The Swarmauri SDK provides a powerful, extensible framework for building AI-powered applications. This repository includes core interfaces, standard abstract base classes, and concrete reference implementations.

Swarmauri offers multiple installation options to suit different needs:

For a full-featured experience with all standard components:

# Install the main namespace package with standard components
pip install swarmauri

# Install the main namespace package with extra standard components
pip install swarmauri[full]

For a minimal installation with just the core interfaces:

# Install only the core components
pip install swarmauri_core

Add additional experimental components:

# Add experimental features (optional)
pip install swarmauri_experimental

Install specific packages for targeted functionality:

# Install only the vector store implementations you need
pip install swarmauri_vectorstore_pinecone
pip install swarmauri_vectorstore_annoy

# Install specific tools
pip install swarmauri_tool_jupyterexportlatex

For contributors or those wanting the latest features:

# Clone the repository
git clone https://github.com/swarmauri/swarmauri-sdk.git
cd swarmauri-sdk

# Install in development mode
pip install -e .

# Or with UV for faster installation
pip install uv
uv pip install -e .

The namespace approach provides a clean, unified interface to all components:

# Import through the swarmauri namespace
from swarmauri.vectorstores import PineconeVectorStore, AnnoyVectorStore
from swarmauri.documents import Document
from swarmauri.tools import JupyterExportLatexTool

# Create a vector store
vector_store = PineconeVectorStore(
    api_key="your-api-key",
    environment="your-environment",
    index_name="your-index"
)

# Create a document
document = Document(
    content="Sample text content",
    metadata={"source": "example"}
)

# Add document to vector store
vector_store.add_document(document)

# Retrieve similar documents
results = vector_store.retrieve("query text", top_k=5)

For more explicit imports or when working with specific packages:

# Import directly from individual packages
from swarmauri_vectorstore_pinecone import PineconeVectorStore
from swarmauri_standard.documents import Document
from swarmauri_tool_jupyterexportlatex import JupyterExportLatexTool

# Use components as before
vector_store = PineconeVectorStore(...)

The Swarmauri SDK is organized into several key packages:

• swarmauri_core: Core interfaces and constants
• swarmauri_base: Abstract base classes for extensibility
• swarmauri_standard: Standard implementations of common components
• swarmauri: Main namespace package that unifies all components
• swarmauri_experimental: Experimental features under development

Individual components follow these naming conventions:

• swarmauri_vectorstore_*: Vector database integrations
• swarmauri_embedding_*: Embedding model implementations
• swarmauri_tool_*: Task-specific tools
• swarmauri_parser_*: Text parsing utilities
• swarmauri_distance_*: Distance calculation methods

If you want to contribute to the Swarmauri SDK, please read our guidelines for contributing and style guide to get started.

Swarmauri uses Python's entry point system for plugin discovery. Here's how to register your component:

# In your pyproject.toml
[project.entry-points.'swarmauri.vectorstores']
YourVectorStore = "swarmauri_vectorstore_yourplugin:YourVectorStore"

# In your implementation file
@ComponentBase.register_type(VectorStoreBase, "YourVectorStore")
class YourVectorStore(VectorStoreBase):
    # Your implementation

The Swarmauri SDK is licensed under the Apache License 2.0. See the LICENSE file for details.