Humanloop

Humanloop enables product teams to build robust AI features with LLMs, using best-in-class tooling for Evaluation, Prompt Management, and Observability.

Getting Started

Use Humanloop to manage prompts across all LiteLLM Providers.

SDK

import os 
import litellm

os.environ["HUMANLOOP_API_KEY"] = "" # [OPTIONAL] set here or in `.completion`

litellm.set_verbose = True # see raw request to provider

resp = litellm.completion(
    model="humanloop/gpt-3.5-turbo",
    prompt_id="test-chat-prompt",
    prompt_variables={"user_message": "this is used"}, # [OPTIONAL]
    messages=[{"role": "user", "content": "<IGNORED>"}],
    # humanloop_api_key="..." ## alternative to setting env var
)

PROXY

1. Setup config.yaml

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: humanloop/gpt-3.5-turbo
      prompt_id: "<humanloop_prompt_id>"
      api_key: os.environ/OPENAI_API_KEY

2. Start the proxy

litellm --config config.yaml --detailed_debug

3. Test it!

CURL

curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
            "role": "user",
            "content": "THIS WILL BE IGNORED"
        }
    ],
    "prompt_variables": {
        "key": "this is used"
    }
}'

OpenAI Python SDK

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={
        "prompt_variables": { # [OPTIONAL]
            "key": "this is used"
        }
    }
)

print(response)

Expected Logs:

POST Request Sent from LiteLLM:
curl -X POST \
https://api.openai.com/v1/ \
-d '{'model': 'gpt-3.5-turbo', 'messages': <YOUR HUMANLOOP PROMPT TEMPLATE>}'

How to set model

How to set model

Set the model on LiteLLM

You can do humanloop/<litellm_model_name>

SDK

litellm.completion(
    model="humanloop/gpt-3.5-turbo", # or `humanloop/anthropic/claude-3-5-sonnet`
    ...
)

PROXY

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: humanloop/gpt-3.5-turbo # OR humanloop/anthropic/claude-3-5-sonnet
      prompt_id: <humanloop_prompt_id>
      api_key: os.environ/OPENAI_API_KEY

Set the model on Humanloop

LiteLLM will call humanloop's https://api.humanloop.com/v5/prompts/<your-prompt-id> endpoint, to get the prompt template.

This also returns the template model set on Humanloop.

{
  "template": [
    {
      ... # your prompt template
    }
  ],
  "model": "gpt-3.5-turbo" # your template model
}