Bizy is the business logic orchestration layer that sits atop multiple AI frameworks to coordinate complex, enterprise-grade workflows. It acts as a meta-orchestrator and provides adapters to frameworks like LangChain, Semantic Kernel, Anthropic MCP, Temporal, FastMCP, and Zep AI, enabling you to define and coordinate business rules across these ecosystems. Once started, Bizy handles the heavy lifting of routing tool usage, managing cross-framework state, and applying domain-specific rules to drive agent actions and process flows. You can use Bizy to build scalable, enterprise AI applications that leverage the strengths of each integrated framework while maintaining centralized business logic.

To use Bizy, install dependencies, start the development server, and interact with its orchestration capabilities through its Python module entrypoint. The Quick Start guidance mirrors typical Python project workflows: install dependencies with Poetry, run tests, and start the server with the -m entrypoint. After startup, you can leverage Bizy to configure cross-framework workflows, register adapters for your preferred toolchains, and define rules that Bizy will orchestrate across LangChain, Semantic Kernel, MCP-based tools, and more. Explore the documentation site to learn how to define business rules, model workflows, and monitor orchestration events across frameworks.

Prerequisites:

• Python 3.8+ (preferably 3.9+)
• Poetry for dependency management
• Git to clone the repository

1. Clone the repository git clone https://github.com/getfounded/bizy.git cd bizy

2. Create and activate a virtual environment (optional but recommended) python -m venv venv

   On macOS/Linux

   source venv/bin/activate

   On Windows

   venv\Scripts\activate.bat

3. Install dependencies with Poetry poetry install

4. Run tests (optional but recommended during development) poetry run pytest

5. Start the Bizy development server poetry run python -m bizy.main

Notes:

• If you encounter dependency issues, ensure Poetry is up to date and that your Python version is compatible with the project requirements.
• For production deployments, consider containerizing Bizy and configuring the mcp_config accordingly.

Tips and common issues:

• Ensure that the Python environment where Bizy runs has network access to all integrated frameworks and services.
• When adding new adapters (e.g., for a new MCP or framework), follow the existing Framework Adapters pattern in Bizy to maintain compatibility.
• Environment variables can be used to configure runtime behavior. If you add variables, document them in your deployment environment and consider adding defaults in the codebase.
• Review the docs.bizy.work site for detailed integration guides, examples, and best practices for cross-framework orchestration.
• If you run into startup or binding issues, check that the required submodules and plugins are installed and that the correct Python path is used when invoking -m bizy.main.