Merge branch 'dev' into aarushikansal/exa-api

.github/dependabot.yml

@@ -7,6 +7,9 @@ updates:
       interval: "weekly"
     open-pull-requests-limit: 10
     target-branch: "dev"
+    commit-message:
+      prefix: "chore(libs/deps)"
+      prefix-development: "chore(libs/deps-dev)"
     groups:
       production-dependencies:
         dependency-type: "production"
@@ -26,6 +29,9 @@ updates:
       interval: "weekly"
     open-pull-requests-limit: 10
     target-branch: "dev"
+    commit-message:
+      prefix: "chore(backend/deps)"
+      prefix-development: "chore(backend/deps-dev)"
     groups:
       production-dependencies:
         dependency-type: "production"
@@ -38,14 +44,16 @@ updates:
         - "minor"
         - "patch"
 
-
   # frontend (Next.js project)
   - package-ecosystem: "npm"
     directory: "autogpt_platform/frontend"
     schedule:
       interval: "weekly"
     open-pull-requests-limit: 10
     target-branch: "dev"
+    commit-message:
+      prefix: "chore(frontend/deps)"
+      prefix-development: "chore(frontend/deps-dev)"
     groups:
       production-dependencies:
         dependency-type: "production"
@@ -58,14 +66,17 @@ updates:
         - "minor"
         - "patch"
 
-
   # infra (Terraform)
   - package-ecosystem: "terraform"
     directory: "autogpt_platform/infra"
     schedule:
       interval: "weekly"
     open-pull-requests-limit: 5
     target-branch: "dev"
+    commit-message:
+      prefix: "chore(infra/deps)"
+      prefix-development: "chore(infra/deps-dev)"
+
     groups:
       production-dependencies:
         dependency-type: "production"
@@ -78,14 +89,16 @@ updates:
         - "minor"
         - "patch"
 
-
   # market (Poetry project)
   - package-ecosystem: "pip"
     directory: "autogpt_platform/market"
     schedule:
       interval: "weekly"
     open-pull-requests-limit: 10
     target-branch: "dev"
+    commit-message:
+      prefix: "chore(market/deps)"
+      prefix-development: "chore(market/deps-dev)"
     groups:
       production-dependencies:
         dependency-type: "production"
@@ -146,6 +159,9 @@ updates:
       interval: "weekly"
     open-pull-requests-limit: 1
     target-branch: "dev"
+    commit-message:
+      prefix: "chore(platform/deps)"
+      prefix-development: "chore(platform/deps-dev)"
     groups:
       production-dependencies:
         dependency-type: "production"
@@ -166,6 +182,8 @@ updates:
       interval: "weekly"
     open-pull-requests-limit: 1
     target-branch: "dev"
+    commit-message:
+      prefix: "chore(docs/deps)"
     groups:
       production-dependencies:
         dependency-type: "production"

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client_test.py

@@ -1,7 +1,8 @@
 import pytest
-from autogpt_libs.feature_flag.client import feature_flag, mock_flag_variation
 from ldclient import LDClient
 
+from autogpt_libs.feature_flag.client import feature_flag, mock_flag_variation
+
 
 @pytest.fixture
 def ld_client(mocker):

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/config.py

@@ -6,7 +6,7 @@ class Settings(BaseSettings):
     launch_darkly_sdk_key: str = Field(
         default="",
         description="The Launch Darkly SDK key",
-        validation_alias="LAUNCH_DARKLY_SDK_KEY"
+        validation_alias="LAUNCH_DARKLY_SDK_KEY",
     )
 
     model_config = SettingsConfigDict(case_sensitive=True, extra="ignore")

autogpt_platform/backend/backend/blocks/jina/fact_checker.py

@@ -0,0 +1,59 @@
+from urllib.parse import quote
+
+import requests
+
+from backend.blocks.jina._auth import (
+    JinaCredentials,
+    JinaCredentialsField,
+    JinaCredentialsInput,
+)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+
+class FactCheckerBlock(Block):
+    class Input(BlockSchema):
+        statement: str = SchemaField(
+            description="The statement to check for factuality"
+        )
+        credentials: JinaCredentialsInput = JinaCredentialsField()
+
+    class Output(BlockSchema):
+        factuality: float = SchemaField(
+            description="The factuality score of the statement"
+        )
+        result: bool = SchemaField(description="The result of the factuality check")
+        reason: str = SchemaField(description="The reason for the factuality result")
+        error: str = SchemaField(description="Error message if the check fails")
+
+    def __init__(self):
+        super().__init__(
+            id="d38b6c5e-9968-4271-8423-6cfe60d6e7e6",
+            description="This block checks the factuality of a given statement using Jina AI's Grounding API.",
+            categories={BlockCategory.SEARCH},
+            input_schema=FactCheckerBlock.Input,
+            output_schema=FactCheckerBlock.Output,
+        )
+
+    def run(
+        self, input_data: Input, *, credentials: JinaCredentials, **kwargs
+    ) -> BlockOutput:
+        encoded_statement = quote(input_data.statement)
+        url = f"https://g.jina.ai/{encoded_statement}"
+
+        headers = {
+            "Accept": "application/json",
+            "Authorization": f"Bearer {credentials.api_key.get_secret_value()}",
+        }
+
+        response = requests.get(url, headers=headers)
+        response.raise_for_status()
+        data = response.json()
+
+        if "data" in data:
+            data = data["data"]
+            yield "factuality", data["factuality"]
+            yield "result", data["result"]
+            yield "reason", data["reason"]
+        else:
+            raise RuntimeError(f"Expected 'data' key not found in response: {data}")

autogpt_platform/backend/backend/data/credit.py

@@ -70,7 +70,11 @@ class UserCredit(UserCreditBase):
     async def get_or_refill_credit(self, user_id: str) -> int:
         cur_time = self.time_now()
         cur_month = cur_time.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
-        nxt_month = cur_month.replace(month=cur_month.month + 1)
+        nxt_month = (
+            cur_month.replace(month=cur_month.month + 1)
+            if cur_month.month < 12
+            else cur_month.replace(year=cur_month.year + 1, month=1)
+        )
 
         user_credit = await UserBlockCredit.prisma().group_by(
             by=["userId"],

autogpt_platform/backend/linter.py

@@ -2,6 +2,7 @@
 import subprocess
 
 directory = os.path.dirname(os.path.realpath(__file__))
+target_dirs = ["../backend", "../autogpt_libs"]
 
 
 def run(*command: str) -> None:
@@ -11,17 +12,17 @@ def run(*command: str) -> None:
 
 def lint():
     try:
-        run("ruff", "check", ".", "--exit-zero")
+        run("ruff", "check", *target_dirs, "--exit-zero")
         run("isort", "--diff", "--check", "--profile", "black", ".")
         run("black", "--diff", "--check", ".")
-        run("pyright")
+        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", ".")
+    run("ruff", "check", "--fix", *target_dirs)
     run("isort", "--profile", "black", ".")
     run("black", ".")
-    run("pyright", ".")
+    run("pyright", *target_dirs)

autogpt_platform/backend/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "autogpt-platform-backend"
-version = "0.3.3"
+version = "0.3.4"
 description = "A platform for building AI-powered agentic workflows"
 authors = ["AutoGPT <[email protected]>"]
 readme = "README.md"

autogpt_platform/frontend/.env.example

@@ -2,6 +2,7 @@ NEXT_PUBLIC_AUTH_CALLBACK_URL=http://localhost:8006/auth/callback
 NEXT_PUBLIC_AGPT_SERVER_URL=http://localhost:8006/api
 NEXT_PUBLIC_AGPT_WS_SERVER_URL=ws://localhost:8001/ws
 NEXT_PUBLIC_AGPT_MARKETPLACE_URL=http://localhost:8015/api/v1/market
+NEXT_PUBLIC_APP_ENV=dev
 
 ## Supabase credentials
 
@@ -15,4 +16,4 @@ AUTH_CALLBACK_URL=http://localhost:3000/auth/callback
 GA_MEASUREMENT_ID=G-FH2XK2W4GN
 
 # When running locally, set NEXT_PUBLIC_BEHAVE_AS=CLOUD to use the a locally hosted marketplace (as is typical in development, and the cloud deployment), otherwise set it to LOCAL to have the marketplace open in a new tab
-NEXT_PUBLIC_BEHAVE_AS=LOCAL
+NEXT_PUBLIC_BEHAVE_AS=LOCAL

autogpt_platform/frontend/package.json

@@ -1,6 +1,6 @@
 {
   "name": "frontend",
-  "version": "0.3.3",
+  "version": "0.3.4",
   "private": true,
   "scripts": {
     "dev": "next dev",

autogpt_platform/frontend/src/components/nav/MarketPopup.tsx

@@ -7,7 +7,15 @@ interface MarketPopupProps extends ButtonHTMLAttributes<HTMLButtonElement> {
 
 export default function MarketPopup({
   className = "",
-  marketplaceUrl = "http://platform.agpt.co/marketplace",
+  marketplaceUrl = (() => {
+    if (process.env.NEXT_PUBLIC_APP_ENV === "prod") {
+      return "https://production-marketplace-url.com";
+    } else if (process.env.NEXT_PUBLIC_APP_ENV === "dev") {
+      return "https://dev-builder.agpt.co/marketplace";
+    } else {
+      return "http://localhost:3000/marketplace";
+    }
+  })(),
   children,
   ...props
 }: MarketPopupProps) {

docs/content/platform/agent-blocks.md

@@ -0,0 +1,86 @@
+# **How to Create an AI Agent as a Block in AutoGPT**
+
+## **Overview**
+
+This guide explains how to create a reusable agent block that can be used as a component in other agents.
+
+<center><iframe width="560" height="315" src="https://www.youtube.com/embed/G5t5wbfomNE?si=dek4KKAPmx8DVOxm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></center>
+
+## **What Are Agent Blocks?**
+
+Agent blocks are pre-configured, reusable AI workflows that can be used as components within larger automation systems. Think of them as "smart building blocks" - each agent block is itself a complete workflow that can:
+
+- Accept specific inputs
+- Process data using AI and traditional automation
+- Produce defined outputs
+- Be easily reused in different contexts
+
+The power of agent blocks lies in their modularity. Once you create an agent with a specific capability (like translating text or analyzing sentiment), you can reuse it as a single block in other workflows. This means you can:
+
+- Combine multiple agent blocks to create more complex automations
+- Reuse proven workflows without rebuilding them
+- Share agent blocks with other users
+- Create hierarchical systems where specialized agents work together
+
+For example, a content creation workflow might combine several agent blocks:
+
+- A research agent block that gathers information
+- A writing agent block that creates the initial draft
+- An editing agent block that polishes the content
+- A formatting agent block that prepares the final output
+
+## **Creating the Base Agent**
+
+### **Required Components**
+
+1. Input Block
+2. AI Text Generator Block
+3. Output Block
+
+### **Step-by-Step Setup**
+
+1. **Add and Configure Blocks**
+    * Add an Input Block
+    * Add an AI Text Generator Block
+    * Add an Output Block
+2. **Connect Components**
+    * Connect Input's result to AI Text Generator's Prompt
+    * Connect AI Text Generator's response to Output's value
+3. **Name the Components**
+    * Name the Input Block: "question"
+    * Name the Output Block: "answer"
+4. **Save the Agent**
+    * Choose a descriptive name (e.g., "Weather Agent")
+    * Click Save
+
+
+
+## **Converting to a Block**
+
+1. **Access the Block Menu**
+    * Go to the Builder interface
+    * Click the Blocks menu
+    * Click the agent tag or search the name of your agent
+2. **Using the Agent Block**
+    * Click on the agent block to add to your workflow
+    * Save the new agent with a descriptive name (e.g., "Weather Agent")
+
+## **Testing the Agent Block**
+
+1. **Run the Agent**
+    * Enter a test question (e.g., "How far is the Earth from the Moon?")
+    * Click Run
+2. **View Results**
+    * Option 1: Check "Agent Outputs" section*
+    * Option 2: Click "View More" for detailed results
+
+*Note: if there is no output block then the "Agent Outputs" button will show up blank. You can see the output under view more or at bottom of the block.
+
+## **Advanced Usage**
+
+* You can make more complex agents by combining multiple agent blocks
+* Chain different agents together for more sophisticated workflows
+
+## **Note**
+
+This is a basic example that can be expanded upon to create more complex agent blocks with additional functionality.

docs/mkdocs.yml

@@ -10,6 +10,7 @@ nav:
   - The AutoGPT Platform 🆕: 
     - Getting Started: platform/getting-started.md
     - Advanced Setup: platform/advanced_setup.md
+    - Agent Blocks: platform/agent-blocks.md
     - Build your own Blocks: platform/new_blocks.md
     - Using Ollama: platform/ollama.md
     - Using D-ID: platform/d_id.md