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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>
238 lines
8.0 KiB
Python
238 lines
8.0 KiB
Python
import json
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from pathlib import Path
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import httpx
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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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UserMessage,
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)
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from nisse_dev.envfile import load_env_file
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from nisse_dev.llm import (
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RATE_LIMIT_RETRY_S,
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LiteLLMClient,
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LLMError,
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RoleMap,
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has_native_adapter,
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select_client,
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)
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EVENTS = [StoredEvent(id=1, ts="t", event=UserMessage(text="hi", source="m:1"))]
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def make_client(handler, sleep=lambda _s: None) -> LiteLLMClient:
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return LiteLLMClient(
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"http://litellm.test/v1",
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"sk-test",
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transport=httpx.MockTransport(handler),
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sleep=sleep, # never wait in unit tests
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)
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def completion(message: dict) -> httpx.Response:
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return httpx.Response(
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200, json={"model": "gpt-test-1", "choices": [{"message": message}]}
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)
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def test_request_shape_and_auth() -> None:
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seen: dict = {}
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def handler(request: httpx.Request) -> httpx.Response:
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seen["url"] = str(request.url)
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seen["auth"] = request.headers.get("authorization")
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seen["body"] = json.loads(request.content)
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return completion({"content": "hello"})
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make_client(handler).complete(EVENTS, tools=None, model="local/qwen", system="s")
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assert seen["url"] == "http://litellm.test/v1/chat/completions"
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assert seen["auth"] == "Bearer sk-test"
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assert seen["body"]["model"] == "local/qwen"
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assert seen["body"]["messages"][0] == {"role": "system", "content": "s"}
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assert seen["body"]["messages"][1] == {"role": "user", "content": "hi"}
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def test_assistant_text_parsed_with_producing_model() -> None:
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client = make_client(lambda request: completion({"content": "hello"}))
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[event] = client.complete(EVENTS, tools=None, model="local/qwen")
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assert event == AssistantText(text="hello", model="gpt-test-1")
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def test_tool_calls_get_locally_minted_canonical_ids() -> None:
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message = {
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"content": None,
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"tool_calls": [
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{
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"id": "call_provider_abc",
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"type": "function",
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"function": {
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"name": "run_tests",
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"arguments": '{"path": "tests/"}',
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},
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}
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],
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}
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client = make_client(lambda request: completion(message))
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[event] = client.complete(EVENTS, tools=None, model="local/qwen")
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assert isinstance(event, ToolCall)
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assert event.tool == "run_tests"
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assert event.arguments == {"path": "tests/"}
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assert event.call_id != "call_provider_abc" # provider ID never a join key
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assert event.call_id.startswith("call-")
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def test_truncated_tool_arguments_raise_llm_error() -> None:
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message = {
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"content": None,
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"tool_calls": [
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{
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"id": "c",
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"type": "function",
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"function": {"name": "submit_plan", "arguments": '{"content": "'},
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}
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],
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}
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client = make_client(lambda request: completion(message))
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with pytest.raises(LLMError, match="non-JSON arguments"):
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client.complete(EVENTS, tools=None, model="local/qwen")
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def test_non_object_tool_arguments_raise_llm_error() -> None:
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message = {
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"content": None,
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"tool_calls": [
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{
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"id": "c",
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"type": "function",
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"function": {"name": "submit_plan", "arguments": '"just a string"'},
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}
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],
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}
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client = make_client(lambda request: completion(message))
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with pytest.raises(LLMError, match="expected object"):
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client.complete(EVENTS, tools=None, model="local/qwen")
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def test_http_error_status_raises_llm_error() -> None:
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client = make_client(lambda request: httpx.Response(500, text="boom"))
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with pytest.raises(LLMError, match="500"):
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client.complete(EVENTS, tools=None, model="local/qwen")
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def test_rate_limit_is_retried_once_then_surfaced() -> None:
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# A 429 is transient. Losing a long delegation to one costs far more
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# than waiting out the window, but retrying forever would hide a real
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# ceiling, so it is retried exactly once.
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attempts: list[int] = []
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waited: list[float] = []
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def handler(request: httpx.Request) -> httpx.Response:
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attempts.append(1)
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return httpx.Response(429, text="rate limited")
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client = make_client(handler, sleep=waited.append)
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with pytest.raises(LLMError, match="429"):
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client.complete(EVENTS, tools=None, model="local/qwen")
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assert len(attempts) == 2
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assert waited == [RATE_LIMIT_RETRY_S]
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def test_rate_limit_that_clears_succeeds() -> None:
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responses = [httpx.Response(429, text="slow down"), completion({"content": "hi"})]
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client = make_client(lambda request: responses.pop(0), sleep=lambda _s: None)
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[event] = client.complete(EVENTS, tools=None, model="local/qwen")
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assert isinstance(event, AssistantText)
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def test_connection_failure_raises_llm_error() -> None:
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def handler(request: httpx.Request) -> httpx.Response:
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raise httpx.ConnectError("refused")
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with pytest.raises(LLMError, match="request failed"):
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make_client(handler).complete(EVENTS, tools=None, model="local/qwen")
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def test_malformed_response_raises_llm_error() -> None:
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client = make_client(lambda request: httpx.Response(200, json={"oops": True}))
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with pytest.raises(LLMError, match="malformed"):
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client.complete(EVENTS, tools=None, model="local/qwen")
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ROLE_ENV = {
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"NISSE_ROLE_EXECUTOR": "local/qwen",
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"NISSE_ROLE_PLANNER": "anthropic/claude-opus-5",
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"NISSE_ROLE_DELEGATE": "anthropic/claude-opus-5",
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"NISSE_ROLE_CONSULTANT": "anthropic/claude-opus-5",
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}
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def test_role_map_resolves_and_pins() -> None:
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roles = RoleMap.from_env(ROLE_ENV)
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assert roles.resolve("executor") == "local/qwen"
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assert roles.pinned() == {
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"executor": "local/qwen",
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"planner": "anthropic/claude-opus-5",
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"delegate": "anthropic/claude-opus-5",
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"consultant": "anthropic/claude-opus-5",
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}
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def test_role_map_missing_env_raises() -> None:
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with pytest.raises(LLMError, match="NISSE_ROLE_PLANNER"):
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RoleMap.from_env({"NISSE_ROLE_EXECUTOR": "local/qwen"})
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def test_unknown_role_raises() -> None:
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with pytest.raises(LLMError, match="unknown role"):
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RoleMap.from_env(ROLE_ENV).resolve("boss")
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def test_client_from_env_requires_config() -> None:
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with pytest.raises(LLMError, match="NISSE_LLM_BASE_URL"):
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LiteLLMClient.from_env({})
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def test_load_env_file(tmp_path: Path) -> None:
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envfile = tmp_path / ".env"
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envfile.write_text(
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"# comment\n\nNISSE_LLM_TOKEN=sk-abc\nNISSE_LLM_BASE_URL = http://x \n"
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)
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env: dict[str, str] = {"NISSE_LLM_TOKEN": "sk-already-set"}
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load_env_file(envfile, env)
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assert env["NISSE_LLM_TOKEN"] == "sk-already-set" # real env wins
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assert env["NISSE_LLM_BASE_URL"] == "http://x"
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def test_load_env_file_missing_is_noop(tmp_path: Path) -> None:
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env: dict[str, str] = {}
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load_env_file(tmp_path / "nope.env", env)
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assert env == {}
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# --- route selection (A5 vendor fluidity) --------------------------------
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def test_anthropic_models_take_the_native_route() -> None:
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normalized, passthrough = object(), object()
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for model in ("claude-haiku-4-5", "claude-opus-5", "anthropic/claude-sonnet-5"):
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assert select_client(model, normalized, passthrough) is passthrough
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def test_other_vendors_take_the_normalized_route() -> None:
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# A13: provisionally accept LiteLLM's normalization for a vendor whose
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# renderer adapter does not exist yet, rather than sending it request
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# bodies shaped for someone else.
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normalized, passthrough = object(), object()
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for model in ("gpt-6-astra", "local-coder", "local-fast", "llama3.2"):
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assert select_client(model, normalized, passthrough) is normalized
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def test_native_adapter_detection_is_explicit() -> None:
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assert has_native_adapter("claude-opus-5")
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assert not has_native_adapter("gpt-6-astra")
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# A model merely mentioning the vendor is not the vendor.
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assert not has_native_adapter("my-claude-clone")
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