Your OpenClaw agents are smart. But right now, they only know what you tell them in the current session. What if they could query what you actually did on screen last week? What you said in your last five meetings? What you looked at in that browser tab two hours ago?
That’s what Screenpipe does — and it ships with an MCP-compatible API that OpenClaw can query directly.
This guide walks through the integration: installing Screenpipe, enabling its MCP server, and connecting it to OpenClaw so your agents can query your real work history.
Important note: This guide is based on Screenpipe’s official website (screenpipe.com) and GitHub README at the time of writing (July 2026). Specific configuration steps and command syntax may evolve. Always refer to the official Screenpipe documentation and GitHub repository for the most current instructions.
What You’ll Need
- A Mac, Windows, or Linux machine
- Screenpipe installed (free, source-available)
- OpenClaw with MCP tool support configured
- About 15–20 minutes
Step 1: Install Screenpipe
Head to screenpipe.com and download the installer for your platform (macOS, Windows, or Linux). Screenpipe is a desktop application — the install is straightforward.
On macOS, you may need to allow the app in System Preferences > Privacy & Security after first launch, because Screenpipe needs screen recording and microphone permissions to do its job.
When you first open the app, it will ask you to grant:
- Screen recording permission — to capture what’s on screen
- Microphone permission — to record and transcribe audio from calls and system audio
Both permissions are handled locally. Nothing is sent to any server unless you explicitly configure a cloud connection.
Step 2: Configure Privacy Settings
Before you start recording, review Screenpipe’s PII redaction settings. By default, Screenpipe includes built-in PII redaction — names, emails, phone numbers, and other sensitive patterns are filtered from the indexed data.
You can configure which apps to exclude from recording. If you have apps you specifically don’t want captured (password managers, banking apps, etc.), add them to the exclusion list in Screenpipe’s settings before starting the recorder.
Screenpipe stores all data locally in SQLite. The default data directory will be shown in the app settings — note this path if you want to back it up or understand where the data lives.
Step 3: Start Recording
Once permissions are granted and privacy settings are configured, start Screenpipe’s recorder. It runs as a background process and begins continuously capturing screen content and audio.
You’ll see a menu bar icon (macOS) or system tray icon (Windows/Linux) confirming it’s running.
The local SQLite index builds up over time. Give it at least a few hours of real work before you query it — the more history it has, the more useful your agent queries will be.
Step 4: Enable the MCP Server
Screenpipe exposes an MCP-compatible API on port 3030. This is what allows AI agents to query your screen/audio history using the Model Context Protocol.
To verify the MCP server is running, open a browser or terminal and check:
http://localhost:3030
If Screenpipe is running, you should see a response from the local API server. For the specific API endpoints, query parameters, and MCP tool schema, refer to the Screenpipe GitHub repository — the README and documentation folder contain the current API reference.
Note: The specific API paths and parameters are documented in the Screenpipe GitHub repo and may change between versions. Do not rely on hardcoded endpoint paths from third-party guides (including this one) — check the official docs for your installed version.
Step 5: Configure OpenClaw to Query Screenpipe
With the MCP server running on localhost:3030, you can configure OpenClaw to include it as an MCP context source.
OpenClaw supports MCP tool configuration through its config system. The exact configuration path and syntax for adding an MCP server depends on your OpenClaw version. Refer to the OpenClaw documentation or your OpenClaw config file for the current format.
In general, the connection tells OpenClaw:
- Where the MCP server is (localhost:3030)
- What tools to expose from that server
- When those tools should be made available to agents
Once configured, your agents can call into the Screenpipe MCP tool to search your recorded work history as part of any conversation or task.
Step 6: Test the Integration
With both Screenpipe recording and OpenClaw configured, start a new OpenClaw session and try a query that would require historical context. For example:
- “What was I working on yesterday afternoon?”
- “Summarize the key points from my meetings this week.”
- “What did I look at related to [project name] last week?”
Your agent should pull context from Screenpipe’s indexed history and incorporate it into the response. If this isn’t working, check that:
- Screenpipe’s recorder is actively running (check the menu bar/tray icon)
- The MCP server is responding at localhost:3030
- Your OpenClaw MCP configuration points to the correct address
- There’s sufficient recording history — a few minutes of data won’t produce useful results
Step 7: Create Your First Pipe (Optional)
Screenpipe’s “pipes” are agent-triggered workflows that run on-device when specific events occur. A simple example is an automatic meeting notes pipe:
---
name: meeting-notes
trigger: meeting_ended
model: local
---
When a meeting ends, write:
- 3-bullet summary
- Action items per person
- Decisions + next steps
Save to ~/notes/{date}_{app}.md
Pipes are defined in Markdown files with a YAML frontmatter header that specifies the trigger condition and model to use. For the current trigger types, model options, and pipe syntax, see the Screenpipe pipes documentation.
Privacy Considerations
Screenpipe records everything unless you configure exclusions. Before deploying this in a work context, review:
- Employer policies on screen recording software
- Data residency requirements for your organization
- What gets included in the SQLite index (you can inspect it directly)
- How long data is retained (configurable in Screenpipe settings)
The local-first architecture means your data stays on your machine — but it does mean your machine now contains a comprehensive record of your work activity. Treat the Screenpipe data directory with appropriate care.
What’s Possible With This Setup
Once your OpenClaw agents can query Screenpipe, the use cases expand significantly:
- Automatic project context: When you open a new conversation about a project, your agent can pull relevant history from Screenpipe without you manually summarizing what’s happened.
- Meeting follow-up: After a call, ask your agent to draft follow-up emails based on what was said — without manually taking notes during the call.
- Progress tracking: Query your agent for a summary of what you’ve worked on in a given period, with Screenpipe providing the actual evidence.
- SOP generation: Screenpipe and your agent together can watch how you do a repetitive task and draft a Standard Operating Procedure from the observed steps.
This is what “AI agents with real work context” looks like in practice. Not hypothetical future AI — tools you can set up this afternoon.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260724-0800
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