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Framelet

Framelet

Local
github.com/liuminxin45Media & Design1Updated 6d ago

About Framelet

Framelet is a native Windows screen recorder for AI-driven software testing and walkthroughs. Its CLI and local MCP support capture, synchronized interaction timelines, and GIF review and editing.

Key features

  1. 01Synchronized keyboard, mouse, and frame timeline
  2. 02Background recording through CLI and MCP
  3. 03GIF trimming and speed adjustment
  4. 04Native frame extraction and comparison
  5. 05Recording rotation and recovery

Use cases

  1. 01Record an agent reproducing a software bug
  2. 02Review actions and frames around a failed test
  3. 03Trim idle time from a recorded walkthrough

MCP support

Its local MCP interface lets an agent control recording and query, extract, and trim captured material.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbframelet-v0.5.1-windows-x64.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Framelet

CLI-first, AI-native Windows recording with keyboard, mouse and frame synchronization.

≈3.6 MiB native EXE · No extra runtime installs · CLI First · AI Native

English · 简体中文 · Download · About

Let an AI operate software while recording its screen and keyboard/mouse events, then query, extract and trim through MCP. Built for AI-driven software tests, bug reproduction, product showcases and walkthroughs. CLI and MCP are the primary interfaces; a native capture frame is also available. No Python, Node.js, .NET or FFmpeg installation is required.

More than a GIF: an interaction timeline

Framelet records keyboard, mouse and frame samples together by default, sharing UTC and monotonic clocks. A timeline and content-integrity manifest accompany the GIF.

  • Unique identities and time: shared observation IDs link input events, original samples and GIF frames, with UTC references and explicit timing offsets.
  • Input independent of FPS: several actions between samples can map to one frame. Unsampled visual changes cannot be recovered.
  • Review and trim with context: locate actions by ID/time, inspect selected pictures, then trim or change speed while preserving original identities and computer-time references.

Keyboard records contain down/up transitions, key codes and scan codes, not clipboard or IME-committed text. Key events can reveal typed content; visual masks are not automatic password protection. Masked mouse coordinates are omitted. Timestamps describe observed intervals, not hardware synchronization; dwell candidates do not identify UI elements.

Small tool, useful capabilities

CapabilityWhat it gives you
Native capture frameMove, resize, record, pause and save; F8 control
Client installation + SkillNative setup/check/uninstall, MCPB and reusable AI recording recipes
CLI + local MCPBackground recording while an agent operates your app
Reliable long recordingNumbered GIFs of at most 300 seconds, automatic rotation and checkpoint recovery
Keyboard/mouse/frame timelineLocate failed steps and review action results
1–60 FPSState checks and high-rate native review; use 30 FPS for browser sharing
Efficient AI reviewFiltered event queries, one-pass batch probes and transient-aware action intervals
GIF editingPrecise time cuts and playback speed changes
Native frame extractionPNGs, contact sheets, timestamp probes and region comparisons
Privacy and guardrailsFixed masks, scoped MCP paths, no-overwrite and resource limits
Isolation and integritySupervised capture process, content hashes and recovery markers

Static acceptance can use 1 FPS; compact GIFs 10–15 FPS; polished demos default to 30 FPS. Actual sampling depends on workload.

Measured review performance

v0.4.1 removes repeated segment checks, skips unused RGB conversions and builds candidate previews in one analysis pass. No runtime dependencies were added.

Local MCP workloadv0.4.0v0.4.1Time saved
Probe 64 times in one recording part633 ms52.5 ms91.7%
Analyze the last 0.5 s of a 20 s GIF2,106 ms790 ms62.5%
Analyze + preview the Bilibili demo971 ms566 ms41.7%

Windows, 10 alternating warm runs per version, medians. The group fixture is 64×64; the window fixture is 640×360/600 frames; Bilibili reuses the demo below. Timings exclude model inference and UI operation and depend on input/hardware. Methodology, hashes, memory tradeoffs and raw results.

Example 1 · Real window and recording

Framelet capture frame

The original small real recording: 620×300 · 3 seconds · 16 FPS · 15,250 bytes. This mostly static moving-marker scene is an example, not a general compression benchmark.

Original recording

Example 2 · Bilibili, operated and recorded through MCP

Start recording → scroll the real browser → stop → probe boundaries → trim idle time.

Real Bilibili recording

29.88 seconds → 7.63 seconds. Native region comparisons and interval jumps located boundaries with eight midpoint probes, avoiding intermediate image exports. Read the real walkthrough.

These demos predate synchronized timelines. Current releases record keyboard/mouse/frame timelines by default; the older media illustrates capture and editing.

For viewing GIFs and inspecting them frame by frame, we recommend Minimal Image Viewer.

Made for local workflows

Windows 10 2004+ / Windows 11 x64, visible desktop capture. No built-in cloud uploads or telemetry. GIF/PNG only; no audio or MP4. Agent guide · Timeline details · Changelog.

By Neo / 刘民心. Inspired by GifCam, independently implemented. MIT.

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