hermes-agent/tests/gateway/test_session_hygiene.py

"""Tests for gateway session hygiene — auto-compression of large sessions.

Verifies that the gateway detects pathologically large transcripts and
triggers auto-compression before running the agent.  (#628)

The hygiene system uses the SAME compression config as the agent:
  compression.threshold × model context length
so CLI and messaging platforms behave identically.
"""

import pytest
from unittest.mock import patch, MagicMock, AsyncMock
from agent.model_metadata import estimate_messages_tokens_rough


# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------

def _make_history(n_messages: int, content_size: int = 100) -> list:
    """Build a fake transcript with n_messages user/assistant pairs."""
    history = []
    content = "x" * content_size
    for i in range(n_messages):
        role = "user" if i % 2 == 0 else "assistant"
        history.append({"role": role, "content": content, "timestamp": f"t{i}"})
    return history


def _make_large_history_tokens(target_tokens: int) -> list:
    """Build a history that estimates to roughly target_tokens tokens."""
    # estimate_messages_tokens_rough counts total chars in str(msg) // 4
    # Each msg dict has ~60 chars of overhead + content chars
    # So for N tokens we need roughly N * 4 total chars across all messages
    target_chars = target_tokens * 4
    # Each message as a dict string is roughly len(content) + 60 chars
    msg_overhead = 60
    # Use 50 messages with appropriately sized content
    n_msgs = 50
    content_size = max(10, (target_chars // n_msgs) - msg_overhead)
    return _make_history(n_msgs, content_size=content_size)


# ---------------------------------------------------------------------------
# Detection threshold tests (model-aware, unified with compression config)
# ---------------------------------------------------------------------------

class TestSessionHygieneThresholds:
    """Test that the threshold logic correctly identifies large sessions.

    Thresholds are derived from model context length × compression threshold,
    matching what the agent's ContextCompressor uses.
    """

    def test_small_session_below_thresholds(self):
        """A 10-message session should not trigger compression."""
        history = _make_history(10)
        approx_tokens = estimate_messages_tokens_rough(history)

        # For a 200k-context model at 85% threshold = 170k
        context_length = 200_000
        threshold_pct = 0.85
        compress_token_threshold = int(context_length * threshold_pct)

        needs_compress = approx_tokens >= compress_token_threshold
        assert not needs_compress

    def test_large_token_count_triggers(self):
        """High token count should trigger compression when exceeding model threshold."""
        # Build a history that exceeds 85% of a 200k model (170k tokens)
        history = _make_large_history_tokens(180_000)
        approx_tokens = estimate_messages_tokens_rough(history)

        context_length = 200_000
        threshold_pct = 0.85
        compress_token_threshold = int(context_length * threshold_pct)

        needs_compress = approx_tokens >= compress_token_threshold
        assert needs_compress

    def test_under_threshold_no_trigger(self):
        """Session under threshold should not trigger, even with many messages."""
        # 250 short messages — lots of messages but well under token threshold
        history = _make_history(250, content_size=10)
        approx_tokens = estimate_messages_tokens_rough(history)

        # 200k model at 85% = 170k token threshold
        context_length = 200_000
        threshold_pct = 0.85
        compress_token_threshold = int(context_length * threshold_pct)

        needs_compress = approx_tokens >= compress_token_threshold
        assert not needs_compress, (
            f"250 short messages (~{approx_tokens} tokens) should NOT trigger "
            f"compression at {compress_token_threshold} token threshold"
        )

    def test_message_count_alone_does_not_trigger(self):
        """Message count alone should NOT trigger — only token count matters.

        The old system used an OR of token-count and message-count thresholds,
        which caused premature compression in tool-heavy sessions with 200+
        messages but low total tokens.
        """
        # 300 very short messages — old system would compress, new should not
        history = _make_history(300, content_size=10)
        approx_tokens = estimate_messages_tokens_rough(history)

        context_length = 200_000
        threshold_pct = 0.85
        compress_token_threshold = int(context_length * threshold_pct)

        # Token-based check only
        needs_compress = approx_tokens >= compress_token_threshold
        assert not needs_compress

    def test_threshold_scales_with_model(self):
        """Different models should have different compression thresholds."""
        # 128k model at 85% = 108,800 tokens
        small_model_threshold = int(128_000 * 0.85)
        # 200k model at 85% = 170,000 tokens
        large_model_threshold = int(200_000 * 0.85)
        # 1M model at 85% = 850,000 tokens
        huge_model_threshold = int(1_000_000 * 0.85)

        # A session at ~120k tokens:
        history = _make_large_history_tokens(120_000)
        approx_tokens = estimate_messages_tokens_rough(history)

        # Should trigger for 128k model
        assert approx_tokens >= small_model_threshold
        # Should NOT trigger for 200k model
        assert approx_tokens < large_model_threshold
        # Should NOT trigger for 1M model
        assert approx_tokens < huge_model_threshold

    def test_custom_threshold_percentage(self):
        """Custom threshold percentage from config should be respected."""
        context_length = 200_000

        # At 50% threshold = 100k
        low_threshold = int(context_length * 0.50)
        # At 90% threshold = 180k
        high_threshold = int(context_length * 0.90)

        history = _make_large_history_tokens(150_000)
        approx_tokens = estimate_messages_tokens_rough(history)

        # Should trigger at 50% but not at 90%
        assert approx_tokens >= low_threshold
        assert approx_tokens < high_threshold

    def test_minimum_message_guard(self):
        """Sessions with fewer than 4 messages should never trigger."""
        history = _make_history(3, content_size=100_000)
        # Even with enormous content, < 4 messages should be skipped
        # (the gateway code checks `len(history) >= 4` before evaluating)
        assert len(history) < 4


class TestSessionHygieneWarnThreshold:
    """Test the post-compression warning threshold (95% of context)."""

    def test_warn_when_still_large(self):
        """If compressed result is still above 95% of context, should warn."""
        context_length = 200_000
        warn_threshold = int(context_length * 0.95)  # 190k
        post_compress_tokens = 195_000
        assert post_compress_tokens >= warn_threshold

    def test_no_warn_when_under(self):
        """If compressed result is under 95% of context, no warning."""
        context_length = 200_000
        warn_threshold = int(context_length * 0.95)  # 190k
        post_compress_tokens = 150_000
        assert post_compress_tokens < warn_threshold


class TestTokenEstimation:
    """Verify rough token estimation works as expected for hygiene checks."""

    def test_empty_history(self):
        assert estimate_messages_tokens_rough([]) == 0

    def test_proportional_to_content(self):
        small = _make_history(10, content_size=100)
        large = _make_history(10, content_size=10_000)
        assert estimate_messages_tokens_rough(large) > estimate_messages_tokens_rough(small)

    def test_proportional_to_count(self):
        few = _make_history(10, content_size=1000)
        many = _make_history(100, content_size=1000)
        assert estimate_messages_tokens_rough(many) > estimate_messages_tokens_rough(few)

    def test_pathological_session_detected(self):
        """The reported pathological case: 648 messages, ~299K tokens.

        With a 200k model at 85% threshold (170k), this should trigger.
        """
        history = _make_history(648, content_size=1800)
        tokens = estimate_messages_tokens_rough(history)
        # Should be well above the 170K threshold for a 200k model
        threshold = int(200_000 * 0.85)
        assert tokens > threshold