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The arrival of non-Anthropic models made a latent bug observable: the
delegate's client was hardcoded to the Anthropic passthrough, so
pointing NISSE_ROLE_DELEGATE at gpt-6-astra or a local model would
have sent Anthropic-shaped /v1/messages bodies to something that does
not speak them. A5 promises the frontier tier is swappable "as a
configuration change, not a refactor", and it was not.
select_client now picks the route from the model: the vendor-native
adapter where one exists, LiteLLM's normalization otherwise — which is
exactly what A13 prescribes for a vendor whose renderer adapter has
not been written, pending validation against the A16 eval set.
Also adds the capability matrix Part H asks for. It says the
thresholds that depend on local capability — A12's failure count,
A14's consult budget, A15's manifest size — must be tuned "on the real
hardware", and that needs measurement rather than reputation. The
harness probes each model for the things that actually break: whether
it calls a tool at all, whether the plan it submits has substance
(llama3.2 emitted "{"), whether it can consume its own tool result,
and whether prompt-level JSON holds. Opt in with
NISSE_CAPABILITY_MODELS; it spends real tokens across several models.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
163 lines
6.0 KiB
Python
163 lines
6.0 KiB
Python
"""Capability matrix across models (spec Part H, A15).
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Part H says the thresholds that depend on local capability — A12's failure
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count, A14's consult budget, A15's manifest size — must be tuned "at
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build-order step 5 on the real hardware". This is the measurement that makes
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that possible, rather than reasoning about model strength from reputation.
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What it measures is the thing that actually breaks: whether a model can hold
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the tool-call contract our loops depend on. llama3.2 could not — it returned
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`{` as an entire plan — and no amount of prompt work fixed it.
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Opt in with NISSE_CAPABILITY_MODELS, comma separated. Skipped otherwise,
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because it spends real tokens across several models.
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"""
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import os
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from pathlib import Path
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import pytest
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from nisse_dev.canonical import (
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AssistantText,
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StoredEvent,
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ToolCall,
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ToolResult,
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UserMessage,
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)
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from nisse_dev.envfile import load_env_file
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from nisse_dev.expert import parse_answer
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from nisse_dev.llm import (
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AnthropicPassthroughClient,
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LiteLLMClient,
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LLMError,
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select_client,
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)
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from nisse_dev.planner import PLANNER_TOOLS
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from nisse_dev.tools import EXECUTOR_TOOLS
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load_env_file(Path(__file__).resolve().parents[2] / ".env")
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SENTINEL = "NISSE_CAPABILITY_MODELS"
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MODELS = [m.strip() for m in os.environ.get(SENTINEL, "").split(",") if m.strip()]
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pytestmark = [
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pytest.mark.integration,
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pytest.mark.skipif(
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not MODELS, reason=f"set {SENTINEL} to a comma-separated model list"
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),
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]
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def clients() -> tuple[LiteLLMClient, AnthropicPassthroughClient]:
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return LiteLLMClient.from_env(), AnthropicPassthroughClient.from_env()
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def user(text: str) -> list[StoredEvent]:
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return [StoredEvent(id=1, ts="t", event=UserMessage(text=text, source="probe"))]
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@pytest.mark.parametrize("model", MODELS)
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def test_model_answers_at_all(model: str) -> None:
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normalized, passthrough = clients()
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client = select_client(model, normalized, passthrough)
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produced = client.complete(
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user("Reply with exactly one word: pong"), None, model, system="Be terse."
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)
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texts = [e.text for e in produced if isinstance(e, AssistantText)]
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assert texts, f"{model} produced no text: {produced!r}"
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@pytest.mark.parametrize("model", MODELS)
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def test_model_holds_the_planner_contract(model: str) -> None:
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"""A4's gating is structural at the tool layer: a model that answers in
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prose cannot be gated, because there is no plan to approve."""
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normalized, passthrough = clients()
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client = select_client(model, normalized, passthrough)
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produced = client.complete(
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user("Draft an architecture plan for a command-line slug generator."),
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PLANNER_TOOLS,
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model,
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system=(
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"You must answer by calling exactly one tool: submit_plan or "
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"ask_human. Plain prose is not accepted."
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),
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)
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calls = [e for e in produced if isinstance(e, ToolCall)]
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assert calls, f"{model} answered in prose instead of calling a tool"
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assert calls[0].tool in {"submit_plan", "ask_human"}
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if calls[0].tool == "submit_plan":
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content = calls[0].arguments.get("content", "")
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# llama3.2 emitted "{" here — structurally a tool call, substantively
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# nothing. Length is a crude proxy, but it separates the two.
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assert len(str(content)) > 80, (
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f"{model} submitted a degenerate plan: {content!r}"
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)
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@pytest.mark.parametrize("model", MODELS)
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def test_model_round_trips_a_tool_result(model: str) -> None:
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"""The executor loop feeds results back; a model that cannot consume its
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own tool result cannot run a milestone."""
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normalized, passthrough = clients()
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client = select_client(model, normalized, passthrough)
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events = user("List the files in the current directory using your tools.")
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produced = client.complete(events, EXECUTOR_TOOLS, model, system="Use a tool.")
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calls = [e for e in produced if isinstance(e, ToolCall)]
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assert calls, f"{model} made no tool call"
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next_id = 2
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for event in produced:
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events.append(StoredEvent(id=next_id, ts="t", event=event))
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next_id += 1
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events.append(
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StoredEvent(
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id=next_id,
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ts="t",
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event=ToolResult(
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call_id=calls[0].call_id, result="app.py\ntests/", success=True
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),
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)
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)
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follow_up = client.complete(events, EXECUTOR_TOOLS, model, system="Use a tool.")
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assert follow_up, f"{model} produced nothing after its tool result"
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@pytest.mark.parametrize("model", MODELS)
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def test_model_holds_the_prompt_level_json_contract(model: str) -> None:
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"""A14 contracts structured output at the prompt level because vendors
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differ on JSON enforcement. This measures whether that holds per model."""
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normalized, passthrough = clients()
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client = select_client(model, normalized, passthrough)
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produced = client.complete(
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user("Is SQLite sufficient for a single-process agent's task state?"),
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None,
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model,
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system=(
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"Reply with a single JSON object and nothing else: "
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'{"answer": "...", "confidence": "high"|"medium"|"low", '
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'"caveats": "..."}'
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),
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)
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text = "\n".join(e.text for e in produced if isinstance(e, AssistantText))
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assert parse_answer(text) is not None, (
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f"{model} did not produce parseable JSON: {text[:200]!r}"
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)
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@pytest.mark.parametrize("model", MODELS)
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def test_route_selection_matches_the_model(model: str) -> None:
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"""A non-Anthropic model must not be sent Anthropic-shaped bodies. This
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was hardcoded until gpt-6-astra made it observable."""
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normalized, passthrough = clients()
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chosen = select_client(model, normalized, passthrough)
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if model.startswith(("claude-", "anthropic/")):
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assert chosen is passthrough
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else:
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assert chosen is normalized
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# And the chosen route must actually work for this model.
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try:
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chosen.complete(user("say pong"), None, model, system="Be terse.")
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except LLMError as exc:
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pytest.fail(f"{model} failed on its selected route: {exc}")
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