mirror of
https://github.com/open-jarvis/OpenJarvis.git
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Jon Saad-FalconandClaude Opus 4.6
852259f18b
Phase 1: Move RoutingContext to core/types.py, add RouterPolicy and QueryAnalyzer ABCs to intelligence/_stubs.py Phase 2: Move memory backends to tools/storage/, convert memory/ to backward-compat shims Phase 3: Add MCPToolAdapter, storage MCP tools, upgrade MCP server to spec 2025-11-25 Phase 4: Add SystemBuilder + JarvisSystem composition layer (system.py) Phase 5: Add InstrumentedEngine for opt-in telemetry, simplify all agents Phase 6: Add LearningPolicy ABC taxonomy with SFTPolicy, AgentAdvisorPolicy, ICLUpdaterPolicy Phase 7: Update config schema (ToolsConfig, MCPConfig, TracesConfig, per-pillar learning policies) Also: update all docs, README (DSPy-inspired), CLAUDE.md, and add logo assets. 1391 tests pass, 32 skipped. Zero new lint errors. Full backward compatibility via shims. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
"""Tests for agent advisor policy."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Optional
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from openjarvis.learning.agent_advisor import AgentAdvisorPolicy
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@dataclass
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class _MockTrace:
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query: str = ""
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model: str = "model-a"
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outcome: str = "success"
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feedback: Optional[float] = 0.8
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steps: list = field(default_factory=list)
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total_latency_seconds: float = 1.0
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class _MockTraceStore:
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def __init__(self, traces):
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self._traces = traces
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def list_traces(self):
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return self._traces
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class TestAgentAdvisorPolicy:
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def test_no_problem_traces(self):
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traces = [_MockTrace(outcome="success") for _ in range(5)]
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policy = AgentAdvisorPolicy()
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store = _MockTraceStore(traces)
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result = policy.update(store)
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assert result["recommendations"] == []
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assert result["confidence"] == 1.0
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def test_detects_failures(self):
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traces = [
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_MockTrace(query="def code()", outcome="failure"),
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_MockTrace(query="def more_code()", outcome="failure"),
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_MockTrace(query="import something", outcome="failure"),
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]
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policy = AgentAdvisorPolicy()
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store = _MockTraceStore(traces)
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result = policy.update(store)
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assert len(result["recommendations"]) > 0
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assert result["confidence"] < 1.0
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def test_detects_slow_traces(self):
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traces = [
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_MockTrace(outcome="success", total_latency_seconds=10.0),
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]
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policy = AgentAdvisorPolicy()
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store = _MockTraceStore(traces)
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result = policy.update(store)
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assert result["problem_traces"] == 1
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def test_empty_traces(self):
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policy = AgentAdvisorPolicy()
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store = _MockTraceStore([])
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result = policy.update(store)
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assert result["recommendations"] == []
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def test_is_agent_policy(self):
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from openjarvis.learning._stubs import AgentLearningPolicy
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assert issubclass(AgentAdvisorPolicy, AgentLearningPolicy)
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assert AgentAdvisorPolicy.target == "agent"
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def test_max_traces_limit(self):
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traces = [
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_MockTrace(outcome="failure") for _ in range(100)
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]
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policy = AgentAdvisorPolicy(max_traces=10)
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store = _MockTraceStore(traces)
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result = policy.update(store)
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# Should only analyze last 10 traces
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assert result["problem_traces"] <= 10
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