meshy-3d-agent

name	meshy-3d-agent
description	Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API. Handles API key detection, task creation, polling, downloading, and full 3D print pipeline with slicer integration. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, 3D print a model, or interact with the Meshy API.
license	MIT-0
compatibility	Requires Python 3 with requests package. Compatible with OpenClaw and all Agent Skills tools.
metadata	{"openclaw":{"primaryEnv":"MESHY_API_KEY","requires":{"env":["MESHY_API_KEY"],"bins":["python3","curl"]},"install":[{"kind":"uv","package":"requests"}]}}
allowed-tools	Bash, Write

Meshy 3D — Generation + Printing

Directly communicate with the Meshy AI API to generate and print 3D assets. Covers the complete lifecycle: API key setup, task creation, exponential backoff polling, downloading, multi-step pipelines, and 3D print preparation with slicer integration.

---

SECURITY MANIFEST

Environment variables accessed:

• MESHY_API_KEY — API authentication token sent in HTTP Authorization: Bearer header only. Never logged, never written to any file except .env in the current working directory when explicitly requested by the user.

External network endpoints:

• https://api.meshy.ai — Meshy AI API (task creation, status polling, model/image downloads)

File system access:

• Read: .env in the current working directory only (API key lookup)
• Write: .env in the current working directory only (API key storage, only on user request)
• Write: ./meshy_output/ in the current working directory (downloaded model files, metadata)
• Read: files explicitly provided by the user (e.g., local images passed for image-to-3D conversion), accessed only at the exact path the user specifies
• No access to home directories, shell profiles, or any path outside the above

Data leaving this machine:

• API requests to api.meshy.ai include the MESHY_API_KEY in the Authorization header and user-provided text prompts or image URLs. No other local data is transmitted. Downloaded model files are saved locally only.

---

IMPORTANT: First-Use Session Notice

When this skill is first activated in a session, inform the user:

All generated files will be saved to meshy_output/ in the current working directory. Each project gets its own folder ({YYYYMMDD_HHmmss}_{prompt}_{id}/) with model files, textures, thumbnails, and metadata. History is tracked in meshy_output/history.json.

This only needs to be said once per session.

---

IMPORTANT: File Organization

All downloaded files MUST go into a structured meshy_output/ directory in the current working directory. Do NOT scatter files randomly.

• Each project: meshy_output/{YYYYMMDD_HHmmss}_{prompt_slug}_{task_id_prefix}/
• Chained tasks (preview → refine → rig) reuse the same project_dir
• Track tasks in metadata.json per project, and global history.json
• Auto-download thumbnails alongside models

---

IMPORTANT: Shell Command Rules

Use only standard POSIX tools. Do NOT use rg, fd, bat, exa/eza.

---

IMPORTANT: Run Long Tasks Properly

Meshy generation takes 1–5 minutes. Write the entire create → poll → download flow as ONE Python script and execute in a single Bash call. Use python3 -u script.py for unbuffered output. Tasks sitting at 99% for 30–120s is normal finalization — do NOT interrupt.

---

Step 0: API Key Detection (ALWAYS RUN FIRST)

Only check the current session environment and the .env file in the current working directory. Do NOT scan home directories or shell profile files.

echo "=== Meshy API Key Detection ==="

# 1. Check current env var
if [ -n "$MESHY_API_KEY" ]; then
  echo "ENV_VAR: FOUND (${MESHY_API_KEY:0:8}...)"
else
  echo "ENV_VAR: NOT_FOUND"
fi

# 2. Check .env in current working directory only
if [ -f ".env" ] && grep -q "MESHY_API_KEY" ".env" 2>/dev/null; then
  echo "DOTENV(.env): FOUND"
  export MESHY_API_KEY=$(grep "^MESHY_API_KEY=" ".env" | head -1 | cut -d'=' -f2- | tr -d '"'"'" )
fi

# 3. Final status
if [ -n "$MESHY_API_KEY" ]; then
  echo "READY: key=${MESHY_API_KEY:0:8}..."
else
  echo "READY: NO_KEY_FOUND"
fi

# 4. Python requests check
python3 -c "import requests; print('PYTHON_REQUESTS: OK')" 2>/dev/null || echo "PYTHON_REQUESTS: MISSING (run: pip install requests)"

echo "=== Detection Complete ==="

Decision After Detection

• Key found → Proceed to Step 1.
• Key NOT found → Go to Step 0a.
• Python requests missing → Run pip install requests.

---

Step 0a: API Key Setup (Only If No Key Found)

Tell the user:

To use the Meshy API, you need an API key:

1. Go to https://www.meshy.ai/settings/api
2. Click "Create API Key", name it, and copy the key (starts with msy_)
3. The key is shown only once — save it somewhere safe

Note: API access requires a Pro plan or above. Free-tier accounts cannot create API keys.

Once the user provides the key, set it for the current session and optionally persist to .env:

# Set for current session only
export MESHY_API_KEY="msy_PASTE_KEY_HERE"

# Verify the key
STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
  -H "Authorization: Bearer $MESHY_API_KEY" \
  https://api.meshy.ai/openapi/v1/balance)

if [ "$STATUS" = "200" ]; then
  BALANCE=$(curl -s -H "Authorization: Bearer $MESHY_API_KEY" https://api.meshy.ai/openapi/v1/balance)
  echo "Key valid. $BALANCE"
else
  echo "Key invalid (HTTP $STATUS). Please check the key and try again."
fi

To persist the key (current project only):

# Write to .env in current working directory
echo 'MESHY_API_KEY=msy_PASTE_KEY_HERE' >> .env
echo "Saved to .env"

# IMPORTANT: add .env to .gitignore to avoid leaking the key
grep -q "^\.env" .gitignore 2>/dev/null || echo ".env" >> .gitignore
echo ".env added to .gitignore"

Security reminder: The key is stored only in .env in your current project directory. Never commit this file to version control. .env has been automatically added to .gitignore.

---

Step 1: Confirm Plan With User Before Spending Credits

CRITICAL: Before creating any task, present the user with a cost summary and wait for confirmation:

I'll generate a 3D model of "<prompt>" using the following plan:

  1. Preview (mesh generation) — 20 credits
  2. Refine (texturing with PBR) — 10 credits
  3. Download as .glb

  Total cost: 30 credits
  Current balance: <N> credits

  Shall I proceed?

For multi-step pipelines (text-to-3d → rig → animate), show the FULL pipeline cost upfront.

Note: Rigging automatically includes walking + running animations at no extra cost. Only add Animate (3 credits) for custom animations beyond those.

Intent → API Mapping

User wants to...	API	Endpoint	Credits
3D model from text	Text to 3D	POST /openapi/v2/text-to-3d	5–20 (preview) + 10 (refine)
3D model from one image	Image to 3D	POST /openapi/v1/image-to-3d	5–30
3D model from multiple images	Multi-Image to 3D	POST /openapi/v1/multi-image-to-3d	5–30
New textures on existing model	Retexture	POST /openapi/v1/retexture	10
Change mesh format/topology	Remesh	POST /openapi/v1/remesh	5
Convert a model to other formats (no remesh)	Convert	POST /openapi/v1/convert	1
Rescale a model to real-world size	Resize	POST /openapi/v1/resize	1
Generate fresh UVs (GLB, ≤40k faces) before external texturing	UV Unwrap	POST /openapi/v1/uv-unwrap	5
Add skeleton to character	Auto-Rigging	POST /openapi/v1/rigging	5
Animate a rigged character	Animation	POST /openapi/v1/animations	3
2D image from text (recommended pre-step before image-to-3d)	Text to Image	POST /openapi/v1/text-to-image	3 / 6 / 9 / 9
Optimize/edit a 2D image (recommended pre-step before image-to-3d)	Image to Image	POST /openapi/v1/image-to-image	3 / 6 / 9 / 12
Photo → styled physical product (figure/lamp/keychain/fridge-magnet)	Creative Lab	POST /openapi/creative-lab/{product}/v1/prototype then .../build	6 + 30
Check FDM printability	Analyze Printability	POST /openapi/v1/print/analyze	0 (free)
Repair non-manifold/degenerate-face/hole topology	Repair Printability	POST /openapi/v1/print/repair	10
Multi-color 3D print	Multi-Color Print	POST /openapi/v1/print/multi-color	10 (+ generation)
3D print a model (white)	→ See Print Pipeline section	—	20
Check credit balance	Balance	GET /openapi/v1/balance	0

---

Step 2: Execute the Workflow

Reusable Script Template

Use this as the base for ALL workflows. It loads the API key securely from environment or .env in the current directory only:

#!/usr/bin/env python3
"""Meshy API task runner. Handles create → poll → download."""
import requests, time, os, sys, re, json
from datetime import datetime

# --- Secure API key loading ---
def load_api_key():
    """Load MESHY_API_KEY from environment, then .env in cwd only."""
    key = os.environ.get("MESHY_API_KEY", "").strip()
    if key:
        return key
    env_path = os.path.join(os.getcwd(), ".env")
    if os.path.exists(env_path):
        with open(env_path) as f:
            for line in f:
                line = line.strip()
                if line.startswith("MESHY_API_KEY=") and not line.startswith("#"):
                    val = line.split("=", 1)[1].strip().strip('"').strip("'")
                    if val:
                        return val
    return ""

API_KEY = load_api_key()
if not API_KEY:
    sys.exit("ERROR: MESHY_API_KEY not set. Run Step 0a to configure it.")

# Never log the full key — only first 8 chars for traceability
print(f"API key loaded: {API_KEY[:8]}...")

BASE = "https://api.meshy.ai"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}
SESSION = requests.Session()
SESSION.trust_env = False  # bypass any system proxy settings

def create_task(endpoint, payload):
    resp = SESSION.post(f"{BASE}{endpoint}", headers=HEADERS, json=payload, timeout=30)
    if resp.status_code == 401:
        sys.exit("ERROR: Invalid API key (401). Re-run Step 0a.")
    if resp.status_code == 402:
        try:
            bal = SESSION.get(f"{BASE}/openapi/v1/balance", headers=HEADERS, timeout=10)
            balance = bal.json().get("balance", "unknown")
            sys.exit(f"ERROR: Insufficient credits (402). Balance: {balance}. Top up at https://www.meshy.ai/pricing")
        except Exception:
            sys.exit("ERROR: Insufficient credits (402). Check balance at https://www.meshy.ai/pricing")
    if resp.status_code == 429:
        sys.exit("ERROR: Rate limited (429). Wait and retry.")
    resp.raise_for_status()
    task_id = resp.json()["result"]
    print(f"TASK_CREATED: {task_id}")
    return task_id

def poll_task(endpoint, task_id, timeout=300):
    """Poll with exponential backoff (5s→30s, fixed 15s at 95%+)."""
    elapsed, delay, max_delay, backoff, finalize_delay, poll_count = 0, 5, 30, 1.5, 15, 0
    while elapsed < timeout:
        poll_count += 1
        resp = SESSION.get(f"{BASE}{endpoint}/{task_id}", headers=HEADERS, timeout=30)
        resp.raise_for_status()
        task = resp.json()
        status = task["status"]
        progress = task.get("progress", 0)
        bar = f"[{'█' * int(progress/5)}{'░' * (20 - int(progress/5))}] {progress}%"
        print(f"  {bar} — {status} ({elapsed}s, poll #{poll_count})", flush=True)
        if status == "SUCCEEDED":
            return task
        if status in ("FAILED", "CANCELED"):
            msg = task.get("task_error", {}).get("message", "Unknown")
            sys.exit(f"TASK_{status}: {msg}")
        current_delay = finalize_delay if progress >= 95 else delay
        time.sleep(current_delay)
        elapsed += current_delay
        if progress < 95:
            delay = min(delay * backoff, max_delay)
    sys.exit(f"TIMEOUT after {timeout}s ({poll_count} polls)")

def download(url, filepath):
    """Download a file into a project directory (within cwd/meshy_output/)."""
    os.makedirs(os.path.dirname(filepath), exist_ok=True)
    print(f"Downloading {filepath}...", flush=True)
    resp = SESSION.get(url, timeout=300, stream=True)
    resp.raise_for_status()
    with open(filepath, "wb") as f:
        for chunk in resp.iter_content(chunk_size=8192):
            f.write(chunk)
    print(f"DOWNLOADED: {filepath} ({os.path.getsize(filepath)/1024/1024:.1f} MB)")

# --- File organization helpers ---
OUTPUT_ROOT = os.path.join(os.getcwd(), "meshy_output")
os.makedirs(OUTPUT_ROOT, exist_ok=True)
HISTORY_FILE = os.path.join(OUTPUT_ROOT, "history.json")

def get_project_dir(task_id, prompt="", task_type="model"):
    slug = re.sub(r'[^a-z0-9]+', '-', (prompt or task_type).lower())[:30].strip('-')
    folder = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{slug}_{task_id[:8]}"
    project_dir = os.path.join(OUTPUT_ROOT, folder)
    os.makedirs(project_dir, exist_ok=True)
    return project_dir

def record_task(project_dir, task_id, task_type, stage, prompt="", files=None):
    meta_path = os.path.join(project_dir, "metadata.json")
    meta = json.load(open(meta_path)) if os.path.exists(meta_path) else {
        "project_name": prompt or task_type, "folder": os.path.basename(project_dir),
        "root_task_id": task_id, "created_at": datetime.now().isoformat(), "tasks": []
    }
    meta["tasks"].append({"task_id": task_id, "task_type": task_type, "stage": stage,
                          "files": files or [], "created_at": datetime.now().isoformat()})
    meta["updated_at"] = datetime.now().isoformat()
    json.dump(meta, open(meta_path, "w"), indent=2)
    history = json.load(open(HISTORY_FILE)) if os.path.exists(HISTORY_FILE) else {"version": 1, "projects": []}
    folder = os.path.basename(project_dir)
    entry = next((p for p in history["projects"] if p["folder"] == folder), None)
    if entry:
        entry.update({"task_count": len(meta["tasks"]), "updated_at": meta["updated_at"]})
    else:
        history["projects"].append({"folder": folder, "prompt": prompt, "task_type": task_type,
            "root_task_id": task_id, "created_at": meta["created_at"],
            "updated_at": meta["updated_at"], "task_count": len(meta["tasks"])})
    json.dump(history, open(HISTORY_FILE, "w"), indent=2)

def save_thumbnail(project_dir, url):
    path = os.path.join(project_dir, "thumbnail.png")
    if os.path.exists(path): return
    try:
        r = SESSION.get(url, timeout=15); r.raise_for_status()
        open(path, "wb").write(r.content)
    except Exception: pass

---

Text to 3D (Preview + Refine)

Append to the template above:

PROMPT = "USER_PROMPT"

# Preview
preview_id = create_task("/openapi/v2/text-to-3d", {
    "mode": "preview",
    "prompt": PROMPT,
    "ai_model": "latest",
    # "pose_mode": "t-pose",   # Use "t-pose" if rigging/animating later
    # "hd_texture": True,      # 4K base color on refine (meshy-6/latest only)
    # "target_formats": ["glb", "3mf"],  # 3mf must be explicitly requested
    # NOTE: symmetry_mode / art_style / is_a_t_pose are deprecated (symmetry_mode & art_style ignored; use pose_mode)
})
task = poll_task("/openapi/v2/text-to-3d", preview_id)
project_dir = get_project_dir(preview_id, prompt=PROMPT)
download(task["model_urls"]["glb"], os.path.join(project_dir, "preview.glb"))
record_task(project_dir, preview_id, "text-to-3d", "preview", prompt=PROMPT, files=["preview.glb"])
if task.get("thumbnail_url"):
    save_thumbnail(project_dir, task["thumbnail_url"])
print(f"\nPREVIEW COMPLETE — Task: {preview_id} | Project: {project_dir}")

# Refine
refine_id = create_task("/openapi/v2/text-to-3d", {
    "mode": "refine",
    "preview_task_id": preview_id,
    "enable_pbr": True,
    "ai_model": "latest",
})
task = poll_task("/openapi/v2/text-to-3d", refine_id)
download(task["model_urls"]["glb"], os.path.join(project_dir, "refined.glb"))
record_task(project_dir, refine_id, "text-to-3d", "refined", prompt=PROMPT, files=["refined.glb"])
print(f"\nREFINE COMPLETE — Task: {refine_id} | Formats: {', '.join(task['model_urls'].keys())}")

Refine compatibility: Refine works with meshy-5, meshy-6, or latest (= Meshy 6) — pick the same family as your preview for consistency. Refine costs 10 credits regardless of model. (meshy-4 is retired and returns 400.)

---

(Optional but strongly recommended) 2D Optimization Pre-Step

Prefer the image-to-3d route over direct text-to-3d — it's higher quality and more controllable, so for a text-only request make a design image first, then 3D-ify.

Image quality directly determines 3D model quality. Before calling /openapi/v1/image-to-3d or /openapi/v1/multi-image-to-3d, evaluate the user's input and proactively suggest a 2D pass:

User input	Recommended pre-step
Only a text description, no reference image	/openapi/v1/text-to-image with nano-banana-pro. For characters add generate_multi_view: True and pose_mode: "a-pose" or "t-pose" for rig-friendly output.
Reference image is low-resolution / cluttered background / unclear subject / bad lighting	/openapi/v1/image-to-image with nano-banana-pro to clean up.
User wants to adjust style / colors / details	/openapi/v1/image-to-image for style transfer, then 3D-ify.

3-9 extra credits typically buy a noticeable quality bump. Skip when the user already provided a clean studio-style image. Also skip for Creative Lab products (figure / lamp / keychain / fridge-magnet): they apply their own built-in stylization — feed the raw photo (or text, for lamp) straight to Creative Lab, not through text-to-image / image-to-image first.

Image to 3D

import base64

# For local files: convert to data URI
# with open("photo.jpg", "rb") as f:
#     image_url = "data:image/jpeg;base64," + base64.b64encode(f.read()).decode()

task_id = create_task("/openapi/v1/image-to-3d", {
    "image_url": "IMAGE_URL_OR_DATA_URI",
    "should_texture": True,
    "enable_pbr": True,
    "ai_model": "latest",
})
task = poll_task("/openapi/v1/image-to-3d", task_id)
project_dir = get_project_dir(task_id, task_type="image-to-3d")
download(task["model_urls"]["glb"], os.path.join(project_dir, "model.glb"))
record_task(project_dir, task_id, "image-to-3d", "complete", files=["model.glb"])

---

Multi-Image to 3D

task_id = create_task("/openapi/v1/multi-image-to-3d", {
    "image_urls": ["URL_1", "URL_2", "URL_3"],  # 1–4 images
    "should_texture": True,
    "enable_pbr": True,
    "ai_model": "latest",
})
task = poll_task("/openapi/v1/multi-image-to-3d", task_id)
project_dir = get_project_dir(task_id, task_type="multi-image-to-3d")
download(task["model_urls"]["glb"], os.path.join(project_dir, "model.glb"))

---

Retexture

IMPORTANT: Ask user for texture style first — text_style_prompt OR image_style_url (one required, image takes precedence if both given).

# REQUIRED: ask user for text_style_prompt OR image_style_url
task_id = create_task("/openapi/v1/retexture", {
    "input_task_id": "PREVIOUS_TASK_ID",
    "text_style_prompt": "wooden texture",     # REQUIRED if no image_style_url
    # "image_style_url": "URL",               # REQUIRED if no prompt (takes precedence)
    "enable_pbr": True,
    # "target_formats": ["glb", "3mf"],  # 3mf must be explicitly requested
})
task = poll_task("/openapi/v1/retexture", task_id)
project_dir = get_project_dir(task_id, task_type="retexture")
download(task["model_urls"]["glb"], os.path.join(project_dir, "retextured.glb"))

---

Remesh / Format Conversion

task_id = create_task("/openapi/v1/remesh", {
    "input_task_id": "TASK_ID",
    "target_formats": ["glb", "fbx", "obj"],
    "topology": "quad",
    "target_polycount": 10000,
})
task = poll_task("/openapi/v1/remesh", task_id)
project_dir = get_project_dir(task_id, task_type="remesh")
for fmt, url in task["model_urls"].items():
    download(url, os.path.join(project_dir, f"remeshed.{fmt}"))

---

Mesh Utilities (Convert / Resize / UV Unwrap)

Lightweight post-processing on a finished model (via input_task_id or model_url):

# Convert to other formats without remeshing (1 credit). Cheapest way to get 3MF/STL.
conv_id = create_task("/openapi/v1/convert", {
    "input_task_id": "TASK_ID",         # or "model_url": "URL"
    "target_formats": ["stl", "3mf"],   # required: glb/fbx/obj/usdz/blend/stl/3mf
})
poll_task("/openapi/v1/convert", conv_id)

# Resize to a real-world size (1 credit). Give EXACTLY ONE resize mode.
resize_id = create_task("/openapi/v1/resize", {
    "input_task_id": "TASK_ID",         # or "model_url": "URL"
    "resize_height": 0.15,              # meters — OR "resize_longest_side": 0.2  OR "auto_size": True
    # "origin_at": "bottom",            # "bottom" | "center"
})
poll_task("/openapi/v1/resize", resize_id)

# UV Unwrap a GLB (5 credits). GLB only, ≤ 40,000 faces (else 400 → remesh down first).
# Output: a GLB "UV white model" (fresh UVs + placeholder grey material) for external texturing.
uv_id = create_task("/openapi/v1/uv-unwrap", {
    "input_task_id": "TASK_ID",         # or "model_url": "GLB_URL"
})
poll_task("/openapi/v1/uv-unwrap", uv_id)

---

Creative Lab Consumer Products

Turn a photo into a styled, printable physical product. Products: figure, lamp, keychain, fridge-magnet. Two stages (replace {product} with one of those):

1. Prototype (6 credits): photo → styled concept image.
2. Build (30 credits): runs image-to-3d on the SUCCEEDED prototype → textured GLB / OBJ+MTL.

PRODUCT = "figure"  # figure | lamp | keychain | fridge-magnet

# Stage 1 — prototype (6 credits)
proto_id = create_task(f"/openapi/creative-lab/{PRODUCT}/v1/prototype", {
    "image_url": "PHOTO_URL_OR_DATA_URI",   # jpg/jpeg/png/webp
    # "name": "My figure",                  # optional, ≤ 100 chars
})
poll_task(f"/openapi/creative-lab/{PRODUCT}/v1/prototype", proto_id)

# Stage 2 — build (30 credits). Must reference a SUCCEEDED prototype of the SAME product.
# Web-app prototypes are rejected with 404 — only API-created prototypes are accepted.
build_id = create_task(f"/openapi/creative-lab/{PRODUCT}/v1/build", {
    "input_task_id": proto_id,
})
build = poll_task(f"/openapi/creative-lab/{PRODUCT}/v1/build", build_id)
project_dir = get_project_dir(build_id, task_type=f"creative-lab-{PRODUCT}")
download(build["model_urls"]["glb"], os.path.join(project_dir, "creative-lab.glb"))

# To MULTICOLOR a Creative Lab result: a Creative Lab model can only be sent to multi-color
# as model_url — pass the build's GLB URL (or a data URI of the downloaded GLB):
#   create_task("/openapi/v1/print/multi-color", {"model_url": build["model_urls"]["glb"], "max_colors": 4})

---

Auto-Rigging + Animation

When the user asks to rig or animate, the generation step MUST use pose_mode: "t-pose".

# Pre-rig check: polycount must be ≤ 300,000
source_endpoint = "/openapi/v2/text-to-3d"  # adjust to match source task endpoint
source_task_id = "TASK_ID"
check = SESSION.get(f"{BASE}{source_endpoint}/{source_task_id}", headers=HEADERS, timeout=30)
check.raise_for_status()
face_count = check.json().get("face_count", 0)
if face_count > 300000:
    sys.exit(f"ERROR: {face_count:,} faces exceeds 300,000 limit. Remesh first.")

# Rig
rig_id = create_task("/openapi/v1/rigging", {
    "input_task_id": source_task_id,
    "height_meters": 1.7,
})
rig_task = poll_task("/openapi/v1/rigging", rig_id)
project_dir = get_project_dir(rig_id, task_type="rigging")
download(rig_task["result"]["rigged_character_glb_url"], os.path.join(project_dir, "rigged.glb"))
download(rig_task["result"]["basic_animations"]["walking_glb_url"], os.path.join(project_dir, "walking.glb"))
download(rig_task["result"]["basic_animations"]["running_glb_url"], os.path.join(project_dir, "running.glb"))

# Custom animation (optional, 3 credits — only if user needs beyond walking/running)
# anim_id = create_task("/openapi/v1/animations", {"rig_task_id": rig_id, "action_id": 1})
# anim_task = poll_task("/openapi/v1/animations", anim_id)
# download(anim_task["result"]["animation_glb_url"], os.path.join(project_dir, "animated.glb"))

---

Text to Image / Image to Image

# Text to Image
task_id = create_task("/openapi/v1/text-to-image", {
    "ai_model": "nano-banana-pro",
    "prompt": "a futuristic spaceship",
})
task = poll_task("/openapi/v1/text-to-image", task_id)
# Result URL: task["image_url"]

# Image to Image
task_id = create_task("/openapi/v1/image-to-image", {
    "ai_model": "nano-banana-pro",
    "prompt": "make it look cyberpunk",
    "reference_image_urls": ["URL"],
})
task = poll_task("/openapi/v1/image-to-image", task_id)

---

3D Printing Workflow

IMPORTANT: When the user's request involves 3D printing, use this section for the ENTIRE workflow — including model generation. Do NOT run the generation workflows above and then come here. This section controls target_formats and other print-specific parameters from the start.

Trigger when the user mentions: print, 3d print, slicer, slice, bambu, orca, prusa, cura, multicolor, multi-color, 3mf, figurine, miniature, statue, physical model, desk toy, phone stand.

Decision: White Model vs Multicolor

1. Detect installed slicers first (see script below)
2. Ask the user: "White model (single-color) or multicolor?"
3. If multicolor: check for multicolor-capable slicer (OrcaSlicer, Bambu Studio, Creality Print, Elegoo Slicer, Anycubic Slicer Next), ask max_colors (1-16, default 4) and max_depth (3-6, default 4), confirm cost: 40 credits (+10 if repair is needed)
4. (Recommended) After generation, run a printability analysis (POST /openapi/v1/print/analyze, FREE). Run POST /openapi/v1/print/repair (10 credits) only if status = error.

Printability Analysis & Repair

# After the textured/final mesh is ready:
INPUT_TASK_ID = refine_id  # or whatever produced the print-ready mesh

# input_task_id MUST refer to a Meshy 6 / Preview task. For Meshy 4/5 outputs,
# pass `model_url` (the GLB download URL) instead.
analyze_id = create_task("/openapi/v1/print/analyze", {
    "input_task_id": INPUT_TASK_ID,
})
analyze_task = poll_task("/openapi/v1/print/analyze", analyze_id)

p = analyze_task.get("printability") or {}
metrics = p.get("metrics", {})
print(f"Printability: {p.get('status')}: {metrics}")

if p.get("status") == "error":
    repair_id = create_task("/openapi/v1/print/repair", {
        "input_task_id": INPUT_TASK_ID,   # output is GLB
    })
    repair_task = poll_task("/openapi/v1/print/repair", repair_id)
    repaired_url = next((u for u in repair_task["model_urls"].values() if u), None)
    # Use repaired_url for the next step (download / multicolor / slicer)

Status meanings: healthy (print as-is) | warning (degenerate faces / holes — repair optional) | error (non-watertight / non-manifold — repair recommended) | unknown (analyze failed). Repair preserves geometry only, not textures — re-texture if needed for multicolor.

Slicer Detection + Opening

import subprocess, shutil, platform, os, glob as glob_mod

SLICER_MAP = {
    "OrcaSlicer":           {"mac_app": "OrcaSlicer",          "win_exe": "orca-slicer.exe",         "win_dir": "OrcaSlicer",          "linux_exe": "orca-slicer"},
    "Bambu Studio":         {"mac_app": "BambuStudio",         "win_exe": "bambu-studio.exe",        "win_dir": "BambuStudio",         "linux_exe": "bambu-studio"},
    "Creality Print":       {"mac_app": "Creality Print",      "win_exe": "CrealityPrint.exe",       "win_dir": "Creality Print*",     "linux_exe": None},
    "Elegoo Slicer":        {"mac_app": "ElegooSlicer",        "win_exe": "elegoo-slicer.exe",       "win_dir": "ElegooSlicer",        "linux_exe": None},
    "Anycubic Slicer Next": {"mac_app": "AnycubicSlicerNext",  "win_exe": "AnycubicSlicerNext.exe",  "win_dir": "AnycubicSlicerNext",  "linux_exe": None},
    "PrusaSlicer":          {"mac_app": "PrusaSlicer",         "win_exe": "prusa-slicer.exe",        "win_dir": "PrusaSlicer",         "linux_exe": "prusa-slicer"},
    "UltiMaker Cura":       {"mac_app": "UltiMaker Cura",      "win_exe": "UltiMaker-Cura.exe",     "win_dir": "UltiMaker Cura*",     "linux_exe": None},
}
MULTICOLOR_SLICERS = {"OrcaSlicer", "Bambu Studio", "Creality Print", "Elegoo Slicer", "Anycubic Slicer Next"}

def detect_slicers():
    found = []
    system = platform.system()
    for name, info in SLICER_MAP.items():
        path = None
        if system == "Darwin":
            app = info.get("mac_app")
            if app and os.path.exists(f"/Applications/{app}.app"):
                path = f"/Applications/{app}.app"
        elif system == "Windows":
            win_dir, win_exe = info.get("win_dir", ""), info.get("win_exe", "")
            for base in [os.environ.get("ProgramFiles", r"C:\Program Files"),
                         os.environ.get("ProgramFiles(x86)", r"C:\Program Files (x86)")]:
                if "*" in win_dir:
                    matches = glob_mod.glob(os.path.join(base, win_dir, win_exe))
                    if matches: path = matches[0]; break
                else:
                    candidate = os.path.join(base, win_dir, win_exe)
                    if os.path.exists(candidate): path = candidate; break
        else:
            exe = info.get("linux_exe")
            if exe: path = shutil.which(exe)
        if path:
            found.append({"name": name, "path": path, "multicolor": name in MULTICOLOR_SLICERS})
    return found

def open_in_slicer(file_path, slicer_name):
    info = SLICER_MAP.get(slicer_name, {})
    system, abs_path = platform.system(), os.path.abspath(file_path)
    if system == "Darwin":
        subprocess.run(["open", "-a", info.get("mac_app", slicer_name), abs_path])
    elif system == "Windows":
        exe_path = shutil.which(info.get("win_exe", ""))
        (subprocess.Popen([exe_path, abs_path]) if exe_path else os.startfile(abs_path))
    else:
        exe_path = shutil.which(info.get("linux_exe", ""))
        (subprocess.Popen([exe_path, abs_path]) if exe_path else subprocess.run(["xdg-open", abs_path]))
    print(f"Opened {abs_path} in {slicer_name}")

# Several SEPARATE/unrelated models (e.g. results from different tasks)? Call this once per
# model with a short gap — Bambu Studio especially may respond to only one if fired
# back-to-back. (Parts of ONE model belong in a single project — not spaced.)
#     import time
#     for f in [model_a, model_b, model_c]:
#         open_in_slicer(f, slicer_name); time.sleep(2)

slicers = detect_slicers()
for s in slicers:
    mc = " [multicolor]" if s["multicolor"] else ""
    print(f"  - {s['name']}{mc}: {s['path']}")

White Model Pipeline

Step	Action	Credits
1	Generate untextured model	20
2	Download OBJ	0
3	Fix OBJ (fix_obj_for_printing)	0
4	Open in slicer	0

Generate with target_formats including "obj", then fix for printing:

# --- Generate for white model printing ---
# Text to 3D:
task_id = create_task("/openapi/v2/text-to-3d", {
    "mode": "preview", "prompt": "USER_PROMPT", "ai_model": "latest",
    "target_formats": ["obj"],  # Only OBJ for white model printing
})
# OR Image to 3D:
# task_id = create_task("/openapi/v1/image-to-3d", {
#     "image_url": "URL", "should_texture": False,
#     "target_formats": ["glb", "obj"],
# })
task = poll_task("/openapi/v2/text-to-3d", task_id)
project_dir = get_project_dir(task_id, "print")

obj_url = task["model_urls"].get("obj") or task["model_urls"].get("glb")
obj_path = os.path.join(project_dir, "model.obj")
download(obj_url, obj_path)

def fix_obj_for_printing(input_path, output_path=None, target_height_mm=75.0):
    if output_path is None: output_path = input_path
    lines = open(input_path, "r").readlines()
    rotated, min_x, max_x, min_y, max_y, min_z, max_z = [], float("inf"), float("-inf"), float("inf"), float("-inf"), float("inf"), float("-inf")
    for line in lines:
        if line.startswith("v "):
            parts = line.split()
            x, y, z = float(parts[1]), float(parts[2]), float(parts[3])
            rx, ry, rz = x, -z, y
            min_x, max_x = min(min_x, rx), max(max_x, rx)
            min_y, max_y = min(min_y, ry), max(max_y, ry)
            min_z, max_z = min(min_z, rz), max(max_z, rz)
            rotated.append(("v", rx, ry, rz, parts[4:]))
        elif line.startswith("vn "):
            parts = line.split()
            rotated.append(("vn", float(parts[1]), -float(parts[3]), float(parts[2]), []))
        else:
            rotated.append(("line", line))
    h = max_z - min_z
    s = target_height_mm / h if h > 1e-6 else 1.0
    xo, yo, zo = -(min_x+max_x)/2*s, -(min_y+max_y)/2*s, -(min_z*s)
    with open(output_path, "w") as f:
        for item in rotated:
            if item[0] == "v":
                _, rx, ry, rz, extra = item
                e = " "+" ".join(extra) if extra else ""
                f.write(f"v {rx*s+xo:.6f} {ry*s+yo:.6f} {rz*s+zo:.6f}{e}\n")
            elif item[0] == "vn":
                f.write(f"vn {item[1]:.6f} {item[2]:.6f} {item[3]:.6f}\n")
            else:
                f.write(item[1])
    print(f"OBJ fixed: scaled to {target_height_mm:.0f}mm, Z-up, centered")

fix_obj_for_printing(obj_path, target_height_mm=75.0)
if slicers: open_in_slicer(obj_path, slicers[0]["name"])

Multicolor Pipeline

Step	Action	Credits
1	Generate + texture	30
2	Multi-color processing	10
3	Download 3MF	0
4	Open in multicolor slicer	0

mc_slicers = [s for s in slicers if s["multicolor"]]
if not mc_slicers:
    print("WARNING: No multicolor slicer detected. Install: OrcaSlicer, Bambu Studio, etc.")

# --- Generate + texture with target_formats including 3mf ---
preview_id = create_task("/openapi/v2/text-to-3d", {
    "mode": "preview", "prompt": "USER_PROMPT", "ai_model": "latest",
    # No target_formats needed — 3MF comes from multi-color API
})
poll_task("/openapi/v2/text-to-3d", preview_id)

refine_id = create_task("/openapi/v2/text-to-3d", {
    "mode": "refine", "preview_task_id": preview_id, "enable_pbr": True,
})
poll_task("/openapi/v2/text-to-3d", refine_id)
project_dir = get_project_dir(preview_id, "multicolor-print")

# --- Multi-color processing ---
mc_task_id = create_task("/openapi/v1/print/multi-color", {
    "input_task_id": refine_id,
    "max_colors": 4,   # 1-16, ask user
    "max_depth": 4,     # 3-6, ask user
})
task = poll_task("/openapi/v1/print/multi-color", mc_task_id)
threemf_path = os.path.join(project_dir, "multicolor.3mf")
download(task["model_urls"]["3mf"], threemf_path)
if mc_slicers: open_in_slicer(threemf_path, mc_slicers[0]["name"])

Printability Checklist

Check	Recommendation
Wall thickness	Min 1.2mm FDM, 0.8mm resin
Overhangs	Keep below 45° or add supports
Manifold mesh	Watertight, no holes
Minimum detail	0.4mm FDM, 0.05mm resin
Base stability	Flat base or add brim/raft in slicer
Floating parts	All parts connected or printed separately

---

Step 3: Report Results

After task succeeds:

1. Downloaded file paths and sizes
2. Task IDs (for follow-up: refine, rig, retexture)
3. Available formats (list model_urls keys)
4. Credits consumed + current balance
5. Suggested next steps:
   • Preview done → "Want to refine (add textures)?"
   • Model done → "Want to rig this character?"
   • Rigged → "Want to apply a custom animation?"
   • Any textured model → "Want to 3D print this? Multicolor printing is available!"
   • Any model → "Want to 3D print this?"

---

Error Recovery

HTTP Status	Meaning	Action
401	Invalid API key	Re-run Step 0; ask user to check key
402	Insufficient credits	Show balance, link https://www.meshy.ai/pricing
422	Cannot process	Explain (e.g., non-humanoid for rigging)
429	Rate limited	Auto-retry after 5s (max 3 times)
5xx	Server error	Auto-retry after 10s (once)

Task FAILED messages:

• "The server is busy..." → retry with backoff (5s, 10s, 20s)
• "Internal server error." → simplify prompt, retry once

---

Known Behaviors & Constraints

• 99% stall: Normal finalization (30–120s). Do NOT interrupt.
• Asset retention: Files deleted after 3 days (non-Enterprise). Download immediately.
• PBR maps: Must set enable_pbr: true explicitly.
• Refine: Works with meshy-5, meshy-6, or latest — pick the same family as your preview for consistency. 10 credits regardless of model. (meshy-4 is retired → 400.)
• Deprecated params: symmetry_mode no longer affects output; art_style is ignored by Meshy-6; use pose_mode instead of the old is_a_t_pose flag.
• consumed_credits: Every task GET response includes consumed_credits — read it to report the real credits spent rather than estimating.
• Rigging: Humanoid bipedal only, polycount ≤ 300,000.
• Printing formats: White model → OBJ with fix_obj_for_printing(). Multicolor → 3MF from Multi-Color Print API. Always detect slicer first.
• Download format: Ask the user which format they need before downloading. GLB (viewing), OBJ (printing), 3MF (multicolor), FBX (games), USDZ (AR). Do NOT download all formats.
• 3MF for multicolor: Multi-Color Print API outputs 3MF directly — no need to request 3MF from generate/refine. For non-print use cases needing 3MF, pass "3mf" in target_formats.
• Timestamps: All API timestamps are Unix epoch milliseconds.

---

Execution Checklist

• Ran API key detection (Step 0) — checked env var and .env only
• API key verified (never printed in full)
• Presented cost summary and got user confirmation
• Wrote complete workflow as single Python script
• Ran with python3 -u for unbuffered output
• Reported file paths, formats, task IDs, and balance
• Suggested next steps

---

Additional Resources

For the complete API endpoint reference including all parameters, response schemas, and error codes, read reference.md.