* feat(terraform): vendor terraform-provider-litellm as source of truth with endpoint drift CI * fix(terraform): address review feedback on vendored provider Replace deprecated io/ioutil with io. Remove the unused org/team CRUD client methods so the endpoint audit only tracks live call sites (54 -> 46). Redact request/response logs by parsing the JSON and recursively masking sensitive fields, which fixes the nested-object leak in the old credential_values regex, with a regex fallback for non-JSON payloads; covered by new unit tests. Docs: stop showing api_key inside vector store litellm_params and document that Sensitive attributes still persist in plaintext state, recommending litellm_credential_name and an encrypted state backend. * fix(terraform): stop persisting server-returned litellm_params into vector store state The vector store Read wrote litellm_params straight back from the API response into state. The proxy redacts secrets in those responses, so the readback overwrote user config with redaction sentinels and caused perpetual diffs, and against a server that returns raw values it would persist secrets into a non-Sensitive attribute. Read now preserves the config value like the credential and model resources do, litellm_params is marked Sensitive, and a regression test pins that a server-returned api_key never lands in state * fix(terraform): send role on team member update and stop persisting server env into MCP state The team member update payload omitted role, and the proxy leaves role unchanged when the field is absent, so a role downgrade reported as applied by Terraform never took effect on the proxy. The update now always sends the configured role (the attribute is Required). The MCP server resource wrote env straight back from API responses into a non-Sensitive attribute, pulling admin-visible secrets into state and, for sanitized responses, blanking user config. Read now preserves the config value, env is marked Sensitive, and the docs warn against passing secrets via args. Regression tests cover both fixes and fail against the previous behavior.
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LiteLLM Terraform Provider
This Terraform provider allows you to manage LiteLLM resources through Infrastructure as Code. It provides support for managing models, teams, team members, and API keys via the LiteLLM REST API.
Source of truth
This directory (terraform/provider/ in BerriAI/litellm) is the source of truth for the provider. BerriAI/terraform-provider-litellm is a thin release mirror that the public Terraform Registry ingests from; do not open PRs there. Changes land here, where CI builds the provider, runs its tests, and statically audits every endpoint the provider calls against the proxy's generated OpenAPI schema (tools/endpointaudit/), so the provider cannot drift from the LiteLLM API silently. Releases are published by mirroring this directory into the split repo and tagging it, which triggers the goreleaser workflow there (see RELEASING.md)
Features
- Manage LiteLLM model configurations
- Associate models with specific teams
- Create and manage teams
- Configure team members and their permissions
- Set usage limits and budgets
- Control access to specific models
- Specify model modes (e.g., completion, embedding, image generation)
- Manage API keys with fine-grained controls
- Support for reasoning effort configuration in the model resource
Requirements
Using the Provider
To use the LiteLLM provider in your Terraform configuration, you need to declare it in the terraform block:
terraform {
required_providers {
litellm = {
source = "BerriAI/litellm"
version = "~> 0.1.1" #HERE UPDATE VERSION ACCORDINGLY
}
}
}
provider "litellm" {
api_base = var.litellm_api_base
api_key = var.litellm_api_key
}
Then, you can use the provider to manage LiteLLM resources. Here's an example of creating a model configuration:
resource "litellm_model" "gpt4" {
model_name = "gpt-4-proxy"
custom_llm_provider = "openai"
model_api_key = var.openai_api_key
model_api_base = "https://api.openai.com/v1"
base_model = "gpt-4"
tier = "paid"
mode = "chat"
reasoning_effort = "medium" # Optional: "low", "medium", or "high"
input_cost_per_million_tokens = 30.0
output_cost_per_million_tokens = 60.0
}
For full details on the litellm_model resource, see the model resource documentation.
Here's an example of creating an API key with various options:
resource "litellm_key" "example_key" {
models = ["gpt-4", "claude-3.5-sonnet"]
max_budget = 100.0
user_id = "user123"
team_id = "team456"
max_parallel_requests = 5
tpm_limit = 1000
rpm_limit = 60
budget_duration = "monthly"
key_alias = "prod-key-1"
duration = "30d"
metadata = {
environment = "production"
}
allowed_cache_controls = ["no-cache", "max-age=3600"]
soft_budget = 80.0
aliases = {
"gpt-4" = "gpt4"
}
config = {
default_model = "gpt-4"
}
permissions = {
can_create_keys = "true"
}
model_max_budget = {
"gpt-4" = 50.0
}
model_rpm_limit = {
"claude-3.5-sonnet" = 30
}
model_tpm_limit = {
"gpt-4" = 500
}
guardrails = ["content_filter", "token_limit"]
blocked = false
tags = ["production", "api"]
}
The litellm_key resource supports the following options:
models: List of allowed models for this keymax_budget: Maximum budget for the keyuser_idandteam_id: Associate the key with a user and teammax_parallel_requests: Limit concurrent requeststpm_limitandrpm_limit: Set tokens and requests per minute limitsbudget_duration: Specify budget duration (e.g., "monthly", "weekly")key_alias: Set a friendly name for the keyduration: Set the key's validity periodmetadata: Add custom metadata to the keyallowed_cache_controls: Specify allowed cache control directivessoft_budget: Set a soft budget limitaliases: Define model aliasesconfig: Set configuration optionspermissions: Specify key permissionsmodel_max_budget,model_rpm_limit,model_tpm_limit: Set per-model limitsguardrails: Apply specific guardrails to the keyblocked: Flag to block/unblock the keytags: Add tags for organization and filtering
For full details on the litellm_key resource, see the key resource documentation.
Available Resources
litellm_model: Manage model configurations. Documentationlitellm_team: Manage teams. Documentationlitellm_team_member: Manage team members. Documentationlitellm_team_member_add: Add multiple members to teams. Documentationlitellm_key: Manage API keys. Documentationlitellm_mcp_server: Manage MCP (Model Context Protocol) servers. Documentationlitellm_credential: Manage credentials for secure authentication. Documentationlitellm_vector_store: Manage vector stores for embeddings and RAG. Documentation
Available Data Sources
litellm_credential: Retrieve information about existing credentials. Documentationlitellm_vector_store: Retrieve information about existing vector stores. Documentation
Development
Project Structure
The project is organized as follows:
terraform-provider-litellm/
├── litellm/
│ ├── provider.go
│ ├── resource_model.go
│ ├── resource_model_crud.go
│ ├── resource_team.go
│ ├── resource_team_member.go
│ ├── resource_key.go
│ ├── resource_key_utils.go
│ ├── types.go
│ └── utils.go
├── main.go
├── go.mod
├── go.sum
├── Makefile
└── ...
Building the Provider
- Clone the repository:
git clone https://github.com/your-username/terraform-provider-litellm.git
- Enter the repository directory:
cd terraform-provider-litellm
- Build and install the provider:
make install
Development Commands
The Makefile provides several useful commands for development:
make build: Builds the providermake install: Builds and installs the providermake test: Runs the test suitemake fmt: Formats the codemake vet: Runs go vetmake lint: Runs golangci-lintmake clean: Removes build artifacts and installed provider
Testing
To run the tests:
make test
Contributing
Contributions are welcome! Please read our contributing guidelines first.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Notes
- Always use environment variables or secure secret management solutions to handle sensitive information like API keys and AWS credentials.
- Refer to the comprehensive documentation in the
docs/directory for detailed usage examples and configuration options. - Make sure to keep your provider version updated for the latest features and bug fixes.
- The provider now supports AWS cross-account access with
aws_session_nameandaws_role_nameparameters in the model resource. - All example configurations have been consolidated into the documentation for better organization and maintenance.