docs: update install and deployment guidance for uv

AGENTS.md

@@ -121,7 +121,7 @@ LiteLLM supports MCP for agent workflows:
 
 ## RUNNING SCRIPTS
 
-Use `poetry run python script.py` to run Python scripts in the project environment (for non-test files).
+Use `uv run python script.py` to run Python scripts in the project environment (for non-test files).
 
 ## GITHUB TEMPLATES
 
@@ -232,16 +232,16 @@ When opening issues or pull requests, follow these templates:
 
 ### Environment
 
-- Poetry is installed in `~/.local/bin`; the update script ensures it is on `PATH`.
+- uv is installed in `~/.local/bin`; the update script ensures it is on `PATH`.
 - Python 3.12, Node 22 are pre-installed.
-- The virtual environment lives under `~/.cache/pypoetry/virtualenvs/`.
+- The project virtual environment lives under `.venv/`.
 
 ### Running the proxy server
 
 Start the proxy with a config file:
 
 ```bash
-poetry run litellm --config dev_config.yaml --port 4000
+uv run litellm --config dev_config.yaml --port 4000
 ```
 
 The proxy takes ~15-20 seconds to fully start (it runs Prisma migrations on boot). Wait for `/health` to return before sending requests. Without a PostgreSQL `DATABASE_URL`, the proxy connects to a default Neon dev database embedded in the `litellm-proxy-extras` package.
@@ -250,17 +250,16 @@ The proxy takes ~15-20 seconds to fully start (it runs Prisma migrations on boot
 
 See `CLAUDE.md` and the `Makefile` for standard commands. Key notes:
 
-- `psycopg-binary` must be installed (`poetry run pip install psycopg-binary`) because the pytest-postgresql plugin requires it and the lock file only includes `psycopg` (no binary).
-- `openapi-core` must be installed (`poetry run pip install openapi-core`) for the OpenAPI compliance tests in `tests/test_litellm/interactions/`.
+- `uv sync --group proxy-dev --extra proxy` installs the Prisma and proxy-side test dependencies used by the standard local workflow.
 - The `--timeout` pytest flag is NOT available; don't pass it.
-- Unit tests: `poetry run pytest tests/test_litellm/ -x -vv -n 4`
+- Unit tests: `uv run pytest tests/test_litellm/ -x -vv -n 4`
 - Black `--check` may report pre-existing formatting issues; this does not block test runs.
-- If `poetry install` fails with "pyproject.toml changed significantly since poetry.lock was last generated", run `poetry lock` first to regenerate the lock file.
+- If `uv sync` fails because the lockfile is outdated, run `uv lock` and retry.
 
 ### Lint
 
 ```bash
-cd litellm && poetry run ruff check .
+cd litellm && uv run ruff check .
 ```
 
 Ruff is the primary fast linter. For the full lint suite (including mypy, black, circular imports), run `make lint` per `CLAUDE.md`.
@@ -271,4 +270,4 @@ Ruff is the primary fast linter. For the full lint suite (including mypy, black,
 - The proxy at port 4000 serves a **pre-built** static UI from `litellm/proxy/_experimental/out/`. After making UI code changes, you must run `npm run build` in the dashboard directory and copy the output: `cp -r ui/litellm-dashboard/out/* litellm/proxy/_experimental/out/` for the proxy to serve the updated UI.
 - SVGs used as provider logos (loaded via `<img>` tags) must NOT use `fill="currentColor"` — replace with an explicit color like `#000000` or use the `-color` variant from lobehub icons, since CSS color inheritance does not work inside `<img>` elements.
 - Provider logos live in `ui/litellm-dashboard/public/assets/logos/` (source) and `litellm/proxy/_experimental/out/assets/logos/` (pre-built). Both locations must have the file for it to work in dev and proxy-served modes.
-- UI Vitest tests: `cd ui/litellm-dashboard && npx vitest run`
+- UI Vitest tests: `cd ui/litellm-dashboard && npx vitest run`

CLAUDE.md

@@ -7,7 +7,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 ### Installation
 - `make install-dev` - Install core development dependencies
 - `make install-proxy-dev` - Install proxy development dependencies with full feature set
-- `make install-test-deps` - Install all test dependencies
+- `make install-test-deps` - Install the full local test environment and generate the Prisma client
 
 ### Testing
 - `make test` - Run all tests
@@ -22,11 +22,11 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 - `make lint-mypy` - Run MyPy type checking only
 
 ### Single Test Files
-- `poetry run pytest tests/path/to/test_file.py -v` - Run specific test file
-- `poetry run pytest tests/path/to/test_file.py::test_function -v` - Run specific test
+- `uv run pytest tests/path/to/test_file.py -v` - Run specific test file
+- `uv run pytest tests/path/to/test_file.py::test_function -v` - Run specific test
 
 ### Running Scripts
-- `poetry run python script.py` - Run Python scripts (use for non-test files)
+- `uv run python script.py` - Run Python scripts (use for non-test files)
 
 ### GitHub Issue & PR Templates
 When contributing to the project, use the appropriate templates:

CONTRIBUTING.md

@@ -122,9 +122,17 @@ Run all unit tests (uses parallel execution for speed):
 make test-unit
 ```
 
+If you're running broader test suites, proxy tests, or anything that touches PostgreSQL-backed fixtures/plugins, install the full local test environment first:
+
+```bash
+make install-test-deps
+```
+
+This syncs the locked test environment used across the repo, including `psycopg` v3 plus `psycopg-binary` (used by `pytest-postgresql`), `psycopg2-binary` (used by some proxy E2E tests), and a generated Prisma client for DB-backed proxy tests, so pytest startup matches CI without manual package installs.
+
 Run specific test files:
 ```bash
-poetry run pytest tests/test_litellm/test_your_file.py -v
+uv run pytest tests/test_litellm/test_your_file.py -v
 ```
 
 ### Running Linting and Formatting Checks
@@ -172,7 +180,7 @@ Run `make help` to see all available commands:
 make help                       # Show all available commands
 make install-dev               # Install development dependencies
 make install-proxy-dev         # Install proxy development dependencies
-make install-test-deps         # Install test dependencies (for running tests)
+make install-test-deps         # Install the full local test environment
 make format                    # Apply Black code formatting
 make format-check              # Check Black formatting (matches CI)
 make lint                      # Run all linting checks
@@ -234,7 +242,7 @@ To run the proxy server locally:
 make install-proxy-dev
 
 # Start the proxy server
-poetry run litellm --config your_config.yaml
+uv run litellm --config your_config.yaml
 ```
 
 ### Docker Development
@@ -319,4 +327,4 @@ Looking for ideas? Check out:
 - 🧪 Test coverage improvements
 - 🔌 New LLM provider integrations
 
-Thank you for contributing to LiteLLM! 🚀 
+Thank you for contributing to LiteLLM! 🚀 

GEMINI.md

@@ -22,11 +22,11 @@ This file provides guidance to Gemini when working with code in this repository.
 - `make lint-mypy` - Run MyPy type checking only
 
 ### Single Test Files
-- `poetry run pytest tests/path/to/test_file.py -v` - Run specific test file
-- `poetry run pytest tests/path/to/test_file.py::test_function -v` - Run specific test
+- `uv run pytest tests/path/to/test_file.py -v` - Run specific test file
+- `uv run pytest tests/path/to/test_file.py::test_function -v` - Run specific test
 
 ### Running Scripts
-- `poetry run python script.py` - Run Python scripts (use for non-test files)
+- `uv run python script.py` - Run Python scripts (use for non-test files)
 
 ### GitHub Issue & PR Templates
 When contributing to the project, use the appropriate templates:
@@ -105,4 +105,4 @@ LiteLLM is a unified interface for 100+ LLM providers with two main components:
 ### Enterprise Features
 - Enterprise-specific code in `enterprise/` directory
 - Optional features enabled via environment variables
-- Separate licensing and authentication for enterprise features
+- Separate licensing and authentication for enterprise features

README.md

@@ -46,7 +46,7 @@
 ### Python SDK
 
 ```shell
-pip install litellm
+uv add litellm
 ```
 
 ```python
@@ -68,7 +68,7 @@ response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"ro
 [**Getting Started - E2E Tutorial**](https://docs.litellm.ai/docs/proxy/docker_quick_start) - Setup virtual keys, make your first request
 
 ```shell
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 litellm --model gpt-4o
 ```
 
@@ -390,8 +390,8 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature
 ### Backend
 1. (In root) create virtual environment `python -m venv .venv`
 2. Activate virtual environment `source .venv/bin/activate`
-3. Install dependencies `pip install -e ".[all]"`
-4. `pip install prisma`
+3. Install dependencies `uv sync --all-extras --group proxy-dev`
+4. `uv run prisma generate`
 5. `prisma generate`
 6. Start proxy backend `python litellm/proxy/proxy_cli.py`
 
@@ -419,7 +419,7 @@ We welcome contributions to LiteLLM! Whether you're fixing bugs, adding features
 
 ## Quick Start for Contributors
 
-This requires poetry to be installed.
+This requires uv to be installed.
 
 ```bash
 git clone https://github.com/BerriAI/litellm.git
@@ -473,4 +473,3 @@ All these checks must pass before your PR can be merged.
 <a href="https://github.com/BerriAI/litellm/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=BerriAI/litellm" />
 </a>
-

docs/my-website/docs/adding_provider/generic_prompt_management_api.md

@@ -378,7 +378,7 @@ if __name__ == "__main__":
 
 1. Install dependencies:
 ```bash
-pip install fastapi uvicorn
+uv add fastapi uvicorn
 ```
 
 2. Save the code above to `prompt_server.py`

docs/my-website/docs/caching/all_caches.md

@@ -23,7 +23,7 @@ import TabItem from '@theme/TabItem';
 
 Install redis
 ```shell
-pip install redis
+uv add redis
 ```
 
 For the hosted version you can setup your own Redis DB here: https://redis.io/try-free/
@@ -55,7 +55,7 @@ response2 = completion(
 For GCP Memorystore Redis with IAM authentication:
 
 ```shell
-pip install google-cloud-iam
+uv add google-cloud-iam
 ```
 
 ```python
@@ -150,7 +150,7 @@ response2 = completion(
 
 Install boto3
 ```shell
-pip install boto3
+uv add boto3
 ```
 
 Set AWS environment variables
@@ -187,7 +187,7 @@ response2 = completion(
 
 Install azure-storage-blob and azure-identity
 ```shell
-pip install azure-storage-blob azure-identity
+uv add azure-storage-blob azure-identity
 ```
 
 ```python
@@ -219,7 +219,7 @@ response2 = completion(
 
 Install redisvl client
 ```shell
-pip install redisvl==0.4.1
+uv add redisvl==0.4.1
 ```
 
 For the hosted version you can setup your own Redis DB here: https://redis.io/try-free/
@@ -366,7 +366,7 @@ response2 = completion(
 Install the disk caching extra:
 
 ```shell
-pip install "litellm[caching]"
+uv add "litellm[caching]"
 ```
 
 Then you can use the disk cache as follows.

docs/my-website/docs/completion/message_sanitization.md

@@ -401,7 +401,7 @@ response = litellm.completion(
 
 3. Ensure you're using a recent version of LiteLLM:
    ```bash
-   pip install --upgrade litellm
+   uv add --upgrade-package litellm litellm
    ```
 
 ### Unexpected Dummy Tool Results

docs/my-website/docs/contributing.md

@@ -29,7 +29,7 @@ general_settings:
 Start the proxy on port 4000:
 
 ```bash
-poetry run litellm --config config.yaml --port 4000
+uv run litellm --config config.yaml --port 4000
 ```
 
 The UI comes pre-built in the repo. Access it at `http://localhost:4000/ui`

docs/my-website/docs/default_code_snippet.md

@@ -16,7 +16,7 @@ If you want to use the non-hosted version, [go here](https://docs.litellm.ai/doc
 
 
 ```
-pip install litellm
+uv add litellm
 ```
 
 <QueryParamReader/>

docs/my-website/docs/extras/contributing_code.md

@@ -41,7 +41,7 @@ git clone https://github.com/BerriAI/litellm.git
 Step 2: Install dev dependencies
 
 ```shell
-poetry install --with dev --extras proxy
+uv sync --group dev --extra proxy
 ```
 
 ### 2. Adding tests

docs/my-website/docs/index.md

@@ -26,13 +26,13 @@ import Image from '@theme/IdealImage';
 ## Installation
 
 ```shell
-pip install litellm
+uv add litellm
 ```
 
 To run the full Proxy Server (LLM Gateway):
 
 ```shell
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 ```
 
 ---
@@ -336,7 +336,7 @@ The proxy is a self-hosted OpenAI-compatible gateway. Any client that works with
 #### Step 1 — Start the proxy
 
 <Tabs>
-<TabItem value="pip" label="pip">
+<TabItem value="cli" label="LiteLLM CLI">
 
 ```shell
 litellm --model huggingface/bigcode/starcoder

docs/my-website/docs/integrations/letta.md

@@ -16,7 +16,7 @@ Letta allows you to build LLM agents that can:
 ## Prerequisites
 
 ```bash
-pip install letta litellm
+uv add letta litellm
 ```
 
 ## Quick Start
@@ -910,7 +910,7 @@ for model in models:
 ```
 
 ### Common SDK Issues
-- **Import errors**: Ensure `pip install litellm letta` is run
+- **Import errors**: Ensure `uv add litellm letta` is run
 - **Model format**: Use `provider/model` format (e.g., `openai/gpt-4`)
 - **API key format**: Different providers have different key formats
 - **Rate limits**: Implement exponential backoff for retries

docs/my-website/docs/langchain/langchain.md

@@ -5,7 +5,7 @@ import TabItem from '@theme/TabItem';
 
 ## Pre-Requisites
 ```shell
-!pip install litellm langchain
+!uv add litellm langchain
 ```
 ## Quick Start
 

docs/my-website/docs/learn/gateway_quickstart.md

@@ -13,7 +13,7 @@ If you need a Docker or database-first setup, use the [Docker + Database tutoria
 ## 1. Install The Gateway
 
 ```bash
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 ```
 
 ## 2. Set One Provider Key

docs/my-website/docs/learn/sdk_quickstart.md

@@ -11,7 +11,7 @@ Use this path if you are integrating LiteLLM directly into application code.
 ## 1. Install LiteLLM
 
 ```bash
-pip install litellm==1.82.6
+uv add 'litellm==1.82.6'
 ```
 
 ## 2. Set Provider Credentials

docs/my-website/docs/load_test.md

@@ -17,7 +17,7 @@ model_list:
       api_base: https://exampleopenaiendpoint-production.up.railway.app/
 ```
 
-2. `pip install locust`
+2. `uv add locust`
 
 3. Create a file called `locustfile.py` on your local machine. Copy the contents from the litellm load test located [here](https://github.com/BerriAI/litellm/blob/main/.github/workflows/locustfile.py)
 

docs/my-website/docs/load_test_advanced.md

@@ -70,7 +70,7 @@ litellm_settings:
   callbacks: ["prometheus"] # Enterprise LiteLLM Only - use prometheus to get metrics on your load test
 ```
 
-2. `pip install locust`
+2. `uv add locust`
 
 3. Create a file called `locustfile.py` on your local machine. Copy the contents from the litellm load test located [here](https://github.com/BerriAI/litellm/blob/main/.github/workflows/locustfile.py)
 
@@ -138,7 +138,7 @@ litellm_settings:
   callbacks: ["prometheus"] # Enterprise LiteLLM Only - use prometheus to get metrics on your load test
 ```
 
-2. `pip install locust`
+2. `uv add locust`
 
 3. Create a file called `locustfile.py` on your local machine. Copy the contents from the litellm load test located [here](https://github.com/BerriAI/litellm/blob/main/.github/workflows/locustfile.py)
 

docs/my-website/docs/mcp_aws_sigv4.md

@@ -175,7 +175,7 @@ SigV4-authenticated MCP servers skip the standard health check on proxy startup.
 Install the `botocore` package:
 
 ```bash
-pip install botocore
+uv add botocore
 ```
 
 `botocore` is used for SigV4 credential handling and is required when using `aws_sigv4` auth.

docs/my-website/docs/mcp_oauth.md

@@ -205,7 +205,7 @@ sequenceDiagram
 Use [BerriAI/mock-oauth2-mcp-server](https://github.com/BerriAI/mock-oauth2-mcp-server) to test locally:
 
 ```bash title="Terminal 1 - Start mock server" showLineNumbers
-pip install fastapi uvicorn
+uv add fastapi uvicorn
 python mock_oauth2_mcp_server.py  # starts on :8765
 ```
 

docs/my-website/docs/observability/braintrust.md

@@ -9,7 +9,7 @@ import TabItem from '@theme/TabItem';
 ## Quick Start
 
 ```python
-# pip install braintrust
+# uv add braintrust
 import litellm
 import os
 

docs/my-website/docs/observability/lago.md

@@ -22,7 +22,7 @@ litellm.callbacks = ["lago"] # logs cost + usage of successful calls to lago
 <TabItem value="sdk" label="SDK">
 
 ```python
-# pip install lago 
+# uv add lago 
 import litellm
 import os
 

docs/my-website/docs/observability/langfuse_integration.md

@@ -26,9 +26,9 @@ For Langfuse v3, we recommend using the [Langfuse OTEL](./langfuse_otel_integrat
 ## Usage with LiteLLM Python SDK
 
 ### Pre-Requisites
-Ensure you have run `pip install langfuse` for this integration
+Ensure you have run `uv add langfuse` for this integration
 ```shell
-pip install langfuse==2.59.7 litellm
+uv add langfuse==2.59.7 litellm
 ```
 
 ### Quick Start
@@ -44,7 +44,7 @@ litellm.success_callback = ["langfuse"]
 litellm.failure_callback = ["langfuse"] # logs errors to langfuse
 ```
 ```python
-# pip install langfuse 
+# uv add langfuse 
 import litellm
 import os
 
@@ -335,7 +335,7 @@ Be aware that if you are continuing an existing trace, and you set `update_trace
 
 ## Troubleshooting & Errors
 ### Data not getting logged to Langfuse ? 
-- Ensure you're on the latest version of langfuse `pip install langfuse -U`. The latest version allows litellm to log JSON input/outputs to langfuse
+- Ensure you're on the latest version of langfuse `uv add langfuse -U`. The latest version allows litellm to log JSON input/outputs to langfuse
 - Follow [this checklist](https://langfuse.com/faq/all/missing-traces) if you don't see any traces in langfuse.
 
 ## Support & Talk to Founders

docs/my-website/docs/observability/langfuse_otel_integration.md

@@ -24,7 +24,7 @@ The Langfuse OpenTelemetry integration allows you to send LiteLLM traces and obs
 2. **API Keys**: Get your public and secret keys from your Langfuse project settings
 3. **Dependencies**: Install required packages:
    ```bash
-   pip install litellm opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp
+   uv add litellm opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp
    ```
 
 ## Configuration

docs/my-website/docs/observability/langsmith_integration.md

@@ -18,7 +18,7 @@ join our [discord](https://discord.gg/wuPM9dRgDw)
 
 ## Pre-Requisites
 ```shell
-pip install litellm
+uv add litellm
 ```
 
 ## Quick Start

docs/my-website/docs/observability/levo_integration.md

@@ -36,7 +36,7 @@ Send all your LLM requests and responses to Levo for monitoring and analysis usi
 **1. Install OpenTelemetry dependencies:**
 
 ```bash
-pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-grpc
+uv add opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-grpc
 ```
 
 **2. Enable Levo callback in your LiteLLM config:**
@@ -133,7 +133,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
    ```
 
 4. **Check for initialization errors**: Look for errors in LiteLLM startup logs. Common issues:
-   - Missing OpenTelemetry packages: Install with `pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-grpc`
+   - Missing OpenTelemetry packages: Install with `uv add opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-grpc`
    - Missing required environment variables: All four required variables must be set
    - Invalid collector URL: Ensure the URL is correct and reachable
 
@@ -150,7 +150,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 - Solution: Set the `LEVOAI_COLLECTOR_URL` environment variable with your collector endpoint URL from Levo support.
 
 **Error: "No module named 'opentelemetry'"**
-- Solution: Install OpenTelemetry packages: `pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-grpc`
+- Solution: Install OpenTelemetry packages: `uv add opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-exporter-otlp-proto-grpc`
 
 ## Additional Resources
 

docs/my-website/docs/observability/literalai_integration.md

@@ -11,7 +11,7 @@ import Image from '@theme/IdealImage';
 Ensure you have the `literalai` package installed:
 
 ```shell
-pip install literalai litellm
+uv add literalai litellm
 ```
 
 ## Quick Start

docs/my-website/docs/observability/logfire_integration.md

@@ -17,11 +17,11 @@ join our [discord](https://discord.gg/wuPM9dRgDw)
 Ensure you have installed the following packages to use this integration
 
 ```shell
-pip install litellm
+uv add litellm
 
-pip install opentelemetry-api==1.25.0
-pip install opentelemetry-sdk==1.25.0
-pip install opentelemetry-exporter-otlp==1.25.0
+uv add opentelemetry-api==1.25.0
+uv add opentelemetry-sdk==1.25.0
+uv add opentelemetry-exporter-otlp==1.25.0
 ```
 
 ## Quick Start
@@ -33,7 +33,7 @@ litellm.callbacks = ["logfire"]
 ```
 
 ```python
-# pip install logfire
+# uv add logfire
 import litellm
 import os
 

docs/my-website/docs/observability/lunary_integration.md

@@ -15,7 +15,7 @@ You can reach out to us anytime by [email](mailto:hello@lunary.ai) or directly [
 ### Pre-Requisites
 
 ```shell
-pip install litellm lunary
+uv add litellm lunary
 ```
 
 ### Quick Start
@@ -124,7 +124,7 @@ my_chain("Chain input")
 ### Step1: Install dependencies and set your environment variables 
 Install the dependencies
 ```shell
-pip install litellm lunary
+uv add litellm lunary
 ```
 
 Get you Lunary public key from from https://app.lunary.ai/settings 

docs/my-website/docs/observability/mlflow.md

@@ -17,7 +17,7 @@ MLflow’s integration with LiteLLM supports advanced observability compatible w
 Install MLflow:
 
 ```shell
-pip install "litellm[mlflow]"
+uv add "litellm[mlflow]"
 ```
 
 To enable MLflow auto tracing for LiteLLM:
@@ -167,7 +167,7 @@ This approach generates a unified trace, combining your custom Python code with
 For using `mlflow` on LiteLLM Proxy Server, you need to install the `mlflow` package on your docker container.
 
 ```shell
-pip install "mlflow>=3.1.4"
+uv add "mlflow>=3.1.4"
 ```
 
 ### Configuration

docs/my-website/docs/observability/openmeter.md

@@ -28,7 +28,7 @@ litellm.callbacks = ["openmeter"] # logs cost + usage of successful calls to ope
 <TabItem value="sdk" label="SDK">
 
 ```python
-# pip install openmeter 
+# uv add openmeter 
 import litellm
 import os
 

docs/my-website/docs/observability/opentelemetry_integration.md

@@ -27,7 +27,7 @@ USE_OTEL_LITELLM_REQUEST_SPAN=true
 Install the OpenTelemetry SDK:
 
 ```
-pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp
+uv add opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp
 ```
 
 Set the environment variables (different providers may require different variables):
@@ -63,7 +63,7 @@ OTEL_EXPORTER_OTLP_PROTOCOL=grpc
 OTEL_EXPORTER_OTLP_HEADERS="api-key=key,other-config-value=value"
 ```
 
-> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+> Note: OTLP gRPC requires `grpcio`. Install via `uv add "litellm[grpc]"` (or `grpcio`).
 
 </TabItem>
 
@@ -75,7 +75,7 @@ OTEL_ENDPOINT="https://api.lmnr.ai:8443"
 OTEL_HEADERS="authorization=Bearer <project-api-key>"
 ```
 
-> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+> Note: OTLP gRPC requires `grpcio`. Install via `uv add "litellm[grpc]"` (or `grpcio`).
 
 </TabItem>
 

docs/my-website/docs/observability/phoenix_integration.md

@@ -22,7 +22,7 @@ Use just 2 lines of code, to instantly log your responses **across all providers
 You can also use the instrumentor option instead of the callback, which you can find [here](https://docs.arize.com/phoenix/tracing/integrations-tracing/litellm).
 
 ```bash
-pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp litellm[proxy]
+uv add opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp litellm[proxy]
 ```
 ```python
 litellm.callbacks = ["arize_phoenix"]
@@ -73,7 +73,7 @@ environment_variables:
     PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/s/<space-name>/v1/traces" # OPTIONAL - For setting the HTTP endpoint
 ```
 
-> Note: If you set the gRPC endpoint, install `grpcio` via `pip install "litellm[grpc]"` (or `grpcio`).
+> Note: If you set the gRPC endpoint, install `grpcio` via `uv add "litellm[grpc]"` (or `grpcio`).
 
 2. Start the proxy
 

docs/my-website/docs/observability/qualifire_integration.md

@@ -23,7 +23,7 @@ Looking for Qualifire Guardrails? Check out the [Qualifire Guardrails Integratio
 2. Get your API key and webhook URL from the Qualifire dashboard
 
 ```bash
-pip install litellm
+uv add litellm
 ```
 
 ## Quick Start

docs/my-website/docs/observability/raw_request_response.md

@@ -12,7 +12,7 @@ See the raw request/response sent by LiteLLM in your logging provider (OTEL/Lang
 <TabItem value="sdk" label="SDK">
 
 ```python
-# pip install langfuse 
+# uv add langfuse 
 import litellm
 import os
 

docs/my-website/docs/observability/scrub_data.md

@@ -60,7 +60,7 @@ litellm.callbacks = [customHandler]
 3. Test it!
 
 ```python
-# pip install langfuse 
+# uv add langfuse 
 
 import os
 import litellm

docs/my-website/docs/observability/signoz.md

@@ -17,7 +17,7 @@ Instrumenting LiteLLM in your AI applications with telemetry ensures full observ
 - A [SigNoz Cloud account](https://signoz.io/teams/) with an active ingestion key
 - Internet access to send telemetry data to SigNoz Cloud
 - [LiteLLM](https://www.litellm.ai/) SDK or Proxy integration
-- For Python: `pip` installed for managing Python packages and _(optional but recommended)_ a Python virtual environment to isolate dependencies
+- For Python: `uv` installed for managing Python packages and _(optional but recommended)_ a Python virtual environment to isolate dependencies
 
 ## Monitoring LiteLLM
 
@@ -37,7 +37,7 @@ No-code auto-instrumentation is recommended for quick setup with minimal code ch
 **Step 1:** Install the necessary packages in your Python environment.
 
 ```bash
-pip install \
+uv add \
   opentelemetry-api \
   opentelemetry-distro \
   opentelemetry-exporter-otlp \
@@ -99,7 +99,7 @@ OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai \
 opentelemetry-instrument <your_run_command>
 ```
 
-> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+> Note: OTLP gRPC requires `grpcio`. Install via `uv add "litellm[grpc]"` (or `grpcio`).
 
 > 📌 Note: We're using `OTEL_PYTHON_DISABLED_INSTRUMENTATIONS=openai` in the run command to disable the OpenAI instrumentor for tracing. This avoids conflicts with LiteLLM's native telemetry/instrumentation, ensuring that telemetry is captured exclusively through LiteLLM's built-in instrumentation.
 
@@ -120,7 +120,7 @@ Code-based instrumentation gives you fine-grained control over your telemetry co
 **Step 1:** Install the necessary packages in your Python environment.
 
 ```bash
-pip install \
+uv add \
   opentelemetry-api \
   opentelemetry-sdk \
   opentelemetry-exporter-otlp \
@@ -338,7 +338,7 @@ You can also check out our custom LiteLLM SDK dashboard [here](https://signoz.i
 **Step 1:** Install the necessary packages in your Python environment.
 
 ```bash
-pip install opentelemetry-api \
+uv add opentelemetry-api \
   opentelemetry-sdk \
   opentelemetry-exporter-otlp \
   'litellm[proxy]'
@@ -364,7 +364,7 @@ export OTEL_METRICS_EXPORTER="otlp"
 export OTEL_LOGS_EXPORTER="otlp"
 ```
 
-> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+> Note: OTLP gRPC requires `grpcio`. Install via `uv add "litellm[grpc]"` (or `grpcio`).
 
 - Set the `<region>` to match your SigNoz Cloud [region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)
 - Replace `<your_ingestion_key>` with your SigNoz [ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)

docs/my-website/docs/observability/slack_integration.md

@@ -13,7 +13,7 @@ join our [discord](https://discord.gg/wuPM9dRgDw)
 
 ### Step 1
 ```shell
-pip install litellm
+uv add litellm
 ```
 
 ### Step 2

docs/my-website/docs/observability/sumologic_integration.md

@@ -25,7 +25,7 @@ join our [discord](https://discord.gg/wuPM9dRgDw)
 For more details, see the [HTTP Logs & Metrics Source](https://www.sumologic.com/help/docs/send-data/hosted-collectors/http-source/logs-metrics/) documentation.
 
 ```shell
-pip install litellm
+uv add litellm
 ```
 
 ## Quick Start

docs/my-website/docs/observability/wandb_integration.md

@@ -21,9 +21,9 @@ join our [discord](https://discord.gg/wuPM9dRgDw)
 ::: 
 
 ## Pre-Requisites
-Ensure you have run `pip install wandb` for this integration
+Ensure you have run `uv add wandb` for this integration
 ```shell
-pip install wandb litellm
+uv add wandb litellm
 ```
 
 ## Quick Start
@@ -33,7 +33,7 @@ Use just 2 lines of code, to instantly log your responses **across all providers
 litellm.success_callback = ["wandb"]
 ```
 ```python
-# pip install wandb 
+# uv add wandb 
 import litellm
 import os
 

docs/my-website/docs/pass_through/bedrock.md

@@ -566,7 +566,7 @@ You can use the [LangChain AWS SDK](https://python.langchain.com/docs/integratio
 **1. Install LangChain AWS**:
 
 ```bash showLineNumbers
-pip install langchain-aws
+uv add langchain-aws
 ```
 
 **2. Setup LiteLLM Proxy**:

docs/my-website/docs/projects/Harbor.md

@@ -5,7 +5,7 @@
 
 ```bash
 # Install
-pip install harbor
+uv add harbor
 
 # Run a benchmark with any LiteLLM-supported model
 harbor run --dataset terminal-bench@2.0 \

docs/my-website/docs/projects/openai-agents.md

@@ -12,7 +12,7 @@ The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) is a lig
 ### 1. Install Dependencies
 
 ```bash
-pip install "openai-agents[litellm]"
+uv add "openai-agents[litellm]"
 ```
 
 ### 2. Add Model to Config

docs/my-website/docs/providers/azure/azure.md

@@ -1143,7 +1143,7 @@ In production, [Router connects to a Redis Cache](#redis-queue) to track usage a
 #### Quick Start
 
 ```python
-pip install litellm
+uv add litellm
 ```
 
 ```python

docs/my-website/docs/providers/azure_ai.md

@@ -121,7 +121,7 @@ response = completion(
 See all litellm.completion supported params [here](../completion/input.md#translated-openai-params)
 
 ```python
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables

docs/my-website/docs/providers/bedrock.md

@@ -16,7 +16,7 @@ ALL Bedrock models (Anthropic, Meta, Deepseek, Mistral, Amazon, etc.) are Suppor
 
 LiteLLM requires `boto3` to be installed on your system for Bedrock requests
 ```shell
-pip install boto3>=1.28.57
+uv add boto3>=1.28.57
 ```
 
 :::info

docs/my-website/docs/providers/bedrock_realtime_with_audio.md

@@ -319,7 +319,7 @@ Complete working examples are available in the LiteLLM repository:
 ## Requirements
 
 ```bash
-pip install litellm websockets pyaudio
+uv add litellm websockets pyaudio
 ```
 
 ## AWS Configuration

docs/my-website/docs/providers/bytez.md

@@ -126,7 +126,7 @@ If you wish to use custom formatting, please let us know via either [help@bytez.
 See all litellm.completion supported params [here](https://docs.litellm.ai/docs/completion/input)
 
 ```py
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables
@@ -160,7 +160,7 @@ Any kwarg supported by huggingface we also support! (Provided the model supports
 Example `repetition_penalty`
 
 ```py
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables

docs/my-website/docs/providers/clarifai.md

@@ -14,7 +14,7 @@ Anthropic, OpenAI, Qwen, xAI, Gemini and most of Open soured LLMs are Supported
 ## Pre-Requisites
 
 ```bash
-pip install litellm
+uv add litellm
 ```
 
 ## Required Environment Variables

docs/my-website/docs/providers/databricks.md

@@ -59,7 +59,7 @@ If no credentials are provided, LiteLLM will use the Databricks SDK for automati
 from litellm import completion
 
 # No environment variables needed - uses Databricks SDK unified auth
-# Requires: pip install databricks-sdk
+# Requires: uv add databricks-sdk
 response = completion(
     model="databricks/databricks-dbrx-instruct",
     messages=[{"role": "user", "content": "Hello!"}],
@@ -220,7 +220,7 @@ response = completion(
 See all litellm.completion supported params [here](../completion/input.md#translated-openai-params)
 
 ```python
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables
@@ -457,7 +457,7 @@ For embedding models, databricks lets you pass in an additional param 'instructi
 
 
 ```python
-# !pip install litellm
+# !uv add litellm
 from litellm import embedding
 import os
 ## set ENV variables

docs/my-website/docs/providers/huggingface.md

@@ -341,7 +341,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 <TabItem value="python" label="python">
 
 ```python
-# pip install openai
+# uv add openai
 from openai import OpenAI
 
 client = OpenAI(

docs/my-website/docs/providers/langgraph.md

@@ -187,7 +187,7 @@ Before using LiteLLM with LangGraph, you need a running LangGraph server.
 ### 1. Install the LangGraph CLI
 
 ```bash
-pip install "langgraph-cli[inmem]"
+uv add "langgraph-cli[inmem]"
 ```
 
 ### 2. Create a new LangGraph project
@@ -200,7 +200,7 @@ cd my-agent
 ### 3. Install dependencies
 
 ```bash
-pip install -e .
+uv add -e .
 ```
 
 ### 4. Set your API key

docs/my-website/docs/providers/oci.md

@@ -50,7 +50,7 @@ Use an OCI SDK `Signer` object for authentication. This method:
 
 To use this method, install the OCI SDK:
 ```bash
-pip install oci
+uv add oci
 ```
 
 This method is an alternative when using the LiteLLM SDK on Oracle Cloud Infrastructure (instances or Oracle Kubernetes Engine).

docs/my-website/docs/providers/ollama.md

@@ -49,7 +49,7 @@ for chunk in response:
 ## Example usage - Streaming + Acompletion
 Ensure you have async_generator installed for using ollama acompletion with streaming
 ```shell
-pip install async_generator
+uv add async_generator
 ```
 
 ```python

docs/my-website/docs/providers/petals.md

@@ -8,7 +8,7 @@ Petals: https://github.com/bigscience-workshop/petals
 ## Pre-Requisites
 Ensure you have `petals` installed
 ```shell
-pip install git+https://github.com/bigscience-workshop/petals
+uv add git+https://github.com/bigscience-workshop/petals
 ```
 
 ## Usage

docs/my-website/docs/providers/predibase.md

@@ -186,7 +186,7 @@ model_list:
 See all litellm.completion supported params [here](https://docs.litellm.ai/docs/completion/input)
 
 ```python
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables
@@ -219,7 +219,7 @@ Send params [not supported by `litellm.completion()`](https://docs.litellm.ai/do
 Example `adapter_id`, `adapter_source` are Predibase specific param - [See List](https://github.com/BerriAI/litellm/blob/8a35354dd6dbf4c2fcefcd6e877b980fcbd68c58/litellm/llms/predibase.py#L54)
 
 ```python
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables

docs/my-website/docs/providers/pydantic_ai_agent.md

@@ -23,7 +23,7 @@ LiteLLM requires Pydantic AI agents to follow the [A2A (Agent-to-Agent) protocol
 #### Install Dependencies
 
 ```bash
-pip install pydantic-ai fasta2a uvicorn
+uv add pydantic-ai fasta2a uvicorn
 ```
 
 #### Create Agent

docs/my-website/docs/providers/replicate.md

@@ -231,7 +231,7 @@ Model Name                  | Function Call
 See all litellm.completion supported params [here](https://docs.litellm.ai/docs/completion/input)
 
 ```python
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables
@@ -264,7 +264,7 @@ Send params [not supported by `litellm.completion()`](https://docs.litellm.ai/do
 Example `seed`, `min_tokens` are Replicate specific param
 
 ```python
-# !pip install litellm
+# !uv add litellm
 from litellm import completion
 import os
 ## set ENV variables

docs/my-website/docs/providers/sap.md

@@ -51,7 +51,7 @@ The resource group is typically configured separately in your AI Core deployment
 ### Step 1: Install LiteLLM
 
 ```bash
-pip install litellm
+uv add litellm
 ```
 
 ### Step 2: Set Your Credentials

docs/my-website/docs/providers/vertex.md

@@ -1216,7 +1216,7 @@ curl http://0.0.0.0:4000/chat/completions \
 </Tabs>
 
 ## Pre-requisites
-* `pip install google-cloud-aiplatform` (pre-installed on proxy docker image)
+* `uv add google-cloud-aiplatform` (pre-installed on proxy docker image)
 * Authentication: 
     * run `gcloud auth application-default login` See [Google Cloud Docs](https://cloud.google.com/docs/authentication/external/set-up-adc)
     * Alternatively you can set `GOOGLE_APPLICATION_CREDENTIALS`

docs/my-website/docs/providers/vllm.md

@@ -517,11 +517,11 @@ curl -X POST http://0.0.0.0:4000/chat/completions \
 </Tabs>
 
 
-## (Deprecated) for `vllm pip package` 
+## (Deprecated) for packaged `vllm` installs
 ### Using - `litellm.completion`
 
 ```
-pip install litellm vllm
+uv add litellm vllm
 ```
 ```python
 import litellm 
@@ -616,4 +616,3 @@ test_vllm_custom_model()
 ```
 
 [Implementation Code](https://github.com/BerriAI/litellm/blob/6b3cb1898382f2e4e80fd372308ea232868c78d1/litellm/utils.py#L1414)
-

docs/my-website/docs/proxy/caching.md

@@ -214,7 +214,7 @@ For GCP Memorystore Redis with IAM authentication, install the required dependen
 :::
 
 ```shell
-pip install google-cloud-iam
+uv add google-cloud-iam
 ```
 
 <Tabs>

docs/my-website/docs/proxy/deploy.md

@@ -32,10 +32,10 @@ docker pull docker.litellm.ai/berriai/litellm:main-latest
 
 </TabItem>
 
-<TabItem value="pip" label="LiteLLM CLI (pip package)">
+<TabItem value="cli" label="LiteLLM CLI">
 
 ```shell
-$ pip install 'litellm[proxy]'
+$ uv tool install 'litellm[proxy]'
 ```
 
 </TabItem>
@@ -155,33 +155,32 @@ EXPOSE 4000/tcp
 CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"]
 ```
 
-### Build from litellm `pip` package
+### Build from published LiteLLM packages
 
-Follow these instructions to build a docker container from the litellm pip package. If your company has a strict requirement around security / building images you can follow these steps.
+Follow these instructions to build a Docker container from published LiteLLM packages. If your company has a strict requirement around security or image provenance, you can follow these steps.
 
-**Note:** You'll need to copy the `schema.prisma` file from the [litellm repository](https://github.com/BerriAI/litellm/blob/main/schema.prisma) to your build directory alongside the Dockerfile and requirements.txt.
+**Note:** Copy the `schema.prisma` file from the [LiteLLM repository](https://github.com/BerriAI/litellm/blob/main/schema.prisma) into your build directory alongside this Dockerfile.
 
 Dockerfile 
 
 ```shell
 FROM cgr.dev/chainguard/python:latest-dev
+ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.10.9
 
 USER root
 WORKDIR /app
 
-ENV HOME=/home/litellm
-ENV PATH="${HOME}/venv/bin:$PATH"
+ENV UV_TOOL_BIN_DIR=/usr/local/bin
 
 # Install runtime dependencies
 RUN apk update && \
     apk add --no-cache gcc python3-dev openssl openssl-dev
 
-RUN python -m venv ${HOME}/venv
-RUN ${HOME}/venv/bin/pip install --no-cache-dir --upgrade pip
+COPY --from=$UV_IMAGE /uv /usr/local/bin/uv
+COPY --from=$UV_IMAGE /uvx /usr/local/bin/uvx
 
-COPY requirements.txt .
-RUN --mount=type=cache,target=${HOME}/.cache/pip \
-    ${HOME}/venv/bin/pip install -r requirements.txt
+RUN uv tool install 'litellm[proxy,proxy-runtime,extra_proxy]==1.57.3' \
+    --python python
 
 # Copy Prisma schema file
 COPY schema.prisma .
@@ -196,22 +195,12 @@ CMD ["--port", "4000"]
 ```
 
 
-Example `requirements.txt`
-
-```shell
-litellm[proxy]==1.57.3 # Specify the litellm version you want to use
-litellm-enterprise
-prometheus_client
-langfuse
-prisma
-```
-
 Build the docker image
 
 ```shell
 docker build \
-  -f Dockerfile.build_from_pip \
-  -t litellm-proxy-with-pip-5 .
+  -f Dockerfile \
+  -t litellm-proxy-from-package-5 .
 ```
 
 Run the docker image
@@ -222,7 +211,7 @@ docker run \
     -e OPENAI_API_KEY="sk-1222" \
     -e DATABASE_URL="postgresql://xxxxxxxxx \
     -p 4000:4000 \
-    litellm-proxy-with-pip-5 \
+    litellm-proxy-from-package-5 \
     --config /app/config.yaml --detailed_debug
 ```
 
@@ -724,7 +713,7 @@ RUN chmod +x ./docker/entrypoint.sh
 EXPOSE 4000/tcp
 
 # 👉 Key Change: Install hypercorn
-RUN pip install hypercorn
+RUN uv add hypercorn
 
 # Override the CMD instruction with your desired command and arguments
 # WARNING: FOR PROD DO NOT USE `--detailed_debug` it slows down response times, instead use the following CMD

docs/my-website/docs/proxy/docker_quick_start.md

@@ -70,15 +70,15 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
 }'
 ```
 
-:::tip Already have pip installed?
-You can skip the curl install and run `litellm --setup` directly after `pip install 'litellm[proxy]'`.
+:::tip Already have uv installed?
+You can skip the curl install and run `litellm --setup` directly after `uv tool install 'litellm[proxy]'`.
 :::
 
 ---
 
 ## Pre-Requisites 
 
-Choose your install method. **Docker Compose** users complete their full setup inside the tab and are done. **Docker** and **pip** users continue with the steps below the tabs.
+Choose your install method. **Docker Compose** users complete their full setup inside the tab and are done. **Docker** and **LiteLLM CLI** users continue with the steps below the tabs.
 
 <Tabs>
 
@@ -92,10 +92,10 @@ docker pull docker.litellm.ai/berriai/litellm:main-latest
 
 </TabItem>
 
-<TabItem value="pip" label="LiteLLM CLI (pip package)">
+<TabItem value="cli" label="LiteLLM CLI">
 
 ```shell
-$ pip install 'litellm[proxy]'
+$ uv tool install 'litellm[proxy]'
 ```
 
 </TabItem>
@@ -269,7 +269,7 @@ Virtual keys let you track spend, set rate limits, and control model access per
 </Tabs>
 
 :::note Docker Compose users
-Your setup is complete — the steps below are for **Docker** and **pip** users only.
+Your setup is complete — the steps below are for **Docker** and **LiteLLM CLI** users only.
 :::
 
 ---
@@ -336,7 +336,7 @@ docker run \
 
 </TabItem>
 
-<TabItem value="pip" label="LiteLLM CLI (pip package)">
+<TabItem value="cli" label="LiteLLM CLI">
 
 ```shell
 $ litellm --config /app/config.yaml --detailed_debug
@@ -463,7 +463,7 @@ Track spend and control model access via virtual keys for the proxy.
 Your Postgres container is already running — skip ahead to [Create Key w/ RPM Limit](#create-key-w-rpm-limit) below.
 :::
 
-**Docker / pip users** — you need a Postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), or self-hosted). Add `general_settings` to your `config.yaml`:
+**Docker / LiteLLM CLI users** — you need a Postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), or self-hosted). Add `general_settings` to your `config.yaml`:
 
 ```yaml
 model_list:

docs/my-website/docs/proxy/guardrails/lasso_security.md

@@ -11,7 +11,7 @@ Use [Lasso Security](https://www.lasso.security/) to protect your LLM applicatio
 The Lasso guardrail requires the `ulid-py` package (version 1.1.0 or higher) for generating unique conversation identifiers:
 
 ```shell
-pip install ulid-py>=1.1.0
+uv add ulid-py>=1.1.0
 ```
 
 This package is used to create lexicographically sortable identifiers for tracking conversations and sessions in the Lasso Security platform.

docs/my-website/docs/proxy/logging.md

@@ -351,7 +351,7 @@ We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this
 **Step 1** Install langfuse
 
 ```shell
-pip install langfuse>=2.0.0
+uv add langfuse>=2.0.0
 ```
 
 **Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
@@ -982,7 +982,7 @@ OTEL_ENDPOINT="http:/0.0.0.0:4317"
 OTEL_HEADERS="x-honeycomb-team=<your-api-key>" # Optional
 ```
 
-> Note: OTLP gRPC requires `grpcio`. Install via `pip install "litellm[grpc]"` (or `grpcio`).
+> Note: OTLP gRPC requires `grpcio`. Install via `uv add "litellm[grpc]"` (or `grpcio`).
 
 Add `otel` as a callback on your `litellm_config.yaml`
 
@@ -1587,7 +1587,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \
 #### Step1: Install dependencies and set your environment variables 
 Install the dependencies
 ```shell
-pip install litellm lunary
+uv add litellm lunary
 ```
 
 Get you Lunary public key from from https://app.lunary.ai/settings 
@@ -2516,7 +2516,7 @@ If api calls fail (llm/database) you can log those to Sentry:
 **Step 1** Install Sentry
 
 ```shell
-pip install --upgrade sentry-sdk
+uv add --upgrade sentry-sdk
 ```
 
 **Step 2**: Save your Sentry_DSN and add `litellm_settings`: `failure_callback`

docs/my-website/docs/proxy/prometheus.md

@@ -9,7 +9,7 @@ LiteLLM Exposes a `/metrics` endpoint for Prometheus to Poll
 
 ## Quick Start
 
-If you're using the LiteLLM CLI with `litellm --config proxy_config.yaml` then you need to `pip install prometheus_client==0.20.0`. **This is already pre-installed on the litellm Docker image**
+If you're using the LiteLLM CLI with `litellm --config proxy_config.yaml` then you need to `uv add prometheus_client==0.20.0`. **This is already pre-installed on the litellm Docker image**
 
 Add this to your proxy config.yaml 
 ```yaml

docs/my-website/docs/proxy/pyroscope_profiling.md

@@ -7,13 +7,13 @@ LiteLLM proxy can send continuous CPU profiles to [Grafana Pyroscope](https://gr
 1. **Install the optional dependency** (required only when enabling Pyroscope):
 
    ```bash
-   pip install pyroscope-io
+   uv add pyroscope-io
    ```
 
    Or install the proxy extra:
 
    ```bash
-   pip install "litellm[proxy]"
+   uv add "litellm[proxy]"
    ```
 
 2. **Set environment variables** before starting the proxy:

docs/my-website/docs/proxy/quick_start.md

@@ -13,7 +13,7 @@ LiteLLM Server (LLM Gateway) manages:
 * **Load Balancing**: between [Multiple Models](#multiple-models---quick-start) + [Deployments of the same model](#multiple-instances-of-1-model) - LiteLLM proxy can handle 1.5k+ requests/second during load tests.
 
 ```shell
-$ pip install 'litellm[proxy]'
+$ uv tool install 'litellm[proxy]'
 ```
 
 ## Quick Start - LiteLLM Proxy CLI

docs/my-website/docs/proxy/user_keys.md

@@ -881,7 +881,7 @@ Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-175
 <TabItem value="aider" label="Aider">
 
 ```shell
-$ pip install aider 
+$ uv add aider 
 
 $ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key
 ```
@@ -889,7 +889,7 @@ $ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key
 <TabItem value="autogen" label="AutoGen">
 
 ```python
-pip install pyautogen
+uv add pyautogen
 ```
 
 ```python

docs/my-website/docs/proxy_api.md

@@ -66,16 +66,16 @@ git clone https://github.com/krrishdholakia/open-interpreter-litellm-fork
 ```
 To run it do: 
 ```
-poetry build 
+uv build 
 
 # call gpt-4 - always add 'litellm_proxy/' in front of the model name
-poetry run interpreter --model litellm_proxy/gpt-4
+uv run interpreter --model litellm_proxy/gpt-4
 
 # call llama-70b - always add 'litellm_proxy/' in front of the model name
-poetry run interpreter --model litellm_proxy/togethercomputer/llama-2-70b-chat
+uv run interpreter --model litellm_proxy/togethercomputer/llama-2-70b-chat
 
 # call claude-2 - always add 'litellm_proxy/' in front of the model name
-poetry run interpreter --model litellm_proxy/claude-2
+uv run interpreter --model litellm_proxy/claude-2
 ```
 
 And that's it! 
@@ -83,4 +83,4 @@ And that's it!
 Now you can call any model you like!
 
 
-Want us to add more models? [Let us know!](https://github.com/BerriAI/litellm/issues/new/choose)
+Want us to add more models? [Let us know!](https://github.com/BerriAI/litellm/issues/new/choose)

docs/my-website/docs/proxy_auth.md

@@ -72,7 +72,7 @@ response = litellm.completion(
 </TabItem>
 </Tabs>
 
-**Required package:** `pip install azure-identity`
+**Required package:** `uv add azure-identity`
 
 ### Generic OAuth2 (Okta, Auth0, Keycloak, etc.)
 

docs/my-website/docs/proxy_server.md

@@ -13,7 +13,7 @@ Docs outdated. New docs 👉 [here](./simple_proxy)
 
 ## Usage 
 ```shell
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 ```
 ```shell 
 $ litellm --model ollama/codellama 
@@ -213,7 +213,7 @@ docker compose up -d
 <TabItem value="autogen" label="AutoGen">
 
 ```python
-pip install pyautogen
+uv add pyautogen
 ```
 
 ```python
@@ -329,7 +329,7 @@ git clone https://github.com/OpenBMB/ChatDev.git
 cd ChatDev
 conda create -n ChatDev_conda_env python=3.9 -y
 conda activate ChatDev_conda_env
-pip install -r requirements.txt
+uv add -r requirements.txt
 ```
 ### Run ChatDev w/ Proxy
 ```shell 
@@ -346,7 +346,7 @@ python3 run.py --task "a script that says hello world" --name "hello world"
 <TabItem value="langroid" label="Langroid">
 
 ```python
-pip install langroid
+uv add langroid
 ```
 
 ```python
@@ -383,7 +383,7 @@ Credits [@pchalasani](https://github.com/pchalasani) and [Langroid](https://gith
 Here's how to use the local proxy to test codellama/mistral/etc. models for different github repos 
 
 ```shell
-pip install litellm
+uv add litellm
 ```
 
 ```shell
@@ -440,7 +440,7 @@ Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-175
 <TabItem value="aider" label="Aider">
 
 ```shell
-$ pip install aider 
+$ uv add aider 
 
 $ aider --openai-api-base http://0.0.0.0:8000 --openai-api-key fake-key
 ```
@@ -448,7 +448,7 @@ $ aider --openai-api-base http://0.0.0.0:8000 --openai-api-key fake-key
 <TabItem value="autogen" label="AutoGen">
 
 ```python
-pip install pyautogen
+uv add pyautogen
 ```
 
 ```python
@@ -564,7 +564,7 @@ git clone https://github.com/OpenBMB/ChatDev.git
 cd ChatDev
 conda create -n ChatDev_conda_env python=3.9 -y
 conda activate ChatDev_conda_env
-pip install -r requirements.txt
+uv add -r requirements.txt
 ```
 ### Run ChatDev w/ Proxy
 ```shell 
@@ -581,7 +581,7 @@ python3 run.py --task "a script that says hello world" --name "hello world"
 <TabItem value="langroid" label="Langroid">
 
 ```python
-pip install langroid
+uv add langroid
 ```
 
 ```python

docs/my-website/docs/rag_ingest.md

@@ -287,7 +287,7 @@ When `vector_store_id` is omitted, LiteLLM automatically creates:
 1. Create a RAG corpus in Vertex AI console or via API
 2. Create a GCS bucket for file uploads
 3. Authenticate via `gcloud auth application-default login`
-4. Install: `pip install 'google-cloud-aiplatform>=1.60.0'`
+4. Install: `uv add 'google-cloud-aiplatform>=1.60.0'`
 :::
 
 ### vector_store (AWS S3 Vectors)

docs/my-website/docs/response_api.md

@@ -831,7 +831,7 @@ The system automatically selects the appropriate mode based on provider capabili
 
 ```python showLineNumbers title="WebSocket with Python"
 import json
-from websocket import create_connection  # pip install websocket-client
+from websocket import create_connection  # uv add websocket-client
 
 # Connect to LiteLLM proxy WebSocket endpoint
 ws = create_connection(

docs/my-website/docs/sdk_custom_pricing.md

@@ -5,7 +5,7 @@ Register custom pricing for sagemaker completion model.
 For cost per second pricing, you **just** need to register `input_cost_per_second`. 
 
 ```python
-# !pip install boto3 
+# !uv add boto3 
 from litellm import completion, completion_cost 
 
 os.environ["AWS_ACCESS_KEY_ID"] = ""
@@ -35,7 +35,7 @@ def test_completion_sagemaker():
 
 
 ```python
-# !pip install boto3 
+# !uv add boto3 
 from litellm import completion, completion_cost 
 
 ## set ENV variables

docs/my-website/docs/secret_managers/azure_key_vault.md

@@ -14,7 +14,7 @@
 
 1. Install Proxy dependencies 
 ```bash
-pip install 'litellm[proxy]' 'litellm[extra_proxy]'
+uv tool install 'litellm[proxy]' 'litellm[extra_proxy]'
 ```
 
 2. Save Azure details in your environment

docs/my-website/docs/troubleshoot/pip_venv_upgrade.md

@@ -1,21 +1,21 @@
-# Upgrading LiteLLM Proxy (pip/venv)
+# Upgrading LiteLLM Proxy (uv/venv)
 
-Guide for upgrading LiteLLM Proxy when installed via pip in a virtual environment.
+Guide for upgrading LiteLLM Proxy when installed via uv in a virtual environment.
 
 :::info Important
 Always activate your virtual environment before running any `litellm` or `prisma` commands. All commands in this guide assume you're working inside an activated venv.
 :::
 
-## How pip/venv Upgrades Work
+## How uv/venv Upgrades Work
 
 There are two pieces that need to stay in sync:
 
 1. **Prisma client** - Generated Python code that talks to the DB
 2. **DB schema** - Tables/columns in PostgreSQL
 
-When you upgrade via pip, the `litellm-proxy-extras` package ships with a new `schema.prisma` and a `migrations/` directory. But unlike the Docker image, pip install does NOT automatically regenerate the Prisma client or run migrations. You have to do both manually.
+When you upgrade via uv, the `litellm-proxy-extras` package ships with a new `schema.prisma` and a `migrations/` directory. But unlike the Docker image, `uv add` does not automatically regenerate the Prisma client or run migrations. You have to do both manually.
 
-## Upgrade Workflow (pip/venv)
+## Upgrade Workflow (uv/venv)
 
 ### 1. Stop the proxy
 
@@ -30,7 +30,7 @@ pg_dump -h <host> -U <user> -d <db> -F c -f backup_$(date +%Y%m%d).dump
 ### 3. Upgrade the package
 
 ```bash
-pip install 'litellm[proxy]==<version>'
+uv add 'litellm[proxy]==<version>'
 ```
 
 ### 4. Regenerate the Prisma client
@@ -91,7 +91,7 @@ litellm --config your_config.yaml --port 4000
 
 ### Before applying migrations: Preview what will change
 
-Run `pip install 'litellm[proxy]==<version>'` first (Step 3) so the new `schema.prisma` is available.
+Run `uv add 'litellm[proxy]==<version>'` first (Step 3) so the new `schema.prisma` is available.
 
 ```bash
 prisma migrate diff \

docs/my-website/docs/tutorials/TogetherAI_liteLLM.md

@@ -4,7 +4,7 @@ https://together.ai/
 
 
 ```python
-!pip install litellm
+!uv add litellm
 ```
 
 

docs/my-website/docs/tutorials/claude_agent_sdk.md

@@ -12,7 +12,7 @@ The Claude Agent SDK provides a high-level interface for building AI agents. By
 ### 1. Install Dependencies
 
 ```bash
-pip install claude-agent-sdk
+uv add claude-agent-sdk
 ```
 
 ### 2. Start LiteLLM Proxy
@@ -104,7 +104,7 @@ See our [cookbook example](https://github.com/BerriAI/litellm/tree/main/cookbook
 # Clone and run the example
 git clone https://github.com/BerriAI/litellm.git
 cd litellm/cookbook/anthropic_agent_sdk
-pip install -r requirements.txt
+uv add -r requirements.txt
 python main.py
 ```
 

docs/my-website/docs/tutorials/claude_non_anthropic_models.md

@@ -22,7 +22,7 @@ LiteLLM automatically translates between different provider formats, allowing yo
 First, install LiteLLM with proxy support:
 
 ```bash
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 ```
 
 ## Configuration

docs/my-website/docs/tutorials/claude_responses_api.md

@@ -28,7 +28,7 @@ This tutorial is based on [Anthropic's official LiteLLM configuration documentat
 First, install LiteLLM with proxy support:
 
 ```bash
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 ```
 
 ### 1. Setup config.yaml

docs/my-website/docs/tutorials/compare_llms.md

@@ -23,7 +23,7 @@ cd litellm/cookbook/benchmark
 
 ### Install Dependencies
 ```
-pip install litellm click tqdm tabulate termcolor
+uv add litellm click tqdm tabulate termcolor
 ```
 
 ### Configuration - Set LLM API Keys + LLMs in benchmark.py
@@ -88,7 +88,7 @@ Benchmark Results for 'When will BerriAI IPO?':
 <!-- 
 ## Pre-requisites:
 ``` python
-!pip install litellm
+!uv add litellm
 ```
 
 ## Example Use Case 1 - Code Generator
@@ -102,7 +102,7 @@ litellm is a light package to simplify calling OpenAI, Azure, Cohere, Anthropic,
 --
 Sample Usage:
 ```
-pip install litellm
+uv add litellm
 from litellm import completion
 ## set ENV variables
 os.environ["OPENAI_API_KEY"] = "openai key"

docs/my-website/docs/tutorials/compare_llms_2.md

@@ -20,7 +20,7 @@ given test set using litellm
 <div class="cell code" id="fBkbl4Qo9pvz">
 
 ``` python
-!pip install litellm
+!uv add litellm
 ```
 
 </div>

docs/my-website/docs/tutorials/elasticsearch_logging.md

@@ -72,7 +72,7 @@ docker run -p 4317:4317 -p 4318:4318 \
 ### 3. Install OpenTelemetry Dependencies
 
 ```bash
-pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp
+uv add opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp
 ```
 
 ### 4. Configure LiteLLM

docs/my-website/docs/tutorials/eval_suites.md

@@ -9,10 +9,10 @@ MLflow provides an API `mlflow.evaluate()` to help evaluate your LLMs https://ml
 
 ### Pre Requisites
 ```shell
-pip install litellm
+uv add litellm
 ```
 ```shell
-pip install mlflow
+uv add mlflow
 ```
 
 
@@ -226,10 +226,10 @@ https://github.com/braintrustdata/autoevals
 
 ### Pre Requisites
 ```shell
-pip install litellm
+uv add litellm
 ```
 ```shell
-pip install autoevals
+uv add autoevals
 ```
 
 ### Quick Start

docs/my-website/docs/tutorials/file_search_responses_api.md

@@ -73,7 +73,7 @@ print(response.output)
 ### 1. Install + set keys
 
 ```bash
-pip install litellm
+uv add litellm
 export OPENAI_API_KEY="sk-..."
 export ANTHROPIC_API_KEY="sk-ant-..."
 ```
@@ -135,7 +135,7 @@ flowchart TD
 ## Prerequisites
 
 ```bash
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 export OPENAI_API_KEY="sk-..."          # for native path
 export ANTHROPIC_API_KEY="sk-ant-..."  # for emulated path
 ```

docs/my-website/docs/tutorials/first_playground.md

@@ -24,7 +24,7 @@ Let's make sure our keys are working. Run this script in any environment of your
 🚨 Don't forget to replace the placeholder key values with your keys!
 
 ```python 
-pip install litellm
+uv add litellm
 ```
 
 ```python
@@ -169,10 +169,10 @@ Now let's run our app:
 cd litellm_playground_fe_template && streamlit run app.py
 ```
 
-If you're missing Streamlit - just pip install it (or check out their [installation guidelines](https://docs.streamlit.io/library/get-started/installation#install-streamlit-on-macoslinux))
+If you're missing Streamlit - just uv add it (or check out their [installation guidelines](https://docs.streamlit.io/library/get-started/installation#install-streamlit-on-macoslinux))
 
 ```zsh
-pip install streamlit
+uv add streamlit
 ```
 
 This is what you should see: 

docs/my-website/docs/tutorials/github_copilot_integration.md

@@ -42,7 +42,7 @@ Before you begin, ensure you have:
 Install LiteLLM with proxy support:
 
 ```bash
-pip install litellm[proxy]
+uv tool install litellm[proxy]
 ```
 
 ### Step 2: Configure LiteLLM Proxy

docs/my-website/docs/tutorials/google_adk.md

@@ -35,7 +35,7 @@ ADK (Agent Development Kit) allows you to build intelligent agents powered by LL
 ## Installation
 
 ```bash showLineNumbers title="Install dependencies"
-pip install google-adk litellm
+uv add google-adk litellm
 ```
 
 ## 1. Setting Up Environment

docs/my-website/docs/tutorials/google_genai_sdk.md

@@ -42,7 +42,7 @@ npm install @google/genai
 <TabItem value="python" label="Python">
 
 ```bash
-pip install google-genai
+uv add google-genai
 ```
 
 </TabItem>

docs/my-website/docs/tutorials/gradio_integration.md

@@ -3,7 +3,7 @@ Simple tutorial for integrating LiteLLM completion calls with streaming Gradio c
 
 ### Install & Import Dependencies
 ```python
-!pip install gradio litellm
+!uv add gradio litellm
 import gradio
 import litellm
 ```

docs/my-website/docs/tutorials/litellm_Test_Multiple_Providers.md

@@ -10,7 +10,7 @@
 
 
 ```python
-!pip install litellm python-dotenv
+!uv add litellm python-dotenv
 ```
 
 

docs/my-website/docs/tutorials/livekit_xai_realtime.md

@@ -12,7 +12,7 @@ The LiveKit Agents framework provides tools for building real-time voice and vid
 ### 1. Install Dependencies
 
 ```bash
-pip install livekit-agents[xai]
+uv add livekit-agents[xai]
 ```
 
 ### 2. Start LiteLLM Proxy

docs/my-website/docs/tutorials/lm_evaluation_harness.md

@@ -34,7 +34,7 @@ source lmharness/bin/activate
 
 Pip install openai==0.28.01 in the venv
 ```shell
-pip install openai==0.28.01
+uv add openai==0.28.01
 ```
 
 **Step 3: Set OpenAI API Base & Key**
@@ -52,9 +52,9 @@ export OPENAI_API_SECRET_KEY=anything
 cd lm-evaluation-harness
 ```
 
-pip install lm harness dependencies in venv
+uv add lm harness dependencies in venv
 ```
-python3 -m pip install -e .
+uv sync
 ```
 
 ```shell

docs/my-website/docs/tutorials/model_fallbacks.md

@@ -4,7 +4,7 @@ Here's how you can implement model fallbacks across 3 LLM providers (OpenAI, Ant
 
 ## 1. Install LiteLLM
 ```python 
-!pip install litellm
+!uv add litellm
 ```
 
 ## 2. Basic Fallbacks Code 

docs/my-website/docs/tutorials/oobabooga.md

@@ -2,7 +2,7 @@
 
 ### Install + Import LiteLLM 
 ```python 
-!pip install litellm
+!uv add litellm
 from litellm import completion 
 import os
 ```

docs/my-website/docs/tutorials/openai_agents_sdk.md

@@ -47,7 +47,7 @@ See the [Docs](https://openai.github.io/openai-agents-python/models/litellm/) fo
 ## Installation
 
 ```bash showLineNumbers title="Install dependencies"
-pip install openai-agents litellm
+uv add openai-agents litellm
 ```
 
 ## 1. Start LiteLLM Proxy

docs/my-website/docs/tutorials/openclaw_integration.md

@@ -23,7 +23,7 @@ Chat apps → OpenClaw Gateway → LiteLLM Proxy → LLM Providers (OpenAI, Anth
 ## Step 1 — Install LiteLLM Proxy
 
 ```bash
-pip install 'litellm[proxy]'
+uv tool install 'litellm[proxy]'
 ```
 
 ## Step 2 — Create a LiteLLM config file

docs/my-website/src/pages/contributing.md

@@ -9,7 +9,7 @@ git clone https://github.com/BerriAI/litellm.git
 
 #### Installation
 ```
-pip install mkdocs
+uv add mkdocs
 ```
 
 #### Locally Serving Docs