Initialization, License, setup, interface for each endpoint and sample ping EP. Autoloader of builtin endpoints and for the future implementations.
0.0.2
Add base models for api call (module llm_proxy_rest.data_models with error.py handling. Decorators to check required params and to measure the response time.
0.0.3
Proper AutoLoading for each found endpoint. Implementation of ApiTypesDispatcher, ApiModelConfig, ModelHandler. Ollama endpoints: /, tags. Added endpoint to full proxy with params. Streaming in case when external api provides stream.
0.0.4
All llama-service endpoints are refactored to llm-proxy-api. Refactoring base ep_run method. Proper handling system message, prompt name, model etc.
0.1.0
Repository name changed from llm-proxy-api to llm-router. Added class HttpRequestExecutor to handle http requests from EndpointWithHttpRequestI. Handled routing between any models: openai -> ollama and ollama -> openai
0.1.1
Prometheus metrics logging. Workers/Threads/Workers class is able to set by environments. Streaming fixes. Multi-providers for single model with default-balanced strategy.
0.2.0
Add balancing strategies: balanced, weighted, dynamic_weighted and first_available which works for streaming and non streaming requests. Included Prometheus metrics logging via /metrics endpoint. First stage of llm_router_lib library, to simply usage of llm-router-api.
0.2.1
Fix stream: OpenAI->Ollama, Ollama->OpenAI. Add Redis caching of availability of model providers (when using first_available strategy). Add llm_router_web module with simple flask-based frontend to manage llm-router config files.
0.2.2
Update dockerfile and requirements. Fix routing with vLLM.
0.2.3
New web configurator: Handling projects, configs for each user separately. First Available strategy is more powerful, a lot of improvements to efficiency.
0.2.4
Anonymizer module, integration anonymization with any endpoint (using dynamic payload analysis and full payload anonymisation), dedicated /api/anonymize_text endpoint as memory only anonymization. Whole router may be run in FORCE_ANONYMISATION mode.
0.3.0
Anonymization available with three strategies: fast_masker, genai, prov_masker.
0.3.1
Refactoring lb.strategies to be more flexible modular. Introduced MaskerPipeline and GuardrailPipeline both configured via env. Removed genai-based masking endpoint.
0.4.0
The main repository is divided into dedicated ones: plugins, services, web — separate repositories. Clean up the whole repository. Examples of integration with llamaindex, langchain, openai, litellm and haystack.
0.4.1
Audit log is stored using GPG. Add bash script (scripts/gen_and_export_gpg.sh to prepare GPG keys and simple scripts/decrypt_auditor_logs.sh to decrypt encrypted audit logs. Moved core functionality from base to module core module. Quickstart.
0.4.2
Fix first_available_optim Strategy. Add KeepAliveMonitor to periodically pings model endpoints to keep them warm.
0.4.3
Add custom Prometheus metrices for logging masker/guardrail inidents. Fix OpenAI compatible v1 /models endpoint. Introduce monitors: services and keep alive models. Fixed guardrail retunr in case when streaming.
0.4.4
Validate unique provider identifiers. Store all hosts with keep‑alive configured in a Redis. UtilsPlugin pipeline with LangChain based simple RAG plugin (extending context to GenAI with locally built databse). Add handling of v1/response endpoint
0.4.5
Fixed sreaming to LMStudio nartive. Refactor streaming module.
0.4.6
Added support for embeddings endpoints across all providers. Extended ApiModel and ApiTypesI with is_embedding flag. Added test_embeddings.py utility for verifying embedding models through the API.