually implement (patterns only vs the real protocol)?
- Claim/lease semantics: what happens to a task whose worker dies mid-run?
- Does an agent's memory bank follow it across backbones (yes, by design) — and
  what does that mean for extraction quality when the backbone is weak?

## 8. Related

- Kanboard epic: architecture + LiteLLM gateway + multi-agent framework.
- Hindsight in-loop extraction (the cheap counterpart to queued reflect).
- Per-agent tool scoping (why Adolf stops carrying every tool).
