gllm-agents-binary 0.3.2b1

A library for managing agents in Gen AI applications.

GLLM Agents

Description

A library for managing agents in Generative AI applications.

Installation

Prerequisites

• Python 3.13+ - Install here
• Pip (if using Pip) - Install here
• Poetry 1.8.1+ (if using Poetry) - Install here
• Git (if using Git) - Install here
• For git installation:
  • Access to the GDP Labs SDK github repository

1. Installation from Artifact Registry

Choose one of the following methods to install the package:

Using pip

pip install gllm-agents-binary

Using Poetry

poetry add gllm-agents-binary

2. Development Installation (Git)

For development purposes, you can install directly from the Git repository:

poetry add "git+ssh://[e-mail]/GDP-ADMIN/gen-ai-internal.git#subdirectory=libs/gllm-agents"

Managing Dependencies

1. Go to root folder of gllm-agents module, e.g. cd libs/gllm-agents.
2. Run poetry shell to create a virtual environment.
3. Run poetry lock to create a lock file if you haven't done it yet.
4. Run poetry install to install the gllm-agents requirements for the first time.
5. Run poetry update if you update any dependency module version at pyproject.toml.

Contributing

Please refer to this Python Style Guide to get information about code style, documentation standard, and SCA that you need to use when contributing to this project

1. Activate pre-commit hooks using pre-commit install
2. Run poetry shell to create a virtual environment.
3. Run poetry lock to create a lock file if you haven't done it yet.
4. Run poetry install to install the gllm-agents requirements for the first time.
5. Run which python to get the path to be referenced at Visual Studio Code interpreter path (Ctrl+Shift+P or Cmd+Shift+P)
6. Try running the unit test to see if it's working:

poetry run pytest -s tests/unit_tests/

Hello World Examples

Prerequisites

• Python 3.13+
• Install the binary package:

pip install gllm-agents-binary

• For OpenAI: Set your API key in the environment:

export OPENAI_API_KEY=your-openai-key

• For Google ADK: Set your API key in the environment:

export GOOGLE_API_KEY=your-google-api-key

Run the Hello World Examples

The example scripts are located in the gllm_agents/examples directory within the library. You can run them individually or use the run_all_examples.py script.

1. Running Individual Examples:

Navigate to the library's root directory (e.g., libs/gllm-agents if you cloned the repository).

LangGraph (OpenAI):

python gllm_agents/examples/hello_world_langgraph.py

LangGraph with BOSA Connector (OpenAI):

python gllm_agents/examples/hello_world_langgraph_bosa_twitter.py

LangGraph Streaming (OpenAI):

python gllm_agents/examples/hello_world_langgraph_stream.py

LangGraph Multi-Agent Coordinator (OpenAI):

python gllm_agents/examples/hello_world_multi_agent_coordinator.py

Google ADK:

python gllm_agents/examples/hello_world_google_adk.py

Google ADK Streaming:

python gllm_agents/examples/hello_world_google_adk_stream.py

LangChain (OpenAI):

python gllm_agents/examples/hello_world_langchain.py

LangChain Streaming (OpenAI):

python gllm_agents/examples/hello_world_langchain_stream.py

2. Running Individual MCP Examples:

• Navigate to the library's root directory (e.g., libs/gllm-agents if you cloned the repository).

• Open a new terminal and navigate to the sample MCP servers directory libs/gllm-agents/gllm_agents/examples/mcp_servers

  Turn on the SSE MCP servers:

  python mcp_server_sse.py
  

• Open a new terminal and navigate to the sample MCP servers directory libs/gllm-agents/gllm_agents/examples/mcp_servers

  Turn on the STDIO MCP servers:

  python mcp_server_stdio.py
  

LangGraph SSE Transport (OpenAI):

python gllm_agents/examples/hello_world_langgraph_mcp_sse.py

LangGraph Streaming SSE Transport (OpenAI):

python gllm_agents/examples/hello_world_langgraph_mcp_sse_stream.py

LangGraph STDIO Transport (OpenAI):

python gllm_agents/examples/hello_world_langgraph_mcp_stdio.py

LangGraph Streaming STDIO Transport (OpenAI):

python gllm_agents/examples/hello_world_langgraph_mcp_stdio_stream.py

Google ADK SSE Transport:

python gllm_agents/examples/hello_world_google_adk_mcp_sse.py

Google ADK Streaming SSE Transport:

python gllm_agents/examples/hello_world_google_adk_mcp_sse_stream.py

Google ADK STDIO Transport:

python gllm_agents/examples/hello_world_google_adk_mcp_stdio.py

Google ADK Streaming STDIO Transport:

python gllm_agents/examples/hello_world_google_adk_mcp_stdio_stream.py

LangChain SSE Transport (OpenAI):

python gllm_agents/examples/hello_world_langchain_mcp_sse.py

LangChain Streaming SSE Transport (OpenAI):

python gllm_agents/examples/hello_world_langchain_mcp_sse_stream.py

LangChain STDIO Transport (OpenAI):

python gllm_agents/examples/hello_world_langchain_mcp_stdio.py

LangChain Streaming STDIO Transport (OpenAI):

python gllm_agents/examples/hello_world_langchain_mcp_stdio_stream.py

3. Running Individual A2A Examples:

• Navigate to the library's root directory (e.g., libs/gllm-agents if you cloned the repository).
• Open a new terminal and navigate to the gllm_agents/examples directory to run the A2A server.

LangChain Server:

python hello_world_a2a_langchain_server.py

• Open a new terminal and navigate to the gllm_agents/examples directory to run the A2A client.

LangChain Client:

python hello_world_a2a_langchain_client.py

LangChain Client Integrated with Agent Workflow:

python hello_world_a2a_langchain_client_agent.py

LangChain Client Streaming:

python hello_world_a2a_langchain_client_stream.py

Architectural Notes

Agent Interface (AgentInterface)

The gllm_agents.agent.interface.AgentInterface class defines a standardized contract for all agent implementations within the GLLM Agents ecosystem. It ensures that different agent types (e.g., LangGraph-based, Google ADK-based) expose a consistent set of methods for core operations.

Key methods defined by AgentInterface typically include:

• arun(): For asynchronous execution of the agent that returns a final consolidated response.
• arun_stream(): For asynchronous execution that streams back partial responses or events from the agent.

By adhering to this interface, users can interact with various agents in a uniform way, making it easier to switch between or combine different agent technologies.

Inversion of Control (IoC) / Dependency Injection (DI)

The agent implementations (e.g., LangGraphAgent, GoogleADKAgent) utilize Dependency Injection. For instance, LangGraphAgent accepts an agent_executor (like one created by LangGraph's create_react_agent) in its constructor. Similarly, GoogleADKAgent accepts a native adk_native_agent. This allows the core execution logic to be provided externally, promoting flexibility and decoupling the agent wrapper from the specific instantiation details of its underlying engine.