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· 2 min read

guardrails, logging, virtual key management, new models

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no call needed

New Features​

✨ Log Guardrail Traces​

Track guardrail failure rate and if a guardrail is going rogue and failing requests. Start here

Traced Guardrail Success​

Traced Guardrail Failure​

/guardrails/list​

/guardrails/list allows clients to view available guardrails + supported guardrail params

curl -X GET 'http://0.0.0.0:4000/guardrails/list'

Expected response

{
"guardrails": [
{
"guardrail_name": "aporia-post-guard",
"guardrail_info": {
"params": [
{
"name": "toxicity_score",
"type": "float",
"description": "Score between 0-1 indicating content toxicity level"
},
{
"name": "pii_detection",
"type": "boolean"
}
]
}
}
]
}

✨ Guardrails with Mock LLM​

Send mock_response to test guardrails without making an LLM call. More info on mock_response here

curl -i http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{"role": "user", "content": "hi my email is ishaan@berri.ai"}
],
"mock_response": "This is a mock response",
"guardrails": ["aporia-pre-guard", "aporia-post-guard"]
}'

Assign Keys to Users​

You can now assign keys to users via Proxy UI

New Models​

  • openrouter/openai/o1
  • vertex_ai/mistral-large@2411

Fixes​

· One min read

batches, guardrails, team management, custom auth


info

Get a free 7-day LiteLLM Enterprise trial here. Start here

No call needed

✨ Cost Tracking, Logging for Batches API (/batches)​

Track cost, usage for Batch Creation Jobs. Start here

✨ /guardrails/list endpoint​

Show available guardrails to users. Start here

✨ Allow teams to add models​

This enables team admins to call their own finetuned models via litellm proxy. Start here

✨ Common checks for custom auth​

Calling the internal common_checks function in custom auth is now enforced as an enterprise feature. This allows admins to use litellm's default budget/auth checks within their custom auth implementation. Start here

✨ Assigning team admins​

Team admins is graduating from beta and moving to our enterprise tier. This allows proxy admins to allow others to manage keys/models for their own teams (useful for projects in production). Start here

· One min read

A new LiteLLM Stable release just went out. Here are 5 updates since v1.52.2-stable.

langfuse, fallbacks, new models, azure_storage

Langfuse Prompt Management​

This makes it easy to run experiments or change the specific models gpt-4o to gpt-4o-mini on Langfuse, instead of making changes in your applications. Start here

Control fallback prompts client-side​

Claude prompts are different than OpenAI

Pass in prompts specific to model when doing fallbacks. Start here

New Providers / Models​

✨ Azure Data Lake Storage Support​

Send LLM usage (spend, tokens) data to Azure Data Lake. This makes it easy to consume usage data on other services (eg. Databricks) Start here

Docker Run LiteLLM​

docker run \
-e STORE_MODEL_IN_DB=True \
-p 4000:4000 \
ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.55.8-stable

Get Daily Updates​

LiteLLM ships new releases every day. Follow us on LinkedIn to get daily updates.

· One min read

key management, budgets/rate limits, logging, guardrails

info

Get a 7 day free trial for LiteLLM Enterprise here.

no call needed

✨ Budget / Rate Limit Tiers​

Define tiers with rate limits. Assign them to keys.

Use this to control access and budgets across a lot of keys.

Start here

curl -L -X POST 'http://0.0.0.0:4000/budget/new' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"budget_id": "high-usage-tier",
"model_max_budget": {
"gpt-4o": {"rpm_limit": 1000000}
}
}'

OTEL Bug Fix​

LiteLLM was double logging litellm_request span. This is now fixed.

Relevant PR

Logging for Finetuning Endpoints​

Logs for finetuning requests are now available on all logging providers (e.g. Datadog).

What's logged per request:

  • file_id
  • finetuning_job_id
  • any key/team metadata

Start Here:

Dynamic Params for Guardrails​

You can now set custom parameters (like success threshold) for your guardrails in each request.

See guardrails spec for more details