Deprecate llama-deploy in favor of workflows-py (#585)

.github/workflows/lint.yml

@@ -16,7 +16,7 @@ jobs:
         uses: astral-sh/setup-uv@v5
 
       - name: Set up Python
-        run: uv python install
+        run: uv python install 3.12
 
       - name: Run linter
         shell: bash

README.md

@@ -11,66 +11,5 @@
 
 # 🦙 LlamaDeploy 🤖
 
-LlamaDeploy (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 LlamaDeploy, 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 LlamaDeploy 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 LlamaDeploy in two ways:
-
-- Using the [`llamactl`](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/40_llamactl/) CLI from a shell.
-- Through the [_LlamaDeploy SDK_](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/30_python_sdk/) from a Python application or script.
-
-Both the SDK and the CLI are part of the LlamaDeploy Python package. To install, just run:
-
-```bash
-pip install -U llama-deploy
-```
-> [!TIP]
-> For a comprehensive guide to LlamaDeploy's architecture and detailed descriptions of its components, visit our
-[official documentation](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/).
-
-## Why LlamaDeploy?
-
-1. **Seamless Deployment**: It bridges the gap between development and production, allowing you to deploy `llama_index`
-   workflows with minimal changes to your code.
-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, LlamaDeploy adds robustness in
-   production environments.
-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 LlamaDeploy on closing the gap between local development and remote execution of agents as services.
-
-## Quick Start with `llamactl`
-
-Spin up a running deployment in minutes using the interactive CLI wizard:
-
-```bash
-# 1. Install the package & CLI
-pip install -U llama-deploy
-
-# 2. Scaffold a new project (interactive)
-llamactl init
-
-#    or non-interactive
-llamactl init --name project-name --template basic
-
-# 3. Enter the project
-cd project-name
-
-# 4. Start the control-plane API server (new terminal)
-python -m llama_deploy.apiserver
-
-# 5. Deploy the generated workflow (another terminal)
-llamactl deploy deployment.yml
-
-# 6. Call it!
-llamactl run --deployment hello-deploy --arg message "Hello world!"
-```
-
-Looking for more templates or integrations? Check the [`examples`](examples) directory for end-to-end demos (message queues, web UIs, etc.) or read the full [documentation](https://docs.llamaindex.ai/en/latest/module_guides/llama_deploy/).
+> [!CAUTION]
+> **This project is deprecated.** To serve workflows, use [llama-agents](https://github.com/run-llama/workflows-py) instead.

llama_deploy/__init__.py

@@ -1,4 +1,5 @@
 import logging
+import warnings
 
 root_logger = logging.getLogger("llama_deploy")
 
@@ -9,3 +10,10 @@ root_logger.addHandler(console_handler)
 
 root_logger.setLevel(logging.INFO)
 root_logger.propagate = True
+
+warnings.warn(
+    "llama-deploy is deprecated. Use llama-agents instead to serve workflows. "
+    "See https://github.com/run-llama/workflows-py for more information.",
+    DeprecationWarning,
+    stacklevel=2,
+)