Kimi Agent
b9b78adaa2
Three optimizations to the agentic loop: 1. Cache loop agent as singleton (avoid repeated warmups) 2. Sliding window for step context (last 2 results, not all) 3. Replace summary LLM call with deterministic summary Saves 1 full LLM inference call per agentic loop invocation (30-60s on local models) and reduces context window pressure. Also fixes pre-existing test_cli.py repl test bugs (missing result= assignment).
117 lines
3.5 KiB
Python
117 lines
3.5 KiB
Python
"""E2E: verify multi-step tool chaining works end-to-end.
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These tests validate the full agentic loop pipeline: planning,
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execution, adaptation, and progress tracking.
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"""
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from timmy.agentic_loop import run_agentic_loop
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def _mock_run(content: str):
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"""Create a mock return value for agent.run()."""
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m = MagicMock()
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m.content = content
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return m
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@pytest.mark.asyncio
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async def test_multistep_chain_completes_all_steps():
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"""GREEN PATH: multi-step prompt executes all steps."""
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mock_agent = MagicMock()
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mock_agent.run = MagicMock(
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side_effect=[
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_mock_run("1. Search AI news\n2. Write to file\n3. Verify"),
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_mock_run("Found 5 articles about AI in March 2026."),
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_mock_run("Wrote summary to /tmp/ai_news.md"),
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_mock_run("File exists, 15 lines."),
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]
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)
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with (
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patch("timmy.agentic_loop._get_loop_agent", return_value=mock_agent),
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patch("timmy.agentic_loop._broadcast_progress", new_callable=AsyncMock),
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):
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result = await run_agentic_loop("Search AI news and write summary to file")
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assert result.status == "completed"
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assert len(result.steps) == 3
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assert mock_agent.run.call_count == 4 # plan + 3 steps
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@pytest.mark.asyncio
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async def test_multistep_chain_adapts_on_failure():
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"""Step failure -> model adapts -> continues."""
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mock_agent = MagicMock()
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mock_agent.run = MagicMock(
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side_effect=[
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_mock_run("1. Read config\n2. Update setting\n3. Verify"),
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_mock_run("Config: timeout=30"),
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Exception("Permission denied"),
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_mock_run("Adapted: wrote to ~/config.yaml instead"),
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_mock_run("Verified: timeout=60"),
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]
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)
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with (
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patch("timmy.agentic_loop._get_loop_agent", return_value=mock_agent),
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patch("timmy.agentic_loop._broadcast_progress", new_callable=AsyncMock),
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):
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result = await run_agentic_loop("Update config timeout to 60")
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assert result.status == "completed"
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assert any(s.status == "adapted" for s in result.steps)
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@pytest.mark.asyncio
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async def test_max_steps_enforced():
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"""Loop stops at max_steps."""
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mock_agent = MagicMock()
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mock_agent.run = MagicMock(
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side_effect=[
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_mock_run("1. A\n2. B\n3. C\n4. D\n5. E"),
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_mock_run("A done"),
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_mock_run("B done"),
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]
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)
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with (
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patch("timmy.agentic_loop._get_loop_agent", return_value=mock_agent),
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patch("timmy.agentic_loop._broadcast_progress", new_callable=AsyncMock),
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):
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result = await run_agentic_loop("Do 5 things", max_steps=2)
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assert len(result.steps) == 2
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assert result.status == "partial"
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@pytest.mark.asyncio
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async def test_progress_events_fire():
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"""Progress callback fires per step."""
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events = []
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async def on_progress(desc, step, total):
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events.append((step, total))
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mock_agent = MagicMock()
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mock_agent.run = MagicMock(
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side_effect=[
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_mock_run("1. Do A\n2. Do B"),
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_mock_run("A done"),
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_mock_run("B done"),
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]
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)
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with (
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patch("timmy.agentic_loop._get_loop_agent", return_value=mock_agent),
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patch("timmy.agentic_loop._broadcast_progress", new_callable=AsyncMock),
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):
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await run_agentic_loop("Do A and B", on_progress=on_progress)
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assert len(events) == 2
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assert events[0] == (1, 2)
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assert events[1] == (2, 2)
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