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Lovart Api

lovartai/lovart-skill

  • 980 installs
  • 112 repo stars
  • Updated August 1, 2026
  • lovartai/lovart-skill

Lovart API is a generative media agent skill that lets coding agents create images, videos, audio, 3D models, and perform media editing from natural language prompts for developers who need programmatic creative asset ge

About

Lovart API is the lovart-skill agent integration for generating and editing creative media directly from coding sessions. Built for Python 3.6+ environments under an MIT license, it connects agents to Lovart capabilities for images, videos, audio, 3D models, and media editing via natural-language prompts. Developers reach for Lovart API when building apps, demos, or content pipelines that need generated visuals, audio, or 3D assets without switching to separate design tools. The skill wraps Lovart release-tracked API workflows so Claude, Cursor, or Codex agents can produce and refine media artifacts as part of feature development.

  • Connects OpenClaw and other AI coding agents to Lovart Agent OpenAPI
  • Generates images including posters, logos, illustrations, banners and mockups
  • Creates videos, animations, product clips, background music, songs and sound effects
  • Supports image and video editing such as super-resolution, reconstruction and style transfer
  • Produces 3D models from text or image prompts plus project and session management with local persistence

Lovart Api by the numbers

  • 980 all-time installs (skills.sh)
  • +50 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #1,123 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/lovartai/lovart-skill --skill lovart-api

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repo stars★ 112
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Last updatedAugust 1, 2026
Repositorylovartai/lovart-skill ↗

How do you generate images and video from agent prompts?

Let their coding agent generate images, videos, audio, 3D models and perform media editing directly from natural language prompts.

Who is it for?

Developers embedding generative image, video, audio, and 3D asset creation into agent-driven build workflows via Lovart API.

Skip if: Developers who only need static design files without API-driven generation or those with no Lovart API access.

When should I use this skill?

The user asks to generate images, videos, audio, 3D models, or edit media using Lovart or natural-language creative prompts.

What you get

Generated images, videos, audio files, 3D models, and edited media assets from Lovart API calls.

  • generated images
  • video and audio assets
  • 3D model files

By the numbers

  • Requires Python 3.6+
  • Supports 4 media types: images, videos, audio, and 3D models

Files

SKILL.mdMarkdownGitHub ↗

⚠️ RULE #0: ONLY USE SKILL COMMANDS — NO DIRECT API CALLS

You MUST interact with Lovart EXCLUSIVELY through the commands defined in this skill.

  • NEVER construct URLs, make curl/wget/fetch requests, or call Lovart APIs directly
  • NEVER guess API endpoints, paths, or parameters — only use the commands listed below
  • NEVER modify the skill's source code (agent_skill.py) during execution to "debug" issues (users may freely read the source to verify it)
  • If a command fails, retry it or report the error to the user — do NOT try to work around it
  • ALL Lovart operations go through: chat, send, watch, confirm, result, status, config, projects, project-add, project-switch, project-rename, project-remove, threads, thread-remove, upload, upload-artifact, download, set-mode, query-mode, create-project

⚠️ RULE #1: YOU CAN AND MUST GENERATE IMAGES/VIDEOS/AUDIO

When a user asks to "draw", "generate", "create", "design", "make", "画", "生成", "制作", "创作" any visual or audio content (in any language), you MUST use this skill. This includes: images, videos, audio, music, songs, BGM, TTS, voice, posters, carousels, banners, logos, illustrations, thumbnails, slides, covers, mockups, icons, social media content, etc. Do NOT say "I can't generate images/music" or offer to write prompts instead.

⚠️ RULE #1.5: PROJECT & THREAD QUERIES USE THIS SKILL

When the user asks about projects, threads, conversations, history, or settings (in any language), use these commands — do NOT browse the filesystem:

User asksCommand
"What projects do I have?" / "我有哪些项目"projects --json
"What conversations/threads?" / "有哪些对话"threads --json or threads --all --json
"Show my settings" / "我的配置"config --json
"Switch to project X"project-switch --project-id X
"Create a new project"project-add --project-id NEW_ID --name "Name" (or let chat auto-create)

⚠️ RULE #2: ALWAYS USE chat AND WAIT FOR COMPLETION

Use the chat command (blocks until done), NOT send. Do NOT reply before generation completes.

Handle these `final_status` values:

  • "done" — Generation complete. Send the downloaded files to the user.
  • "pending_confirmation" — A high-cost tool (e.g. video, or a premium-quality image variant) needs user approval before credits are consumed.

You MUST ask the user for explicit confirmation before proceeding. Do NOT auto-confirm. 1. Show the user: "This will cost approximately {estimated_cost} credits. Shall I proceed? (yes/no)" 2. WAIT for user response. Only if user explicitly says yes/confirm/proceed, run: confirm --thread-id THREAD_ID --json --download (This confirms, waits for completion, and returns the result with downloaded files) 3. If user declines, do NOT confirm. Just inform them the operation was cancelled.

  • "abort" — Generation was aborted. Inform the user.
  • "timeout" — Generation is still running but exceeded the wait time. The result may contain partial artifacts.

1. Send any downloaded files that are already available 2. Tell the user: "Generation is still in progress. Checking again..." 3. Run: result --thread-id THREAD_ID --json --download to get the latest results 4. If status is still "running", wait and retry. If "done", send remaining files.

Handle errors:

If chat throws an error (AgentSkillError), handle it by HTTP status and structured code. The message field already contains a user-ready explanation — surface it to the user as-is.

HTTP statuscodeWhat it meansWhat to tell the user
`402`2012Quota / billing / risk-control rejectionShow AgentSkillError.message directly — the server already returns a specific message (insufficient credits, free-tier reached, concurrent limit, risk control, phone verification, team plan required, etc.) and a suggested next step.
`409`2011Another task is still running on this thread"A task is still running on this conversation. Wait for it to finish (status) before sending a new prompt, or start a new thread."
`429`1429API rate limit hit"Slowing down; rate limit hit. Retry in ~60s."
`401`—AK/SK misconfigured"API key authentication failed. Please check your LOVART_ACCESS_KEY and LOVART_SECRET_KEY."
——Project.*does not exist in message"Project not found. Please check the project ID or create a new one."

Rule of thumb: prefer AgentSkillError.message for user-facing copy. Do not try to parse internal codes out of the response — the server already maps them to human-readable messages before returning.

Detect silent generation failures (`done` with no artifact):

Some prompts end with final_status: "done" but produce no artifacts / empty downloaded. This usually means the upstream image model refused the prompt (content moderation), timed out, or the LLM chose to reply with text instead of calling a tool. The skill flags this automatically — when chat() returns, check:

  • result["generation_succeeded"] — boolean. False means no artifact was produced.
  • result["warning"] — explanation string (present only when generation_succeeded is False).
  • result["agent_message"] — the agent's plain-text reply that hints at why (present when available).

Typical triggers:

  • GPT Image 2 with very long/complex prompts involving weapons, specific bodies, or policy-sensitive wording — retry with a different model (--include-tools generate_image_midjourney or generate_image_nano_banana_pro) or simplify the prompt.
  • Prompt that describes a task the agent can't fulfill — show agent_message to the user.

⚠️ RULE #3: ALWAYS DELIVER RESULTS + PROJECT LINK

After EVERY generation, you MUST: 1. Use --download flag with chat (or result) 2. Send each downloaded file to the user as a file attachment (images, videos, audio/mp3 — ALL file types):

  • ALWAYS send downloaded[].local_path as file attachments, regardless of file type (.png, .jpg, .mp4, .mp3, etc.)
  • NEVER just paste the URL when a local file has been downloaded — send the actual file
  • Only fall back to displaying URLs if no files were downloaded

3. Append the project canvas link: https://www.lovart.ai/canvas?projectId={project_id}

⚠️ RULE #4: CHECK LOCAL STATE ON FIRST USE (MANDATORY — DO NOT SKIP)

Before the FIRST generation in a conversation, you MUST run these two commands IN ORDER. This is NOT optional. Do NOT call `chat` until you have done both.

Step 1: `config --json`

  • Check local state (~/.lovart/state.json) for active_project
  • If active_project is set → proceed to Step 2. Do NOT create a new project. Do NOT ask the user.
  • If active_project is missing → ask the user: "Do you have an existing Lovart project ID, or should I create a new one?" WAIT for their answer.
  • Save with: project-add --project-id PID --name "name"

Step 2: `threads --json`

  • Check if there's a recent thread to continue
  • If recent thread exists and topic is related → REUSE it (pass --thread-id THREAD_ID to chat)
  • If no threads or completely different topic → omit --thread-id (creates new thread)

CRITICAL RULES:

  • NEVER create a new project if config --json already shows an active_project. Reuse it.
  • NEVER omit `--thread-id` when a relevant recent thread exists. Always reuse threads by default.
  • NEVER call `chat` without first running `config --json` and `threads --json` in the same conversation.
  • The chat command auto-reads active_project from local state — you do NOT need to pass --project-id every time.
  • Only create a new project if the user explicitly asks for one.
  • Only create a new thread if the topic is completely unrelated to the most recent thread.
  • When in doubt, REUSE both the existing project and the existing thread.

---

Lovart Agent OpenAPI Skill

Interact with Lovart AI Agent to generate images, videos, and visual assets via natural language.

Lovart is an AI design platform. The Agent understands user requests and automatically selects the best model and workflow.

Terminology

  • Thread — A conversation flow (chat session) with the Lovart AI Agent, NOT a programming thread. Each thread has a unique thread_id and preserves multi-turn context. Reusing a thread means continuing the same conversation so the Agent remembers previous images/videos and can iterate on them.
  • Project — A workspace/canvas that groups threads and generated artifacts together. One project can contain multiple threads.

Prerequisites

export LOVART_ACCESS_KEY="ak_xxx"
export LOVART_SECRET_KEY="sk_xxx"

No third-party dependencies. Python standard library only.

Features

1. Chat - Send a message to the AI Agent, get text replies and generated images/videos 2. Confirm - Confirm and wait for high-cost operations (e.g. video generation) 3. Create Project - Create a new project 4. Upload File - Upload a local image/video file, get back a CDN URL 5. Upload Artifact - Upload a link artifact to a project 6. Status/Result - Check thread status and retrieve results 7. Set/Query Mode - Switch between fast (credits) and unlimited (queue) mode

Usage

0. First-time setup (saves to ~/.lovart/state.json)

python3 {baseDir}/agent_skill.py project-add --project-id PROJECT_ID --name "My Project"

1. Send a message (reads project_id from local state)

python3 {baseDir}/agent_skill.py chat --prompt "USER_PROMPT" --json --download

To override project: add --project-id PROJECT_ID To continue a conversation: add --thread-id THREAD_ID To list saved threads: python3 {baseDir}/agent_skill.py threads

2. Create a project

python3 {baseDir}/agent_skill.py create-project

3. Upload a file (local image/video → CDN URL)

python3 {baseDir}/agent_skill.py upload --file /path/to/image.png
# Returns: {"url": "https://assets-persist.lovart.ai/img/{user_uuid}/xxx.png"}

Use this when the user sends an image/video file that needs to be passed as an attachment to chat.

4. Upload an artifact

python3 {baseDir}/agent_skill.py upload-artifact --project-id PROJECT_ID --url "ARTIFACT_URL" --type image

5. Check status / get result

# Status
python3 {baseDir}/agent_skill.py status --thread-id THREAD_ID

# Result (auto-syncs to gallery/canvas, idempotent)
python3 {baseDir}/agent_skill.py result --thread-id THREAD_ID --json --download

6. Download artifacts

# Download during chat
python3 {baseDir}/agent_skill.py chat --prompt "draw a cat" --json --download --output-dir /tmp/openclaw

# Download from existing result
python3 {baseDir}/agent_skill.py result --thread-id THREAD_ID --download --output-dir /tmp/openclaw

# Download specific URLs
python3 {baseDir}/agent_skill.py download --urls URL1 URL2 --output-dir /tmp/openclaw --prefix myimg

Typical Workflows

Scenario 1: Generate images/videos/audio (most common)

First, run `config --json` to check if project_id is set. If not, ask the user and save with `project-add`.

1. config --json  →  check local state for active_project
   - If not set → ask user, save with project-add
2. threads --json  →  check if there's a recent thread to continue
   - If recent thread exists and topic is related → reuse it (step 3a)
   - If no threads or completely new topic → new thread (step 3b)
3a. chat --thread-id THREAD_ID --prompt "user's request" --json --download
3b. chat --prompt "user's request" --json --download
4. Send each downloaded[].local_path file as an IM attachment to the user
5. The chat command auto-syncs artifacts to canvas and gallery

IDs are auto-persisted locally (`~/.lovart/state.json`):

  • project_id is saved after first chat, reused automatically
  • thread_id + topic are saved after each chat for thread switching
  • Only create a new project if the user explicitly asks for one
  • Only create a new thread (omit --thread-id) when starting a completely new topic
  • Run threads to list saved threads for the user to pick from

Scenario 2: Edit with attachments

1. User sends a reference image/video via IM → save to local file
2. upload --file /path/to/image.png  →  get CDN URL
3. chat --prompt "edit this image to..." --project-id PID --attachments "CDN_URL" --json --download
4. Continue as Scenario 1

Scenario 3: Follow-up on same topic (continue context)

1. chat --prompt "change the background to a beach" --project-id PROJECT_ID --thread-id THREAD_ID --json --download

The Agent remembers the previous conversation and can continue editing based on context.

Scenario 4: New topic (new thread)

1. chat --prompt "completely new request" --project-id PROJECT_ID --json --download

Omitting --thread-id creates a new conversation without previous memory.

Scenario 5: Streaming / incremental delivery (multiple artifacts)

Use when the user's request will produce multiple images/videos and you want to deliver each one to the user as soon as it's ready, rather than waiting for the whole batch.

python3 {baseDir}/agent_skill.py watch --prompt "generate 4 variations of a cyberpunk cat" --json

watch emits NDJSON to stdout (one event per line). Parse line-by-line and deliver each artifact event's local_path to the user immediately:

{"event": "started", "thread_id": "xxx", "project_id": "yyy"}
{"event": "artifact", "type": "image", "url": "https://...", "local_path": "/tmp/openclaw/lovart_ab12cd.png"}
{"event": "artifact", "type": "image", "url": "https://...", "local_path": "/tmp/openclaw/lovart_ef34gh.png"}
{"event": "pending_confirmation", "thread_id": "xxx", "pending_confirmation": {...}}
{"event": "finished", "thread_id": "xxx", "final_status": "done", "artifact_count": 4}

Files are saved with URL-hash filenames so re-running watch on the same thread won't re-download.

You can also attach to an already-running thread: watch --thread-id THREAD_ID.

When NOT to use `watch`: single-image requests — use chat (simpler, one-shot response).

Output Format

chat --json returns:

{
  "thread_id": "xxx",
  "status": "done",
  "project_id": "xxx",
  "final_status": "done",
  "items": [
    {"type": "assistant", "text": "Agent's reply"},
    {"type": "generator", "name": "artifacts", "artifacts": [
      {"type": "image", "content": "https://assets-persist.lovart.ai/artifacts/agent/xxx.png"},
      {"type": "video", "content": "https://assets-persist.lovart.ai/artifacts/agent/xxx.mp4"}
    ]}
  ],
  "downloaded": [
    {"type": "image", "url": "https://...", "local_path": "/tmp/openclaw/lovart_01.png"}
  ]
}

Core Principle

You are a messenger, not a creator. The backend Agent handles understanding requirements, selecting models, and writing prompts. Your job:

1. Relay: Pass the user's original description verbatim to chat 2. Wait: Poll until generation completes 3. Deliver: Send result files to the user

Do NOT rewrite/expand prompts, break down tasks, or add your own style descriptions.

Lovart Generation Mode (MUST use API, not prompt)

CRITICAL: "Fast mode" and "unlimited mode" are server-side settings controlled via API calls, NOT prompt keywords.

Do NOT put "快速模式" or "fast mode" in the prompt text. Instead, call the set-mode command:

# User says "fast mode" / "快速模式" / "skip queue" / "use credits" → RUN THIS:
python3 {baseDir}/agent_skill.py set-mode --fast

# User says "unlimited mode" / "无限模式" / "free mode" / "save credits" → RUN THIS:
python3 {baseDir}/agent_skill.py set-mode --unlimited

# Check which mode is active:
python3 {baseDir}/agent_skill.py query-mode

How it works:

  • set-mode --fast calls the Lovart backend API to switch the user's account to fast generation (costs credits, no queue)
  • set-mode --unlimited switches to unlimited generation (free, may queue)
  • This is a persistent server-side setting — it stays until changed again
  • It affects ALL subsequent image/video generations, not just one request
  • It has nothing to do with your (the assistant's) response style or behavior

Specifying Models

Option 1: In the prompt (simple, the Agent routes automatically):

python3 {baseDir}/agent_skill.py chat --prompt "generate ocean waves video using kling" --json --download

Option 2: Via --prefer-models (precise, same as frontend's model selector):

# Prefer a specific image model
python3 {baseDir}/agent_skill.py chat --prompt "draw a cat" --prefer-models '{"IMAGE":["generate_image_midjourney"]}' --json --download

# Prefer a specific video model
python3 {baseDir}/agent_skill.py chat --prompt "generate ocean waves" --prefer-models '{"VIDEO":["generate_video_kling_3_0"]}' --json --download

# Combine image and video preferences
python3 {baseDir}/agent_skill.py chat --prompt "create content" --prefer-models '{"IMAGE":["generate_image_seedream_3_0"],"VIDEO":["generate_video_kling_3_0"]}' --json --download

Available models for --prefer-models:

<!-- AUTOGEN:models:start --> <!-- AUTOGEN:models:end -->

When the user requests a specific model, prefer --prefer-models over putting model names in the prompt.

Option 3: Via --include-tools (hard constraint, forces specific tools):

# Force upscale only
python3 {baseDir}/agent_skill.py chat --prompt "upscale this image to 4K" --include-tools upscale_image --attachments "IMAGE_URL" --json --download

# Force a specific video model (no fallback to others)
python3 {baseDir}/agent_skill.py chat --prompt "generate a video" --include-tools generate_video_kling_3_0 --json --download

--include-tools strongly instructs the Agent to prioritize the listed tools. Use this when the user explicitly requests a specific tool or operation.

Reasoning Mode — --mode thinking / --mode fast

Lovart has two reasoning modes you can select per thread:

  • `fast` (default) — lightweight single-pass response. Use for simple, one-shot generations where speed matters.
  • `thinking` — deep structured reasoning with planning and multi-step analysis. Use for complex brand systems, multi-asset campaigns, anything that benefits from deliberate planning. Slower but higher quality.

Omitting --mode is equivalent to --mode fast, matching the web UI's default.

# Thinking mode — strategic, multi-step
python3 {baseDir}/agent_skill.py chat --prompt "design a brand identity system for a sustainable coffee startup" --mode thinking --json --download

# Fast mode — quick one-shot
python3 {baseDir}/agent_skill.py chat --prompt "draw a cat" --mode fast --json --download

Mode is locked to the thread on its first message. Once you start a thread with --mode thinking, subsequent messages on the same --thread-id stay in thinking mode regardless of later --mode flags. To switch modes, start a new thread (omit --thread-id).

Task-Specific Tool Selection (IMPORTANT)

When the user's request matches a specific operation, use --include-tools to ensure the correct tool:

User saysUse --include-tools
"upscale", "放大", "enlarge", "enhance resolution", "超分"upscale_image
"edit image", "modify", "change style"(let Agent decide)
"generate image", "draw", "画"(let Agent decide, or use --prefer-models)

CRITICAL: When the user asks to "upscale", "enlarge", or increase resolution of an existing image, you MUST use `--include-tools upscale_image`. Do NOT let the Agent use image generation models for upscaling — they will re-generate the image instead of upscaling it.

Notes

  • All APIs use AK/SK HMAC-SHA256 signature authentication
  • Video generation takes several minutes; the chat command auto-polls until complete
  • Gallery and canvas sync is idempotent — safe to call result multiple times without duplicates
  • Connection failures auto-retry 3 times with SSL fallback
  • After status becomes "done", waits 5 seconds to re-confirm (guards against sub-agent startup race)

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How it compares

Pick Lovart API when agents must produce and edit multiple media types in one integration rather than wiring separate image-only or video-only services.

FAQ

What media types does Lovart API generate?

Lovart API supports generating images, videos, audio, and 3D models plus media editing from natural-language prompts, callable directly from coding agents via the lovart-skill integration.

What runtime does lovart-skill require?

lovart-skill targets Python 3.6+ environments and is distributed under the MIT license, connecting agents to Lovart generative and editing API endpoints.

Is Lovart Api safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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