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c2e06890ad
* fix: redact config and MCP secrets in read-only admin views GET /config/field/info and the MCP server list/detail endpoints returned secret-bearing fields to any caller with an admin view, including read-only admins. They now return those fields in full only to a full PROXY_ADMIN; every other caller gets the reduced, non-admin view, while non-sensitive fields remain readable. Regression tests cover the role-based visibility on both endpoints, including that a full admin still sees everything needed to populate the edit form. * fix: redact nested secrets in config field info for non-admins /config/field/info returned structured general_settings fields verbatim to any admin-view caller, so a view-only admin reading database_args received the nested aws_web_identity_token (a DynamoDB role-assumption credential) in plaintext. Recurse into dict/list field values and redact secret leaves for non-PROXY_ADMIN callers, leaving non-secret siblings and full-admin reads unchanged * fix: redact secret config values in /config/list for non-admins /config/list shared the same _user_has_admin_view gate as /config/field/info but returned each field value unredacted, so a view-only admin reading the list received pass_through_endpoints upstream Authorization headers verbatim. Route every general_settings value through a shared role-aware redactor (extracted from /config/field/info) covering the top-level and nested field paths, so non-PROXY_ADMIN callers get secret-bearing fields redacted while full-admin reads stay unchanged * chore(ci): allowlist _redact_secret_values_in_obj in recursive_detector The config secret redactor recurses over JsonValue, which is acyclic, and its depth is bounded by the operator-authored general_settings schema. Add it to the recursive_detector ignore list alongside the other bounded nested-redaction helpers (mask_dict, _redact_sensitive_litellm_params) * proxy: cap recursive secret redaction depth at 10 Match the cap on _redact_sensitive_litellm_params (the closest analog in the proxy, also recursive, key-name driven, returns a sentinel). The previous justification — bounded by operator-authored schema depth, JsonValue acyclic — is true today but is a property of the threat model, not an enforced invariant of the function. If a code path is ever added that pipes external input into general_settings (config import, migration tooling, JWT-driven settings, …) the assumption silently breaks. A local cap makes the invariant local. The cap branch fails closed: at _REDACT_SECRET_MAX_DEPTH the whole subtree is replaced with 'REDACTED' rather than returned verbatim. A future refactor that flips this to fail-open would let a deeply nested credential leak; the new regression test test_redact_secret_values_in_obj_fails_closed_at_max_depth guards against that. Updates the recursive_detector ignore-list rationale to point at the numeric cap rather than the structural argument. * test: actually exercise the depth cap in fails-closed test The previous fixture stored the leaf under the secret-named key 'aws_web_identity_token', which the recursor's key-name short-circuit redacts regardless of the cap — so the test passed both with and without the cap in place. Empirically confirmed: under an uncapped mutant the old fixture still hides the secret (key-name catches it), the new fixture leaks it (only the cap can stop it). Swap the leaf key to a non-secret name so the cap is the only redaction path exercised, making the test fail on mutation as advertised. |
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| .. | ||
| _experimental | ||
| a2a | ||
| agent_endpoints | ||
| analytics_endpoints | ||
| anthropic_endpoints | ||
| auth | ||
| batches_endpoints | ||
| client | ||
| common_utils | ||
| config_management_endpoints | ||
| container_endpoints | ||
| credential_endpoints | ||
| custom_hooks | ||
| db | ||
| discovery_endpoints | ||
| example_config_yaml | ||
| fine_tuning_endpoints | ||
| google_endpoints | ||
| guardrails | ||
| health_check_utils | ||
| health_endpoints | ||
| hooks | ||
| image_endpoints | ||
| management_endpoints | ||
| management_helpers | ||
| memory | ||
| middleware | ||
| ocr_endpoints | ||
| openai_evals_endpoints | ||
| openai_files_endpoints | ||
| pass_through_endpoints | ||
| policy_engine | ||
| prompts | ||
| public_endpoints | ||
| rag_endpoints | ||
| realtime_endpoints | ||
| rerank_endpoints | ||
| response_api_endpoints | ||
| response_polling | ||
| search_endpoints | ||
| shutdown | ||
| spend_tracking | ||
| swagger | ||
| test_prompts | ||
| types_utils | ||
| ui_crud_endpoints | ||
| vector_store_endpoints | ||
| vector_store_files_endpoints | ||
| vertex_ai_endpoints | ||
| video_endpoints | ||
| workflows | ||
| .gitignore | ||
| __init__.py | ||
| _lazy_features.py | ||
| _lazy_openapi_snapshot.json | ||
| _lazy_openapi_snapshot.py | ||
| _logging.py | ||
| _new_new_secret_config.yaml | ||
| _new_secret_config.yaml | ||
| _super_secret_config.yaml | ||
| _types.py | ||
| cached_logo.jpg | ||
| caching_routes.py | ||
| common_request_processing.py | ||
| compliance_checks.py | ||
| custom_auth_auto.py | ||
| custom_prompt_management.py | ||
| custom_sso.py | ||
| custom_validate.py | ||
| dd_span_tagger.py | ||
| dev_config.yaml | ||
| enterprise | ||
| health_check.py | ||
| lambda.py | ||
| litellm_pre_call_utils.py | ||
| llamaguard_prompt.txt | ||
| logo.jpg | ||
| mcp_registry.json | ||
| mcp_tools.py | ||
| model_config.yaml | ||
| openapi.json | ||
| openapi_registry.json | ||
| plugin_routes.py | ||
| post_call_rules.py | ||
| prisma_migration.py | ||
| prometheus_cleanup.py | ||
| proxy_cli.py | ||
| proxy_config.yaml | ||
| proxy_server.py | ||
| README.md | ||
| route_llm_request.py | ||
| schema.prisma | ||
| start.sh | ||
| utils.py | ||
| wildcard_config.yaml | ||
litellm-proxy
A local, fast, and lightweight OpenAI-compatible server to call 100+ LLM APIs.
usage
$ uv tool install litellm
$ litellm --model ollama/codellama
#INFO: Ollama running on http://0.0.0.0:8000
replace openai base
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
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
See how to call Huggingface,Bedrock,TogetherAI,Anthropic, etc.
Folder Structure
Routes
proxy_server.py- all openai-compatible routes -/v1/chat/completion,/v1/embedding+ model info routes -/v1/models,/v1/model/info,/v1/model_group_inforoutes.health_endpoints/-/health,/health/liveliness,/health/readinessmanagement_endpoints/key_management_endpoints.py- all/key/*routesmanagement_endpoints/team_endpoints.py- all/team/*routesmanagement_endpoints/internal_user_endpoints.py- all/user/*routesmanagement_endpoints/ui_sso.py- all/sso/*routes