screenpipe/screenpipe
screenpipe
YC (S26) | AI that knows what you've seen, said, or heard. Records everything you do, say, hear 24/7, local, private, secure. Connect to OpenClaw, Hermes agent and 100+ apps
Usage guide
screenpipe is an open-source project around agents, agi, ai-memory with 19,537 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
- YC (S26) AI that knows what you've seen, said, or heard. Records everything you do, say, hear 24/7, local, private, secure. Connect to OpenClaw, Hermes agent and 100+ apps
- Implemented mainly in Rust, useful for judging integration effort in a similar stack.
- GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms 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 screenpipe for Rust AI workflows.
- Comparing a GitHub project with 19,537 stars and current repository activity.
Pros
- screenpipe has visible GitHub traction with 19,537 stars. Topics: agents, agi, ai.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- No license was detected, so usage risk needs manual review.
Production readiness
screenpipe should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
GitHub did not report a license, which usually requires manual legal review before production use.
screenpipe architecture preview
screenpipe's main path starts at the entry surface, runs through Agent orchestration runtime, combines Optional AI model, Runtime context, MCP tools, and returns Assistant response / action result.
Entry
CLI / terminal entry
screenpipe is primarily entered through a developer command or terminal workflow.
npx screenpipe record
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
Optional AI model
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
MCP tools
Tool adapters let the runtime act outside the model through MCP tools.
MCP tools
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Featured video
screenpipe
screenpipe released 2 new AI models that outperforms big tech models
1,913 views ยท 2026-05-11
Install tutorial
Before you install
- Node.js and the package manager used by the project
- Local build tools for compiling the project
- A clean working directory for the first test run
Check the runtime environment
screenpipe may require a local build toolchain. Check the compiler, package manager, and system dependencies first.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/screenpipe/screenpipe.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npx screenpipe recordAdoption guidance and sources
Practical use cases
YC (S26) AI that knows what you've seen, said, or heard. Records every
This is one of the documented reasons to evaluate screenpipe before choosing a stack.
Focus area: agents
This is one of the documented reasons to evaluate screenpipe before choosing a stack.
All project comparison
Compare screenpipe with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official screenpipe 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 screenpipe 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 screenpipe?
screenpipe is an open-source all project. YC (S26) | AI that knows what you've seen, said, or heard. Records everything you do, say, hear 24/7, local, private, secure. Connect to OpenClaw, Hermes agent and 100+ apps
How do I install screenpipe?
Start with the official README. The first detected setup step is: git clone https://github.com/screenpipe/screenpipe.git.
Is screenpipe beginner-friendly?
If you already know the Rust ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can screenpipe be used commercially?
GitHub did not detect a repository license, so commercial permission is unconfirmed. Review the repository terms and any model weights, datasets, dependencies, or external services before commercial adoption.
Does screenpipe 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 screenpipe?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.