feat: Add ModelsLabImageGenerationTool for image generation

lib/crewai-tools/src/crewai_tools/tools/__init__.py

@@ -37,6 +37,7 @@
 )
 from crewai_tools.tools.csv_search_tool.csv_search_tool import CSVSearchTool
 from crewai_tools.tools.dalle_tool.dalle_tool import DallETool
+from crewai_tools.tools.modelslab_tool.modelslab_tool import ModelsLabImageGenerationTool
 from crewai_tools.tools.databricks_query_tool.databricks_query_tool import (
     DatabricksQueryTool,
 )
@@ -202,6 +203,7 @@
     "CrewaiPlatformTools",
     "DOCXSearchTool",
     "DallETool",
+    "ModelsLabImageGenerationTool",
     "DatabricksQueryTool",
     "DirectoryReadTool",
     "DirectorySearchTool",

lib/crewai-tools/src/crewai_tools/tools/modelslab_tool/README.md

@@ -0,0 +1,76 @@
+# ModelsLab Image Generation Tool
+
+Generates images from text prompts using [ModelsLab's](https://modelslab.com) text-to-image API.
+
+ModelsLab provides access to 200+ AI models including Flux, SDXL, Stable Diffusion, and thousands of community fine-tunes via a single unified API.
+
+## Installation
+
+```bash
+pip install 'crewai-tools'
+pip install requests
+```
+
+## Prerequisites
+
+Set your ModelsLab API key as an environment variable:
+
+```bash
+export MODELSLAB_API_KEY="your-api-key"
+```
+
+Get your API key at https://modelslab.com/dashboard/api-keys
+
+## Usage
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import ModelsLabImageGenerationTool
+
+# Basic usage with default Flux model
+image_tool = ModelsLabImageGenerationTool()
+
+# Custom model and dimensions
+image_tool = ModelsLabImageGenerationTool(
+    model="flux",          # or "sdxl", "realistic-vision-v51", etc.
+    width=1024,
+    height=1024,
+    negative_prompt="blurry, low quality",
+)
+
+designer = Agent(
+    role="Visual Designer",
+    goal="Create stunning images for the project",
+    backstory="An experienced visual designer with a great eye for aesthetics.",
+    tools=[image_tool],
+    verbose=True,
+)
+
+task = Task(
+    description="Create an image of a futuristic city at night with neon lights.",
+    agent=designer,
+    expected_output="A URL to the generated image.",
+)
+
+crew = Crew(agents=[designer], tasks=[task])
+result = crew.kickoff()
+print(result)
+```
+
+## Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `model` | str | `"flux"` | Model ID (e.g., `"flux"`, `"sdxl"`, `"realistic-vision-v51"`) |
+| `width` | int | `512` | Image width in pixels (divisible by 8) |
+| `height` | int | `512` | Image height in pixels (divisible by 8) |
+| `samples` | int | `1` | Number of images to generate (1–4) |
+| `num_inference_steps` | int | `30` | Denoising steps (1–50) |
+| `guidance_scale` | float | `7.5` | Prompt adherence (1–20) |
+| `negative_prompt` | str | `None` | Content to avoid in the image |
+| `seed` | int | `None` | Random seed for reproducibility |
+
+## API Reference
+
+- Docs: https://docs.modelslab.com/image-generation/community-models/text2img
+- Available models: https://modelslab.com/models

lib/crewai-tools/src/crewai_tools/tools/modelslab_tool/__init__.py

@@ -0,0 +1,3 @@
+from crewai_tools.tools.modelslab_tool.modelslab_tool import ModelsLabImageGenerationTool
+
+__all__ = ["ModelsLabImageGenerationTool"]

lib/crewai-tools/src/crewai_tools/tools/modelslab_tool/modelslab_tool.py

@@ -0,0 +1,163 @@
+import json
+import time
+from typing import Optional
+
+import requests
+from crewai.tools import BaseTool, EnvVar
+from pydantic import BaseModel, Field
+
+MODELSLAB_BASE_URL = "https://modelslab.com/api/v6"
+MODELSLAB_POLLING_INTERVAL = 3  # seconds between polls
+MODELSLAB_POLLING_TIMEOUT = 300  # 5 minute max
+
+
+class ModelsLabImageSchema(BaseModel):
+    """Input for ModelsLab Image Generation Tool."""
+
+    image_description: str = Field(
+        description="Text description of the image to generate."
+    )
+
+
+class ModelsLabImageGenerationTool(BaseTool):
+    """Generates images using ModelsLab's text-to-image API.
+
+    ModelsLab provides access to 200+ AI models including Flux, SDXL,
+    and thousands of community fine-tunes via a unified API.
+
+    Docs: https://docs.modelslab.com/image-generation/community-models/text2img
+    """
+
+    name: str = "ModelsLab Image Generation Tool"
+    description: str = (
+        "Generates images from text prompts using ModelsLab's AI API. "
+        "Supports Flux, SDXL, Stable Diffusion, and 200+ community models. "
+        "Returns a URL to the generated image."
+    )
+    args_schema: type[BaseModel] = ModelsLabImageSchema
+
+    model: str = "flux"
+    width: int = 512
+    height: int = 512
+    samples: int = 1
+    num_inference_steps: int = 30
+    guidance_scale: float = 7.5
+    negative_prompt: Optional[str] = None
+    seed: Optional[int] = None
+    api_base_url: str = MODELSLAB_BASE_URL
+
+    env_vars: list[EnvVar] = Field(
+        default_factory=lambda: [
+            EnvVar(
+                name="MODELSLAB_API_KEY",
+                description="API key for ModelsLab services. Get yours at https://modelslab.com/dashboard/api-keys",
+                required=True,
+            ),
+        ]
+    )
+
+    def _poll_for_result(self, generation_id: int, api_key: str) -> list[str]:
+        """Poll the fetch endpoint until image generation completes."""
+        fetch_url = f"{self.api_base_url.rstrip('/')}/images/fetch/{generation_id}"
+        start = time.time()
+
+        while True:
+            if time.time() - start > MODELSLAB_POLLING_TIMEOUT:
+                raise TimeoutError(
+                    f"ModelsLab image generation timed out after {MODELSLAB_POLLING_TIMEOUT}s "
+                    f"(generation_id={generation_id})."
+                )
+            response = requests.post(
+                fetch_url,
+                json={"key": api_key},
+                headers={"Content-Type": "application/json"},
+                timeout=30,
+            )
+            response.raise_for_status()
+            data = response.json()
+            status = data.get("status", "")
+
+            if status == "success":
+                return data.get("output", [])
+            elif status == "error":
+                raise RuntimeError(
+                    f"ModelsLab error: {data.get('message', 'unknown error')}"
+                )
+            elif status == "processing":
+                time.sleep(MODELSLAB_POLLING_INTERVAL)
+            else:
+                raise RuntimeError(
+                    f"ModelsLab: unexpected status '{status}' for generation {generation_id}"
+                )
+
+    def _run(self, **kwargs) -> str:
+        import os
+
+        image_description = kwargs.get("image_description", "")
+        if not image_description:
+            return "Image description is required."
+
+        api_key = os.environ.get("MODELSLAB_API_KEY", "")
+        if not api_key:
+            return "MODELSLAB_API_KEY environment variable is not set."
+
+        payload: dict = {
+            "key": api_key,
+            "prompt": image_description,
+            "model_id": self.model,
+            "width": self.width,
+            "height": self.height,
+            "samples": self.samples,
+            "num_inference_steps": self.num_inference_steps,
+            "guidance_scale": self.guidance_scale,
+            "safety_checker": "no",
+        }
+        if self.negative_prompt:
+            payload["negative_prompt"] = self.negative_prompt
+        if self.seed is not None:
+            payload["seed"] = self.seed
+
+        text2img_url = f"{self.api_base_url.rstrip('/')}/images/text2img"
+        try:
+            response = requests.post(
+                text2img_url,
+                json=payload,
+                headers={"Content-Type": "application/json"},
+                timeout=60,
+            )
+            response.raise_for_status()
+            data = response.json()
+        except requests.RequestException as e:
+            return f"ModelsLab API request failed: {e}"
+
+        status = data.get("status", "")
+
+        if status == "error":
+            return f"ModelsLab error: {data.get('message', 'unknown error')}"
+
+        if status == "processing":
+            generation_id = data.get("id")
+            if not generation_id:
+                return "ModelsLab returned 'processing' status without a generation ID."
+            try:
+                image_urls = self._poll_for_result(
+                    generation_id=generation_id, api_key=api_key
+                )
+            except (TimeoutError, RuntimeError) as e:
+                return f"ModelsLab polling failed: {e}"
+        elif status == "success":
+            image_urls = data.get("output", [])
+        else:
+            return f"ModelsLab: unexpected response status '{status}'."
+
+        if not image_urls:
+            return "ModelsLab returned no images."
+
+        return json.dumps(
+            {
+                "image_url": image_urls[0],
+                "all_image_urls": image_urls,
+                "model": self.model,
+                "prompt": image_description,
+            }
+        )