---
title: "Humanloop"
url: "/docs/observability/humanloop"
canonical_url: "https://docs.litellm.ai/docs/observability/humanloop"
type: "docs"
last_updated: "2026-10-04"
summary: "Humanloop enables product teams to build robust AI features with LLMs, using best-in-class tooling for Evaluation, Prompt Management, and Observability."
related:
  - "/docs/observability/helicone_integration"
  - "/docs/observability/langfuse_integration"
---
# Humanloop

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


[Humanloop](https://humanloop.com/docs/v5/getting-started/overview) 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**

```python
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-5.6-luna",
    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

```yaml
model_list:
  - model_name: gpt-5.6-luna
    litellm_params:
      model: humanloop/gpt-5.6-luna
      prompt_id: "<humanloop_prompt_id>"
      api_key: os.environ/OPENAI_API_KEY
```

2. Start the proxy

```bash
litellm --config config.yaml --detailed_debug
```

3. Test it! 

**CURL**

```bash
curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-d '{
    "model": "gpt-5.6-luna",
    "messages": [
        {
            "role": "user",
            "content": "THIS WILL BE IGNORED"
        }
    ],
    "prompt_variables": {
        "key": "this is used"
    }
}'
```
**OpenAI Python SDK**

```python
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-5.6-luna",
    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-5.6-luna', '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**

```python
litellm.completion(
    model="humanloop/gpt-5.6-luna", # or `humanloop/anthropic/claude-sonnet-5`
    # ...
)
```

**PROXY**

```yaml
model_list:
  - model_name: gpt-5.6-luna
    litellm_params:
      model: humanloop/gpt-5.6-luna # OR humanloop/anthropic/claude-sonnet-5
      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.

```bash
{
  "template": [
    {
      ... # your prompt template
    }
  ],
  "model": "gpt-5.6-luna" # your template model
}
```

## Related pages

- [Helicone](https://docs.litellm.ai/docs/observability/helicone_integration.md)
- [Langfuse](https://docs.litellm.ai/docs/observability/langfuse_integration.md)