Recall boosts Claude Code with offline memory for smooth project continuity

Claude Code users know the pain of repetition. Every cold start means restating goals, context, and code history — wasting tokens and patience. The Recall plugin for Claude Code promises to eliminate this friction entirely offline: no API keys, no external calls, no hidden costs. Instead, Recall persistently and privately saves session context on your own disk, so every project picks up exactly where you left off. If you are frustrated with throwing away credits and time, Recall offers a local-first, privacy-first way to keep your AI workflow humming — and its own GitHub repository documents exactly how it works, so you can verify every claim below against the source.

What the Recall plugin actually is

Recall is a free, offline plugin designed for developers running Claude Code locally. Its purpose is narrow: persistent session memory for Claude Code in a way that respects privacy and adds no metered cost. Recall keeps a local append-only log of your sessions — prompts, replies, files touched, commands run — captured to disk and never leaving your machine.

Instead of re-explaining your goals and project structure every time you open Claude Code, Recall generates a concise summary from this log. That summary is built entirely on your local machine using a classical Python summarizer, not a large language model, and gets injected into your next session. Your assistant understands the state of your project without you burning tokens on context dumps.

This is the architectural bet that separates Recall from most memory tools: no dependency on external APIs, keys, or cloud services. Where typical AI memory tools upload session logs or run cloud summarization with billable model calls, Recall's design is local-first. You can browse the plugin's code and confirm the process yourself at the official GitHub repo.

Key features, per the project README:

• Purely local memory — nothing leaves your machine.
• Summarization with a classical Python algorithm, not a metered LLM.
• No API key, account, or remote service required.
• Durable, resume-ready session context every time you open Claude Code.

How Recall stretches a Claude Code subscription

Token waste compounds when every session restarts from zero. Recall breaks this cycle through two mechanisms, both documented in the project's own description:

1. Local summarization spends zero model tokens. Normally, creating a session summary via an LLM means sending prompts and context to a model, eating quota or credits even though the summary is never user-facing output. Recall sidesteps this: the session log is condensed by a classical Python summarization algorithm running on your local CPU. Not a single Claude API call, and zero token cost for building or updating the summary.

2. Resuming from a compact summary instead of re-explaining. Session resumes are expensive when you re-feed goals, context, and progress with every start. Recall provides a compact summary — the README describes it as roughly 1,000 to 2,000 tokens — capturing what you are building, next steps, files edited, and open threads. Instead of hundreds or thousands of tokens per session wasted on recap, you spend only what the injected summary costs.

One scoping note the README itself respects: the zero-cost claim covers the summarization, not the subscription. Recall is free, but it runs on top of a Claude Code subscription you already pay for — it stretches your usage limits rather than replacing them. If you want the broader discipline of keeping agent sessions legible and cheap, pair it with structured prompting habits from agent sessions that stay on track and a repository layout agents can navigate in agent-readable repository structure.

How to install and use Recall on your local machine

Setting up Recall is deliberately low-friction: no package install, no local model to run, no key to configure, and it works offline from the moment the plugin loads.

Step 1: Install the Recall plugin.

Clone the project or download the release from the GitHub repo. Place the plugin files in your Claude Code local instance's plugin directory:

# Example: clone directly into a typical Claude Code plugins folder
git clone https://github.com/raiyanyahya/recall ~/.claude-code/plugins/recall

Step 2: Start Claude Code locally, as usual.

Recall is purpose-built for local Claude Code environments you control. Launch your usual session — no flags, no API keys, no extra config files.

Step 3: Automatic memory capture begins.

The moment the plugin loads, every session interaction — prompts, replies, file changes, commands — is appended to a local log in your project directory. The summarizer runs locally and maintains the condensed summary. Both files live under .recall/ in your project:

.recall/history.md  # Append-only, high-fidelity log of every session.
.recall/context.md  # Overwritten each run; compact, session-ready summary.

You never manually save, trigger recaps, or curate memory. Recall works in the background.

Step 4: Smooth session resumes.

When you restart Claude Code and load your project, Recall injects the latest summary into the AI context up front. The agent picks up your last task, code, and open TODOs without you repeating yourself.

Tip: To confirm Recall is working, open your project's .recall directory. You should see both history.md and context.md updating as you interact. If the files do not appear, check the plugin's placement and permissions, and confirm sessions are not running in ephemeral or sandboxed environments that discard local writes.

Why Recall's privacy story checks out

AI session memory is almost always a privacy risk. Most plugins that promise long-term context achieve it by uploading transcripts or workspace data to remote endpoints or cloud LLM providers — every recap risks leaking code, directory structure, and even secrets upstream.

Recall blocks this risk by default. All memory — transcripts, code, paths, commands, mistakes — is logged and summarized purely on your workstation. The classical Python summarizer works without internet access and never involves a third-party LLM. The project backs this with a full privacy policy in PRIVACY.md, which states the guarantee plainly: your project never leaves your control at any point in the workflow.

For sensitive repositories, client codebases, regulated industries, or anyone unwilling to ship working context to a vendor, this is the shape memory tooling has to take. Recall's privacy guarantee is as strong as your local disk permissions — no network, no export, no leak path. If you ship AI features to production, the same paranoia belongs in your checklist — see AI app security checklist — and if you standardize agent conventions per repo, Cursor rules for Next.js shows the pattern.

Requirements and limitations, honestly stated

Before adding Recall to every coding stack, check the target environment:

• Claude Code must run locally on a subscription. Recall attaches to your own instance — it does not help with hosted, cloud, or chat-based Claude sessions.
• No LLM is used for summarization. The classical Python summarizer eliminates compute and cost, but its summaries are as good as traditional algorithms get: well-suited to code and project logs, not to nuanced natural-language reasoning.
• Local storage is required. Logs and summaries live as files under .recall/ in each project. Large histories can grow, though daily use across multi-week projects is unlikely to trouble modern storage.
• Summaries are compact by design. At roughly 1,000 to 2,000 tokens, the summary covers most project context but is not a complete session replay.
• The plugin is free; the subscription is not. Recall costs nothing itself, but it is not a workaround for running Claude Code without a valid subscription.

Scaling across machines? The privacy model relies on every machine staying strictly local. Network sync or backup is your responsibility — plan it before you depend on memory that lives on one laptop.

How Recall compares to other memory tools

Virtually every other persistent AI memory or session summarization tool takes one of two approaches: send session logs to a metered API or cloud LLM for summarization, or store context in cloud databases — risking exposure and incurring cost with every use.

Recall's niche is clear: persistent, affordable, frictionless project context without the privacy and data-exposure risks of cloud summarization. Alternatives charge per token, bill monthly, or demand trust in a third party to store session logs. Recall never touches the internet, never asks for a key, and never requires an account. For developers who value privacy, cost control, and direct ownership of working context on a local Claude Code setup, there is little direct competition.

Closing takeaway

If you run Claude Code on your own hardware, Recall is the missing piece: frictionless, free, and private. Stop burning credits on repetitive context dumps and keep sessions flowing, securely and efficiently. Download it from the Recall repo and let coding sessions pick up right where you left off — no more cold starts.

Want a codebase your agent can resume into without the recap tax? Start from OTF templates — production-grade starters structured so agents stay oriented from the first prompt.

Sources

• Recall — GitHub repository — local append-only log, classical Python summarizer, .recall/history.md plus context.md, no API key, subscription-scoped savings claims.
• Recall PRIVACY.md — full privacy policy behind the nothing-leaves-your-machine guarantee.