Simplify LLM Integration with litellm
Makes it easy to call 100+ LLM APIs in OpenAI format from Python, with cost tracking, and more
Automations like: when a new customer signs up, use an LLM to generate a personalized onboarding email in Notion.
Saves development time when using multiple LLM APIs, and helps track costs associated with using these APIs.
"A solo developer building a customer support chatbot can use litellm to easily integrate with multiple LLM APIs, like OpenAI and Anthropic, to create more accurate and personalized responses. To do this, they simply install the Python SDK, set up their APIs, and write a simple script to call the LLMs for every new chat."
Pick this up once you're familiar with Python and basic AI concepts.
Senior engineers use litellm when building large-scale chatbots, or other applications that require integrating multiple LLM APIs.
Don't assume litellm is just another OpenAI wrapper - it supports many other LLM APIs and has features like cost tracking.
Litellm is a Python SDK and proxy server that simplifies calling over 100 LLM APIs from various providers, including OpenAI, Azure, and Google Vertex AI, with features like cost tracking, guardrails, load balancing, and logging.
pip install litellm- โขWhen you need to integrate multiple LLM APIs into your application and want to manage them through a single interface
- โขWhen you want to track and control costs associated with LLM API calls across different providers
- โขWhen you need to implement guardrails and load balancing to ensure reliable and efficient LLM API usage
- 1Clone the Litellm GitHub repository using `git clone https://github.com/BerriAI/litellm.git`
- 2Create a configuration file `config.yaml` to specify your LLM API credentials and settings, as shown in the example `config.example.yaml` file
- 3Run the Litellm proxy server using `litellm serve --config config.yaml`
- 4Use the Litellm Python SDK to call LLM APIs, such as `from litellm import Client; client = Client('openai'); response = client.complete(prompt='Hello, world!')`
- 5Verify the cost tracking and logging features by checking the `logs` directory and the `costs` table in the database
from litellm import Client; client = Client('openai'); response = client.complete(prompt='Write a short story about a character who discovers a hidden world.', max_tokens=512)Topics
Simplify LLM Integration with litellm is a very large project (~1M lines across 4635 code files, plus 2870 test files). Setup is light: installs like a normal app โ add your API keys. Reading the code is optional. Last commit this month, Other license, has a test suite.
- 1README.mdStart here โ what it does and how to install it
- 2AGENTS.mdThe instructions the AI actually follows
- 3litellm/main.pyWhere the program starts running
- 4package.jsonDependencies and the commands it exposes
- 5.env.exampleThe API keys and settings you must provide
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