feat(image-editing): introduce image generation and editing tool with updated documentation

backend/agent/prompt.py

@@ -89,7 +89,31 @@ You have the ability to execute operations using both Python and CLI tools:
   * Supported formats include JPG, PNG, GIF, WEBP, and other common image formats.
   * Maximum file size limit is 10 MB.
 
-### 2.2.7 DATA PROVIDERS
+### 2.2.7 IMAGE GENERATION & EDITING
+- Use the 'image_edit_or_generate' tool to generate new images from a prompt or to edit an existing image file (no mask support).
+  * To generate a new image, set mode="generate" and provide a descriptive prompt.
+  * To edit an existing image, set mode="edit", provide the prompt, and specify the image_path.
+  * The image_path can be a full URL or a relative path to the `/workspace` directory.
+  * Example (generate):
+      <function_calls>
+      <invoke name="image_edit_or_generate">
+      <parameter name="mode">generate</parameter>
+      <parameter name="prompt">A futuristic cityscape at sunset</parameter>
+      </invoke>
+      </function_calls>
+  * Example (edit):
+      <function_calls>
+      <invoke name="image_edit_or_generate">
+      <parameter name="mode">edit</parameter>
+      <parameter name="prompt">Add a red hat to the person in the image</parameter>
+      <parameter name="image_path">http://example.com/images/person.png</parameter>
+      </invoke>
+      </function_calls>
+  * ALWAYS use this tool for any image creation or editing tasks. Do not attempt to generate or edit images by any other means.
+  * You must use edit mode when the user asks you to edit an image or change an existing image in any way.
+  * Once the image is generated or edited, you must display the image using the ask tool.
+
+### 2.2.8 DATA PROVIDERS
 - You have access to a variety of data providers that you can use to get data for your tasks.
 - You can use the 'get_data_provider_endpoints' tool to get the endpoints for a specific data provider.
 - You can use the 'execute_data_provider_call' tool to execute a call to a specific data provider endpoint.

backend/agent/run.py

@@ -25,6 +25,7 @@ from utils.logger import logger
 from utils.auth_utils import get_account_id_from_thread
 from services.billing import check_billing_status
 from agent.tools.sb_vision_tool import SandboxVisionTool
+from agent.tools.sb_image_edit_tool import SandboxImageEditTool
 from services.langfuse import langfuse
 from langfuse.client import StatefulTraceClient
 from services.langfuse import langfuse
@@ -107,6 +108,7 @@ async def run_agent(
         thread_manager.add_tool(MessageTool)
         thread_manager.add_tool(SandboxWebSearchTool, project_id=project_id, thread_manager=thread_manager)
         thread_manager.add_tool(SandboxVisionTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
+        thread_manager.add_tool(SandboxImageEditTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
         if config.RAPID_API_KEY:
             thread_manager.add_tool(DataProvidersTool)
     else:

backend/agent/tools/sb_image_edit_tool.py

@@ -0,0 +1,167 @@
+from typing import Optional
+from agentpress.tool import ToolResult, openapi_schema, xml_schema
+from sandbox.tool_base import SandboxToolsBase
+from agentpress.thread_manager import ThreadManager
+from openai import OpenAI
+import httpx
+import os
+from io import BytesIO
+import uuid
+
+
+class SandboxImageEditTool(SandboxToolsBase):
+    """Tool for generating or editing images using OpenAI DALL-E via OpenAI SDK (no mask support)."""
+
+    def __init__(self, project_id: str, thread_id: str, thread_manager: ThreadManager):
+        super().__init__(project_id, thread_manager)
+        self.thread_id = thread_id
+        self.thread_manager = thread_manager
+        self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+    @openapi_schema(
+        {
+            "type": "function",
+            "function": {
+                "name": "image_edit_or_generate",
+                "description": "Generate a new image from a prompt, or edit an existing image (no mask support) using OpenAI DALL-E via OpenAI SDK. Stores the result in the thread context.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "mode": {
+                            "type": "string",
+                            "enum": ["generate", "edit"],
+                            "description": "'generate' to create a new image from a prompt, 'edit' to edit an existing image.",
+                        },
+                        "prompt": {
+                            "type": "string",
+                            "description": "Text prompt describing the desired image or edit.",
+                        },
+                        "image_path": {
+                            "type": "string",
+                            "description": "(edit mode only) Path to the image file to edit, relative to /workspace. Required for 'edit'.",
+                        },
+                    },
+                    "required": ["mode", "prompt"],
+                },
+            },
+        }
+    )
+    @xml_schema(
+        tag_name="image-edit-or-generate",
+        mappings=[
+            {"param_name": "mode", "node_type": "attribute", "path": "."},
+            {"param_name": "prompt", "node_type": "attribute", "path": "."},
+            {"param_name": "image_path", "node_type": "attribute", "path": "."},
+        ],
+        example="""
+        <function_calls>
+        <invoke name="image_edit_or_generate">
+        <parameter name="mode">generate</parameter>
+        <parameter name="prompt">A futuristic cityscape at sunset</parameter>
+        </invoke>
+        </function_calls>
+        """,
+    )
+    async def image_edit_or_generate(
+        self,
+        mode: str,
+        prompt: str,
+        image_path: Optional[str] = None,
+    ) -> ToolResult:
+        """Generate or edit images using OpenAI DALL-E via OpenAI SDK (no mask support)."""
+        try:
+            await self._ensure_sandbox()
+
+            if mode == "generate":
+                response = self.client.images.generate(
+                    prompt=prompt, n=1, size="1024x1024"
+                )
+            elif mode == "edit":
+                if not image_path:
+                    return self.fail_response("'image_path' is required for edit mode.")
+
+                image_bytes = await self._get_image_bytes(image_path)
+                if isinstance(image_bytes, ToolResult):  # Error occurred
+                    return image_bytes
+
+                # Create BytesIO object with proper filename to set MIME type
+                image_io = BytesIO(image_bytes)
+                image_io.name = "image.png"  # Set filename to ensure proper MIME type detection
+                
+                response = self.client.images.edit(
+                    image=image_io, prompt=prompt, n=1, size="1024x1024"
+                )
+            else:
+                return self.fail_response("Invalid mode. Use 'generate' or 'edit'.")
+
+            # Download and save the generated image to sandbox
+            image_filename = await self._process_image_response(response)
+            if isinstance(image_filename, ToolResult):  # Error occurred
+                return image_filename
+
+            return self.success_response(
+                f"Successfully generated image using mode '{mode}'. Image saved as: {image_filename}. You can use the ask tool to display the image."
+            )
+
+        except Exception as e:
+            return self.fail_response(
+                f"An error occurred during image generation/editing: {str(e)}"
+            )
+
+    async def _get_image_bytes(self, image_path: str) -> bytes | ToolResult:
+        """Get image bytes from URL or local file path."""
+        if image_path.startswith(("http://", "https://")):
+            return await self._download_image_from_url(image_path)
+        else:
+            return await self._read_image_from_sandbox(image_path)
+
+    async def _download_image_from_url(self, url: str) -> bytes | ToolResult:
+        """Download image from URL."""
+        try:
+            async with httpx.AsyncClient() as client:
+                response = await client.get(url)
+                response.raise_for_status()
+                return response.content
+        except Exception:
+            return self.fail_response(f"Could not download image from URL: {url}")
+
+    async def _read_image_from_sandbox(self, image_path: str) -> bytes | ToolResult:
+        """Read image from sandbox filesystem."""
+        try:
+            cleaned_path = self.clean_path(image_path)
+            full_path = f"{self.workspace_path}/{cleaned_path}"
+
+            # Check if file exists and is not a directory
+            file_info = self.sandbox.fs.get_file_info(full_path)
+            if file_info.is_dir:
+                return self.fail_response(
+                    f"Path '{cleaned_path}' is a directory, not an image file."
+                )
+
+            return self.sandbox.fs.download_file(full_path)
+
+        except Exception as e:
+            return self.fail_response(
+                f"Could not read image file from sandbox: {image_path} - {str(e)}"
+            )
+
+    async def _process_image_response(self, response) -> str | ToolResult:
+        """Download generated image and save to sandbox with random name."""
+        try:
+            original_url = response.data[0].url
+
+            async with httpx.AsyncClient() as client:
+                img_response = await client.get(original_url)
+                img_response.raise_for_status()
+
+            # Generate random filename
+
+            random_filename = f"generated_image_{uuid.uuid4().hex[:8]}.png"
+            sandbox_path = f"{self.workspace_path}/{random_filename}"
+
+            # Save image to sandbox
+            self.sandbox.fs.upload_file(sandbox_path, img_response.content)
+            return random_filename
+
+        except Exception as e:
+            return self.fail_response(f"Failed to download and save image: {str(e)}")

backend/poetry.lock

@@ -1428,14 +1428,14 @@ openai = ["openai (>=0.27.8)"]
 
 [[package]]
 name = "litellm"
-version = "1.66.1"
+version = "1.72.2"
 description = "Library to easily interface with LLM API providers"
 optional = false
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 groups = ["main"]
 files = [
-    {file = "litellm-1.66.1-py3-none-any.whl", hash = "sha256:1f601fea3f086c1d2d91be60b9db115082a2f3a697e4e0def72f8b9c777c7232"},
-    {file = "litellm-1.66.1.tar.gz", hash = "sha256:98f7add913e5eae2131dd412ee27532d9a309defd9dbb64f6c6c42ea8a2af068"},
+    {file = "litellm-1.72.2-py3-none-any.whl", hash = "sha256:51e70f5cd98748a603d725ef29ede0ecad3d55e1a89cbbcec8d12d6fff55bff4"},
+    {file = "litellm-1.72.2.tar.gz", hash = "sha256:b50c7f7a0df67117889479264a12b0dea9c566a02173d4c3159540a13760d38b"},
 ]
 
 [package.dependencies]
@@ -1453,7 +1453,8 @@ tokenizers = "*"
 
 [package.extras]
 extra-proxy = ["azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (==0.11.0)", "redisvl (>=0.4.1,<0.5.0) ; python_version >= \"3.9\" and python_version < \"3.14\"", "resend (>=0.8.0,<0.9.0)"]
-proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "boto3 (==1.34.34)", "cryptography (>=43.0.1,<44.0.0)", "fastapi (>=0.115.5,<0.116.0)", "fastapi-sso (>=0.16.0,<0.17.0)", "gunicorn (>=23.0.0,<24.0.0)", "litellm-proxy-extras (==0.1.7)", "mcp (==1.5.0) ; python_version >= \"3.10\"", "orjson (>=3.9.7,<4.0.0)", "pynacl (>=1.5.0,<2.0.0)", "python-multipart (>=0.0.18,<0.0.19)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.29.0,<0.30.0)", "uvloop (>=0.21.0,<0.22.0)", "websockets (>=13.1.0,<14.0.0)"]
+proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "boto3 (==1.34.34)", "cryptography (>=43.0.1,<44.0.0)", "fastapi (>=0.115.5,<0.116.0)", "fastapi-sso (>=0.16.0,<0.17.0)", "gunicorn (>=23.0.0,<24.0.0)", "litellm-enterprise (==0.1.7)", "litellm-proxy-extras (==0.2.3)", "mcp (==1.5.0) ; python_version >= \"3.10\"", "orjson (>=3.9.7,<4.0.0)", "pynacl (>=1.5.0,<2.0.0)", "python-multipart (>=0.0.18,<0.0.19)", "pyyaml (>=6.0.1,<7.0.0)", "rich (==13.7.1)", "rq", "uvicorn (>=0.29.0,<0.30.0)", "uvloop (>=0.21.0,<0.22.0) ; sys_platform != \"win32\"", "websockets (>=13.1.0,<14.0.0)"]
+utils = ["numpydoc"]
 
 [[package]]
 name = "mailtrap"
@@ -3904,4 +3905,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.11"
-content-hash = "09a851f3db2d0b1f130405a69c1661c453f82ce23e078256bc6749662af897a7"
+content-hash = "3b983fbe8614f4e59280b2087fa4bcc574502d58fc75aa73a44426279f99e3d2"

backend/pyproject.toml

@@ -19,7 +19,7 @@ classifiers = [
 [tool.poetry.dependencies]
 python = "^3.11"
 python-dotenv = "1.0.1"
-litellm = "1.66.1"
+litellm = "1.72.2"
 click = "8.1.7"
 questionary = "2.0.1"
 requests = "^2.31.0"

backend/requirements.txt

@@ -1,5 +1,5 @@
 python-dotenv==1.0.1
-litellm==1.66.1
+litellm==1.72.2
 click==8.1.7
 questionary==2.0.1
 requests>=2.31.0