Merge branch 'main' into feat/file-writer-uses-o1-model

.github/workflows/docs.yml

@@ -2,7 +2,7 @@ name: docs
 on:
   push:
     branches:
-      - master 
+      - master
       - main
 permissions:
   contents: write
@@ -23,12 +23,12 @@ jobs:
         with:
           python-version: "3.10"
 
-      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV 
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
       - uses: actions/cache@v3
         with:
           key: mkdocs-material-${{ env.cache_id }}
           path: .cache
           restore-keys: |
             mkdocs-material-
       - run: pip install -r requirements_docs.txt
-      - run: mkdocs gh-deploy --force
+      - run: mkdocs gh-deploy --force

.github/workflows/publish.yml

@@ -19,4 +19,4 @@ jobs:
       - name: Publish package
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          password: ${{ secrets.PYPI_API_TOKEN }}
+          password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -162,4 +162,4 @@ cython_debug/
 settings.json
 .DS_Store
 tests/test_agents/
-test_agents/
+test_agents/

.pre-commit-config.yaml

@@ -0,0 +1,17 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-toml
+      - id: debug-statements
+        language_version: python3
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.7.4
+    hooks:
+      - id: ruff
+        args: [--fix, --select=I]
+      - id: ruff-format

CONTRIBUTING.md

@@ -1,5 +1,5 @@
 # Contributing to Agency Swarm
-Each agent or tool you add to Agency Swarm will automatically be available for import by the Genesis Swarm, which will help us create an exponentially larger and smarter system.  
+Each agent or tool you add to Agency Swarm will automatically be available for import by the Genesis Swarm, which will help us create an exponentially larger and smarter system.
 
 This document provides guidelines for contributing new agents to the framework.
 
@@ -23,7 +23,7 @@ agency_swarm/agents/AgentName/
 
 ### Creating an Agent
 
-1. Follow the structure below in your `AgentName.py` as a guideline. 
+1. Follow the structure below in your `AgentName.py` as a guideline.
 2. All tools (except schemas) should be imported in `AgentName.py` from the `agency_swarm/tools/...` folder.
 
 ```python
@@ -42,4 +42,4 @@ class AgentName(Agent):
 
 ---
 
-Thank you for contributing to Agency Swarm! Your efforts help us build a more robust and versatile framework.
+Thank you for contributing to Agency Swarm! Your efforts help us build a more robust and versatile framework.

README.md

@@ -18,7 +18,7 @@ Agency Swarm started as a desire and effort of Arsenii Shatokhin (aka VRSEN) to
 
 - **Customizable Agent Roles**: Define roles like CEO, virtual assistant, developer, etc., and customize their functionalities with [Assistants API](https://platform.openai.com/docs/assistants/overview).
 - **Full Control Over Prompts**: Avoid conflicts and restrictions of pre-defined prompts, allowing full customization.
-- **Tool Creation**: Tools within Agency Swarm are created using [Instructor](https://github.com/jxnl/instructor), which provides a convenient interface and automatic type validation. 
+- **Tool Creation**: Tools within Agency Swarm are created using [Instructor](https://github.com/jxnl/instructor), which provides a convenient interface and automatic type validation.
 - **Efficient Communication**: Agents communicate through a specially designed "send message" tool based on their own descriptions.
 - **State Management**: Agency Swarm efficiently manages the state of your assistants on OpenAI, maintaining it in a special `settings.json` file.
 - **Deployable in Production**: Agency Swarm is designed to be reliable and easily deployable in production environments.
@@ -45,45 +45,45 @@ Define your custom tools with [Instructor](https://github.com/jxnl/instructor):
     ```python
     from agency_swarm.tools import BaseTool
     from pydantic import Field
-    
+
     class MyCustomTool(BaseTool):
         """
-        A brief description of what the custom tool does. 
+        A brief description of what the custom tool does.
         The docstring should clearly explain the tool's purpose and functionality.
         """
-    
+
         # Define the fields with descriptions using Pydantic Field
         example_field: str = Field(
             ..., description="Description of the example field, explaining its purpose and usage."
         )
-    
+
         # Additional fields as required
         # ...
-    
+
         def run(self):
             """
             The implementation of the run method, where the tool's main functionality is executed.
             This method should utilize the fields defined above to perform its task.
             Doc string description is not required for this method.
             """
-    
+
             # Your custom tool logic goes here
             do_something(self.example_field)
-    
+
             # Return the result of the tool's operation
             return "Result of MyCustomTool operation"
     ```
-    
+
     or convert from OpenAPI schemas:
-    
+
     ```python
     from agency_swarm.tools import ToolFactory
     # using local file
     with open("schemas/your_schema.json") as f:
         tools = ToolFactory.from_openapi_schema(
             f.read(),
         )
-    
+
     # using requests
     tools = ToolFactory.from_openapi_schema(
         requests.get("https://api.example.com/openapi.json").json(),
@@ -94,13 +94,13 @@ Define your custom tools with [Instructor](https://github.com/jxnl/instructor):
 
     ```python
     from agency_swarm import Agent
-    
+
     ceo = Agent(name="CEO",
                 description="Responsible for client communication, task planning and management.",
                 instructions="You must converse with other agents to ensure complete task execution.", # can be a file like ./instructions.md
                 files_folder="./files", # files to be uploaded to OpenAI
                 schemas_folder="./schemas", # OpenAPI schemas to be converted into tools
-                tools=[MyCustomTool], 
+                tools=[MyCustomTool],
                 temperature=0.5, # temperature for the agent
                 max_prompt_tokens=25000, # max tokens in conversation history
                 )
@@ -111,29 +111,29 @@ Define your custom tools with [Instructor](https://github.com/jxnl/instructor):
    ```bash
    agency-swarm import-agent --name "Devid" --destination "./"
    ```
-   
+
    This will import Devid (Software Developer) Agent locally, including all source code files, so you have full control over your system. Currently, available agents are: `Devid`, `BrowsingAgent`.
 
 
 
-4. **Define Agency Communication Flows**: 
+4. **Define Agency Communication Flows**:
 Establish how your agents will communicate with each other.
 
     ```python
     from agency_swarm import Agency
     # if importing from local files
     from Developer import Developer
     from VirtualAssistant import VirtualAssistant
-   
+
     dev = Developer()
     va = VirtualAssistant()
-    
+
     agency = Agency([
            ceo,  # CEO will be the entry point for communication with the user
            [ceo, dev],  # CEO can initiate communication with Developer
            [ceo, va],   # CEO can initiate communication with Virtual Assistant
            [dev, va]    # Developer can initiate communication with Virtual Assistant
-         ], 
+         ],
          shared_instructions='agency_manifesto.md', #shared instructions for all agents
          temperature=0.5, # default temperature for all agents
          max_prompt_tokens=25000 # default max tokens in conversation history
@@ -142,23 +142,23 @@ Establish how your agents will communicate with each other.
 
      In Agency Swarm, communication flows are directional, meaning they are established from left to right in the agency_chart definition. For instance, in the example above, the CEO can initiate a chat with the developer (dev), and the developer can respond in this chat. However, the developer cannot initiate a chat with the CEO. The developer can initiate a chat with the virtual assistant (va) and assign new tasks.
 
-5. **Run Demo**: 
+5. **Run Demo**:
 Run the demo to see your agents in action!
-    
+
     Web interface:
 
     ```python
     agency.demo_gradio(height=900)
     ```
-    
+
     Terminal version:
-    
+
     ```python
     agency.run_demo()
     ```
-    
+
     Backend version:
-    
+
     ```python
     completion_output = agency.get_completion("Please create a new website for our client.")
     ```
@@ -213,12 +213,12 @@ When you run the `create-agent-template` command, it creates the following folde
 └── AgentName/                  # Directory for the specific agent
     ├── files/                  # Directory for files that will be uploaded to openai
     ├── schemas/                # Directory for OpenAPI schemas to be converted into tools
-    ├── tools/                  # Directory for tools to be imported by default. 
+    ├── tools/                  # Directory for tools to be imported by default.
     ├── AgentName.py            # The main agent class file
     ├── __init__.py             # Initializes the agent folder as a Python package
     ├── instructions.md or .txt # Instruction document for the agent
     └── tools.py                # Custom tools specific to the agent
-    
+
 ```
 
 This structure ensures that each agent has its dedicated space with all necessary files to start working on its specific tasks. The `tools.py` can be customized to include tools and functionalities specific to the agent's role.

agency_swarm/__init__.py

@@ -1,8 +1,5 @@
 from .agency import Agency
 from .agents import Agent
 from .tools import BaseTool
-from .util import set_openai_key
-from .util import set_openai_client
-from .util import get_openai_client
+from .util import get_openai_client, llm_validator, set_openai_client, set_openai_key
 from .util.streaming import AgencyEventHandler
-from .util import llm_validator