Recall boosts Claude Code with offline memory for smooth project continuity

Claude Code users know the pain of repetition. Each "cold start" means restating goals, context, and code history—wasting tokens and patience. The Recall plugin for Claude Code durable memory eliminates this friction entirely offline. No API keys, no external calls, no hidden costs. Instead, Recall persistently and privately saves your session context, so every project picks up exactly where you left off. If you're frustrated with throwing away credits and time, Recall provides a zero-cost, privacy-first solution to keep your AI workflow humming—saving tokens and keeping your project discussions out of the cloud.

What is the Recall plugin for Claude Code durable memory?

Recall is a free, offline plugin designed for developers running Claude Code locally. Its sole purpose: provide persistent session memory for Claude Code in a way that’s privacy-respecting and costs nothing extra. How does it work? Recall keeps a local append-only log of all your sessions. Every prompt, reply, file touched, and command run gets captured to disk, never leaving your machine.

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

This approach has no dependency on external APIs, keys, or cloud services. Unlike many “AI memory” tools that upload session logs or use cloud summarization with billable model calls, Recall’s design is fundamentally local-first. Your usage never exposes code, paths, or secrets outside your machine. You can browse the plugin’s code and see the process yourself at the official GitHub repo.

Key features:

• 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 does Recall save tokens and extend your Claude Code subscription?

Token waste compounds when every session restarts from zero. Recall breaks this cycle and makes your Claude Code subscription go much further. There are two main mechanisms driving these savings:

1. Local summarization spends zero model tokens.
Normally, creating a session summary via an LLM requires sending prompts (and context) to a model, eating up valuable quota or credits—even if the summary isn't needed for user-facing output. Recall circumvents this: your session log is condensed using a classical Python summarization algorithm that runs entirely 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 have to re-feed your project’s goals, context, and progress with every new Claude Code start. Recall provides a compact summary (typically around 1,000–2,000 tokens) that encapsulates your session state: what you’re building, next steps, files edited, and open threads. Instead of hundreds or thousands of tokens per session wasted on recap, you spend only what’s needed to inject the summary—shrinking your overall usage dramatically.

If you’re on a metered Claude Code subscription or API, this translates to real savings. No more burning up your monthly usage limit just to keep the agent up to speed. Instead, you can get several times the mileage out of each credit because memory and recap work is shifted fully offline.

Takeaway: Recall turns session context from a recurring expense into a fixed, zero-cost feature. Your subscription covers just your actual prompts and completions—never redundant context setup.

How to install and use Recall with Claude Code on your local machine

Setting up Recall is as straightforward as it gets—zero config, no new accounts, no external dependencies. Here’s all it takes:

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  ~/.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 Claude Code session—no flags, no API keys, no extra config files.

Step 3: Automatic memory capture begins

The moment the plugin loads:

• Every session interaction—your prompts, Claude’s replies, changes to files, commands you run—gets appended to a local log file in your project directory.
• The summarization step runs locally. It continually updates a summary.txt, condensing your progress and open threads.
• Both files live under .recall/ in your project directory:

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

You’ll never need to manually save, trigger recaps, or curate memory. Recall simply works in the background.

Step 4: smooth session resumes

When you restart Claude Code and load your project:

• Recall injects the latest summary.txt 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 log.txt and summary.txt updating as you interact. If the files don’t appear, ensure you’re running a supported local Claude Code build and have the plugin in the correct directory.

Troubleshooting common issues:

• If you see no .recall files: double-check the plugin’s placement and permissions.
• If summarization seems stale, confirm the plugin’s write permissions and that sessions aren’t being run in ephemeral or sandboxed environments.

Why is Recall a privacy-first solution for AI memory?

AI session memory is almost always a privacy risk. Most plugins and tools that promise "long-term context" or "durable AI memory" achieve it by uploading transcripts or workspace data to remote endpoints or cloud LLM providers. Every time you recap your project for the agent, your code, directory structure, and even secrets risk leaking upstream.

Recall is designed to block this risk by default. All memory—session transcripts, code, paths, commands, and even mistakes—is logged and summarized purely on your workstation. Nothing is ever sent to any API, cloud service, or model endpoint. The classical Python summarizer does its work without internet access, never involving a third-party LLM. Your project never leaves your control at any point in the workflow.

For sensitive repositories, client codebases, regulated industries, or anyone simply unwilling to ship their working context to a vendor, this is the only viable AI memory option. Recall’s privacy guarantee is as strong as your local disk permissions—no network, no export, no leak path.

Takeaway: If you need memory but can’t compromise on privacy, Recall is the right tool. Local-first by design, not just marketing.

What are the system requirements and limitations of Recall?

Before you add Recall to every coding stack, double-check the target environment:

• Claude Code must be running locally on a subscription. Recall attaches to your own instance—it won’t help with hosted, cloud, or chat-based Claude sessions.
• No LLM required for summarization. Recall uses a classical Python summarizer, not an LLM like Claude itself or GPT. This eliminates compute and cost, but means the summaries are as good as traditional algorithms get—well-suited to code/project logs, but not for nuanced natural language reasoning.
• Local storage is required. All logs and summaries are kept as files under .recall/ in each project. Depending on project size and session history, large logs could grow, though daily use cases (dozens of sessions, multi-week projects) are unlikely to outpace modern storage.
• Summary length is compact. Summaries target 1,000–2,000 tokens; large enough to cover most project context, but not a complete session replay.
• Plugin is free, but Claude Code subscription is required. There’s no charge for Recall itself, but it’s not a workaround for running Claude Code without a valid subscription.

If you’re considering scaling this across many machines or projects: the privacy model relies on every machine staying strictly local. Network sync or backup is your responsibility.

How does Recall compare to other AI memory and summarization tools?

Virtually every other persistent AI memory or session summarization tool takes one of two approaches:

• Send your session logs to a metered API or cloud LLM for summarization and context injection.
• Store context in cloud databases, risking exposure and incurring costs with every usage.

Recall’s niche is clear: if you want persistent, Affordable, frictionless project context—without the privacy and data exposure risks of cloud summarization—there is simply no competition for local Claude Code setups. Alternatives charge per token, bill monthly, or demand trust in a third-party to store your session logs. Recall never touches the internet, never asks for a key, and never requires an account.

It’s the right solution for any developer who values privacy, cost control, and direct ownership of their working context.

Closing takeaway

If you’re running Claude Code on your own hardware, the Recall plugin for Claude Code durable memory is the missing piece: frictionless, free, and private. Stop burning credits on repetitive context dumps and keep your sessions flowing, securely and efficiently—all while never compromising on privacy or cost. Download Recall and let your coding sessions finally pick up right where you left off—no more cold starts.

For next steps on optimizing your Claude Code setup, see “How to Optimize Token Usage in LLM-based Coding Assistants,” “Local AI Development Environments: Setup and Best Practices,” and “Privacy and Security Best Practices for AI Code Tools.”