crewplus 0.3.1

Base services for CrewPlus AI applications

CrewPlus

CrewPlus provides the foundational services and core components for building advanced AI applications. It is the heart of the CrewPlus ecosystem, designed for scalability, extensibility, and seamless integration.

Overview

This repository, crewplus-base, contains the core crewplus Python package. It includes essential building blocks for interacting with large language models, managing vector databases, and handling application configuration. Whether you are building a simple chatbot or a complex multi-agent system, CrewPlus offers the robust foundation you need.

The CrewPlus Ecosystem

CrewPlus is designed as a modular and extensible ecosystem of packages. This allows you to adopt only the components you need for your specific use case.

• crewplus (This package): The core package containing foundational services for chat, model load balancing, and vector stores.
• crewplus-agent: crewplus agent core: agentic task planner and executor, with context-aware memory.
• crewplus-ingestion: Provides robust pipelines for knowledge ingestion and data processing.
• crewplus-memory: Provides agent memory services for Crewplus AI Agents.
• crewplus-integrations: A collection of third-party integrations to connect CrewPlus with other services and platforms.

Features

• Chat Services: A unified interface for interacting with various chat models:
  • GeminiChatModel - Google Gemini models via Google AI or Vertex AI
  • ClaudeChatModel - Anthropic Claude models via Google Vertex AI
  • TracedAzureChatOpenAI - Azure OpenAI with built-in tracing
• Model Load Balancer: Intelligently distribute requests across multiple LLM endpoints.
• Vector DB Services: working with popular vector stores (e.g. Milvus, Zilliz Cloud) for retrieval-augmented generation (RAG) and agent memory.
• Observability & Tracing: Automatic integration with tracing tools like Langfuse, with an extensible design for adding others (e.g., Helicone, ...).

Documentation

For detailed guides and API references, please see the docs/ folder.

• GeminiChatModel Documentation: A comprehensive guide to using the GeminiChatModel for text, image, and video understanding.
• ClaudeChatModel Documentation: A comprehensive guide to using Claude models via Google Vertex AI with full LangChain compatibility.

Installation

To install the core crewplus package, run the following command:

pip install crewplus

Getting Started

Using GeminiChatModel

Here is a simple example of how to use the GeminiChatModel to start a conversation with Google Gemini.

from crewplus.services import GeminiChatModel

# Initialize the model
llm = GeminiChatModel(
    model_name="gemini-2.0-flash",
    google_api_key="your-google-api-key"
)

# Start a conversation
response = llm.invoke("Hello, what is CrewPlus?")
print(response.content)

Using ClaudeChatModel

Here is an example of how to use the ClaudeChatModel to interact with Claude via Google Vertex AI.

from crewplus.services import ClaudeChatModel

# Authenticate with GCP first: gcloud auth application-default login

# Initialize the model
llm = ClaudeChatModel(
    model_name="claude-opus-4-5",
    project_id="your-gcp-project-id",
    region="global",  # or "us-east1", "europe-west1" for regional endpoints
    max_tokens=1024
)

# Start a conversation
response = llm.invoke("Hello, what is CrewPlus?")
print(response.content)

# Streaming example
for chunk in llm.stream("Tell me about AI agents"):
    print(chunk.content, end="", flush=True)

Project Structure

The crewplus-base repository is organized to separate core logic, tests, and documentation.

crewplus-base/                    # GitHub repo name
├── pyproject.toml
├── README.md
├── LICENSE
├── CHANGELOG.md
├── crewplus/                 # PyPI package name
│   └──  __init__.py
│   └──  services/
│       └──  __init__.py
│       └──  gemini_chat_model.py
│       └──  claude_chat_model.py
│       └──  azure_chat_model.py
│       └──  model_load_balancer.py
│       └──  tracing_manager.py
│       └──  ...
│   └──  vectorstores/milvus
│       └──  __init__.py
│       └──  schema_milvus.py
│       └──  vdb_service.py
│   └──  utils/
│       └──  __init__.py
│       └──  schema_action.py
│       └──  ...
├── tests/
│   └── ...
├── docs/
│   └── ...
└── notebooks/
    └── ...

Version Update

0.2.91

• Add ClaudeChatModel for Anthropic Claude models via Google Vertex AI
• Full support for Claude Opus 4.5, Sonnet 4.5, and Haiku 4.5
• Async operations support with AsyncAnthropicVertex
• Streaming support for both sync and async modes
• Automatic Langfuse tracing integration

0.2.80

• Add FeedbackManager to support LangSmith-style feedback with Langfuse score

0.2.50

• Add async aget_vector_store to enable async vector search

Deploy to PyPI

Clean Previous Build Artifacts: Remove the dist/, build/, and *.egg-info/ directories to ensure that no old files are included in the new build.

rm -rf dist build *.egg-info

install deployment tool

pip install twine

build package

python -m build

deploy to TestPyPI (Test first)

python -m twine upload --repository testpypi dist/*

install from TestPyPI

pip install -i https://test.pypi.org/simple/ crewplus

Deploy to official PyPI

python -m twine upload dist/*