diff --git a/apps/dojo/src/agents.ts b/apps/dojo/src/agents.ts index 02db44e28..fdac352b9 100644 --- a/apps/dojo/src/agents.ts +++ b/apps/dojo/src/agents.ts @@ -345,6 +345,34 @@ export const agentsIntegrations: AgentIntegrationConfig[] = [ }; }, }, + { + id: "microsoft-agent-framework-python", + agents: async () => { + return { + agentic_chat: new HttpAgent({ + url: `${envVars.agentFrameworkPythonUrl}/agentic_chat`, + }), + backend_tool_rendering: new HttpAgent({ + url: `${envVars.agentFrameworkPythonUrl}/backend_tool_rendering`, + }), + human_in_the_loop: new HttpAgent({ + url: `${envVars.agentFrameworkPythonUrl}/human_in_the_loop`, + }), + agentic_generative_ui: new HttpAgent({ + url: `${envVars.agentFrameworkPythonUrl}/agentic_generative_ui`, + }), + shared_state: new HttpAgent({ + url: `${envVars.agentFrameworkPythonUrl}/shared_state`, + }), + tool_based_generative_ui: new HttpAgent({ + url: `${envVars.agentFrameworkPythonUrl}/tool_based_generative_ui`, + }), + predictive_state_updates: new HttpAgent({ + url: `${envVars.agentFrameworkPythonUrl}/predictive_state_updates`, + }), + }; + }, + }, { id: "a2a-basic", agents: async () => { diff --git a/apps/dojo/src/env.ts b/apps/dojo/src/env.ts index 569c6cfdc..fecebef11 100644 --- a/apps/dojo/src/env.ts +++ b/apps/dojo/src/env.ts @@ -11,6 +11,7 @@ type envVars = { crewAiUrl: string; pydanticAIUrl: string; adkMiddlewareUrl: string; + agentFrameworkPythonUrl: string; a2aUrl: string; a2aMiddlewareBuildingsManagementUrl: string; a2aMiddlewareFinanceUrl: string; @@ -40,6 +41,7 @@ export default function getEnvVars(): envVars { crewAiUrl: process.env.CREW_AI_URL || 'http://localhost:9002', pydanticAIUrl: process.env.PYDANTIC_AI_URL || 'http://localhost:9000', adkMiddlewareUrl: process.env.ADK_MIDDLEWARE_URL || 'http://localhost:8000', + agentFrameworkPythonUrl: process.env.AGENT_FRAMEWORK_PYTHON_URL || 'http://localhost:8888', springAiUrl: process.env.SPRING_AI_URL || 'http://localhost:8080', a2aUrl: process.env.A2A_URL || 'http://localhost:10002', a2aMiddlewareBuildingsManagementUrl: process.env.A2A_MIDDLEWARE_BUILDINGS_MANAGEMENT_URL || 'http://localhost:9001', diff --git a/apps/dojo/src/files.json b/apps/dojo/src/files.json index 4fdd3b9d6..91e489e90 100644 --- a/apps/dojo/src/files.json +++ b/apps/dojo/src/files.json @@ -1641,7 +1641,7 @@ }, { "name": "tool_based_generative_ui.py", - "content": "\"\"\"\nAn example demonstrating tool-based generative UI.\n\"\"\"\n\nfrom crewai.flow.flow import Flow, start\nfrom litellm import completion\nfrom ..sdk import copilotkit_stream, CopilotKitState\n\n\n# This tool generates a haiku on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nGENERATE_HAIKU_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"description\": \"Generate a haiku in Japanese and its English translation\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"japanese\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in Japanese\"\n },\n \"english\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in English\"\n },\n \"image_names\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"Names of 3 relevant images from the provided list\"\n }\n },\n \"required\": [\"japanese\", \"english\", \"image_names\"]\n }\n }\n}\n\n\nclass ToolBasedGenerativeUIFlow(Flow[CopilotKitState]):\n \"\"\"\n A flow that demonstrates tool-based generative UI.\n \"\"\"\n\n @start()\n async def chat(self):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n system_prompt = \"You assist the user in generating a haiku. When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter. Dont provide the relavent image names in your final response to the user. \"\n\n\n # 1. Run the model and stream the response\n # Note: In order to stream the response, wrap the completion call in\n # copilotkit_stream and set stream=True.\n response = await copilotkit_stream(\n completion(\n\n # 1.1 Specify the model to use\n model=\"openai/gpt-4o\",\n messages=[\n {\n \"role\": \"system\", \n \"content\": system_prompt\n },\n *self.state.messages\n ],\n\n # 1.2 Bind the available tools to the model\n tools=[ GENERATE_HAIKU_TOOL ],\n\n # 1.3 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n stream=True\n )\n )\n message = response.choices[0].message\n\n # 2. Append the message to the messages in state\n self.state.messages.append(message)\n\n # 3. If there are tool calls, append a tool message to the messages in state\n if message.tool_calls:\n self.state.messages.append(\n {\n \"tool_call_id\": message.tool_calls[0].id,\n \"role\": \"tool\",\n \"content\": \"Haiku generated.\"\n }\n )\n", + "content": "\"\"\"\nAn example demonstrating tool-based generative UI.\n\"\"\"\n\nfrom crewai.flow.flow import Flow, start\nfrom litellm import completion\nfrom ag_ui.core import MessagesSnapshotEvent, EventType\nfrom ..sdk import copilotkit_stream, CopilotKitState\n\n\n# This tool generates a haiku on the server.\n# The tool call will be streamed to the frontend as it is being generated.\nGENERATE_HAIKU_TOOL = {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"generate_haiku\",\n \"description\": \"Generate a haiku in Japanese and its English translation\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"japanese\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in Japanese\"\n },\n \"english\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"An array of three lines of the haiku in English\"\n },\n \"image_names\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"Names of 3 relevant images from the provided list\"\n }\n },\n \"required\": [\"japanese\", \"english\", \"image_names\"]\n }\n }\n}\n\n\nclass ToolBasedGenerativeUIFlow(Flow[CopilotKitState]):\n \"\"\"\n A flow that demonstrates tool-based generative UI.\n \"\"\"\n\n @start()\n async def chat(self):\n \"\"\"\n The main function handling chat and tool calls.\n \"\"\"\n system_prompt = \"You assist the user in generating a haiku. When generating a haiku using the 'generate_haiku' tool, you MUST also select exactly 3 image filenames from the following list that are most relevant to the haiku's content or theme. Return the filenames in the 'image_names' parameter. Dont provide the relavent image names in your final response to the user. \"\n\n\n # 1. Run the model and stream the response\n # Note: In order to stream the response, wrap the completion call in\n # copilotkit_stream and set stream=True.\n response = await copilotkit_stream(\n completion(\n\n # 1.1 Specify the model to use\n model=\"openai/gpt-4o\",\n messages=[\n {\n \"role\": \"system\", \n \"content\": system_prompt\n },\n *self.state.messages\n ],\n\n # 1.2 Bind the available tools to the model\n tools=[ GENERATE_HAIKU_TOOL ],\n\n # 1.3 Disable parallel tool calls to avoid race conditions,\n # enable this for faster performance if you want to manage\n # the complexity of running tool calls in parallel.\n parallel_tool_calls=False,\n stream=True\n )\n )\n message = response.choices[0].message\n\n # 2. Append the message to the messages in state\n self.state.messages.append(message)\n\n # 3. If there are tool calls, append a tool message to the messages in state\n if message.tool_calls:\n self.state.messages.append(\n {\n \"tool_call_id\": message.tool_calls[0].id,\n \"role\": \"tool\",\n \"content\": \"Haiku generated.\"\n }\n )\n \n # 4. Emit MessagesSnapshotEvent to notify frontend about tool result\n yield MessagesSnapshotEvent(\n type=EventType.MESSAGES_SNAPSHOT,\n messages=self.state.messages\n )\n", "language": "python", "type": "file" } diff --git a/apps/dojo/src/menu.ts b/apps/dojo/src/menu.ts index 36959a218..bb8a41c19 100644 --- a/apps/dojo/src/menu.ts +++ b/apps/dojo/src/menu.ts @@ -168,6 +168,19 @@ export const menuIntegrations: MenuIntegrationConfig[] = [ "tool_based_generative_ui", ], }, + { + id: "microsoft-agent-framework-python", + name: "Microsoft Agent Framework (Python)", + features: [ + "agentic_chat", + "backend_tool_rendering", + "human_in_the_loop", + "agentic_generative_ui", + "predictive_state_updates", + "shared_state", + "tool_based_generative_ui", + ], + }, { id: "a2a", name: "A2A", diff --git a/integrations/microsoft-agent-framework/python/examples/.env.example b/integrations/microsoft-agent-framework/python/examples/.env.example new file mode 100644 index 000000000..bbf8e4e40 --- /dev/null +++ b/integrations/microsoft-agent-framework/python/examples/.env.example @@ -0,0 +1,13 @@ +# Environment variables for Microsoft Agent Framework examples + +# Azure OpenAI Configuration +AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/ +AZURE_OPENAI_API_KEY=your_azure_openai_api_key_here +AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=your_deployment_name + +# OpenAI Configuration (alternative to Azure OpenAI) +# OPENAI_API_KEY=your_openai_api_key_here +# OPENAI_CHAT_MODEL_ID=your_chat_model_id_here + +# Server Configuration +PORT=8888 diff --git a/integrations/microsoft-agent-framework/python/examples/.gitignore b/integrations/microsoft-agent-framework/python/examples/.gitignore new file mode 100644 index 000000000..80d049379 --- /dev/null +++ b/integrations/microsoft-agent-framework/python/examples/.gitignore @@ -0,0 +1,48 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Virtual environments +venv/ +ENV/ +env/ +.venv + +# Poetry +poetry.lock + +# Environment variables +.env +.env.local + +# IDEs +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db + +# Logs +*.log diff --git a/integrations/microsoft-agent-framework/python/examples/README.md b/integrations/microsoft-agent-framework/python/examples/README.md new file mode 100644 index 000000000..c9fe61287 --- /dev/null +++ b/integrations/microsoft-agent-framework/python/examples/README.md @@ -0,0 +1,119 @@ +# Microsoft Agent Framework AG-UI Integration + +This directory contains examples for using the Microsoft Agent Framework with the AG-UI protocol in the Dojo application. + +## Prerequisites + +- Python 3.10 or higher +- [uv](https://docs.astral.sh/uv/) for dependency management +- An OpenAI API key or Azure OpenAI endpoint + +## Installation + +1. Install dependencies: + +```bash +cd integrations/microsoft-agent-framework/python/examples +uv sync +``` + +2. Create a `.env` file based on `.env.example`: + +```bash +cp .env.example .env +``` + +3. Add your API credentials to `.env`: + +```bash +# For OpenAI +OPENAI_API_KEY=your_api_key_here +OPENAI_CHAT_MODEL_ID=your_model_here + +# Or for Azure OpenAI +AZURE_OPENAI_ENDPOINT=your_endpoint_here +AZURE_OPENAI_API_KEY=your_api_key_here +AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=your_deployment_here +``` + +## Running the Examples + +### 1. Start the Backend Server + +In the examples directory, start the Dojo backend server: + +```bash +cd integrations/microsoft-agent-framework/python/examples +uv run dev +``` + +The server will start on `http://localhost:8888` by default. + +### 2. Start the Dojo Frontend + +In a separate terminal, start the Dojo web application: + +```bash +cd apps/dojo +pnpm dev +``` + +The Dojo frontend will be available at `http://localhost:3000`. + +### 3. Connect to Your Agent + +1. Open `http://localhost:3000` in your browser +2. Configure the server URL to `http://localhost:8888` +3. Select one "Microsoft Agent Framework (Python)" from the dropdown +4. Start exploring the samples + +## Available Endpoints + +The server exposes the following example agents demonstrating all 7 AG-UI features: + +- `/agentic_chat` - Basic conversational agent with tool calling (Feature 1: Agentic Chat) +- `/backend_tool_rendering` - Agent demonstrating backend tool rendering (Feature 2: Backend Tool Rendering) +- `/human_in_the_loop` - Agent with human-in-the-loop workflows (Feature 3: Human in the Loop) +- `/agentic_generative_ui` - Agent that breaks down tasks into steps with streaming updates (Feature 4: Agentic Generative UI) +- `/tool_based_generative_ui` - Agent that generates custom UI components (Feature 5: Tool-based Generative UI) +- `/shared_state` - Agent with bidirectional state synchronization (Feature 6: Shared State) +- `/predictive_state_updates` - Agent with predictive state updates during tool execution (Feature 7: Predictive State Updates) + +## Project Structure + +``` +examples/ +├── agents/ +│ ├── agentic_chat/ # Feature 1: Basic chat agent +│ ├── backend_tool_rendering/ # Feature 2: Backend tool rendering +│ ├── human_in_the_loop/ # Feature 3: Human-in-the-loop +│ ├── agentic_generative_ui/ # Feature 4: Streaming state updates +│ ├── tool_based_generative_ui/ # Feature 5: Custom UI components +│ ├── shared_state/ # Feature 6: Bidirectional state sync +│ ├── predictive_state_updates/ # Feature 7: Predictive state updates +│ └── dojo.py # FastAPI application setup +├── pyproject.toml # Dependencies and scripts +├── .env.example # Environment variable template +└── README.md # This file +``` + +## Development + +To add a new example agent: + +1. Create a new directory under `agents/` +2. Add an `agent.py` file with your agent implementation +3. Import and register it in `agents/dojo.py` + +## Dependencies + +This integration uses: + +- `agent-framework-ag-ui` - Microsoft Agent Framework AG-UI adapter +- `fastapi` - Web framework for the server +- `uvicorn` - ASGI server +- `python-dotenv` - Environment variable management + +## License + +MIT diff --git a/integrations/microsoft-agent-framework/python/examples/agents/__init__.py b/integrations/microsoft-agent-framework/python/examples/agents/__init__.py new file mode 100644 index 000000000..62617ff1c --- /dev/null +++ b/integrations/microsoft-agent-framework/python/examples/agents/__init__.py @@ -0,0 +1 @@ +"""Agent package.""" diff --git a/integrations/microsoft-agent-framework/python/examples/agents/dojo.py b/integrations/microsoft-agent-framework/python/examples/agents/dojo.py new file mode 100644 index 000000000..e880b5a3d --- /dev/null +++ b/integrations/microsoft-agent-framework/python/examples/agents/dojo.py @@ -0,0 +1,71 @@ +"""Microsoft Agent Framework Python Dojo Example Server. + +This provides a FastAPI application that demonstrates how to use the +Microsoft Agent Framework with the AG-UI protocol. It includes examples for +each of the AG-UI dojo features: +- Agentic Chat +- Human in the Loop +- Backend Tool Rendering +- Agentic Generative UI +- Tool-based Generative UI +- Shared State +- Predictive State Updates + +All agent implementations are from the agent-framework-ag-ui package examples. +Reference: https://github.com/microsoft/agent-framework/tree/main/python/packages/ag-ui/examples/agents +""" + +import os +import uvicorn +from fastapi import FastAPI +from dotenv import load_dotenv + +from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint +from agent_framework_ag_ui_examples.agents import ( + simple_agent, + weather_agent, + human_in_the_loop_agent, + task_steps_agent_wrapped, + ui_generator_agent, + recipe_agent, + document_writer_agent, +) + +load_dotenv() + +app = FastAPI(title="Microsoft Agent Framework Python Dojo") + +# Agentic Chat - simple_agent +add_agent_framework_fastapi_endpoint(app, simple_agent, "/agentic_chat") + +# Backend Tool Rendering - weather_agent +add_agent_framework_fastapi_endpoint(app, weather_agent, "/backend_tool_rendering") + +# Human in the Loop - human_in_the_loop_agent with state configuration +add_agent_framework_fastapi_endpoint( + app, + human_in_the_loop_agent, + "/human_in_the_loop", +) + +# Agentic Generative UI - task_steps_agent_wrapped +add_agent_framework_fastapi_endpoint(app, task_steps_agent_wrapped, "/agentic_generative_ui") + +# Tool-based Generative UI - ui_generator_agent +add_agent_framework_fastapi_endpoint(app, ui_generator_agent, "/tool_based_generative_ui") + +# Shared State - recipe_agent +add_agent_framework_fastapi_endpoint(app, recipe_agent, "/shared_state") + +# Predictive State Updates - document_writer_agent +add_agent_framework_fastapi_endpoint(app, document_writer_agent, "/predictive_state_updates") + + +def main(): + """Main function to start the FastAPI server.""" + port = int(os.getenv("PORT", "8888")) + uvicorn.run(app, host="0.0.0.0", port=port) + + +if __name__ == "__main__": + main() diff --git a/integrations/microsoft-agent-framework/python/examples/pyproject.toml b/integrations/microsoft-agent-framework/python/examples/pyproject.toml new file mode 100644 index 000000000..774ea6fba --- /dev/null +++ b/integrations/microsoft-agent-framework/python/examples/pyproject.toml @@ -0,0 +1,25 @@ +[project] +name = "microsoft-agent-framework-dojo" +version = "0.1.0" +description = "Microsoft Agent Framework Python examples for AG-UI Dojo" +authors = [{ name = "Microsoft Agent Framework Team" }] +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "agent-framework-ag-ui>=1.0.0b251106.post1", + "agent-framework-core>=1.0.0b251105", + "python-dotenv>=1.0.0", +] + +[tool.hatch.metadata] +allow-direct-references = true + +[project.scripts] +dev = "agents.dojo:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["agents"] diff --git 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