* feat(terraform): add litellm_jwt_key_mapping resource Adds a Terraform resource for the proxy's JWT to virtual key mappings, so a JWT client identified by a claim such as client_id, azp or sub maps to a virtual key and inherits its models, budgets, rate limits and spend tracking. Covers the four mapping endpoints: /jwt/key/mapping/new, /info, /update and /delete. is_active is applied through a follow-up update because the create endpoint always starts a mapping active, a dropped description is sent as an empty string because the update endpoint ignores absent fields, changing the mapped key rotates it in place, and changing the claim name or value forces replacement since the update endpoint cannot change them. * fix(terraform): revert key on failed jwt_key_mapping update Classic SDKv2 persists a failed Update's diff-applied values to state regardless of the error, so a rejected key rotation left the new key in state while the proxy kept the old one and the next plan falsely converged. Revert key via GetChange and resync description/is_active/computed fields from a post-failure Read, since Read alone can't recover key (the proxy never returns it). Also drop the case-insensitive "mapping not found" body match: the proxy raises 404 for all three not-found paths (info, update, delete), so checking the status code alone is sufficient. Clarify the docs: referencing a litellm_key resource's write-only key is not a null-then-400 situation, it's a static "Missing required argument" error at plan time, in every apply ordering. * fix(terraform): stop leaving an active mapping behind on failed cleanup Two issues flagged by review: - Create has no way to ask the proxy for an inactive mapping, so an is_active=false mapping is briefly active while the follow-up deactivation runs. If that deactivation call itself fails, the mapping used to stay active and untracked. It's now deleted instead, closing the exposure rather than leaving it open indefinitely. - On a failed update, only `key` was reverted before the recovery read. If that read also failed, description/is_active kept the rejected values, so a later plan could report false convergence. Now all three are reverted before the read runs. Both come with regression tests, mutation-verified against the pre-fix code. * fix(deps): bump restrictedpython to 8.5 for GHSA-ffg3-p8fm-mjx2 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore: retrigger ci Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(tests): stub anthropic judge credentials in funnel seeding test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * revert(deps): keep uv.lock unchanged to keep the PR terraform-only Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Fabrice Pont <fabrice.pont@doctolib.com> Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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LiteLLM Provider
The LiteLLM provider allows Terraform to manage LiteLLM resources. LiteLLM is a proxy service that standardizes the input/output across different LLM APIs, providing a unified interface for various language model providers.
Example Usage
terraform {
required_providers {
litellm = {
source = "registry.terraform.io/BerriAI/litellm"
}
}
}
provider "litellm" {
api_base = "https://your-litellm-proxy.com"
api_key = var.litellm_api_key
}
# Basic model configuration
resource "litellm_model" "gpt4" {
model_name = "gpt-4-proxy"
custom_llm_provider = "openai"
model_api_key = var.openai_api_key
base_model = "gpt-4"
tier = "paid"
mode = "chat"
input_cost_per_million_tokens = 30.0
output_cost_per_million_tokens = 60.0
}
# Team configuration
resource "litellm_team" "dev_team" {
team_alias = "development-team"
models = [litellm_model.gpt4.model_name]
max_budget = 100.0
}
Available Resources
The LiteLLM provider supports the following resources:
litellm_model- Manage LiteLLM model configurationslitellm_team- Manage teams and their permissionslitellm_team_member- Manage team member configurationslitellm_team_member_add- Add members to teamslitellm_key- Manage API keyslitellm_mcp_server- Manage MCP (Model Context Protocol) serverslitellm_credential- Manage credentials for various providerslitellm_vector_store- Manage vector storeslitellm_jwt_key_mapping- Map JWT claim values to virtual keys
Available Data Sources
The LiteLLM provider supports the following data sources:
litellm_credential- Retrieve credential informationlitellm_vector_store- Retrieve vector store information
Authentication
The LiteLLM provider requires an API key and base URL for authentication. These can be provided in the provider configuration block or via environment variables.
Environment Variables
LITELLM_API_BASE- The base URL of your LiteLLM instanceLITELLM_API_KEY- Your LiteLLM API key
Example with Environment Variables
export LITELLM_API_BASE="https://your-litellm-proxy.com"
export LITELLM_API_KEY="your-api-key"
terraform {
required_providers {
litellm = {
source = "registry.terraform.io/BerriAI/litellm"
}
}
}
# Provider will automatically use environment variables
provider "litellm" {}
Provider Arguments
The following arguments are supported in the provider block:
api_base- (Required) The base URL of your LiteLLM instance. This can also be provided via theLITELLM_API_BASEenvironment variable.api_key- (Required) The API key used to authenticate with LiteLLM. This can also be provided via theLITELLM_API_KEYenvironment variable.
Getting Started
- Install the provider by adding it to your Terraform configuration
- Configure your LiteLLM instance URL and API key
- Start creating resources like models, teams, and credentials
- Use data sources to reference existing configurations
For detailed examples and configuration options, see the individual resource and data source documentation pages.
Examples
This repository includes an examples/ directory with curated, ready-to-run HCL examples that demonstrate common and advanced usages of the provider. Examples are grouped by resource and illustrate provider-specific configuration, handling of sensitive values, and advanced options such as additional_litellm_params.
See:
examples/model_additional_params.tf— demonstrates how to useadditional_litellm_params(booleans, integers, floats, and strings).- Other example files will be added to
examples/for credentials, vector stores, and MCP servers.
You can reference these examples directly or copy snippets into your Terraform configurations for quick starts.
For detailed examples and configuration options, see the individual resource and data source documentation pages.