thedotmack/claude-mem
claude-mem
Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
Usage guide
claude-mem is an open-source project around ai-agents, ai-memory, anthropic with 84,905 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in JavaScript, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating claude-mem for JavaScript AI workflows.
- Comparing a GitHub project with 84,905 stars and current repository activity.
Pros
- claude-mem has visible GitHub traction with 84,905 stars. Topics: ai, ai-agents, ai-memory.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
claude-mem should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
claude-mem architecture preview
claude-mem's main path starts at the entry surface, runs through Coding agent runtime, combines OpenAI / Claude / Gemini, SQLite / Files / repository context, External tool adapters, and returns Code changes / developer feedback.
Entry
CLI / terminal entry
claude-mem is primarily entered through a developer command or terminal workflow.
npx claude-mem install
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
OpenAI / Claude / Gemini
Model calls are likely routed through OpenAI, Claude, Gemini based on README and topic signals.
OpenAI, Claude, Gemini
Context
SQLite / Files / repository context
Context comes from SQLite, Files / repository context, which constrains what the model or runtime can use.
SQLite, Files / repository context
Tools
External tool adapters
Tool adapters let the runtime act outside the model through External tool adapters.
tool signal
Output
Code changes / developer feedback
The final result is code edits, explanations, repository actions, or developer-facing feedback.
coding output
Install tutorial
Before you install
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
claude-mem uses a Node.js-style toolchain. Confirm the Node version and package manager before installing.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/thedotmack/claude-mem.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npx claude-mem installAdoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
Persistent Context Across Sessions for Every Agent – Captures everythi
This is one of the documented reasons to evaluate claude-mem before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate claude-mem before choosing a stack.
RAG project comparison
Compare claude-mem with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official claude-mem setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented claude-mem example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is claude-mem?
claude-mem is an open-source rag project. Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
How do I install claude-mem?
Start with the official README. The first detected setup step is: git clone https://github.com/thedotmack/claude-mem.git.
Is claude-mem beginner-friendly?
If you already know the JavaScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can claude-mem be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does claude-mem need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt claude-mem?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.