dm-aioaiagent 0.6.1

This is my custom aioaiagent client

DM-aioaiagent

Urls

• PyPI
• GitHub

* Package contains both asynchronous and synchronous clients

Installation

By default, the package ships with OpenAI support. Other providers are optional extras:

pip install dm-aioaiagent                       # OpenAI only
pip install dm-aioaiagent[anthropic]            # + Anthropic
pip install dm-aioaiagent[anthropic,gemini]     # several at once
pip install dm-aioaiagent[all]                  # every supported provider

Available extras: anthropic, gemini, groq, mistral, deepseek, ollama, all.

If you call a model from a provider whose package is not installed, init_chat_model will raise an ImportError with the exact pip install command you need.

Providers

Provider resolution is delegated to LangChain's init_chat_model — the agent picks the provider automatically by model name prefix when possible. For everything else, use the "provider:model" mask.

# Auto-detected from model prefix (rules come from LangChain's init_chat_model)
agent = DMAioAIAgent(model="gpt-4o-mini")              # → openai
agent = DMAioAIAgent(model="claude-3-5-sonnet-latest") # → anthropic
agent = DMAioAIAgent(model="gemini-2.0-flash")         # → google_vertexai (see note below)

# Explicit provider via "provider:model" mask
agent = DMAioAIAgent(model="google_genai:gemini-2.0-flash")
agent = DMAioAIAgent(model="groq:llama-3.1-70b-versatile")
agent = DMAioAIAgent(model="mistralai:mistral-large-latest")
agent = DMAioAIAgent(model="deepseek:deepseek-chat")
agent = DMAioAIAgent(model="ollama:llama3.1")

# OpenAI-compatible gateway (OpenRouter, Together, vLLM, LiteLLM proxy, ...)
# Works without installing any extra — just point to the OpenAI-compatible URL.
agent = DMAioAIAgent(
    model="meta-llama/llama-3.1-70b-instruct",
    llm_provider_base_url="https://openrouter.ai/api/v1",
    llm_provider_api_key="sk-or-...",
)

Note about Gemini. LangChain's auto-detect maps the gemini* prefix to google_vertexai (Google Cloud Vertex AI, requires a GCP service account). If you have a regular Google AI Studio API key (GOOGLE_API_KEY), use the google_genai: mask explicitly:

agent = DMAioAIAgent(model="google_genai:gemini-2.0-flash")

Supported provider keys for the "provider:model" mask (list inherited from LangChain): openai, anthropic, azure_openai, azure_ai, google_vertexai, google_genai, bedrock, bedrock_converse, cohere, fireworks, together, mistralai, huggingface, groq, ollama, google_anthropic_vertex, deepseek, ibm, nvidia, xai, perplexity.

Note about parallel tool calls

parallel_tool_calls is currently mapped only for OpenAI and Anthropic (their APIs use different formats). For other providers the parameter is silently ignored — extend per-provider mapping if you need it.

Usage

Analogue to DMAioAIAgent is the synchronous client DMAIAgent.

Windows Setup

import asyncio
import sys

if sys.platform == "win32":
    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

Api Key Setup

Each provider reads its API key from a dedicated environment variable, e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, GROQ_API_KEY, MISTRAL_API_KEY, etc. Alternatively, pass the key explicitly via the llm_provider_api_key argument — useful for multi-tenant setups, custom gateways, or runtime key rotation.

Use load_dotenv to load the .env file.

from dotenv import load_dotenv
load_dotenv()

Use agent with inner memory and run single message

By default, agent use inner memory to store the conversation history.

(You can set max count messages in memory by max_memory_messages init argument)

import asyncio
from dm_aioaiagent import DMAioAIAgent


async def main():
    # define a system message
    system_message = "Your custom system message with role, backstory and goal"

    # (optional) define a list of tools, if you want to use them
    tools = [...]

    # define a openai model, default is "gpt-4o-mini"
    model_name = "gpt-4o"

    # create an agent
    ai_agent = DMAioAIAgent(system_message, tools, model=model_name)
    # if you don't want to see the input and output messages from agent
    # you can set `input_output_logging=False` init argument

    # call an agent
    answer = await ai_agent.run("Hello!")

    # call an agent
    answer = await ai_agent.run("I want to know the weather in Kyiv")

    # get full conversation history
    conversation_history = ai_agent.memory_messages

    # clear conversation history
    ai_agent.clear_memory_messages()


if __name__ == "__main__":
    asyncio.run(main())

Use agent without inner memory and run multiple messages

If you want to control the memory of the agent, you can disable it by setting is_memory_enabled=False

import asyncio
from dm_aioaiagent import DMAioAIAgent


async def main():
    # define a system message
    system_message = "Your custom system message with role, backstory and goal"

    # (optional) define a list of tools, if you want to use them
    tools = [...]

    # define a openai model, default is "gpt-4o-mini"
    model_name = "gpt-4o"

    # create an agent
    ai_agent = DMAioAIAgent(system_message, tools, model=model_name,
                            is_memory_enabled=False)
    # if you don't want to see the input and output messages from agent
    # you can set input_output_logging=False

    # define the conversation message(s)
    messages = [
        {"role": "user", "content": "Hello!"}
    ]

    # call an agent
    new_messages = await ai_agent.run_messages(messages)

    # add new_messages to messages
    messages.extend(new_messages)

    # define the next conversation message
    messages.append(
        {"role": "user", "content": "I want to know the weather in Kyiv"}
    )

    # call an agent
    new_messages = await ai_agent.run_messages(messages)


if __name__ == "__main__":
    asyncio.run(main())

Image vision

from dm_aioaiagent import DMAIAgent, OpenAIImageMessageContent


def main():
    # create an agent
    ai_agent = DMAIAgent(agent_name="image_vision", model="gpt-4o")

    # create an image message content
    # NOTE: text argument is optional
    img_content = OpenAIImageMessageContent(image_url="https://your.domain/image",
                                            text="Hello, what is shown in the photo?")

    # define the conversation messages
    messages = [
        {"role": "user", "content": "Hello!"},
        {"role": "user", "content": img_content},
    ]

    # call an agent
    new_messages = ai_agent.run_messages(messages)
    answer = new_messages[-1].content


if __name__ == "__main__":
    main()