When you run Claude Code locally, every session starts “cold” with no memory of what came before. Recall solves that by keeping a complete, append-only log in .recall/history.md and shaving it down into a compact .recall/context.md each time you save (manually with /recall:save or automatically on session end). That context file—about 1–2K tokens—captures your project goal, files touched, commands run, open threads and next steps. Since the summarization runs entirely on your machine via a Python script (TF-IDF sentence vectors, cosine-similarity graph, TextRank) and never calls an LLM API, you spend zero extra model tokens beyond your existing subscription. Unlike CLAUDE.md, where you hand-write instructions, or the --resume flag that reloads a full transcript, Recall’s summary is automatic, deterministic and tiny. You log prompts, responses and actions to history.md as they happen, then let the local summarizer pick the most central sentences and wrap them with deterministic facts from your transcript and git status. No network calls ever, no API key needed, no third-party model. It even redacts common secret patterns before writing, though you should still review history.md and context.md before committing. Installation is drop-and-play in your project root—no pip install, no local model to spin up, no configuration hassles. Add the plugin via `/plugin marketplace add raiyanyahya/recall` and `/plugin install recall@reca`. The default settings live in .recall/config and include eight summary sentences, redaction on, git stats included, and an output cap of 200 000 input chars. You can pause logging with a capture-paused file or tweak any of the seven config keys (output_dir, capture_history, auto_save_context, summary_sentences, redact, include_git, max_input_chars). The result is a zero-friction memory layer for Claude Code with full privacy and no extra cost.