agentboard

AgentBoard: Quickstart Tutorial

DeepNLP AgentBoard provides the visualization and tooling to visualize and monitor the agent loops and key entities of AI Agents development, such as messages, tools/functions, workflow and raw data types including text, dict or json, image, audio, video, etc.

agentboard agent loop workflow

Key Features

Easy APIs to log various data types for LLM calling and AI Agent development: text, dict or json, image, audio, video, etc.
AI Agent Key Entities Visualization: Visualize key entities of chat history messages, memories, tools/functions schema and input/output dict, etc.
Workflow of AI Agent Loop Running, Visualize the plan/act/react/reflect stage in the complete workflow chart. Can be modified and displayed for papers and technical reports.
Multi-Agents Support
Autonomous Agent Simulation Enviroment Support. Multiple build-in AI agent environment AI community, X(twitter) style mininal website for AI agents to interact (comment/like/follow/etc).
Chat Visualizer: A Chatbot Visualizer with multiple mainstream chat(WhatsApp, WeChat, ChatGPT, etc.) UI

Installation

You can install agentboard through pip and start the Flask based web app using the command line agentboard, the port can be changed with “–port=5000” parameters. After installation, you can visit (http://127.0.0.1:5000) to see the web console of agentboard. There is build in log file to visualize multiple data types.

# installation
pip install agentboard

# cmd line start service
agentboard

# Change log dir and port agentboard --logdir=./log --static=./static --port=5000

AgentBoard Visualize Workflow of Agent Loop

Let’s start with an example of a basic asynchronously AI Agent Loop, consists of 3 stages: PLAN, ACT, REFLECT. And plot the stage and result on a workflow chart on agentboard. We can use the basic Asynchronous Agent Loop in the AutoAgent Package, you can also use agentboard with many other agent framework, such as AutoGen and LangChain.

Each line of log is written as dict with some fields, for example if we want to plot input, execution and output related information of PLAN stage of an agent loop.

agent name	process_id	name	data	data_type
agent 1	PLAN	PLAN START	{“args1”: “1”, “args2”: “2”}	agent_loop
agent 1	PLAN	PLAN Execution	{“duration”: “30”	agent_loop
agent 1	PLAN	PLAN END	{“result”: “2”	agent_loop

To use agentboard with basic Async Agent Class, we need to first rewrtie the agent with logger in the desired place.

    cd exmaples/async_agents/

    # write logs and static file, default to ./log and ./static
    python run_agentboard_autoagent.py

    # run agentboard and visualize agent loop
    agentboard --logdir=./log --static=./static --port=5000

agentboard agent loop workflow

AgentBoard Log Messages Chat Visualizer

Let’s start with an example of calling OpenAI LLM api with user input prompt.

    import agentboard as ab

    messages= []
    logdir="./log"
    with ab.summary.FileWriter(logdir=logdir) as writer:
        prompt = "Can you give me an example of python code usage of async await functions"
        messages.append({"role": "user", "content": prompt})

        ## Calling OpenAI Chat Completion API            
        #        completion = client.chat.completions.create(
        #            model="gpt-3.5-turbo",
        #            messages=[
        #                {"role": "system", "content": "You are a helpful assistant."},
        #                {"role": "user", "content": prompt}
        #            ]
        #        )

        response_message = {"role":"assistant","content":"Sure! Here's an example of Python code that uses async/await functions:\n\n```python\nimport asyncio\n\nasync def print_numbers():\n    for i in range(1, 6):\n        print(i)\n        await asyncio.sleep(1)\n\nasync def main():\n    task1 = asyncio.create_task(print_numbers())\n    task2 = asyncio.create_task(print_numbers())\n    await task1\n    await task2\n\nasyncio.run(main())\n```\n\nIn this example, we define an async function `print_numbers` that prints the numbers 1 to 5 with a one-second delay between each number using `await asyncio.sleep(1)`. We also define an async function `main` that creates two tasks using `asyncio.create_task` to run the `print_numbers` function concurrently. We then use `await` to wait for both tasks to complete.\n\nWhen we run the `main` function using `asyncio.run(main())`, the numbers will be printed concurrently by the two tasks."}

        messages.append(response_message)

        ab.summary.messages(name="OpenAI Chat History", data=messages, agent_name="assistant")

Then you can go visit the agentboard (http://127.0.0.1:5000/log/messages) to see the chat visualizer of the chat completion history.

Note the log writing –logdir and agentboard loading –logdir should match.

agentboard summary messages function

AgentBoard Log Tools

Let’s start with an example of calling OpenAI Tool Usage API with user defined functions get_weather().

    import agentboard as ab
    from agentboard.utils import function_to_schema

    def calling_bing_tools(keyword:str, limit:int) -> str:
        url="https://www.bing.com/search?q=%s&limit=%d" % (keyword, limit)
        return url

    with ab.summary.FileWriter(logdir="./log", static="./static") as writer:

        tools = [calling_bing_tools]
        tools_map = {tool.__name__:tool for tool in tools}
        tools_schema = [function_to_schema(tool) for tool in tools]

        ## Calling ChatGPT Tool Usage code omitted
        # Omitted
        # arguments = json.loads(tool_call['function']['arguments'])

        arguments = {"keyword": "agentboard document", "limit": 10}


        ab.summary.tool(name="Act RAG Tool Bing", data=[calling_bing_tools], agent_name="agent 2", process_id="ACT")
        ab.summary.dict(name="Act RAG Argument Input", data=[arguments], agent_name="agent 2", process_id="ACT")

agentboard tool function

AgentBoard Display Image Tensor

Let’s log a random pytorch tensor with shape [8, 3, 400, 600] and display it in the agentboard.

import torch
import agentboard as ab

with ab.summary.FileWriter(logdir="./log", static="./static") as writer:

    input_image = torch.mul(torch.rand(8, 3, 400, 600), 255).to(torch.int64)
    ab.summary.image(name="Plan Input Image", data=input_image, agent_name="agent 1", process_id="plan")

agentboard image function

AgentBoard Display Audio Tensor

Let’s log a random pytorch tensor of audio with 2 channels, 16000 sample_rate lasting for 2 seconds.

import torch
import agentboard as ab
import math

with ab.summary.FileWriter(logdir="./log", static="./static") as writer:
    sample_rate = 16000  # 16 kHz
    duration_seconds = 2  # 2 seconds
    frequency = 440.0  # 440 Hz (A4 note)
    t = torch.linspace(0, duration_seconds, int(sample_rate * duration_seconds), dtype=torch.float32)
    waveform = (0.5 * torch.sin(2 * math.pi * frequency * t)).unsqueeze(0)  # Add channel dimension
    waveform = torch.unsqueeze(waveform, dim=0)
    ab.summary.audio(name="ASR Input Audio", data=waveform, agent_name="agent 1", process_id="asr")

agentboard audio function

AgentBoard Display Video Tensor

Let’s log a random pytorch tensor of video clip with 30 frames, 64x64 resolution, 3 color channels and use agentboard to visualize it.

import torch
import agentboard as ab

with ab.summary.FileWriter(logdir="./log", static="./static") as writer:
    T, H, W, C = 30, 64, 64, 3  # 30 frames, 64x64 resolution, 3 color channels
    video_tensor = torch.randint(0, 256, (T, H, W, C), dtype=torch.uint8)
    frame_rate = 24  # Frames per second
    ab.summary.video(name="Text2Video Output", data=video_tensor, agent_name="agent 1", 
        process_id="act", file_ext = ".mp4", frame_rate = 24, video_codecs = "mpeg4")

agentboard audio function

AI Services Reviews and Ratings

agentboard

AgentBoard: Quickstart Tutorial

Key Features

Installation

AgentBoard Visualize Workflow of Agent Loop

AgentBoard Log Messages Chat Visualizer

AgentBoard Log Tools

AgentBoard Display Image Tensor

AgentBoard Display Audio Tensor

AgentBoard Display Video Tensor

Agents Related Pipeline Workflow and Document

AI Services Reviews and Ratings

AI Agent

Chatbot

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AI Video Generation

AI Education

Robotics

AI Tools

AI Widgets

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