Merge branch 'master' into draft-llamafile-support

.github/workflows/autogpt-builder-ci.yml

@@ -0,0 +1,36 @@
+name: AutoGPT Builder CI
+
+on:
+  push:
+    branches: [ master ]
+    paths:
+      - '.github/workflows/autogpt-builder-ci.yml'
+      - 'rnd/autogpt_builder/**'
+  pull_request:
+    paths:
+      - '.github/workflows/autogpt-builder-ci.yml'
+      - 'rnd/autogpt_builder/**'
+
+defaults:
+  run:
+    shell: bash
+    working-directory: rnd/autogpt_builder
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '21'
+
+    - name: Install dependencies
+      run: |
+        npm install
+
+    - name: Run lint
+      run: |
+        npm run lint

.github/workflows/autogpt-server-ci.yml

@@ -265,4 +265,4 @@ jobs:
         uses: actions/upload-artifact@v4
         with:
           name: autogptserver-AppImage-${{ matrix.platform-os }}
-          path: /Users/runner/work/AutoGPT/AutoGPT/rnd/autogpt_server/build/*.AppImage
+          path: /Users/runner/work/AutoGPT/AutoGPT/rnd/autogpt_server/dist/*.AppImage

.gitignore

@@ -162,11 +162,13 @@ agbenchmark/reports/
 
 # Nodejs
 package-lock.json
-package.json
+
 
 # Allow for locally private items
 # private
 pri* 
 # ignore
 ig*
 .github_access_token
+LICENSE.rtf
+rnd/autogpt_server/settings.py

.pre-commit-config.yaml

@@ -97,7 +97,7 @@ repos:
         alias: pyright-benchmark
         entry: poetry -C benchmark run pyright
         args: [-p, benchmark, benchmark]
-        files: ^benchmark/(agbenchmark|tests)/
+        files: ^benchmark/(agbenchmark/|tests/|poetry\.lock$)
         types: [file]
         language: system
         pass_filenames: false

QUICKSTART.md → FORGE-QUICKSTART.md

@@ -2,11 +2,11 @@
 
 > For the complete getting started [tutorial series](https://aiedge.medium.com/autogpt-forge-e3de53cc58ec) <- click here
 
-Welcome to the Quickstart Guide! This guide will walk you through the process of setting up and running your own AutoGPT agent. Whether you're a seasoned AI developer or just starting out, this guide will provide you with the necessary steps to jumpstart your journey in the world of AI development with AutoGPT.
+Welcome to the Quickstart Guide! This guide will walk you through setting up, building, and running your own AutoGPT agent. Whether you're a seasoned AI developer or just starting out, this guide will provide you with the steps to jumpstart your journey in AI development with AutoGPT.
 
 ## System Requirements
 
-This project supports Linux (Debian based), Mac, and Windows Subsystem for Linux (WSL). If you are using a Windows system, you will need to install WSL. You can find the installation instructions for WSL [here](https://learn.microsoft.com/en-us/windows/wsl/).
+This project supports Linux (Debian-based), Mac, and Windows Subsystem for Linux (WSL). If you use a Windows system, you must install WSL. You can find the installation instructions for WSL [here](https://learn.microsoft.com/en-us/windows/wsl/).
 
 
 ## Getting Setup
@@ -18,11 +18,11 @@ This project supports Linux (Debian based), Mac, and Windows Subsystem for Linux
    - In the top-right corner of the page, click Fork.
 
    ![Create Fork UI](docs/content/imgs/quickstart/002_fork.png)
-   - On the next page, select your GitHub account to create the fork under.
+   - On the next page, select your GitHub account to create the fork.
    - Wait for the forking process to complete. You now have a copy of the repository in your GitHub account.
 
 2. **Clone the Repository**
-   To clone the repository, you need to have Git installed on your system. If you don't have Git installed, you can download it from [here](https://git-scm.com/downloads). Once you have Git installed, follow these steps:
+   To clone the repository, you need to have Git installed on your system. If you don't have Git installed, download it from [here](https://git-scm.com/downloads). Once you have Git installed, follow these steps:
    - Open your terminal.
    - Navigate to the directory where you want to clone the repository.
    - Run the git clone command for the fork you just created
@@ -34,11 +34,11 @@ This project supports Linux (Debian based), Mac, and Windows Subsystem for Linux
    ![Open the Project in your IDE](docs/content/imgs/quickstart/004_ide.png)
 
 4. **Setup the Project**
-    Next we need to setup the required dependencies. We have a tool for helping you do all the tasks you need to on the repo.
+    Next, we need to set up the required dependencies. We have a tool to help you perform all the tasks on the repo.
     It can be accessed by running the `run` command by typing `./run` in the terminal.
 
-    The first command you need to use is `./run setup` This will guide you through the process of setting up your system.
-    Initially you will get instructions for installing flutter, chrome and setting up your github access token like the following image:
+    The first command you need to use is `./run setup.` This will guide you through setting up your system.
+    Initially, you will get instructions for installing Flutter and Chrome and setting up your GitHub access token like the following image:
 
     ![Setup the Project](docs/content/imgs/quickstart/005_setup.png)
 
@@ -47,7 +47,7 @@ This project supports Linux (Debian based), Mac, and Windows Subsystem for Linux
 If you're a Windows user and experience issues after installing WSL, follow the steps below to resolve them. 
 
 #### Update WSL 
-Run the following command in Powershell or Command Prompt to:
+Run the following command in Powershell or Command Prompt:
 1. Enable the optional WSL and Virtual Machine Platform components.
 2. Download and install the latest Linux kernel.
 3. Set WSL 2 as the default.
@@ -73,7 +73,7 @@ dos2unix ./run
 After executing the above commands, running `./run setup` should work successfully. 
 
 #### Store Project Files within the WSL File System
-If you continue to experience issues, consider storing your project files within the WSL file system instead of the Windows file system. This method avoids issues related to path translations and permissions and provides a more consistent development environment.
+If you continue to experience issues, consider storing your project files within the WSL file system instead of the Windows file system. This method avoids path translations and permissions issues and provides a more consistent development environment.
 
 You can keep running the command to get feedback on where you are up to with your setup. 
 When setup has been completed, the command will return an output like this:
@@ -83,7 +83,7 @@ When setup has been completed, the command will return an output like this:
 ## Creating Your Agent
 
 After completing the setup, the next step is to create your agent template.
-Execute the command `./run agent create YOUR_AGENT_NAME`, where `YOUR_AGENT_NAME` should be replaced with a name of your choosing.
+Execute the command `./run agent create YOUR_AGENT_NAME`, where `YOUR_AGENT_NAME` should be replaced with your chosen name.
 
 Tips for naming your agent:
 * Give it its own unique name, or name it after yourself
@@ -101,21 +101,21 @@ This starts the agent on the URL: `http://localhost:8000/`
 
 ![Start the Agent](docs/content/imgs/quickstart/009_start_agent.png)
 
-The frontend can be accessed from `http://localhost:8000/`, you will first need to login using either a google account or your github account.
+The front end can be accessed from `http://localhost:8000/`; first, you must log in using either a Google account or your GitHub account.
 
 ![Login](docs/content/imgs/quickstart/010_login.png)
 
-Upon logging in you will get a page that looks something like this. With your task history down the left hand side of the page and the 'chat' window to send tasks to your agent.
+Upon logging in, you will get a page that looks something like this: your task history down the left-hand side of the page, and the 'chat' window to send tasks to your agent.
 
 ![Login](docs/content/imgs/quickstart/011_home.png)
 
-When you have finished with your agent, or if you just need to restart it, use Ctl-C to end the session then you can re-run the start command.
+When you have finished with your agent or just need to restart it, use Ctl-C to end the session. Then, you can re-run the start command.
 
-If you are having issues and want to ensure the agent has been stopped there is a `./run agent stop` command which will kill the process using port 8000, which should be the agent. 
+If you are having issues and want to ensure the agent has been stopped, there is a `./run agent stop` command, which will kill the process using port 8000, which should be the agent. 
 
 ## Benchmarking your Agent
 
-The benchmarking system can also be accessed using the cli too:
+The benchmarking system can also be accessed using the CLI too:
 
 ```bash
 agpt % ./run benchmark
@@ -163,7 +163,7 @@ The benchmark has been split into different categories of skills you can test yo
 ![Login](docs/content/imgs/quickstart/012_tests.png)
 
 
-Finally you can run the benchmark with
+Finally, you can run the benchmark with
 
 ```bash
 ./run benchmark start YOUR_AGENT_NAME

README.md

@@ -24,7 +24,7 @@ Be part of the revolution! **AutoGPT** is here to stay, at the forefront of AI i
  | 
 **🚀 [Contributing](CONTRIBUTING.md)**
  | 
-**🛠️ [Build your own Agent - Quickstart](QUICKSTART.md)**
+**🛠️ [Build your own Agent - Quickstart](FORGE-QUICKSTART.md)**
 
 ## 🧱 Building blocks
 

autogpt/autogpt/agent_factory/configurators.py

@@ -104,5 +104,5 @@ def create_agent_state(
             allow_fs_access=not app_config.restrict_to_workspace,
             use_functions_api=app_config.openai_functions,
         ),
-        history=Agent.default_settings.history.copy(deep=True),
+        history=Agent.default_settings.history.model_copy(deep=True),
     )

autogpt/autogpt/agent_factory/profile_generator.py

@@ -137,7 +137,7 @@ class AgentProfileGeneratorConfiguration(SystemConfiguration):
                     required=True,
                 ),
             },
-        ).dict()
+        ).model_dump()
     )
 
 
@@ -156,7 +156,7 @@ def __init__(
         self._model_classification = model_classification
         self._system_prompt_message = system_prompt
         self._user_prompt_template = user_prompt_template
-        self._create_agent_function = CompletionModelFunction.parse_obj(
+        self._create_agent_function = CompletionModelFunction.model_validate(
             create_agent_function
         )
 
@@ -222,7 +222,7 @@ async def generate_agent_profile_for_task(
     AIConfig: The AIConfig object tailored to the user's input
     """
     agent_profile_generator = AgentProfileGenerator(
-        **AgentProfileGenerator.default_configuration.dict()  # HACK
+        **AgentProfileGenerator.default_configuration.model_dump()  # HACK
     )
 
     prompt = agent_profile_generator.build_prompt(task)

autogpt/autogpt/agents/agent.py

@@ -100,7 +100,9 @@ def __init__(
         super().__init__(settings)
 
         self.llm_provider = llm_provider
-        prompt_config = OneShotAgentPromptStrategy.default_configuration.copy(deep=True)
+        prompt_config = OneShotAgentPromptStrategy.default_configuration.model_copy(
+            deep=True
+        )
         prompt_config.use_functions_api = (
             settings.config.use_functions_api
             # Anthropic currently doesn't support tools + prefilling :(
@@ -111,14 +113,18 @@ def __init__(
 
         # Components
         self.system = SystemComponent()
-        self.history = ActionHistoryComponent(
-            settings.history,
-            lambda x: self.llm_provider.count_tokens(x, self.llm.name),
-            llm_provider,
-            ActionHistoryConfiguration(
-                model_name=app_config.fast_llm, max_tokens=self.send_token_limit
-            ),
-        ).run_after(WatchdogComponent)
+        self.history = (
+            ActionHistoryComponent(
+                settings.history,
+                lambda x: self.llm_provider.count_tokens(x, self.llm.name),
+                llm_provider,
+                ActionHistoryConfiguration(
+                    model_name=app_config.fast_llm, max_tokens=self.send_token_limit
+                ),
+            )
+            .run_after(WatchdogComponent)
+            .run_after(SystemComponent)
+        )
         if not app_config.noninteractive_mode:
             self.user_interaction = UserInteractionComponent()
         self.file_manager = FileManagerComponent(file_storage, settings)
@@ -156,7 +162,7 @@ async def propose_action(self) -> OneShotAgentActionProposal:
         constraints = await self.run_pipeline(DirectiveProvider.get_constraints)
         best_practices = await self.run_pipeline(DirectiveProvider.get_best_practices)
 
-        directives = self.state.directives.copy(deep=True)
+        directives = self.state.directives.model_copy(deep=True)
         directives.resources += resources
         directives.constraints += constraints
         directives.best_practices += best_practices

autogpt/autogpt/agents/prompt_strategies/one_shot.py

@@ -28,15 +28,13 @@
 
 class AssistantThoughts(ModelWithSummary):
     observations: str = Field(
-        ..., description="Relevant observations from your last action (if any)"
+        description="Relevant observations from your last action (if any)"
     )
-    text: str = Field(..., description="Thoughts")
-    reasoning: str = Field(..., description="Reasoning behind the thoughts")
-    self_criticism: str = Field(..., description="Constructive self-criticism")
-    plan: list[str] = Field(
-        ..., description="Short list that conveys the long-term plan"
-    )
-    speak: str = Field(..., description="Summary of thoughts, to say to user")
+    text: str = Field(description="Thoughts")
+    reasoning: str = Field(description="Reasoning behind the thoughts")
+    self_criticism: str = Field(description="Constructive self-criticism")
+    plan: list[str] = Field(description="Short list that conveys the long-term plan")
+    speak: str = Field(description="Summary of thoughts, to say to user")
 
     def summary(self) -> str:
         return self.text
@@ -96,7 +94,9 @@ def __init__(
         logger: Logger,
     ):
         self.config = configuration
-        self.response_schema = JSONSchema.from_dict(OneShotAgentActionProposal.schema())
+        self.response_schema = JSONSchema.from_dict(
+            OneShotAgentActionProposal.model_json_schema()
+        )
         self.logger = logger
 
     @property
@@ -182,7 +182,7 @@ def build_system_prompt(
         )
 
     def response_format_instruction(self, use_functions_api: bool) -> tuple[str, str]:
-        response_schema = self.response_schema.copy(deep=True)
+        response_schema = self.response_schema.model_copy(deep=True)
         assert response_schema.properties
         if use_functions_api and "use_tool" in response_schema.properties:
             del response_schema.properties["use_tool"]
@@ -274,5 +274,8 @@ def parse_response_content(
                 raise InvalidAgentResponseError("Assistant did not use a tool")
             assistant_reply_dict["use_tool"] = response.tool_calls[0].function
 
-        parsed_response = OneShotAgentActionProposal.parse_obj(assistant_reply_dict)
+        parsed_response = OneShotAgentActionProposal.model_validate(
+            assistant_reply_dict
+        )
+        parsed_response.raw_message = response.copy()
         return parsed_response

autogpt/autogpt/app/agent_protocol_server.py

@@ -314,7 +314,7 @@ async def execute_step(self, task_id: str, step_request: StepRequestBody) -> Ste
                             ""
                             if tool_result is None
                             else (
-                                orjson.loads(tool_result.json())
+                                orjson.loads(tool_result.model_dump_json())
                                 if not isinstance(tool_result, ActionErrorResult)
                                 else {
                                     "error": str(tool_result.error),
@@ -327,7 +327,7 @@ async def execute_step(self, task_id: str, step_request: StepRequestBody) -> Ste
                 if last_proposal and tool_result
                 else {}
             ),
-            **assistant_response.dict(),
+            **assistant_response.model_dump(),
         }
 
         task_cumulative_cost = agent.llm_provider.get_incurred_cost()
@@ -451,15 +451,17 @@ def _get_task_llm_provider(self, task: Task, step_id: str = "") -> MultiProvider
         """
         task_llm_budget = self._task_budgets[task.task_id]
 
-        task_llm_provider_config = self.llm_provider._configuration.copy(deep=True)
+        task_llm_provider_config = self.llm_provider._configuration.model_copy(
+            deep=True
+        )
         _extra_request_headers = task_llm_provider_config.extra_request_headers
         _extra_request_headers["AP-TaskID"] = task.task_id
         if step_id:
             _extra_request_headers["AP-StepID"] = step_id
         if task.additional_input and (user_id := task.additional_input.get("user_id")):
             _extra_request_headers["AutoGPT-UserID"] = user_id
 
-        settings = self.llm_provider._settings.copy()
+        settings = self.llm_provider._settings.model_copy()
         settings.budget = task_llm_budget
         settings.configuration = task_llm_provider_config
         task_llm_provider = self.llm_provider.__class__(

autogpt/autogpt/app/config.py

@@ -5,15 +5,15 @@
 import os
 import re
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 import forge
 from forge.config.base import BaseConfig
 from forge.llm.providers import CHAT_MODELS, ModelName
 from forge.llm.providers.openai import OpenAICredentials, OpenAIModelName
 from forge.logging.config import LoggingConfig
 from forge.models.config import Configurable, UserConfigurable
-from pydantic import SecretStr, validator
+from pydantic import SecretStr, ValidationInfo, field_validator
 
 logger = logging.getLogger(__name__)
 
@@ -91,15 +91,15 @@ class AppConfig(BaseConfig):
         default=AZURE_CONFIG_FILE, from_env="AZURE_CONFIG_FILE"
     )
 
-    @validator("openai_functions")
-    def validate_openai_functions(cls, v: bool, values: dict[str, Any]):
-        if v:
-            smart_llm = values["smart_llm"]
+    @field_validator("openai_functions")
+    def validate_openai_functions(cls, value: bool, info: ValidationInfo):
+        if value:
+            smart_llm = info.data["smart_llm"]
             assert CHAT_MODELS[smart_llm].has_function_call_api, (
                 f"Model {smart_llm} does not support tool calling. "
                 "Please disable OPENAI_FUNCTIONS or choose a suitable model."
             )
-        return v
+        return value
 
 
 class ConfigBuilder(Configurable[AppConfig]):