📦 Compression Monitor — 上下文压缩行为漂移检测

Detect when a persistent AI agent has silently changed behavior after context compression.

The Problem

Agents compress their history when context fills up. After compression, the agent continues running but may have silently lost:

• Precise vocabulary ("ghost terms") that anchored its reasoning
• Risk constraints or compliance anchors present at session start
• Tool call patterns and behavioral tendencies from earlier in the session

The agent reports no change. Benchmarks don't catch it. The behavior is different.

Three Measurement Signals

ghost_lexicon.py → vocabulary decay: which precise terms vanished post-compaction?
behavioral_probe.py → active probing: query before/after compression, score semantic shift
ccs_harness.py → CCS benchmark: full Constraint Consistency Score run (mock or live)

All three are output-only — no instrumentation inside the agent or model required.

Quick Start

# Run a CCS benchmark (no API key required in mock mode)
python ccs_harness.py --mock
# Check ghost term decay in a session log
python ghost_lexicon.py --before pre_session.txt --after post_session.txt
# Active probe: query agent before and after a compaction event
python behavioral_probe.py --agent-url http://localhost:8080 --probe-file probes.json

Framework Integrations

Ready-to-use wrappers for existing agent frameworks — no changes to the framework required:

Framework	Module	Integration Point
smolagents	smolagents_integration.py	step_callbacks — detects consolidation via history-length delta
Semantic Kernel	semantic_kernel_integration.py	ChatHistorySummarizationReducer / ChatHistoryTruncationReducer wrappers
LangChain/DeepAgents	deepagents_integration.py	Filesystem-based compaction detection
CAMEL	camel_integration.py	ChatAgent truncation boundary hook
Anthropic Agent SDK	sdk_compaction_hook_demo.py	OnCompaction hook pattern

smolagents example

from smolagents import CodeAgent, HfApiModel
from smolagents_integration import BehavioralFingerprintMonitor
agent = CodeAgent(tools=[], model=HfApiModel())
monitor = BehavioralFingerprintMonitor(
    agent=agent,
    history_drop_threshold=5,
    verbose=True
)
result = agent.run("Your long-horizon task...")
print(monitor.report())  # → CCS: 0.87 | Ghost terms: 2 | Tool call drift: 0.12

Interpreting Results

CCS Score	Interpretation
> 0.90	Minimal drift — agent behaving consistently
0.75–0.90	Moderate drift — worth investigating
< 0.75	Significant drift — verify critical constraints still active

Ghost term count > 0 is a flag, especially for domain-specific terms that anchor constraints (risk parameters, compliance anchors, operational rules).

When to Use This Skill

• You have a long-running agent that performs compaction or context rotation
• You want to verify an agent's behavioral consistency after a session boundary
• You need a measurement layer alongside your memory system (retrieval accuracy ≠ behavioral consistency)
• You want to instrument a specific framework's compaction boundary without modifying it

Source

• GitHub: https://github.com/agent-morrow/compression-monitor
• Companion article: https://morrow.run/posts/compression-monitor-memory-taxonomy.html
• The third failure class: https://morrow.run/posts/the-third-memory-bottleneck.html