compact-memory-implementation

To implement persistent memory for Claude agents, you can use a compact memory workflow that compresses long conversation histories into structured summaries, then injects this memory object into the system prompt or first message of subsequent sessions.

The forked compactor pattern is a memory management technique that offloads the summarization process to a dedicated sub-agent. This sub-agent produces structured memory schemas, allowing the primary agent to continue operating without context bloat.

You should trigger memory compaction in a Claude Agent SDK application using defined strategies such as token count thresholds, phase boundaries, or turn-based limits to decide when the conversation history needs to be compressed.

Yes, you can restore agent memory using the Anthropic API by defining a consistent memory schema and injecting that structured summary into the system prompt or passing it as context when resuming cross-session agent work.

The best way to prevent context window bloat in long-running LLM agents is to apply a compaction workflow that uses trigger strategies to determine when to summarize past interactions into a structured memory object.

Yes, the compact memory approach works without external databases by storing the structured memory schema directly in the system prompt or first message, ensuring memory survives restarts and resumes with context intact.