pie-studio

PIE record, replay, observe, and input injection for ue-mcp. Built for Unreal Engine 5.8.

Install

ue-mcp plugin install pie-studio

This installs the npm package, deploys the native C++ module to your project's Plugins/ directory, and adds the plugin to your ue-mcp.yml. Rebuild the UE project before launching the editor.

Editor UI

PIE Studio adds a toolbar button group to the UE5 editor (next to the Play controls) and a dockable panel accessible from the three-dot dropdown or Tools > PIE Studio.

Toolbar

Panel Sections

Recorder — status display showing current state, recording ID, frame count, and elapsed time.

Time Scale — slider and quick-set buttons (1%, 10%, 25%, 50%, 100%, 200%) to control PIE playback speed. Persists across PIE restarts.

Recordings — lists all saved recordings with:

• Play button to replay (arms replayer + active observation profiles, starts PIE, captures viewport frames, generates GIF)
• Delete button
• Collapsible GIF list per recording with Open (launches in default viewer) and Delete

Observation Profiles — manages UMCPObservationProfile data assets:

• Checkbox toggles to mark profiles as active
• Active profiles automatically observe during replay
• Create, Edit (opens UE asset editor), Delete, Refresh
• Multiple profiles run simultaneously, each producing independent output

Observation Profiles

Observation profiles are UDataAssets that control what gets sampled during replay. Create them from the panel or the Content Browser.

Frame Capture

When capture_frame_every is set, replay grabs viewport frames as JPEGs (kept on disk under <recording>/frames/) and composes a single labeled contact sheet at <recording>/captures/contact_<timestamp>.jpg — a grid montage of keyframes with the frame index drawn on each cell. A vision model reads stills, so the frames and the contact sheet are the useful artifacts; the paths come back in replay_status.last_result (frame_dir, frame_count, contact_sheet_path).

Animated GIF is now opt-in (encode_gif=true). A vision model cannot parse GIF animation, so it is off by default and exists only for human eyeballing.

capture grabs frames from the live PIE viewport on demand, decoupled from replay, so observe and inject flows can be visual too.

Session Errors

session_errors returns the deduped errors and warnings captured from the Output Log during a PIE session (including Blueprint script exceptions), so an agent can ask "what errored" first. session_log returns the paged raw log with verbosity/category/substring filters. Both default to the live or most-recent session; pass a session id to read a finished one from Saved/MCPSessions/.

Unattended replay

replay_run lets an AI agent drive a recording end-to-end with no human touching the editor. It arms the replayer and starts PIE in a single call, then ends PIE and writes drift.json when the run completes. So "hey Claude, solve this bug in ./some-recording" becomes:

pie(action="replay_run", recording_dir="C:/proj/Saved/MCPRecordings/some-recording")
# poll until PIE has torn down:
pie(action="replay_status")            # -> pie_active: false, last_result: { drift_report_path, ... }
pie(action="record_read", id="some-recording", file="drift")

replay_run takes the same params as replay_arm (recording_id, inline steps, time_scale, drift_thresholds, capture_frame_every, …). It defaults auto_stop_pie=true; pass false to leave PIE up for inspection. recording_dir alone names the source folder directly. Contrast with replay_arm, which only arms and still needs a separate editor(action="play_in_editor") (and a human) to start PIE.

MCP Actions

45 actions in the pie category (provisioned by the plugin; call as pie(action="...")):

• Recording — record_arm, record_disarm, record_stop, record_status, record_list, record_read, record_delete, mark
• Replay — replay_arm, replay_run (unattended), replay_disarm, replay_stop, replay_status with drift tracking and viewport capture
• Analysis — replay_analyze (first-divergence lead + errors + images), replay_state (deterministic scrub/snapshot)
• Profiling — perf_summary (frametime p50/p99, GPU, hitches), trace_start, trace_stop (Unreal Insights .utrace)
• Reproduction tests — test_scaffold, test_run, test_list
• Observation — observe_arm, observe_disarm, observe_stop, observe_status, observe_list, observe_read with profile-based sampling
• Input injection — inject_input, inject_input_start, inject_input_update, inject_input_stop, inject_input_tape
• Profiles — profile_create, profile_read, profile_update, profile_delete, profile_list
• Diff / Snapshot — record_diff, snapshot
• PIE inspection — anim_state, anim_properties, subsystem_state
• Session log — session_errors, session_log
• Capture — capture (standalone viewport frames + contact sheet)

Data Layout

<Project>/Saved/MCPRecordings/
  <recording-id>/
    manifest.json        # recording metadata
    sequence.json        # input sequence
    recording.csv        # frame-by-frame state
    drift.json           # replay drift report
    frames/              # per-frame viewport JPEGs (kept)
      frame_00000.jpg
      frame_00001.jpg
    captures/
      contact_20260527_171430.jpg   # labeled grid montage
      replay_20260527_183200.gif    # only when encode_gif=true

<Project>/Saved/MCPSessions/
  <timestamp>/
    session_log.jsonl    # captured Output Log
    session_errors.json  # deduped errors + warnings

<Project>/Saved/MCPObservations/
  obs_<profile>_<timestamp>/
    manifest.json
    observation.csv
    tracked.jsonl

Develop

npm install
npm run build

See ue-mcp plugin docs for the full author contract.