anomalyco_opencode/packages/opencode/specs/simulation/simulated-network-llm.md

Simulated Network And Driver-Scripted LLM

Status: design for the Phase 2 network and LLM items in simulation-phases.md.

Summary

Simulation replaces the HttpClient.HttpClient platform node with a simulated network. The LLM is not a separate fake: it is one registered route in that network (api.openai.com), answered by the external driver over the existing control WebSocket. When the app issues a provider request, the backend forwards it to the driver and the driver streams response chunks back. There is no enqueueing and no scripted-response store; the driver is the model.

Everything above the HTTP boundary runs real: catalog and auth resolution, LLMClient, request body construction, SSE framing, the OpenAI protocol event schema, the step state machine, Lifecycle grammar, tool-argument accumulation, the session runner, tools, and permissions.

Why the network seam

LLMClient.stream sits on a stack that ends in one platform node:

LLMClient.stream(request)
  route.body.from            LLMRequest -> OpenAI JSON body        (real)
  transport.prepare          body + endpoint + auth -> HttpRequest (real)
  RequestExecutor.execute    status/error taxonomy                 (real)
    HttpClient.HttpClient    <- replaced by the simulated network
  Framing.sse                bytes -> frames                       (real)
  protocol.stream.event      frame -> OpenAIChatEvent, validated   (real)
  protocol.stream.step       state machine -> LLMEvents            (real)

Replacing httpClient (already a LayerNode in app-node-platform.ts, already used by simulationReplacements mechanics) keeps the entire pipeline under test and gives wire-fidelity observation of what would have been sent to the provider. Failure injection (429s, malformed SSE, truncated streams) exercises real error paths that a typed LLMClient fake cannot reach.

Components

1. Simulated network (packages/simulation/src/backend/network.ts)

Replaces httpClient in simulationReplacements. An in-memory route table:

• register(matcher, responder) where matcher is method + URL pattern and responder is (HttpClientRequest) => Effect<HttpClientResponse>.
• Unknown requests fail loudly with a typed simulation error (spec: deny unknown external network by default).
• Optional loopback allowance for the app's own server is not required server-side (the server does not call itself over HTTP); revisit if a consumer needs it.
• Every request/response summary is traced.

2. OpenAI endpoint route (packages/simulation/src/backend/openai.ts)

Registered in the network at startup for POST {DEFAULT_BASE_URL}{PATH} from protocols/openai-chat.ts (https://api.openai.com/v1/chat/completions).

On request:

1. Allocate an exchange id. Parse the real OpenAI request body (available to the driver for assertions).
2. Publish a request record to the LLM exchange service (below) and create a chunk Queue.
3. Return HttpClientResponse with content-type: text/event-stream whose body stream reads from the queue, encoding each item as an SSE data: frame, terminated by [DONE].

Chunks are constructed through the OpenAIChatEvent schema so drift in the protocol schema breaks the build, not the runtime.

The response stream is interruptible like a real HTTP response: if the runner cancels (user interrupt), the exchange closes and the driver is notified.

3. LLM exchange service (packages/simulation/src/backend/llm-exchange.ts)

Process-global simulation service owning pending exchanges:

Exchange = { id, body, queue: Queue<Item | Error | Done>, deferred lifecycle }

• requests() — stream of newly opened exchanges (consumed by the control route).
• push(id, item) — append one response item to an open exchange.
• finish(id, reason) / fail(id, failure) — terminate the exchange.
• Exchanges that receive no driver within a configurable timeout fail the provider request with a simulation error (surfaces in the real provider-error path).

4. Backend control WebSocket (simulation-gated)

Started when OPENCODE_DRIVE names a registry manifest: a loopback JSON-RPC 2.0 WebSocket at that manifest's exact backend endpoint, hosted by the backend process. Drivers connect to it directly — the standalone topology has exactly one backend per TUI, so there is no proxying through the frontend. This socket is also the headless-simulation interface: it works with no TUI at all.

Server -> driver notification (after llm.attach; pending exchanges are replayed on attach so late-attaching drivers miss nothing):

{ "jsonrpc": "2.0", "method": "llm.request",
  "params": { "id": "ex_1", "url": "...", "body": { ...openai request body... } } }

Driver -> server methods:

llm.attach                                subscribe to llm.request notifications
llm.chunk   { id, items: Item[] }         append response items
llm.finish  { id, reason?: "stop" | ... } finish the exchange
llm.pending                               list open exchanges
network.log                               simulated network request log

Item is the response vocabulary the driver speaks:

{ type: "textDelta",      text }
{ type: "reasoningDelta", text }
{ type: "toolCall",       id, name, input }
{ type: "raw",            chunk }        // escape hatch: raw OpenAIChatEvent JSON

The backend compiles items to OpenAI chunks (delta.content, delta.tool_calls[].function.arguments, finish_reason); raw passes through unmodified. Streaming granularity is the driver's choice: many small llm.chunk calls stream word by word; one call with many items plus llm.finish responds at once.

Failure injection (llm.fail: HTTP status instead of SSE) is specced but not yet implemented.

5. Driver topology

A driver manages two loopback WebSocket connections:

• TUI control server (manifest endpoints.ui) — UI state, actions, render, trace.
• Backend control server (manifest endpoints.backend) — LLM exchanges, network log.

Both speak the same JSON-RPC shape. Headless drivers use only the backend socket plus the normal HTTP API. Multiple drivers are out of scope; last attach wins.

6. Pacing and the clock

No server-side pacing by default: the driver controls timing by when it sends chunks, which is the point of driver-in-the-loop. A convenience llm.chunk option { delayMs } may sleep via Effect.sleep between items server-side; because that uses the fiber Clock, scoping a controllable clock to the exchange stream (Stream.provideService(Clock.Clock, simClock)) remains available for deterministic replay without touching app time. Defer until replay work needs it.

7. Catalog and auth seeding

The driver-facing model must be selectable in the TUI. Simulation seeds config (via the snapshot filesystem) defining a provider on the openai-chat route with baseURL left at the OpenAI default and a dummy apiKey (satisfies Catalog.available()). No catalog code changes.

End-to-end flow

driver                TUI drive server             backend + drive WS
  |                            |                          |
  |-- ui.action (submit) ----->|                          |
  |                            |-- (normal app HTTP) ---->|  session runner starts
  |                            |                          |  llm.stream -> HttpClient
  |                            |                          |  simulated network matches openai route
  |<================== llm.request {ex_1} ===============|  exchange ex_1 opened
  |-- llm.chunk {ex_1,[...]} ============================>|  SSE frames flow into the real
  |-- llm.chunk {ex_1,[...]} ============================>|  decode -> step -> LLMEvents ->
  |-- llm.finish {ex_1} =================================>|  runner publishes, TUI renders
  |                            |                          |
  |   (if toolCall was sent: runner executes the real tool against the
  |    fake filesystem, then issues the next provider turn -> new exchange
  |    ex_2 -> driver decides the next response)

The driver observes the TUI through ui.state while chunks stream, so mid-stream UI assertions need no clock control at all: the driver simply has not sent the rest yet.

Implementation order

1. network.ts: simulated HttpClient + route table + deny-unknown + trace. Replace httpClient in simulationReplacements.
2. llm-exchange.ts + openai.ts: exchange service and the OpenAI SSE route (schema-constructed chunks, [DONE], interruption).
3. control.ts: backend-hosted control WebSocket (llm.attach|chunk|finish|pending, network.log), started when the simulation module loads.
4. Config seeding for the sim provider; end-to-end verification via packages/server/script/e2e-sim.ts (headless) and packages/tui/script/sim-llm-driver.ts (TUI + backend sockets).
5. Trace records for network and LLM exchange activity.

Consequences

• No enqueue/script store to keep consistent; the driver is the single source of model behavior.
• Deterministic tests write drivers (respond to llm.request programmatically) instead of pre-baked scripts; replay (Phase 4) records exchanges and replays them as an automatic driver.
• Provider-coupling is confined to openai.ts (one wire encoder against a schema that lives in the repo); a second simulated provider (e.g. Anthropic) is another route file if ever needed.