Merge branch 'dev' into ntindle/open-2093-integration-test-build-an-a…

…gent-with-a-few-blocks-including

.github/workflows/platform-backend-ci.yml

@@ -6,11 +6,13 @@ on:
     paths:
       - ".github/workflows/platform-backend-ci.yml"
       - "autogpt_platform/backend/**"
+      - "autogpt_platform/autogpt_libs/**"
   pull_request:
     branches: [master, dev, release-*]
     paths:
       - ".github/workflows/platform-backend-ci.yml"
       - "autogpt_platform/backend/**"
+      - "autogpt_platform/autogpt_libs/**"
   merge_group:
 
 concurrency:

.pre-commit-config.yaml

@@ -98,6 +98,11 @@ repos:
         files: ^autogpt_platform/autogpt_libs/
         args: [--fix]
 
+      - id: ruff-format
+        name: Format (Ruff) - AutoGPT Platform - Libs
+        alias: ruff-lint-platform-libs
+        files: ^autogpt_platform/autogpt_libs/
+
   - repo: local
     # isort needs the context of which packages are installed to function, so we
     # can't use a vendored isort pre-commit hook (which runs in its own isolated venv).
@@ -140,7 +145,7 @@ repos:
     # everything in .gitignore, so it works fine without any config or arguments.
     hooks:
       - id: black
-        name: Lint (Black)
+        name: Format (Black)
 
   - repo: https://github.com/PyCQA/flake8
     rev: 7.0.0

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client.py

@@ -72,7 +72,7 @@ def feature_flag(
     """
 
     def decorator(
-        func: Callable[P, Union[T, Awaitable[T]]]
+        func: Callable[P, Union[T, Awaitable[T]]],
     ) -> Callable[P, Union[T, Awaitable[T]]]:
         @wraps(func)
         async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> T:

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -23,7 +23,6 @@
 
 
 class LoggingConfig(BaseSettings):
-
     level: str = Field(
         default="INFO",
         description="Logging level",

autogpt_platform/autogpt_libs/autogpt_libs/logging/test_utils.py

@@ -24,10 +24,10 @@
         ),
         ("", ""),
         ("hello", "hello"),
-        ("hello\x1B[31m world", "hello world"),
-        ("\x1B[36mHello,\x1B[32m World!", "Hello, World!"),
+        ("hello\x1b[31m world", "hello world"),
+        ("\x1b[36mHello,\x1b[32m World!", "Hello, World!"),
         (
-            "\x1B[1m\x1B[31mError:\x1B[0m\x1B[31m file not found",
+            "\x1b[1m\x1b[31mError:\x1b[0m\x1b[31m file not found",
             "Error: file not found",
         ),
     ],

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/config.py

@@ -0,0 +1,31 @@
+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class RateLimitSettings(BaseSettings):
+    redis_host: str = Field(
+        default="redis://localhost:6379",
+        description="Redis host",
+        validation_alias="REDIS_HOST",
+    )
+
+    redis_port: str = Field(
+        default="6379", description="Redis port", validation_alias="REDIS_PORT"
+    )
+
+    redis_password: str = Field(
+        default="password",
+        description="Redis password",
+        validation_alias="REDIS_PASSWORD",
+    )
+
+    requests_per_minute: int = Field(
+        default=60,
+        description="Maximum number of requests allowed per minute per API key",
+        validation_alias="RATE_LIMIT_REQUESTS_PER_MINUTE",
+    )
+
+    model_config = SettingsConfigDict(case_sensitive=True, extra="ignore")
+
+
+RATE_LIMIT_SETTINGS = RateLimitSettings()

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/limiter.py

@@ -0,0 +1,51 @@
+import time
+from typing import Tuple
+
+from redis import Redis
+
+from .config import RATE_LIMIT_SETTINGS
+
+
+class RateLimiter:
+    def __init__(
+        self,
+        redis_host: str = RATE_LIMIT_SETTINGS.redis_host,
+        redis_port: str = RATE_LIMIT_SETTINGS.redis_port,
+        redis_password: str = RATE_LIMIT_SETTINGS.redis_password,
+        requests_per_minute: int = RATE_LIMIT_SETTINGS.requests_per_minute,
+    ):
+        self.redis = Redis(
+            host=redis_host,
+            port=int(redis_port),
+            password=redis_password,
+            decode_responses=True,
+        )
+        self.window = 60
+        self.max_requests = requests_per_minute
+
+    async def check_rate_limit(self, api_key_id: str) -> Tuple[bool, int, int]:
+        """
+        Check if request is within rate limits.
+
+        Args:
+            api_key_id: The API key identifier to check
+
+        Returns:
+            Tuple of (is_allowed, remaining_requests, reset_time)
+        """
+        now = time.time()
+        window_start = now - self.window
+        key = f"ratelimit:{api_key_id}:1min"
+
+        pipe = self.redis.pipeline()
+        pipe.zremrangebyscore(key, 0, window_start)
+        pipe.zadd(key, {str(now): now})
+        pipe.zcount(key, window_start, now)
+        pipe.expire(key, self.window)
+
+        _, _, request_count, _ = pipe.execute()
+
+        remaining = max(0, self.max_requests - request_count)
+        reset_time = int(now + self.window)
+
+        return request_count <= self.max_requests, remaining, reset_time

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/middleware.py

@@ -0,0 +1,32 @@
+from fastapi import HTTPException, Request
+from starlette.middleware.base import RequestResponseEndpoint
+
+from .limiter import RateLimiter
+
+
+async def rate_limit_middleware(request: Request, call_next: RequestResponseEndpoint):
+    """FastAPI middleware for rate limiting API requests."""
+    limiter = RateLimiter()
+
+    if not request.url.path.startswith("/api"):
+        return await call_next(request)
+
+    api_key = request.headers.get("Authorization")
+    if not api_key:
+        return await call_next(request)
+
+    api_key = api_key.replace("Bearer ", "")
+
+    is_allowed, remaining, reset_time = await limiter.check_rate_limit(api_key)
+
+    if not is_allowed:
+        raise HTTPException(
+            status_code=429, detail="Rate limit exceeded. Please try again later."
+        )
+
+    response = await call_next(request)
+    response.headers["X-RateLimit-Limit"] = str(limiter.max_requests)
+    response.headers["X-RateLimit-Remaining"] = str(remaining)
+    response.headers["X-RateLimit-Reset"] = str(reset_time)
+
+    return response

autogpt_platform/autogpt_libs/pyproject.toml

@@ -21,7 +21,7 @@ supabase = "^2.10.0"
 
 [tool.poetry.group.dev.dependencies]
 redis = "^5.2.0"
-ruff = "^0.8.0"
+ruff = "^0.8.1"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -1,7 +1,7 @@
 import logging
 import time
 from enum import Enum
-from typing import Any, Dict
+from typing import Any
 
 import httpx
 
@@ -64,16 +64,16 @@ def __init__(self):
             },
         )
 
-    def _get_headers(self, api_key: str) -> Dict[str, str]:
+    def _get_headers(self, api_key: str) -> dict[str, str]:
         """Get headers for FAL API requests."""
         return {
             "Authorization": f"Key {api_key}",
             "Content-Type": "application/json",
         }
 
     def _submit_request(
-        self, url: str, headers: Dict[str, str], data: Dict[str, Any]
-    ) -> Dict[str, Any]:
+        self, url: str, headers: dict[str, str], data: dict[str, Any]
+    ) -> dict[str, Any]:
         """Submit a request to the FAL API."""
         try:
             response = httpx.post(url, headers=headers, json=data)
@@ -83,7 +83,7 @@ def _submit_request(
             logger.error(f"FAL API request failed: {str(e)}")
             raise RuntimeError(f"Failed to submit request: {str(e)}")
 
-    def _poll_status(self, status_url: str, headers: Dict[str, str]) -> Dict[str, Any]:
+    def _poll_status(self, status_url: str, headers: dict[str, str]) -> dict[str, Any]:
         """Poll the status endpoint until completion or failure."""
         try:
             response = httpx.get(status_url, headers=headers)

autogpt_platform/backend/backend/blocks/github/triggers.py

@@ -111,7 +111,9 @@ class Output(GitHubTriggerBase.Output):
     def __init__(self):
         from backend.integrations.webhooks.github import GithubWebhookType
 
-        example_payload = json.loads(self.EXAMPLE_PAYLOAD_FILE.read_text())
+        example_payload = json.loads(
+            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
+        )
 
         super().__init__(
             id="6c60ec01-8128-419e-988f-96a063ee2fea",

autogpt_platform/backend/backend/data/model.py

@@ -120,7 +120,7 @@ def SchemaField(
     title: Optional[str] = None,
     description: Optional[str] = None,
     placeholder: Optional[str] = None,
-    advanced: Optional[bool] = None,
+    advanced: Optional[bool] = False,
     secret: bool = False,
     exclude: bool = False,
     hidden: Optional[bool] = None,

autogpt_platform/backend/linter.py

@@ -2,7 +2,10 @@
 import subprocess
 
 directory = os.path.dirname(os.path.realpath(__file__))
-target_dirs = ["../backend", "../autogpt_libs"]
+
+BACKEND_DIR = "."
+LIBS_DIR = "../autogpt_libs"
+TARGET_DIRS = [BACKEND_DIR, LIBS_DIR]
 
 
 def run(*command: str) -> None:
@@ -12,17 +15,19 @@ def run(*command: str) -> None:
 
 def lint():
     try:
-        run("ruff", "check", *target_dirs, "--exit-zero")
-        run("isort", "--diff", "--check", "--profile", "black", ".")
-        run("black", "--diff", "--check", ".")
-        run("pyright", *target_dirs)
+        run("ruff", "check", *TARGET_DIRS, "--exit-zero")
+        run("ruff", "format", "--diff", "--check", LIBS_DIR)
+        run("isort", "--diff", "--check", "--profile", "black", BACKEND_DIR)
+        run("black", "--diff", "--check", BACKEND_DIR)
+        run("pyright", *TARGET_DIRS)
     except subprocess.CalledProcessError as e:
         print("Lint failed, try running `poetry run format` to fix the issues: ", e)
         raise e
 
 
 def format():
-    run("ruff", "check", "--fix", *target_dirs)
-    run("isort", "--profile", "black", ".")
-    run("black", ".")
-    run("pyright", *target_dirs)
+    run("ruff", "check", "--fix", *TARGET_DIRS)
+    run("ruff", "format", LIBS_DIR)
+    run("isort", "--profile", "black", BACKEND_DIR)
+    run("black", BACKEND_DIR)
+    run("pyright", *TARGET_DIRS)