AI工程SynapseB类

Local-First Proxy: Solving Synapse Cache Sync Delays

TL;DR

  • Synapse cache invalidation suffers 200ms+ delay under peak load
  • Local proxy layer decouples Claude Code from direct online dependency
  • TTL + version hash dual expiration prevents stale data reads
  • State change events trigger targeted sync instead of full polling
  • SQLite-backed cache with content versioning maintains consistency

The Problem

Last Wednesday at 3:47 PM, Claude Code attempted to fetch “Q3 2024 sales data” while our Synapse knowledge base had already updated that information three hours prior. We spent two hours debugging before discovering the root cause: when Synapse pushed information source updates, Claude Code’s local cache did not invalidate. The system’s cache strategy relied on TTL-based expiration, but TTL refreshes only occurred during active queries. During idle periods, stale data persisted silently.

Network logs showed complete push records and HTTP 200 responses—the push mechanism worked. However, Synapse pushed metadata events (“source A updated”), not actual data. Claude Code needed to pull fresh content based on these events, but without active sessions, no pull occurred. The real bottleneck was not network latency but ambiguity about which component should initiate synchronization and when.

Synapse typically delivers 50-80ms sync latency, but this degrades to 200-500ms under peak load exceeding 50 requests per second. Our solution was a local-first information source proxy layer running on the Claude Code host. This proxy, implemented in synapse_local_proxy.py, maintains a SQLite cache with a dual expiration strategy: TTL-based time decay plus version consistency checks. When cache entries expire, the proxy triggers selective synchronization rather than full data pulls.

Key Takeaways

If you manage AI agents with external data dependencies, decouple synchronization from online availability by adding a local cache layer.

If you observe consistent timing mismatches between data updates and agent reads, audit your cache invalidation triggers, not just network connectivity.

If your system experiences variable latency under load, implement content versioning rather than relying solely on time-based expiration.

If you need reliable cache invalidation, prefer state-change triggers over scheduled polling intervals.

If you require persistence across agent sessions, store cache state in a local database rather than in-memory structures.

Read the Full Article (Chinese)

This is an abstract. The full technical walkthrough, including the complete SQLite schema implementation and dual expiration strategy details, is available in the original Chinese article.