feat: LLMonitor is now Lunary

README.md

@@ -25,27 +25,26 @@
 </h4>
 
 LiteLLM manages:
+
 - Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
 - [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']`
 - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
 - Set Budgets & Rate limits per project, api key, model [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
 
-
 [**Jump to OpenAI Proxy Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs) <br>
 [**Jump to Supported LLM Providers**](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-provider-docs)
 
 Support for more providers. Missing a provider or LLM Platform, raise a [feature request](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+).
 
 # Usage ([**Docs**](https://docs.litellm.ai/docs/))
+
 > [!IMPORTANT]
 > LiteLLM v1.0.0 now requires `openai>=1.0.0`. Migration guide [here](https://docs.litellm.ai/docs/migration)
 
-
 <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
 
-
 ```shell
 pip install litellm
 ```
@@ -87,8 +86,10 @@ print(response)
 ```
 
 ## Streaming ([Docs](https://docs.litellm.ai/docs/completion/stream))
+
 liteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response.  
 Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.)
+
 ```python
 from litellm import completion
 response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
@@ -102,20 +103,22 @@ for part in response:
 ```
 
 ## Logging Observability ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
-LiteLLM exposes pre defined callbacks to send data to Langfuse, DynamoDB, s3 Buckets, LLMonitor, Helicone, Promptlayer, Traceloop, Athina, Slack
+
+LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack
+
 ```python
 from litellm import completion
 
 ## set env variables for logging tools
 os.environ["LANGFUSE_PUBLIC_KEY"] = ""
 os.environ["LANGFUSE_SECRET_KEY"] = ""
-os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
+os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
 os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
 
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["langfuse", "llmonitor", "athina"] # log input/output to langfuse, llmonitor, supabase, athina etc
+litellm.success_callback = ["langfuse", "lunary", "athina"] # log input/output to langfuse, lunary, supabase, athina etc
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
@@ -126,6 +129,7 @@ response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content
 Set Budgets & Rate limits across multiple projects
 
 The proxy provides:
+
 1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth)
 2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class)
 3. [Cost tracking](https://docs.litellm.ai/docs/proxy/virtual_keys#tracking-spend)
@@ -140,6 +144,7 @@ pip install 'litellm[proxy]'
 ```
 
 ### Step 1: Start litellm proxy
+
 ```shell
 $ litellm --model huggingface/bigcode/starcoder
 
@@ -147,6 +152,7 @@ $ litellm --model huggingface/bigcode/starcoder
 ```
 
 ### Step 2: Make ChatCompletions Request to Proxy
+
 ```python
 import openai # openai v1.0.0+
 client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
@@ -162,6 +168,7 @@ print(response)
 ```
 
 ## Proxy Key Management ([Docs](https://docs.litellm.ai/docs/proxy/virtual_keys))
+
 UI on `/ui` on your proxy server
 ![ui_3](https://github.com/BerriAI/litellm/assets/29436595/47c97d5e-b9be-4839-b28c-43d7f4f10033)
 
@@ -169,6 +176,7 @@ Set budgets and rate limits across multiple projects
 `POST /key/generate`
 
 ### Request
+
 ```shell
 curl 'http://0.0.0.0:8000/key/generate' \
 --header 'Authorization: Bearer sk-1234' \
@@ -177,6 +185,7 @@ curl 'http://0.0.0.0:8000/key/generate' \
 ```
 
 ### Expected Response
+
 ```shell
 {
     "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
@@ -185,8 +194,9 @@ curl 'http://0.0.0.0:8000/key/generate' \
 ```
 
 ## Supported Providers ([Docs](https://docs.litellm.ai/docs/providers))
+
 | Provider                                                                            | [Completion](https://docs.litellm.ai/docs/#basic-usage) | [Streaming](https://docs.litellm.ai/docs/completion/stream#streaming-responses) | [Async Completion](https://docs.litellm.ai/docs/completion/stream#async-completion) | [Async Streaming](https://docs.litellm.ai/docs/completion/stream#async-streaming) | [Async Embedding](https://docs.litellm.ai/docs/embedding/supported_embedding) | [Async Image Generation](https://docs.litellm.ai/docs/image_generation) |
-| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
+| ----------------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ----------------------------------------------------------------------- |
 | [openai](https://docs.litellm.ai/docs/providers/openai)                             | ✅                                                      | ✅                                                                              | ✅                                                                                  | ✅                                                                                | ✅                                                                            | ✅                                                                      |
 | [azure](https://docs.litellm.ai/docs/providers/azure)                               | ✅                                                      | ✅                                                                              | ✅                                                                                  | ✅                                                                                | ✅                                                                            | ✅                                                                      |
 | [aws - sagemaker](https://docs.litellm.ai/docs/providers/aws_sagemaker)             | ✅                                                      | ✅                                                                              | ✅                                                                                  | ✅                                                                                | ✅                                                                            |
@@ -216,25 +226,28 @@ curl 'http://0.0.0.0:8000/key/generate' \
 | [voyage ai](https://docs.litellm.ai/docs/providers/voyage)                          |                                                         |                                                                                 |                                                                                     |                                                                                   | ✅                                                                            |
 | [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) |                                                         |                                                                                 |                                                                                     |                                                                                   | ✅                                                                            |
 
-
 [**Read the Docs**](https://docs.litellm.ai/docs/)
 
 ## Contributing
+
 To contribute: Clone the repo locally -> Make a change -> Submit a PR with the change.
 
 Here's how to modify the repo locally:
 Step 1: Clone the repo
+
 ```
 git clone https://github.com/BerriAI/litellm.git
 ```
 
 Step 2: Navigate into the project, and install dependencies:
+
 ```
 cd litellm
 poetry install
 ```
 
 Step 3: Test your change:
+
 ```
 cd litellm/tests # pwd: Documents/litellm/litellm/tests
 poetry run flake8
@@ -242,16 +255,19 @@ poetry run pytest .
 ```
 
 Step 4: Submit a PR with your changes! 🚀
+
 - push your fork to your GitHub repo
 - submit a PR from there
 
 # Support / talk with founders
+
 - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
 - [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
 - Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
 - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
 
 # Why did we build this
+
 - **Need for simplicity**: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.
 
 # Contributors
@@ -268,4 +284,3 @@ Step 4: Submit a PR with your changes! 🚀
 <a href="https://github.com/BerriAI/litellm/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=BerriAI/litellm" />
 </a>
-

cookbook/proxy-server/readme.md

@@ -33,7 +33,7 @@
   - Call all models using the OpenAI format - `completion(model, messages)`
   - Text responses will always be available at `['choices'][0]['message']['content']`
 - **Error Handling** Using Model Fallbacks (if `GPT-4` fails, try `llama2`)
-- **Logging** - Log Requests, Responses and Errors to `Supabase`, `Posthog`, `Mixpanel`, `Sentry`, `LLMonitor`,`Athina`, `Helicone` (Any of the supported providers here: https://litellm.readthedocs.io/en/latest/advanced/
+- **Logging** - Log Requests, Responses and Errors to `Supabase`, `Posthog`, `Mixpanel`, `Sentry`, `Lunary`,`Athina`, `Helicone` (Any of the supported providers here: https://litellm.readthedocs.io/en/latest/advanced/
 
   **Example: Logs sent to Supabase**
   <img width="1015" alt="Screenshot 2023-08-11 at 4 02 46 PM" src="https://github.com/ishaan-jaff/proxy-server/assets/29436595/237557b8-ba09-4917-982c-8f3e1b2c8d08">

docs/my-website/docs/getting_started.md

@@ -30,13 +30,15 @@ Email us @ krrish@berri.ai
 Next Steps 👉 [Call all supported models - e.g. Claude-2, Llama2-70b, etc.](./proxy_api.md#supported-models)
 
 More details 👉
-* [Completion() function details](./completion/)
+
-* [All supported models / providers on LiteLLM](./providers/)
+- [Completion() function details](./completion/)
-* [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main)
+- [All supported models / providers on LiteLLM](./providers/)
+- [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main)
 
 ## streaming
 
 Same example from before. Just pass in `stream=True` in the completion args.
+
 ```python
 from litellm import completion
 
@@ -56,8 +58,9 @@ print(response)
 ```
 
 More details 👉
-* [streaming + async](./completion/stream.md)
+
-* [tutorial for streaming Llama2 on TogetherAI](./tutorials/TogetherAI_liteLLM.md)
+- [streaming + async](./completion/stream.md)
+- [tutorial for streaming Llama2 on TogetherAI](./tutorials/TogetherAI_liteLLM.md)
 
 ## exception handling
 
@@ -76,25 +79,28 @@ except OpenAIError as e:
 ```
 
 ## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
-LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
+
+LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack
+
 ```python
 from litellm import completion
 
 ## set env variables for logging tools
+os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
 os.environ["LANGFUSE_PUBLIC_KEY"] = ""
 os.environ["LANGFUSE_SECRET_KEY"] = ""
-os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
 
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
+litellm.success_callback = ["lunary", "langfuse"] # log input/output to langfuse, lunary, supabase
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
 ```
 
 More details 👉
-* [exception mapping](./exception_mapping.md)
+
-* [retries + model fallbacks for completion()](./completion/reliable_completions.md)
+- [exception mapping](./exception_mapping.md)
-* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md)
+- [retries + model fallbacks for completion()](./completion/reliable_completions.md)
+- [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md)

docs/my-website/docs/index.md

@@ -5,7 +5,6 @@ import TabItem from '@theme/TabItem';
 
 https://github.com/BerriAI/litellm
 
-
 ## **Call 100+ LLMs using the same Input/Output Format**
 
 - Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
@@ -14,6 +13,7 @@ https://github.com/BerriAI/litellm
 - Track spend & set budgets per project [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
 
 ## Basic usage
+
 <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
@@ -21,6 +21,7 @@ https://github.com/BerriAI/litellm
 ```shell
 pip install litellm
 ```
+
 <Tabs>
 <TabItem value="openai" label="OpenAI">
 
@@ -113,7 +114,6 @@ response = completion(
 
 </TabItem>
 
-
 <TabItem value="ollama" label="Ollama">
 
 ```python
@@ -125,6 +125,7 @@ response = completion(
             api_base="http://localhost:11434"
 )
 ```
+
 </TabItem>
 <TabItem value="or" label="Openrouter">
 
@@ -140,11 +141,13 @@ response = completion(
   messages = [{ "content": "Hello, how are you?","role": "user"}],
 )
 ```
+
 </TabItem>
 
 </Tabs>
 
 ## Streaming
+
 Set `stream=True` in the `completion` args.
 <Tabs>
 <TabItem value="openai" label="OpenAI">
@@ -243,7 +246,6 @@ response = completion(
 
 </TabItem>
 
-
 <TabItem value="ollama" label="Ollama">
 
 ```python
@@ -256,6 +258,7 @@ response = completion(
             stream=True,
 )
 ```
+
 </TabItem>
 <TabItem value="or" label="Openrouter">
 
@@ -272,6 +275,7 @@ response = completion(
   stream=True,
 )
 ```
+
 </TabItem>
 
 </Tabs>
@@ -293,25 +297,28 @@ except OpenAIError as e:
 ```
 
 ## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
-LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
+
+LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack
+
 ```python
 from litellm import completion
 
 ## set env variables for logging tools
 os.environ["LANGFUSE_PUBLIC_KEY"] = ""
 os.environ["LANGFUSE_SECRET_KEY"] = ""
-os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
+os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
 
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
+litellm.success_callback = ["lunary", "langfuse"] # log input/output to lunary, langfuse, supabase
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
 ```
 
 ## Track Costs, Usage, Latency for streaming
+
 Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
 
 ```python
@@ -351,6 +358,7 @@ Track spend across multiple projects/people
 ![ui_3](https://github.com/BerriAI/litellm/assets/29436595/47c97d5e-b9be-4839-b28c-43d7f4f10033)
 
 The proxy provides:
+
 1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth)
 2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class)
 3. [Cost tracking](https://docs.litellm.ai/docs/proxy/virtual_keys#tracking-spend)
@@ -365,6 +373,7 @@ pip install 'litellm[proxy]'
 ```
 
 #### Step 1: Start litellm proxy
+
 ```shell
 $ litellm --model huggingface/bigcode/starcoder
 
@@ -372,6 +381,7 @@ $ litellm --model huggingface/bigcode/starcoder
 ```
 
 #### Step 2: Make ChatCompletions Request to Proxy
+
 ```python
 import openai # openai v1.0.0+
 client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
@@ -387,6 +397,7 @@ print(response)
 ```
 
 ## More details
-* [exception mapping](./exception_mapping.md)
+
-* [retries + model fallbacks for completion()](./completion/reliable_completions.md)
+- [exception mapping](./exception_mapping.md)
-* [proxy virtual keys & spend management](./tutorials/fallbacks.md)
+- [retries + model fallbacks for completion()](./completion/reliable_completions.md)
+- [proxy virtual keys & spend management](./tutorials/fallbacks.md)

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

@@ -7,7 +7,7 @@ liteLLM provides `input_callbacks`, `success_callbacks` and `failure_callbacks`,
 liteLLM supports:
 
 - [Custom Callback Functions](https://docs.litellm.ai/docs/observability/custom_callback)
-- [LLMonitor](https://llmonitor.com/docs)
+- [Lunary](https://lunary.ai/docs)
 - [Helicone](https://docs.helicone.ai/introduction)
 - [Traceloop](https://traceloop.com/docs)
 - [Athina](https://docs.athina.ai/)
@@ -22,15 +22,15 @@ from litellm import completion
 
 # set callbacks
 litellm.input_callback=["sentry"] # for sentry breadcrumbing - logs the input being sent to the api
-litellm.success_callback=["posthog", "helicone", "llmonitor", "athina"]
+litellm.success_callback=["posthog", "helicone", "lunary", "athina"]
-litellm.failure_callback=["sentry", "llmonitor"]
+litellm.failure_callback=["sentry", "lunary"]
 
 ## set env variables
 os.environ['SENTRY_DSN'], os.environ['SENTRY_API_TRACE_RATE']= ""
 os.environ['POSTHOG_API_KEY'], os.environ['POSTHOG_API_URL'] = "api-key", "api-url"
 os.environ["HELICONE_API_KEY"] = ""
 os.environ["TRACELOOP_API_KEY"] = ""
-os.environ["LLMONITOR_APP_ID"] = ""
+os.environ["LUNARY_PUBLIC_KEY"] = ""
 os.environ["ATHINA_API_KEY"] = ""
 
 response = completion(model="gpt-3.5-turbo", messages=messages)

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

@@ -1,65 +0,0 @@
-# LLMonitor Tutorial
-
-[LLMonitor](https://llmonitor.com/) is an open-source observability platform that provides cost tracking, user tracking and powerful agent tracing.
-
-<video controls width='900' >
-  <source src='https://llmonitor.com/videos/demo-annotated.mp4'/>
-</video>
-
-## Use LLMonitor to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM)
-
-liteLLM provides `callbacks`, making it easy for you to log data depending on the status of your responses.
-
-:::info
-We want to learn how we can make the callbacks better! Meet the [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or
-join our [discord](https://discord.gg/wuPM9dRgDw)
-::: 
-
-### Using Callbacks
-
-First, sign up to get an app ID on the [LLMonitor dashboard](https://llmonitor.com).
-
-Use just 2 lines of code, to instantly log your responses **across all providers** with llmonitor:
-
-```python
-litellm.success_callback = ["llmonitor"]
-litellm.failure_callback = ["llmonitor"]
-```
-
-Complete code
-
-```python
-from litellm import completion
-
-## set env variables
-os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
-# Optional: os.environ["LLMONITOR_API_URL"] = "self-hosting-url"
-
-os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", ""
-
-# set callbacks
-litellm.success_callback = ["llmonitor"]
-litellm.failure_callback = ["llmonitor"]
-
-#openai call
-response = completion(
-  model="gpt-3.5-turbo", 
-  messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}],
-  user="ishaan_litellm"
-)
-
-#cohere call
-response = completion(
-  model="command-nightly", 
-  messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}],
-  user="ishaan_litellm"
-)
-```
-
-## Support & Talk to Founders
-
-- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
-- [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
-- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
-- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
-- Meet the LLMonitor team on [Discord](http://discord.com/invite/8PafSG58kK) or via [email](mailto:vince@llmonitor.com).

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

@@ -0,0 +1,82 @@
+# Lunary - Logging and tracing LLM input/output
+
+[Lunary](https://lunary.ai/) is an open-source AI developer platform providing observability, prompt management, and evaluation tools for AI developers.
+
+<video controls width='900' >
+  <source src='https://lunary.ai/videos/demo-annotated.mp4'/>
+</video>
+
+## Use Lunary to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM)
+
+liteLLM provides `callbacks`, making it easy for you to log data depending on the status of your responses.
+
+:::info
+We want to learn how we can make the callbacks better! Meet the [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or
+join our [discord](https://discord.gg/wuPM9dRgDw)
+:::
+
+### Using Callbacks
+
+First, sign up to get a public key on the [Lunary dashboard](https://lunary.ai).
+
+Use just 2 lines of code, to instantly log your responses **across all providers** with lunary:
+
+```python
+litellm.success_callback = ["lunary"]
+litellm.failure_callback = ["lunary"]
+```
+
+Complete code
+
+```python
+from litellm import completion
+
+## set env variables
+os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
+
+os.environ["OPENAI_API_KEY"] = ""
+
+# set callbacks
+litellm.success_callback = ["lunary"]
+litellm.failure_callback = ["lunary"]
+
+#openai call
+response = completion(
+  model="gpt-3.5-turbo",
+  messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}],
+  user="ishaan_litellm"
+)
+```
+
+## Templates
+
+You can use Lunary to manage prompt templates and use them across all your LLM providers.
+
+Make sure to have `lunary` installed:
+
+```bash
+pip install lunary
+```
+
+Then, use the following code to pull templates into Lunary:
+
+```python
+from litellm import completion
+from lunary
+
+template = lunary.render_template("template-slug", {
+  "name": "John", # Inject variables
+})
+
+litellm.success_callback = ["lunary"]
+
+result = completion(**template)
+```
+
+## Support & Talk to Founders
+
+- Meet the Lunary team via [email](mailto:hello@lunary.ai).
+- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
+- [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
+- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
+- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

docs/my-website/sidebars.js

@@ -22,18 +22,18 @@ const sidebars = {
       type: "category",
       label: "💥 OpenAI Proxy Server",
       link: {
-        type: 'generated-index',
+        type: "generated-index",
-        title: '💥 OpenAI Proxy Server',
+        title: "💥 OpenAI Proxy Server",
         description: `Proxy Server to call 100+ LLMs in a unified interface & track spend, set budgets per virtual key/user`,
-        slug: '/simple_proxy',
+        slug: "/simple_proxy",
       },
       items: [
         "proxy/quick_start",
         "proxy/configs",
         {
-          type: 'link',
+          type: "link",
-          label: '📖 All Endpoints',
+          label: "📖 All Endpoints",
-          href: 'https://litellm-api.up.railway.app/',
+          href: "https://litellm-api.up.railway.app/",
         },
         "proxy/enterprise",
         "proxy/user_keys",
@@ -45,43 +45,33 @@ const sidebars = {
         "proxy/debugging",
         "proxy/pii_masking",
         {
-          "type": "category",
+          type: "category",
-          "label": "🔥 Load Balancing",
+          label: "🔥 Load Balancing",
-          "items": [
+          items: ["proxy/load_balancing", "proxy/reliability"],
-            "proxy/load_balancing", 
-            "proxy/reliability",
-          ]
         },
         "proxy/caching",
         {
-          "type": "category",
+          type: "category",
-          "label": "Logging, Alerting",
+          label: "Logging, Alerting",
-          "items": [
+          items: ["proxy/logging", "proxy/alerting", "proxy/streaming_logging"],
-            "proxy/logging", 
-            "proxy/alerting",
-            "proxy/streaming_logging",
-          ]
         },
         {
-          "type": "category",
+          type: "category",
-          "label": "Content Moderation",
+          label: "Content Moderation",
-          "items": [
+          items: ["proxy/call_hooks", "proxy/rules"],
-            "proxy/call_hooks",
-            "proxy/rules",
-          ]
         },
         "proxy/deploy",
         "proxy/cli",
-      ]
+      ],
     },
     {
       type: "category",
       label: "Completion()",
       link: {
-        type: 'generated-index',
+        type: "generated-index",
-        title: 'Completion()',
+        title: "Completion()",
-        description: 'Details on the completion() function',
+        description: "Details on the completion() function",
-        slug: '/completion',
+        slug: "/completion",
       },
       items: [
         "completion/input",
@@ -104,17 +94,18 @@ const sidebars = {
         "embedding/supported_embedding",
         "embedding/async_embedding",
         "embedding/moderation",
-        "image_generation"
+        "image_generation",
       ],
     },
     {
       type: "category",
       label: "Supported Models & Providers",
       link: {
-        type: 'generated-index',
+        type: "generated-index",
-        title: 'Providers',
+        title: "Providers",
-        description: 'Learn how to deploy + call models from different providers on LiteLLM',
+        description:
-        slug: '/providers',
+          "Learn how to deploy + call models from different providers on LiteLLM",
+        slug: "/providers",
       },
       items: [
         "providers/openai",
@@ -148,7 +139,7 @@ const sidebars = {
         "providers/openrouter",
         "providers/custom_openai_proxy",
         "providers/petals",
-      ]
+      ],
     },
     "proxy/custom_pricing",
     "routing",
@@ -163,9 +154,10 @@ const sidebars = {
       type: "category",
       label: "Logging & Observability",
       items: [
-        'debugging/local_debugging',
+        "debugging/local_debugging",
         "observability/callbacks",
         "observability/custom_callback",
+        "observability/lunary_integration",
         "observability/langfuse_integration",
         "observability/sentry",
         "observability/promptlayer_integration",
@@ -174,7 +166,6 @@ const sidebars = {
         "observability/slack_integration",
         "observability/traceloop_integration",
         "observability/athina_integration",
-        "observability/llmonitor_integration",
         "observability/helicone_integration",
         "observability/supabase_integration",
         `observability/telemetry`,
@@ -182,18 +173,18 @@ const sidebars = {
     },
     "caching/redis_cache",
     {
-      type: 'category',
+      type: "category",
-      label: 'Tutorials',
+      label: "Tutorials",
       items: [
-        'tutorials/azure_openai',
+        "tutorials/azure_openai",
-        'tutorials/oobabooga',
+        "tutorials/oobabooga",
         "tutorials/gradio_integration",
-        'tutorials/huggingface_codellama',
+        "tutorials/huggingface_codellama",
-        'tutorials/huggingface_tutorial', 
+        "tutorials/huggingface_tutorial",
-        'tutorials/TogetherAI_liteLLM', 
+        "tutorials/TogetherAI_liteLLM",
-        'tutorials/finetuned_chat_gpt',
+        "tutorials/finetuned_chat_gpt",
-        'tutorials/sagemaker_llms',
+        "tutorials/sagemaker_llms",
-        'tutorials/text_completion',
+        "tutorials/text_completion",
         "tutorials/first_playground",
         "tutorials/model_fallbacks",
       ],
@@ -201,24 +192,23 @@ const sidebars = {
     {
       type: "category",
       label: "LangChain, LlamaIndex Integration",
-      items: [
+      items: ["langchain/langchain"],
-        "langchain/langchain"
-      ],
     },
     {
-      type: 'category',
+      type: "category",
-      label: 'Extras',
+      label: "Extras",
       items: [
-        'extras/contributing',
+        "extras/contributing",
         "proxy_server",
         {
           type: "category",
           label: "❤️ 🚅 Projects built on LiteLLM",
           link: {
-            type: 'generated-index',
+            type: "generated-index",
-            title: 'Projects built on LiteLLM',
+            title: "Projects built on LiteLLM",
-            description: 'Learn how to deploy + call models from different providers on LiteLLM',
+            description:
-            slug: '/project',
+              "Learn how to deploy + call models from different providers on LiteLLM",
+            slug: "/project",
           },
           items: [
             "projects/Docq.AI",
@@ -234,7 +224,7 @@ const sidebars = {
             "projects/GPT Migrate",
             "projects/YiVal",
             "projects/LiteLLM Proxy",
-          ]
+          ],
         },
       ],
     },

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

@@ -5,7 +5,6 @@ import TabItem from '@theme/TabItem';
 
 https://github.com/BerriAI/litellm
 
-
 ## **Call 100+ LLMs using the same Input/Output Format**
 
 - Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
@@ -14,6 +13,7 @@ https://github.com/BerriAI/litellm
 - Track spend & set budgets per project [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
 
 ## Basic usage
+
 <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
 </a>
@@ -21,6 +21,7 @@ https://github.com/BerriAI/litellm
 ```shell
 pip install litellm
 ```
+
 <Tabs>
 <TabItem value="openai" label="OpenAI">
 
@@ -113,7 +114,6 @@ response = completion(
 
 </TabItem>
 
-
 <TabItem value="ollama" label="Ollama">
 
 ```python
@@ -125,6 +125,7 @@ response = completion(
             api_base="http://localhost:11434"
 )
 ```
+
 </TabItem>
 <TabItem value="or" label="Openrouter">
 
@@ -140,11 +141,13 @@ response = completion(
   messages = [{ "content": "Hello, how are you?","role": "user"}],
 )
 ```
+
 </TabItem>
 
 </Tabs>
 
 ## Streaming
+
 Set `stream=True` in the `completion` args.
 <Tabs>
 <TabItem value="openai" label="OpenAI">
@@ -243,7 +246,6 @@ response = completion(
 
 </TabItem>
 
-
 <TabItem value="ollama" label="Ollama">
 
 ```python
@@ -256,6 +258,7 @@ response = completion(
             stream=True,
 )
 ```
+
 </TabItem>
 <TabItem value="or" label="Openrouter">
 
@@ -272,6 +275,7 @@ response = completion(
   stream=True,
 )
 ```
+
 </TabItem>
 
 </Tabs>
@@ -293,25 +297,28 @@ except OpenAIError as e:
 ```
 
 ## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
-LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
+
+LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack
+
 ```python
 from litellm import completion
 
 ## set env variables for logging tools
 os.environ["LANGFUSE_PUBLIC_KEY"] = ""
 os.environ["LANGFUSE_SECRET_KEY"] = ""
-os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
+os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
 
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
+litellm.success_callback = ["langfuse", "lunary"] # log input/output to lunary, langfuse, supabase
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
 ```
 
 ## Track Costs, Usage, Latency for streaming
+
 Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
 
 ```python
@@ -351,6 +358,7 @@ Track spend across multiple projects/people
 ![ui_3](https://github.com/BerriAI/litellm/assets/29436595/47c97d5e-b9be-4839-b28c-43d7f4f10033)
 
 The proxy provides:
+
 1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth)
 2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class)
 3. [Cost tracking](https://docs.litellm.ai/docs/proxy/virtual_keys#tracking-spend)
@@ -365,6 +373,7 @@ pip install 'litellm[proxy]'
 ```
 
 #### Step 1: Start litellm proxy
+
 ```shell
 $ litellm --model huggingface/bigcode/starcoder
 
@@ -372,6 +381,7 @@ $ litellm --model huggingface/bigcode/starcoder
 ```
 
 #### Step 2: Make ChatCompletions Request to Proxy
+
 ```python
 import openai # openai v1.0.0+
 client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
@@ -387,6 +397,7 @@ print(response)
 ```
 
 ## More details
-* [exception mapping](./exception_mapping.md)
+
-* [retries + model fallbacks for completion()](./completion/reliable_completions.md)
+- [exception mapping](./exception_mapping.md)
-* [proxy virtual keys & spend management](./tutorials/fallbacks.md)
+- [retries + model fallbacks for completion()](./completion/reliable_completions.md)
+- [proxy virtual keys & spend management](./tutorials/fallbacks.md)

docs/my-website/src/pages/observability/callbacks.md

@@ -6,7 +6,7 @@ liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for
 
 liteLLM supports:
 
-- [LLMonitor](https://llmonitor.com/docs)
+- [Lunary](https://lunary.ai/docs)
 - [Helicone](https://docs.helicone.ai/introduction)
 - [Sentry](https://docs.sentry.io/platforms/python/)
 - [PostHog](https://posthog.com/docs/libraries/python)
@@ -18,8 +18,8 @@ liteLLM supports:
 from litellm import completion
 
 # set callbacks
-litellm.success_callback=["posthog", "helicone", "llmonitor"]
+litellm.success_callback=["posthog", "helicone", "lunary"]
-litellm.failure_callback=["sentry", "llmonitor"]
+litellm.failure_callback=["sentry", "lunary"]
 
 ## set env variables
 os.environ['SENTRY_DSN'], os.environ['SENTRY_API_TRACE_RATE']= ""

litellm/integrations/llmonitor.py

@@ -1,127 +0,0 @@
-#### What this does ####
-#    On success + failure, log events to aispend.io
-import datetime
-import traceback
-import dotenv
-import os
-import requests
-
-dotenv.load_dotenv()  # Loading env variables using dotenv
-
-
-# convert to {completion: xx, tokens: xx}
-def parse_usage(usage):
-    return {
-        "completion": usage["completion_tokens"] if "completion_tokens" in usage else 0,
-        "prompt": usage["prompt_tokens"] if "prompt_tokens" in usage else 0,
-    }
-
-
-def parse_messages(input):
-    if input is None:
-        return None
-
-    def clean_message(message):
-        # if is strin, return as is
-        if isinstance(message, str):
-            return message
-
-        if "message" in message:
-            return clean_message(message["message"])
-        text = message["content"]
-        if text == None:
-            text = message.get("function_call", None)
-
-        return {
-            "role": message["role"],
-            "text": text,
-        }
-
-    if isinstance(input, list):
-        if len(input) == 1:
-            return clean_message(input[0])
-        else:
-            return [clean_message(msg) for msg in input]
-    else:
-        return clean_message(input)
-
-
-class LLMonitorLogger:
-    # Class variables or attributes
-    def __init__(self):
-        # Instance variables
-        self.api_url = os.getenv("LLMONITOR_API_URL") or "https://app.llmonitor.com"
-        self.app_id = os.getenv("LLMONITOR_APP_ID")
-
-    def log_event(
-        self,
-        type,
-        event,
-        run_id,
-        model,
-        print_verbose,
-        input=None,
-        user_id=None,
-        response_obj=None,
-        start_time=datetime.datetime.now(),
-        end_time=datetime.datetime.now(),
-        error=None,
-    ):
-        # Method definition
-        try:
-            print_verbose(f"LLMonitor Logging - Logging request for model {model}")
-
-            if response_obj:
-                usage = (
-                    parse_usage(response_obj["usage"])
-                    if "usage" in response_obj
-                    else None
-                )
-                output = response_obj["choices"] if "choices" in response_obj else None
-            else:
-                usage = None
-                output = None
-
-            if error:
-                error_obj = {"stack": error}
-
-            else:
-                error_obj = None
-
-            data = [
-                {
-                    "type": type,
-                    "name": model,
-                    "runId": run_id,
-                    "app": self.app_id,
-                    "event": "start",
-                    "timestamp": start_time.isoformat(),
-                    "userId": user_id,
-                    "input": parse_messages(input),
-                },
-                {
-                    "type": type,
-                    "runId": run_id,
-                    "app": self.app_id,
-                    "event": event,
-                    "error": error_obj,
-                    "timestamp": end_time.isoformat(),
-                    "userId": user_id,
-                    "output": parse_messages(output),
-                    "tokensUsage": usage,
-                },
-            ]
-
-            print_verbose(f"LLMonitor Logging - final data object: {data}")
-
-            response = requests.post(
-                self.api_url + "/api/report",
-                headers={"Content-Type": "application/json"},
-                json={"events": data},
-            )
-
-            print_verbose(f"LLMonitor Logging - response: {response}")
-        except:
-            # traceback.print_exc()
-            print_verbose(f"LLMonitor Logging Error - {traceback.format_exc()}")
-            pass

litellm/integrations/lunary.py

@@ -0,0 +1,167 @@
+#### What this does ####
+#    On success + failure, log events to aispend.io
+import datetime
+import traceback
+import dotenv
+import os
+import requests
+
+dotenv.load_dotenv()  # Loading env variables using dotenv
+import traceback
+import datetime, subprocess, sys
+import litellm
+
+# convert to {completion: xx, tokens: xx}
+def parse_usage(usage):
+    return {
+        "completion": usage["completion_tokens"] if "completion_tokens" in usage else 0,
+        "prompt": usage["prompt_tokens"] if "prompt_tokens" in usage else 0,
+    }
+
+
+def parse_messages(input):
+    if input is None:
+        return None
+
+    def clean_message(message):
+        # if is strin, return as is
+        if isinstance(message, str):
+            return message
+
+        if "message" in message:
+            return clean_message(message["message"])
+       
+        
+        serialized = {
+            "role": message.get("role"),
+            "content": message.get("content"),
+        }
+
+        # Only add tool_calls and function_call to res if they are set
+        if message.get("tool_calls"):
+            serialized["tool_calls"] = message.get("tool_calls")
+        if message.get("function_call"):
+            serialized["function_call"] = message.get("function_call")
+
+        return serialized
+
+    if isinstance(input, list):
+        if len(input) == 1:
+            return clean_message(input[0])
+        else:
+            return [clean_message(msg) for msg in input]
+    else:
+        return clean_message(input)
+
+
+class LunaryLogger:
+    # Class variables or attributes
+    def __init__(self):
+        try:
+            import lunary
+            # lunary.__version__ doesn't exist throws if lunary is not installed
+            if not hasattr(lunary, "track_event"):
+                raise ImportError
+            
+            self.lunary_client = lunary
+        except ImportError:
+            print("Lunary not installed. Installing now...")
+            subprocess.check_call([sys.executable, "-m", "pip", "install", "lunary", "--upgrade"])
+            import importlib
+            import lunary
+            importlib.reload(lunary)
+
+            self.lunary_client = lunary
+
+
+    def log_event(
+        self,
+        kwargs,
+        type,
+        event,
+        run_id,
+        model,
+        print_verbose,
+        extra=None,
+        input=None,
+        user_id=None,
+        response_obj=None,
+        start_time=datetime.datetime.now(),
+        end_time=datetime.datetime.now(),
+        error=None,
+    ):
+        # Method definition
+        try:
+            
+            print_verbose(f"Lunary Logging - Logging request for model {model}")
+
+            litellm_params = kwargs.get("litellm_params", {})
+            metadata = (
+                litellm_params.get("metadata", {}) or {}
+            )
+
+            tags = litellm_params.pop("tags", None) or []
+
+            template_id = extra.pop("templateId", None),
+
+            for param, value in extra.items():
+                if not isinstance(value, (str, int, bool, float)):
+                    try:
+                        extra[param] = str(value)
+                    except:
+                        # if casting value to str fails don't block logging
+                        pass
+
+            if response_obj:
+                usage = (
+                    parse_usage(response_obj["usage"])
+                    if "usage" in response_obj
+                    else None
+                )
+                output = response_obj["choices"] if "choices" in response_obj else None
+            else:
+                usage = None
+                output = None
+
+            if error:
+                error_obj = {"stack": error}
+            else:
+                error_obj = None
+
+            print(start_time.isoformat())
+
+            self.lunary_client.track_event(
+                type,
+                "start",
+                run_id,
+                user_id=user_id,
+                name=model,
+                input=parse_messages(input),
+                timestamp=start_time.isoformat(),
+                # template_id=template_id,
+                metadata=metadata,
+                runtime="litellm",
+                tags=tags,
+                extra=extra,
+                # user_props=user_props,
+            )
+
+            self.lunary_client.track_event(
+                type,
+                event,
+                run_id,
+                timestamp=end_time.isoformat(),
+                runtime="litellm",
+                error=error_obj,
+                output=parse_messages(output),
+                token_usage={
+                    "prompt": usage.get("prompt_tokens"),
+                    "completion": usage.get("completion_tokens"),
+                }
+            )
+
+
+        except:
+            # traceback.print_exc()
+            print_verbose(f"Lunary Logging Error - {traceback.format_exc()}")
+            pass

litellm/tests/test_llmonitor_integration.py

@@ -1,76 +0,0 @@
-# #### What this tests ####
-# #    This tests if logging to the llmonitor integration actually works
-# # Adds the parent directory to the system path
-# import sys
-# import os
-
-# sys.path.insert(0, os.path.abspath("../.."))
-
-# from litellm import completion, embedding
-# import litellm
-
-# litellm.success_callback = ["llmonitor"]
-# litellm.failure_callback = ["llmonitor"]
-
-# litellm.set_verbose = True
-
-
-# def test_chat_openai():
-#     try:
-#         response = completion(
-#             model="gpt-3.5-turbo",
-#             messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}],
-#             user="ishaan_from_litellm"
-#         )
-
-#         print(response)
-
-#     except Exception as e:
-#         print(e)
-
-
-# def test_embedding_openai():
-#     try:
-#         response = embedding(model="text-embedding-ada-002", input=["test"])
-#         # Add any assertions here to check the response
-#         print(f"response: {str(response)[:50]}")
-#     except Exception as e:
-#         print(e)
-
-
-# test_chat_openai()
-# # test_embedding_openai()
-
-
-# def test_llmonitor_logging_function_calling():
-#     function1 = [
-#         {
-#             "name": "get_current_weather",
-#             "description": "Get the current weather in a given location",
-#             "parameters": {
-#                 "type": "object",
-#                 "properties": {
-#                     "location": {
-#                         "type": "string",
-#                         "description": "The city and state, e.g. San Francisco, CA",
-#                     },
-#                     "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
-#                 },
-#                 "required": ["location"],
-#             },
-#         }
-#     ]
-#     try:
-#         response = completion(model="gpt-3.5-turbo",
-#                               messages=[{
-#                                   "role": "user",
-#                                   "content": "what's the weather in boston"
-#                               }],
-#                               temperature=0.1,
-#                               functions=function1,
-#             )
-#         print(response)
-#     except Exception as e:
-#         print(e)
-
-# # test_llmonitor_logging_function_calling()

litellm/tests/test_lunary.py

@@ -0,0 +1,70 @@
+import sys
+import os
+import io
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+from litellm import completion
+import litellm
+
+litellm.success_callback = ["lunary"]
+litellm.set_verbose = True
+import time
+
+
+def test_lunary_logging():
+    try:
+        response = completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "what llm are u"}],
+            max_tokens=10,
+            temperature=0.2,
+        )
+        print(response)
+    except Exception as e:
+        print(e)
+
+
+test_lunary_logging()
+
+
+def test_lunary_logging_with_metadata():
+    try:
+        response = completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "what llm are u"}],
+            max_tokens=10,
+            temperature=0.2,
+            metadata={
+                "run_name": "litellmRUN",
+                "project_name": "litellm-completion",
+            },
+        )
+        print(response)
+    except Exception as e:
+        print(e)
+
+
+# test_lunary_logging_with_metadata()
+
+
+def test_lunary_logging_with_streaming_and_metadata():
+    try:
+        response = completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "what llm are u"}],
+            max_tokens=10,
+            temperature=0.2,
+            metadata={
+                "run_name": "litellmRUN",
+                "project_name": "litellm-completion",
+            },
+            stream=True,
+        )
+        for chunk in response:
+            continue
+    except Exception as e:
+        print(e)
+
+
+# test_lunary_logging_with_streaming_and_metadata()

litellm/utils.py

@@ -60,7 +60,7 @@ from .integrations.helicone import HeliconeLogger
 from .integrations.aispend import AISpendLogger
 from .integrations.berrispend import BerriSpendLogger
 from .integrations.supabase import Supabase
-from .integrations.llmonitor import LLMonitorLogger
+from .integrations.lunary import LunaryLogger
 from .integrations.prompt_layer import PromptLayerLogger
 from .integrations.langsmith import LangsmithLogger
 from .integrations.weights_biases import WeightsBiasesLogger
@@ -126,7 +126,7 @@ dynamoLogger = None
 s3Logger = None
 genericAPILogger = None
 clickHouseLogger = None
-llmonitorLogger = None
+lunaryLogger = None
 aispendLogger = None
 berrispendLogger = None
 supabaseClient = None
@@ -788,7 +788,7 @@ class CallTypes(Enum):
 
 # Logging function -> log the exact model details + what's being sent | Non-BlockingP
 class Logging:
-    global supabaseClient, liteDebuggerClient, promptLayerLogger, weightsBiasesLogger, langsmithLogger, capture_exception, add_breadcrumb, llmonitorLogger
+    global supabaseClient, liteDebuggerClient, promptLayerLogger, weightsBiasesLogger, langsmithLogger, capture_exception, add_breadcrumb, lunaryLogger
 
     def __init__(
         self,
@@ -1327,27 +1327,28 @@ class Logging:
                             end_time=end_time,
                             print_verbose=print_verbose,
                         )
-                    if callback == "llmonitor":
+                    if callback == "lunary":
-                        print_verbose("reaches llmonitor for logging!")
+                        print_verbose("reaches lunary for logging!")
                         model = self.model
 
                         input = self.model_call_details.get(
                             "messages", self.model_call_details.get("input", None)
                         )
 
-                        # if contains input, it's 'embedding', otherwise 'llm'
                         type = (
                             "embed"
                             if self.call_type == CallTypes.embedding.value
                             else "llm"
                         )
 
-                        llmonitorLogger.log_event(
+                        lunaryLogger.log_event(
                             type=type,
+                            kwargs=self.model_call_details,
                             event="end",
                             model=model,
                             input=input,
                             user_id=self.model_call_details.get("user", "default"),
+                            extra=self.model_call_details.get("optional_params", {}),
                             response_obj=result,
                             start_time=start_time,
                             end_time=end_time,
@@ -1842,8 +1843,8 @@ class Logging:
                             call_type=self.call_type,
                             stream=self.stream,
                         )
-                    elif callback == "llmonitor":
+                    elif callback == "lunary":
-                        print_verbose("reaches llmonitor for logging error!")
+                        print_verbose("reaches lunary for logging error!")
 
                         model = self.model
 
@@ -1855,7 +1856,7 @@ class Logging:
                             else "llm"
                         )
 
-                        llmonitorLogger.log_event(
+                        lunaryLogger.log_event(
                             type=_type,
                             event="error",
                             user_id=self.model_call_details.get("user", "default"),
@@ -5593,7 +5594,7 @@ def validate_environment(model: Optional[str] = None) -> dict:
 
 
 def set_callbacks(callback_list, function_id=None):
-    global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, langsmithLogger, dynamoLogger, s3Logger
+    global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, langsmithLogger, dynamoLogger, s3Logger
     try:
         for callback in callback_list:
             print_verbose(f"callback: {callback}")
@@ -5653,8 +5654,8 @@ def set_callbacks(callback_list, function_id=None):
                 print_verbose("Initialized Athina Logger")
             elif callback == "helicone":
                 heliconeLogger = HeliconeLogger()
-            elif callback == "llmonitor":
+            elif callback == "lunary":
-                llmonitorLogger = LLMonitorLogger()
+                lunaryLogger = LunaryLogger()
             elif callback == "promptlayer":
                 promptLayerLogger = PromptLayerLogger()
             elif callback == "langfuse":
@@ -5692,7 +5693,7 @@ def set_callbacks(callback_list, function_id=None):
 
 # NOTE: DEPRECATING this in favor of using failure_handler() in Logging:
 def handle_failure(exception, traceback_exception, start_time, end_time, args, kwargs):
-    global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger
+    global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, lunaryLogger
     try:
         # print_verbose(f"handle_failure args: {args}")
         # print_verbose(f"handle_failure kwargs: {kwargs}")