| Name | llama-deploy JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2024-11-15 16:24:47 |
| maintainer | Logan Markewich |
| docs_url | None |
| author | Logan Markewich |
| requires_python | <4.0,>=3.10 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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[](https://pypi.org/project/llama-deploy)
[](https://pypi.org/project/llama-deploy)
[](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/)
[](https://github.com/run-llama/llama_deploy/actions/workflows/unit_test.yml)
[](https://github.com/run-llama/llama_deploy/actions/workflows/e2e_test.yml)
[](https://coveralls.io/github/run-llama/llama_deploy?branch=main)
# 🦙 Llama Deploy 🤖
Llama Deploy (formerly `llama-agents`) is an async-first framework for deploying, scaling, and productionizing agentic
multi-service systems based on [workflows from `llama_index`](https://docs.llamaindex.ai/en/stable/understanding/workflows/).
With Llama Deploy, you can build any number of workflows in `llama_index` and then run them as services, accessible
through a HTTP API by a user interface or other services part of your system.
The goal of Llama Deploy is to easily transition something that you built in a notebook to something running on the
cloud with the minimum amount of changes to the original code, possibly zero. In order to make this transition a
pleasant one, you can interact with Llama Deploy in two ways:
- Using the [`llamactl`](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/50_llamactl/) CLI from a shell.
- Through the [_LLama Deploy SDK_](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/40_python_sdk/) from a Python application or script.
Both the SDK and the CLI are part of the Llama Deploy Python package. To install, just run:
```bash
pip install llama_deploy
```
> [!TIP]
> For a comprehensive guide to Llama Deploy's architecture and detailed descriptions of its components, visit our
[official documentation](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/).
## Why Llama Deploy?
1. **Seamless Deployment**: It bridges the gap between development and production, allowing you to deploy `llama_index`
workflows with minimal changes to your code.
2. **Scalability**: The microservices architecture enables easy scaling of individual components as your system grows.
3. **Flexibility**: By using a hub-and-spoke architecture, you can easily swap out components (like message queues) or
add new services without disrupting the entire system.
4. **Fault Tolerance**: With built-in retry mechanisms and failure handling, Llama Deploy adds robustness in
production environments.
5. **State Management**: The control plane manages state across services, simplifying complex multi-step processes.
6. **Async-First**: Designed for high-concurrency scenarios, making it suitable for real-time and high-throughput
applications.
> [!NOTE]
> This project was initially released under the name `llama-agents`, but the introduction of [Workflows](https://docs.llamaindex.ai/en/stable/module_guides/workflow/#workflows) in `llama_index` turned out to be the most intuitive way for our users to develop agentic applications. We then decided to add new agentic features in `llama_index` directly, and focus Llama Deploy on closing the gap between local development and remote execution of agents as services.
## Getting Started
The fastest way to start using Llama Deploy is playing with a practical example. This repository contains a few applications you can use as a reference:
- [Quick start](examples/quick_start)
- [Use a deployment from a web-based user interface](examples/python_fullstack)
- [Message queue examples](examples/message-queue-integrations)
We recommend to start from the [Quick start](examples/quick_start) example and move
to [Use a deployment from a web-based user interface](examples/python_fullstack)
immediately after. Each folder contains a README file that will guide you through
the process.
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