ShinkaEvolve/docs/support_local_models.md

Local and OpenRouter Models

Shinka supports dynamic LLM backend routing in LLMClient and AsyncLLMClient. It also supports dynamic embedding backend routing in EmbeddingClient and AsyncEmbeddingClient. You can use:

• models listed in the provider pricing CSVs (existing behavior)
• dynamic OpenRouter model IDs
• local OpenAI-compatible servers via inline endpoint URIs

Supported Model Name Formats

1) Known models (from pricing.csv)

evo_config:
  llm_models:
    - gpt-5-mini
    - claude-sonnet-4-6

2) Dynamic OpenRouter models

Prefix with openrouter/:

evo_config:
  llm_models:
    - openrouter/qwen/qwen3-coder
    - openrouter/deepseek/deepseek-r1

Set env var:

OPENROUTER_API_KEY=...

3) Local OpenAI-compatible models

Use local/<model>@<http(s)://endpoint>:

evo_config:
  llm_models:
    - local/qwen2.5-coder@http://localhost:11434/v1

Set optional env var:

LOCAL_OPENAI_API_KEY=local

If not set, Shinka uses "local" as a default token.

Local Embeddings

The same inline local format also works for embedding_model.

evo_config:
  embedding_model: local/text-embeddings-inference@http://localhost:8080/v1

Common local embedding backends:

• Hugging Face TEI: local/text-embeddings-inference@http://localhost:8080/v1
• vLLM or another OpenAI-compatible embedding server: local/BAAI/bge-small-en-v1.5@http://localhost:8000/v1
• Ollama OpenAI-compatible endpoint: local/embeddinggemma@http://localhost:11434/v1

Notes

• Dynamic OpenRouter/local model IDs are allowed even if not listed in pricing.csv.
• If a model has no pricing entry and the provider does not return cost metadata, Shinka records cost as 0.0.
• Local OpenAI-compatible backend path currently uses chat-completions style calls.
• Local embedding backends use the OpenAI-compatible /v1/embeddings path.
• Structured output is not supported yet for local/...@... models.

Applies to Which Clients

These formats work across all LLM consumers that use LLMClient / AsyncLLMClient, including:

• mutation LLMs (llm_models)
• meta LLMs (meta_llm_models)
• novelty judge LLMs (novelty_llm_models)
• prompt evolution LLMs (prompt_llm_models)

For embeddings, the same format applies to:

• code similarity embeddings (embedding_model)