supermemoryai/supermemory
supermemory
Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.
Usage guide
supermemory is an open-source project around agent-memory, ai-memory, cloudflare-kv with 27,875 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 TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the MIT 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 supermemory for TypeScript AI workflows.
- Comparing a GitHub project with 27,875 stars and current repository activity.
Pros
- supermemory has visible GitHub traction with 27,875 stars. Topics: agent-memory, ai-memory, cloudflare-kv.
- 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 MIT terms fit your use case.
Production readiness
supermemory should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
supermemory architecture preview
supermemory's main path starts at the entry surface, runs through Agent orchestration runtime, combines OpenAI, PostgreSQL, Discord / APIs / webhooks, and returns Assistant response / action result.
Entry
CLI / terminal entry
supermemory is primarily entered through a developer command or terminal workflow.
npm install supermemory
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
OpenAI
Model calls are likely routed through OpenAI based on README and topic signals.
OpenAI
Context
PostgreSQL
Context comes from PostgreSQL, which constrains what the model or runtime can use.
PostgreSQL
Tools
Discord / APIs / webhooks
Tool adapters let the runtime act outside the model through Discord / APIs / webhooks.
Discord, APIs / webhooks
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Featured video
Prajwal Tomar
SuperMemory AI MCP connects all your AI tools together with a shared memory
19,088 views ยท 2025-10-04
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
supermemory 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/supermemoryai/supermemory.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npm install supermemoryAdoption guidance and sources
Practical use cases
Memory and context engine + app that is extremely fast, scalable, and
This is one of the documented reasons to evaluate supermemory before choosing a stack.
Focus area: agent-memory
This is one of the documented reasons to evaluate supermemory before choosing a stack.
All project comparison
Compare supermemory with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official supermemory 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 supermemory 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 supermemory?
supermemory is an open-source all project. Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.
How do I install supermemory?
Start with the official README. The first detected setup step is: git clone https://github.com/supermemoryai/supermemory.git.
Is supermemory beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can supermemory be used commercially?
GitHub detected the MIT 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 supermemory 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 supermemory?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.