When an AI agent runs for many turns, it eventually hits context limits and must compress or discard earlier messages. This is often invisible, yet critical - lost context can cause the agent to forget constraints, user preferences, or prior decisions. The framework moves on. The agent keeps running. And somewhere in those discarded turns is a security finding, a constraint, a decision that the rest of the session quietly proceeds without.
Context Compaction Visualizer makes that process visible - not after something breaks, but as an inspectable artifact of every run. It is a visualization platform that helps teams understand how long-running AI agents manage and compress context over time - upload execution traces from LangSmith, OpenTelemetry, AgentOps, or any custom format, and explore exactly which context was retained, compressed, or discarded, and at what cost.
What This Platform Does
The core problem is that compaction happens inside the framework's internals. There is no standard output that tells you which messages survived, which were summarized, and which were dropped - or what any of that cost in tokens. This platform reconstructs that picture from execution traces.
A trace file is uploaded with a format selected, and the platform rebuilds the full session: every message at every turn, its fate - retained verbatim, summarized, or discarded - and any compaction events that occurred along the way. A D3.js stacked-bar timeline renders token consumption across all turns with color-coded regions for each outcome. A session replay steps through turn by turn, surfacing a diff at the exact point a compaction event fires. Token analytics compute the total cost and compression efficiency of the session. A Claude-powered information loss detector scores the risk of each compaction event and names specifically what may have been lost.
