README.md

metadata

title: Agentic Space Factory
sdk: docker
hf_oauth: true
hf_oauth_expiration_minutes: 480
hf_oauth_scopes:
  - read-repos
  - write-repos
  - manage-repos
  - gated-repos
  - inference-api
  - jobs
  - read-billing

v175 — Anonymous cross-run evaluation archive

• Adds an optional operator-owned evaluation bucket mounted into Jobs, separate from each user's private run bucket.
• Workers publish eval_record.json plus a compact event timeline with hashed run/user/Space identifiers, structured hardware/Pi/recovery/validation metrics, and no raw generated code, prompts, tokens, or private bucket paths.
• Controlled by ASF_EVAL_ENABLED, ASF_EVAL_BUCKET_SOURCE, ASF_EVAL_BUCKET_PATH, ASF_EVAL_BUCKET_MOUNT, and ASF_EVAL_SALT.
• The Run storage card now indicates whether anonymous project evaluation is enabled for the instance.

v174 — Bucket run-prefix mount and robust worker bootstrap writes

• Mounts only the runs/ bucket prefix into Jobs at /output/runs, while the worker still writes to /output/runs/<run_id>/....
• Keeps API-side writes on canonical hf://buckets/<namespace>/<bucket>/runs/<run_id>/... object URIs, with HfFileSystem attempts followed by a sync_bucket fallback for fresh prefixes.
• Removes the v172 parent-prefix makedirs() workaround, because Bucket prefixes are virtual object paths rather than git-style folders.
• Requires huggingface_hub[oauth]>=1.11.0 so Buckets, Jobs volumes and sync helpers are available in fresh installs.

v172 — Fresh bucket writes and pre-scan polish

• Keeps the Hugging Face Space metadata at the very top of README.md, so the app cannot boot with a configuration error after archive edits.
• Makes Bucket writes resilient for fresh run prefixes by creating parent prefixes before writing _worker.py, launch metadata, state, summaries, and events.
• Refreshes the Model pre-scan card into a calmer status panel with compact metadata chips and less oversized green treatment.

v170 — Real bucket recursive run deletion

• DELETE /api/runs/{run_id} now removes concrete bucket objects under runs/<run_id>/ using recursive discovery instead of relying only on virtual-folder deletion.
• The API returns a cleanup report with deleted and remaining object counts.
• Frontend cache invalidation remains as a protection layer, but deletion is now a real backend bucket cleanup.

Agentic Space Factory

• v170 fixes run deletion so the backend recursively removes all concrete Bucket objects for the run prefix.
• v168 refreshes Pi assistant model choices and surfaces requested/effective provider model routing in Active Run.
• v165 fixes fresh-install recovery failures, README metadata preservation, timeline failure anchoring, premium pre-scan/progress polish, and broader Pi assistant model choices.
• v161 fixes duplicate delete modal triggers and stuck Deleting… states with single-flight delete guards.

Agentic Space Factory

Agentic Space Factory turns a Hugging Face model card into a private, testable Gradio Space using a Hugging Face Job, Pi, a coding model through Hugging Face Inference Providers, and a private Storage Bucket for run state, traces, reports, generated files, and validation artifacts.

It is a Hugging Face-native productization of the agentic ZeroGPU Spaces workflow:

model card → agentic build job → private Space → hardware if available → live validation → traces/report/artifacts

What the app does

• Creates private generated Spaces under the signed-in user's namespace.
• Launches long-running build and validation work as Hugging Face Jobs.
• Uses Pi with a coding model served through Hugging Face Inference Providers.
• Injects the configured GitHub gist as Pi's operational instruction source.
• Stores each run in a per-user private Storage Bucket.
• Attempts ZeroGPU first when requested, then optional fixed-GPU fallback.
• Supports manual hardware selection when automatic assignment is unavailable. Automatic fallback avoids high/restricted tiers such as A100/H200; use Space Settings manually if an account or organization has access.
• Distinguishes bootability from real inference.
• Measures live inference latency when a generation smoke test succeeds.
• Uses measured latency to recommend @spaces.GPU(duration=...) when the generated Space uses ZeroGPU.

Current UI

The product root / is a custom Docker/FastAPI dashboard. /custom is kept as an alias.

The UI is organized into three main areas:

v122 stability note

The v122 line intentionally rolls back the experimental v121 client-side caching/polling optimizations after real UI testing exposed flicker and broken build/run behavior. The app keeps the v120 Agent recovery UI and the v118/v119 core blockage protocol, but returns to the proven polling and run-selection model for production stability.

Left column

• Run storage — check or create the signed-in user's private Bucket.
• New Build — starts from Tongyi-MAI/Z-Image-Turbo by default, fast pre-scans the model card, then launches a private Space build only after an acceptable scan verdict.
• Billing & compute — show payment readiness, link to HF Billing, and centralize the compute-cost note for Jobs, Inference Providers, and safe hardware fallbacks.

Center workspace

Two tabs share the main detail area:

• Active Run — build progress, run actions, Space/Settings/Job/Artifacts links, manual hardware guidance, inference latency, a vector-icon latest-events feed, and curated trace documents.
• Space Test — validation run details, actionnable Gradio endpoint picker, endpoint params, validation status, latency, point-based progress, and validation actions.

Selecting a build run opens Active Run. Selecting a validation run opens Space Test. The Runs Explorer is intentionally compact: each row prioritizes the model or Space, status, relative freshness, and a secondary context line while keeping the raw run ID available in the row.

Right column

• Runs Explorer — navigation-only list of build and validation runs with filters, search, status badges, and inline delete.
• Run Stats — aggregate metrics across indexed runs: totals, success/pass rates, validation latency median/p95, and issue counts.

Agent recovery visibility

When a build or runtime blockage appears, the Active Run panel shows an Agent recovery card. It summarizes Pi's diagnosis decision, the selected recovery action (wait_for_logs, factory_rebuild_same_code, patch_code, manual hardware, or blocker), key evidence, and the next step. This makes the blockage protocol visible without turning the UI into a raw log viewer.

The Run traces dock also links to the unified agent_trace.jsonl, the diagnosis REPAIR_DECISION.json, repair artifacts, and blockage details when they exist.

Model pre-scan

New builds are gated by a fast model-card pre-scan. The scan is metadata-only and does not download model weights. It checks:

• model-card guidance and runnable usage examples;
• Diffusers signals such as library_name, model_index.json, pipeline class, and from_pretrained(...) examples;
• safetensors versus unsafe or legacy weight formats;
• gated/private access and unsupported task signals;
• custom-code indicators such as auto_map, custom pipeline files, or trust_remote_code hints.

Verdicts are intentionally product-facing: safe and caution allow launch, risky requires explicit acknowledgement, and unsupported blocks launch. Tongyi-MAI/Z-Image-Turbo is treated as the known-good default example because it has a clear Diffusers model card and works in the app's default test flow.

Run storage

Each signed-in user writes to their own private Storage Bucket. The default bucket is:

https://huggingface.co/buckets/<your-username>/space-factory-runs

For local dev or deployment, configure only the bucket name; the namespace is derived from the signed-in OAuth user:

SPACE_FACTORY_BUCKET_NAME=space-factory-runs
SPACE_FACTORY_BUCKET_MOUNT=/output

Typical run layout:

runs/<run_id>/launch.json
runs/<run_id>/state.json
runs/<run_id>/summary.json
runs/<run_id>/events.jsonl
runs/<run_id>/report.md
runs/<run_id>/generated/
runs/<run_id>/tests/
runs/<run_id>/artifacts/
runs/<run_id>/traces/raw/
runs/<run_id>/traces/raw/agent_trace.jsonl
runs/<run_id>/traces/redacted/
runs/<run_id>/traces/redacted/agent_trace.jsonl
runs/<run_id>/logs/

Main workflow

1. Build from model card

Paste a Hugging Face model ID or model-card URL. The app launches an HF Job that:

• fetches model metadata and repo information;
• writes a strict GOAL.md for Pi;
• asks Pi to follow the configured GitHub gist before coding;
• generates a Gradio app;
• creates and uploads a private Space;
• attempts ZeroGPU/fixed-GPU assignment when requested;
• runs health/API checks and a live generation smoke test when possible;
• writes events, report, traces, and structured artifacts to the user's Bucket.

2. Validate Space

Validation is a separate HF Job. Use it after a build succeeds or after manually selecting hardware in Space Settings.

The validation job:

• waits for the target Space;
• discovers Gradio endpoints when possible;
• calls the selected endpoint;
• checks the returned output type;
• stores returned artifacts;
• measures latency;
• recommends a conservative ZeroGPU duration when relevant;
• writes validation state, events, report data, and smoke-test artifacts.

Result statuses

• full_inference_success: a live generation smoke test returned the expected output type.
• manual_hardware_required: the Space was generated, but hardware must be selected manually before validation.
• full_inference_candidate_health_passed: the Space boots and contains inference signals, but generation was not fully smoke-tested.
• health_only: the Space boots, but no real inference path was validated.
• technical_blocker: concrete blockers such as multi-GPU requirements, missing licenses, custom CUDA, or unclear usage.
• failed: the build, runtime, or validation job failed.

Guarantees

• Generated Spaces are private by default.
• Nothing is published automatically.
• Runs are persisted in the configured per-user Storage Bucket.
• The app distinguishes health checks from full inference validation.
• Hardware assignment is best-effort and can fall back to manual user action.
• Raw and redacted Pi traces are kept separate.

Unified agent trace

Pi can be invoked more than once during a run: the initial build, a blockage diagnosis pass, and an optional structured repair patch. The Factory now writes a canonical run-level agent journal to traces/raw/agent_trace.jsonl and traces/redacted/agent_trace.jsonl so those phases read as one auditable agentic loop instead of separate conversations. Specialized stdout logs remain available under logs/, but the Active Run Pi RAW / Pi redacted buttons prefer the unified trace when it exists.

Non-goals and limits

The app does not guarantee that every model card becomes a working Space. Some models require multi-GPU execution, Docker, unavailable CUDA extensions, external services, accepted licenses, very large hardware, or manual fixes.

The app does not mirror live spend/quota totals from Hugging Face Billing. Users should consult the HF Billing dashboard for exact usage.

Local development

Install dependencies and run tests:

pip install -r requirements.txt
pytest

The Docker Space starts the app with one uvicorn process from the Dockerfile. Do not add an extra uvicorn.run(...) launcher inside app.py.

Documentation

See:

• docs/ARCHITECTURE.md
• docs/PUBLIC_PREVIEW.md
• docs/SECURITY.md
• docs/NEXT_STEPS.md

Default example model: Tongyi-MAI/Z-Image-Turbo. It is treated as a known-good Diffusers text-to-image model for the fast pre-scan calibration and public-preview smoke tests.

Pi diagnosis and structured recovery

When the generated Space fails live validation, the factory now enters a diagnosis-first blockage protocol instead of asking Pi to patch immediately. The worker collects HF build/runtime logs, Space status and validation errors, writes repair/INCIDENT_BRIEF.md, then asks Pi/Qwen Coder to choose one bounded action in repair/REPAIR_DECISION.json: wait for delayed logs, inspect again, force a same-code factory rebuild, patch code, request manual hardware, or declare a technical blocker.

A code repair is allowed only when Pi selects patch_code and the Factory accepts the decision. Empty/no-reason HF failures are routed through wait/rebuild/blocker paths rather than speculative code edits. Patch repair still writes REPAIR_BRIEF.md, REPAIR_PLAN.md, and REPAIR_SUMMARY.md, uploads once, then revalidates the live Space with the normal API checks.

Repair mode is intentionally strict: it must preserve the original model and real-inference contract, avoid fake/static outputs, keep a cheap health endpoint, and report blockers instead of hiding runtime errors.

v123 — Critical Job launch + failure timeline fix

• Fixed a production-critical HF Job startup failure where the large embedded worker script could make Python fail before startup with argument list too long.
• The worker script is now persisted as runs/<run_id>/_worker.py in the mounted run bucket before launch, and the Job receives only WORKER_SCRIPT_PATH.
• Kept a small WORKER_SCRIPT_B64 fallback only for compatibility/manual launch paths; normal app launches no longer put the worker source in env/argv.
• Added a failure-log fallback so pre-worker Job failures such as argument list too long still create an explicit failed timeline event and red failure point.

v124 — Timeline and Run Explorer stabilization

• Fixed the compact Overall Progress timeline so dynamically visible repair/failure groups cannot wrap dots onto a second row.
• Removed smooth timeline auto-scroll that could cause visible flicker during polling.
• Simplified Run Explorer type/status badges to neutral type labels and short status labels such as Success, Failed, Running and Manual.
• Added regression checks for timeline single-row behavior and compact Run Explorer labels.

v127 — Billing usefulness + UI event/timeline audit

• Reworked the Billing & compute card into an actionable compute checklist: payment readiness, Pi Inference Providers, build Job cost, generated Space hardware and high-tier GPU guardrails.
• Kept exact spend/quotas delegated to the Hugging Face Billing dashboard instead of inventing usage totals.
• Audited JS event bindings for one-shot/delegated listeners across navigation, run rows and timeline scroll.
• Reduced timeline auto-scroll churn: identical timeline renders no longer force keep-visible scrolling after user interaction.
• Confirmed run selection keeps the immediate cached/optimistic render path before network hydration.

Active Run source documents

Run traces now renders as a horizontal document dock: compact source-file icons for Pi traces, repair decisions, reports, smoke results, and blockers. Agent recovery remains the human-readable recovery summary; the dock is the raw source-file vault.

v164 timeline

The Active Run timeline uses compact product milestones backed by exact worker steps. Grouped milestones expose clockwise partial progress rings and substep counts, while recovery details remain in Agent recovery and source files remain in Run traces.

v167 note: run artifact manifest

The worker writes runs/<run_id>/artifact_manifest.json as the source of truth for run artifacts. This lets the API expose Run traces from what the worker actually wrote, including failed runs where the Space was created but later entered build/runtime error.

v171 notes

Run deletion now supports an explicit opt-in option to delete the associated generated Space for build runs. Validation run deletion remains run-artifact-only by default. The Run Storage card also summarizes bucket setup errors so fresh installs remain visually clean.