Files
OpenJarvis/tests/engine/test_litellm.py
T
Jon Saad-FalconandClaude Opus 4.6 8d538cd1b0 Add orchestrator training, channels, LiteLLM engine, and simplify learning taxonomy
Major changes across parallel sessions:

- Add orchestrator SFT & GRPO training subpackage (learning/orchestrator/)
  with episode types, multi-objective reward, prompt registry, policy model,
  RL environment, and registered learning policies
- Add structured THOUGHT/TOOL/INPUT/FINAL_ANSWER mode to OrchestratorAgent
- Add 15 channel backends (Discord, Slack, Telegram, Email, Webhook, IRC,
  Matrix, Teams, WhatsApp, Signal, Mattermost, BlueBubbles, Feishu,
  Google Chat, Webchat) with channel tools and config
- Add LiteLLM engine backend for unified LLM provider access
- Add RLM agent and REPL tool
- Remove ToolLearningPolicy — learning taxonomy now only targets
  Intelligence (LM weights/routing) and Agents (logic/ICL/tool strategies)
- Rename SFTPolicy to SFTRouterPolicy (backward-compat alias kept)
- Remove OpenClaw agent infrastructure (openclaw*.py, openclaw_bridge.py)
- Fix async streaming tests (asyncio.run vs deprecated get_event_loop)
- Fix server channel route tests (pytest.importorskip for optional fastapi)
- Track uv.lock for reproducibility
- Update CLAUDE.md and docs to reflect all changes

1676 tests pass, 37 skipped.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-23 18:32:32 +00:00

208 lines
7.2 KiB
Python

"""Tests for the LiteLLM engine backend."""
from __future__ import annotations
from types import SimpleNamespace
from unittest import mock
from openjarvis.core.registry import EngineRegistry
from openjarvis.core.types import Message, Role
from openjarvis.engine.litellm import LiteLLMEngine
class TestLiteLLMEngineHealth:
def test_health_importable(self) -> None:
fake_litellm = mock.MagicMock()
with mock.patch.dict("sys.modules", {"litellm": fake_litellm}):
engine = LiteLLMEngine()
assert engine.health() is True
def test_health_not_importable(self) -> None:
with mock.patch.dict("sys.modules", {"litellm": None}):
engine = LiteLLMEngine()
assert engine.health() is False
class TestLiteLLMEngineGenerate:
def test_generate(self) -> None:
fake_usage = SimpleNamespace(
prompt_tokens=10, completion_tokens=5, total_tokens=15
)
fake_choice = SimpleNamespace(
message=SimpleNamespace(content="Hello!", tool_calls=None),
finish_reason="stop",
)
fake_resp = SimpleNamespace(
choices=[fake_choice], usage=fake_usage, model="gpt-4o"
)
fake_litellm = mock.MagicMock()
fake_litellm.completion.return_value = fake_resp
fake_litellm.completion_cost.return_value = 0.001
with mock.patch.dict("sys.modules", {"litellm": fake_litellm}):
engine = LiteLLMEngine()
result = engine.generate(
[Message(role=Role.USER, content="Hi")], model="gpt-4o"
)
assert result["content"] == "Hello!"
assert result["usage"]["prompt_tokens"] == 10
assert result["usage"]["completion_tokens"] == 5
assert result["usage"]["total_tokens"] == 15
assert result["model"] == "gpt-4o"
assert result["finish_reason"] == "stop"
assert result["cost_usd"] == 0.001
def test_generate_with_tools(self) -> None:
fake_tool_call = SimpleNamespace(
id="call_123",
function=SimpleNamespace(
name="calculator",
arguments='{"expression": "2+2"}',
),
)
fake_usage = SimpleNamespace(
prompt_tokens=20, completion_tokens=10, total_tokens=30
)
fake_choice = SimpleNamespace(
message=SimpleNamespace(
content="",
tool_calls=[fake_tool_call],
),
finish_reason="tool_calls",
)
fake_resp = SimpleNamespace(
choices=[fake_choice], usage=fake_usage, model="gpt-4o"
)
fake_litellm = mock.MagicMock()
fake_litellm.completion.return_value = fake_resp
fake_litellm.completion_cost.return_value = 0.002
tools = [
{
"type": "function",
"function": {
"name": "calculator",
"description": "Evaluate math",
"parameters": {"type": "object", "properties": {}},
},
}
]
with mock.patch.dict("sys.modules", {"litellm": fake_litellm}):
engine = LiteLLMEngine()
result = engine.generate(
[Message(role=Role.USER, content="What is 2+2?")],
model="gpt-4o",
tools=tools,
)
assert "tool_calls" in result
assert len(result["tool_calls"]) == 1
tc = result["tool_calls"][0]
assert tc["id"] == "call_123"
assert tc["type"] == "function"
assert tc["function"]["name"] == "calculator"
assert tc["function"]["arguments"] == '{"expression": "2+2"}'
def test_generate_with_api_base(self) -> None:
fake_usage = SimpleNamespace(
prompt_tokens=5, completion_tokens=3, total_tokens=8
)
fake_choice = SimpleNamespace(
message=SimpleNamespace(content="Hi!", tool_calls=None),
finish_reason="stop",
)
fake_resp = SimpleNamespace(
choices=[fake_choice], usage=fake_usage, model="custom-model"
)
fake_litellm = mock.MagicMock()
fake_litellm.completion.return_value = fake_resp
fake_litellm.completion_cost.return_value = 0.0
with mock.patch.dict("sys.modules", {"litellm": fake_litellm}):
engine = LiteLLMEngine(api_base="http://localhost:8080")
engine.generate(
[Message(role=Role.USER, content="Hi")], model="custom-model"
)
call_kwargs = fake_litellm.completion.call_args
assert call_kwargs[1]["api_base"] == "http://localhost:8080"
def test_generate_cost_error_fallback(self) -> None:
fake_usage = SimpleNamespace(
prompt_tokens=10, completion_tokens=5, total_tokens=15
)
fake_choice = SimpleNamespace(
message=SimpleNamespace(content="Hello!", tool_calls=None),
finish_reason="stop",
)
fake_resp = SimpleNamespace(
choices=[fake_choice], usage=fake_usage, model="unknown/model"
)
fake_litellm = mock.MagicMock()
fake_litellm.completion.return_value = fake_resp
fake_litellm.completion_cost.side_effect = Exception("Unknown model")
with mock.patch.dict("sys.modules", {"litellm": fake_litellm}):
engine = LiteLLMEngine()
result = engine.generate(
[Message(role=Role.USER, content="Hi")], model="unknown/model"
)
assert result["cost_usd"] == 0.0
class TestLiteLLMEngineStream:
def test_stream(self) -> None:
chunk1 = SimpleNamespace(
choices=[SimpleNamespace(delta=SimpleNamespace(content="Hel"))]
)
chunk2 = SimpleNamespace(
choices=[SimpleNamespace(delta=SimpleNamespace(content="lo!"))]
)
chunk3 = SimpleNamespace(
choices=[SimpleNamespace(delta=SimpleNamespace(content=None))]
)
fake_litellm = mock.MagicMock()
fake_litellm.completion.return_value = iter([chunk1, chunk2, chunk3])
with mock.patch.dict("sys.modules", {"litellm": fake_litellm}):
engine = LiteLLMEngine()
import asyncio
async def collect() -> list[str]:
tokens: list[str] = []
async for token in engine.stream(
[Message(role=Role.USER, content="Hi")], model="gpt-4o"
):
tokens.append(token)
return tokens
tokens = asyncio.run(collect())
assert tokens == ["Hel", "lo!"]
class TestLiteLLMEngineListModels:
def test_list_models_default(self) -> None:
engine = LiteLLMEngine()
assert engine.list_models() == []
def test_list_models_with_default_model(self) -> None:
engine = LiteLLMEngine(default_model="anthropic/claude-sonnet-4-20250514")
assert engine.list_models() == ["anthropic/claude-sonnet-4-20250514"]
class TestLiteLLMEngineRegistry:
def test_registry_key(self) -> None:
EngineRegistry.register_value("litellm", LiteLLMEngine)
assert EngineRegistry.contains("litellm")
cls = EngineRegistry.get("litellm")
assert cls is LiteLLMEngine