"""Capability matrix across models (spec Part H, A15). Part H says the thresholds that depend on local capability — A12's failure count, A14's consult budget, A15's manifest size — must be tuned "at build-order step 5 on the real hardware". This is the measurement that makes that possible, rather than reasoning about model strength from reputation. What it measures is the thing that actually breaks: whether a model can hold the tool-call contract our loops depend on. llama3.2 could not — it returned `{` as an entire plan — and no amount of prompt work fixed it. Opt in with NISSE_CAPABILITY_MODELS, comma separated. Skipped otherwise, because it spends real tokens across several models. """ import os from pathlib import Path import pytest from nisse_dev.canonical import ( AssistantText, StoredEvent, ToolCall, ToolResult, UserMessage, ) from nisse_dev.envfile import load_env_file from nisse_dev.expert import parse_answer from nisse_dev.llm import ( AnthropicPassthroughClient, LiteLLMClient, LLMError, select_client, ) from nisse_dev.planner import PLANNER_TOOLS from nisse_dev.tools import EXECUTOR_TOOLS load_env_file(Path(__file__).resolve().parents[2] / ".env") SENTINEL = "NISSE_CAPABILITY_MODELS" MODELS = [m.strip() for m in os.environ.get(SENTINEL, "").split(",") if m.strip()] pytestmark = [ pytest.mark.integration, pytest.mark.skipif( not MODELS, reason=f"set {SENTINEL} to a comma-separated model list" ), ] def clients() -> tuple[LiteLLMClient, AnthropicPassthroughClient]: return LiteLLMClient.from_env(), AnthropicPassthroughClient.from_env() def user(text: str) -> list[StoredEvent]: return [StoredEvent(id=1, ts="t", event=UserMessage(text=text, source="probe"))] @pytest.mark.parametrize("model", MODELS) def test_model_answers_at_all(model: str) -> None: normalized, passthrough = clients() client = select_client(model, normalized, passthrough) produced = client.complete( user("Reply with exactly one word: pong"), None, model, system="Be terse." ) texts = [e.text for e in produced if isinstance(e, AssistantText)] assert texts, f"{model} produced no text: {produced!r}" @pytest.mark.parametrize("model", MODELS) def test_model_holds_the_planner_contract(model: str) -> None: """A4's gating is structural at the tool layer: a model that answers in prose cannot be gated, because there is no plan to approve.""" normalized, passthrough = clients() client = select_client(model, normalized, passthrough) produced = client.complete( user("Draft an architecture plan for a command-line slug generator."), PLANNER_TOOLS, model, system=( "You must answer by calling exactly one tool: submit_plan or " "ask_human. Plain prose is not accepted." ), ) calls = [e for e in produced if isinstance(e, ToolCall)] assert calls, f"{model} answered in prose instead of calling a tool" assert calls[0].tool in {"submit_plan", "ask_human"} if calls[0].tool == "submit_plan": content = calls[0].arguments.get("content", "") # llama3.2 emitted "{" here — structurally a tool call, substantively # nothing. Length is a crude proxy, but it separates the two. assert len(str(content)) > 80, ( f"{model} submitted a degenerate plan: {content!r}" ) @pytest.mark.parametrize("model", MODELS) def test_model_round_trips_a_tool_result(model: str) -> None: """The executor loop feeds results back; a model that cannot consume its own tool result cannot run a milestone.""" normalized, passthrough = clients() client = select_client(model, normalized, passthrough) events = user("List the files in the current directory using your tools.") produced = client.complete(events, EXECUTOR_TOOLS, model, system="Use a tool.") calls = [e for e in produced if isinstance(e, ToolCall)] assert calls, f"{model} made no tool call" next_id = 2 for event in produced: events.append(StoredEvent(id=next_id, ts="t", event=event)) next_id += 1 events.append( StoredEvent( id=next_id, ts="t", event=ToolResult( call_id=calls[0].call_id, result="app.py\ntests/", success=True ), ) ) follow_up = client.complete(events, EXECUTOR_TOOLS, model, system="Use a tool.") assert follow_up, f"{model} produced nothing after its tool result" @pytest.mark.parametrize("model", MODELS) def test_model_holds_the_prompt_level_json_contract(model: str) -> None: """A14 contracts structured output at the prompt level because vendors differ on JSON enforcement. This measures whether that holds per model.""" normalized, passthrough = clients() client = select_client(model, normalized, passthrough) produced = client.complete( user("Is SQLite sufficient for a single-process agent's task state?"), None, model, system=( "Reply with a single JSON object and nothing else: " '{"answer": "...", "confidence": "high"|"medium"|"low", ' '"caveats": "..."}' ), ) text = "\n".join(e.text for e in produced if isinstance(e, AssistantText)) assert parse_answer(text) is not None, ( f"{model} did not produce parseable JSON: {text[:200]!r}" ) @pytest.mark.parametrize("model", MODELS) def test_route_selection_matches_the_model(model: str) -> None: """A non-Anthropic model must not be sent Anthropic-shaped bodies. This was hardcoded until gpt-6-astra made it observable.""" normalized, passthrough = clients() chosen = select_client(model, normalized, passthrough) if model.startswith(("claude-", "anthropic/")): assert chosen is passthrough else: assert chosen is normalized # And the chosen route must actually work for this model. try: chosen.complete(user("say pong"), None, model, system="Be terse.") except LLMError as exc: pytest.fail(f"{model} failed on its selected route: {exc}")