My agent kept "forgetting" things mid-conversation found a technique that actually solves it (LCM)

I've been building a multi-step research agent, and after about 30–40 turns, I keep running into the same problem: the model starts contradicting itself, forgets hard constraints, and asks for information that was already provided. I've tried the usual approaches, but they all have significant limitations: Summaries / handoff.md \- Too lossy. \- Important details inevitably get dropped. LangGraph MemorySaver \- Great for persisting state across restarts. \- Doesn't solve context-window overflow. \- You still hit token limits. Rolling summarize-and-replace \- Eventually becomes a summary of a summary of a summary. \- Fine-grained details disappear over time. A Better Approach: Lossless Context Management (LCM) I've been reading about Lossless Context Management (LCM), introduced in the Voltropy paper. The core idea is straightforward: context compression shouldn't require destroying information. I recently found openlcm ("pip install openlcm"), which implements this approach. Every original message is stored verbatim in SQLite, while the model is given a compressed hierarchy of summaries. The result is that the model operates on a small, efficient context window but can still retrieve the exact text of a decision made dozens of turns earlier. Unlike traditional summarization approaches—or Claude Code–style compaction—there isn't an inherent "compress = lose information" tradeoff. For LangGraph users, it's essentially a drop-in replacement for "MemorySaver": from openlcm.adapters.langgraph import LCMCheckpointer graph = StateGraph(MyState).compile( checkpointer=LCMCheckpointer(llm=my\_llm) ) It also supports AutoGen, CrewAI, and direct OpenAI/Anthropic integrations. One feature I particularly like is the local dashboard, which lets you inspect exactly what the model sees versus what has been summarized out of the active context. Thought I'd share since I don't see LCM discussed very often, and it seems like a promising direction for long-running agent workflows.