Stop wasting tokens and re explaining your project between sessions
Key takeaways
- Recall — fully-local project memory for Claude Code
- Recall keeps a local log of your sessions and condenses it into a resume-ready summary — entirely on your machine.
- It solves the cold-start problem — no more re-explaining the project each session — without a metered summarizer running up a bill.
Recall — fully-local project memory for Claude Code
Claude Code starts every session cold. Recall keeps a local log of your sessions and condenses it into a resume-ready summary — entirely on your machine. No API key, no external model, nothing sent anywhere. It's built for people running Claude Code locally on a subscription: the only AI in the loop is Claude Code itself; the summarization is done by a classical Python summarizer.
Free on your subscription. It solves the cold-start problem — no more re-explaining the project each session — without a metered summarizer running up a bill. The summary is a local algorithm, not an LLM call, so persistent memory costs you nothing beyond the subscription you already pay for. Saves your usage credits. Two ways: (1) the summary is built locally, so capturing and updating your memory spends zero model tokens; and (2) resuming from a compact context.md (~1–2K tokens) instead of re-explaining the project from scratch each session means far fewer tokens spent per session — stretching your subscription's usage limits (or, on the API, lowering billed credits). Nothing leaves your machine. Your transcripts (code, paths, sometimes secrets) are never sent to any API. Most "memory" tools pipe your context to a model endpoint; Recall makes a privacy guarantee they can't. Zero-friction. No pip install, no local model to run, no key to configure, works offline. It starts working the moment the plugin loads. Two files, written into your project under .recall/: