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https://github.com/open-jarvis/OpenJarvis.git
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Prathap PandGitHub
c2756964a7
* fix(channels): wire channel→agent handler and fix Telegram send pipeline * format code * add supported tests
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
"""Tests for the GEPA agent optimizer (mocked -- no gepa dependency required)."""
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from __future__ import annotations
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import time
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from unittest.mock import MagicMock, patch
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class TestGEPAOptimizerConfig:
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def test_default_config(self) -> None:
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from openjarvis.core.config import GEPAOptimizerConfig
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cfg = GEPAOptimizerConfig()
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assert cfg.max_metric_calls == 150
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assert cfg.population_size == 10
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assert cfg.min_traces == 20
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def test_optimizer_init(self) -> None:
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from openjarvis.core.config import GEPAOptimizerConfig
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from openjarvis.learning.agents.gepa_optimizer import GEPAAgentOptimizer
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cfg = GEPAOptimizerConfig()
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optimizer = GEPAAgentOptimizer(cfg)
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assert optimizer.config is cfg
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class TestGEPAOptimizerOptimize:
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def test_too_few_traces_skipped(self) -> None:
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from openjarvis.core.config import GEPAOptimizerConfig
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from openjarvis.learning.agents.gepa_optimizer import GEPAAgentOptimizer
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optimizer = GEPAAgentOptimizer(GEPAOptimizerConfig(min_traces=10))
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mock_store = MagicMock()
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mock_store.list_traces.return_value = []
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result = optimizer.optimize(mock_store)
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assert result["status"] == "skipped"
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def test_no_gepa_reports_error(self) -> None:
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from openjarvis.core.config import GEPAOptimizerConfig
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from openjarvis.core.types import StepType, Trace, TraceStep
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from openjarvis.learning.agents.gepa_optimizer import GEPAAgentOptimizer
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cfg = GEPAOptimizerConfig(min_traces=1)
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optimizer = GEPAAgentOptimizer(cfg)
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now = time.time()
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traces = [
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Trace(
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query="test",
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agent="native_react",
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model="qwen3:8b",
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result="result",
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outcome="success",
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feedback=0.9,
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started_at=now,
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ended_at=now + 1,
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total_tokens=100,
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total_latency_seconds=1.0,
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steps=[
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TraceStep(
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step_type=StepType.GENERATE,
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timestamp=now,
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duration_seconds=0.5,
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)
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],
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)
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]
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mock_store = MagicMock()
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mock_store.list_traces.return_value = traces
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# Ensure gepa is not available
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with patch.dict("sys.modules", {"gepa": None}):
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with patch(
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"openjarvis.learning.agents.gepa_optimizer.HAS_GEPA",
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False,
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):
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result = optimizer.optimize(mock_store)
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assert result["status"] == "error"
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assert "gepa" in result["reason"].lower()
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class TestOpenJarvisGEPAAdapter:
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def test_adapter_init(self) -> None:
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from openjarvis.core.config import GEPAOptimizerConfig
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from openjarvis.learning.agents.gepa_optimizer import (
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OpenJarvisGEPAAdapter,
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)
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mock_store = MagicMock()
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adapter = OpenJarvisGEPAAdapter(
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mock_store,
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"native_react",
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GEPAOptimizerConfig(),
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)
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assert adapter.agent_name == "native_react"
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