LLM Configuration

Configure LLM provider API keys, model selection, Azure OpenAI, and custom endpoints for Reflexio.

LLM Configuration

Reflexio uses LiteLLM for multi-provider LLM support. You must configure an API key for at least one provider.

Enterprise

Reflexio Enterprise users can configure LLM provider API keys and model selection through the Settings page in the web portal under Advanced Settings.

API Keys

Method 1: Environment Variables (Recommended)

Set provider-specific environment variables in your .env file. LiteLLM picks them up automatically.

# .env — set one or more provider keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
DEEPSEEK_API_KEY=...

OpenAI is a common starting point for text generation. Embeddings default to Reflexio's local model unless you override embedding_model_name.

Method 2: Programmatic Configuration

Set API keys via the Config object. Programmatic keys take precedence over environment variables.

from reflexio.models.config_schema import APIKeyConfig, OpenAIConfig

config = client.get_config()
config.api_key_config = APIKeyConfig(
    openai=OpenAIConfig(api_key="sk-your-key-here")
)
client.set_config(config)

curl -X GET "${REFLEXIO_URL:-https://www.reflexio.ai}/api/get_config" \
  -H "User-Agent: my-agent-reflexio" \
  -H "Authorization: Bearer $REFLEXIO_API_KEY"

curl -X POST "${REFLEXIO_URL:-https://www.reflexio.ai}/api/set_config" \
  -H "User-Agent: my-agent-reflexio" \
  -H "Authorization: Bearer $REFLEXIO_API_KEY" \
  -H "Content-Type: application/json" \
  --data @- <<'JSON'
{
  "...": "updated full config object"
}
JSON

Supported Providers:

Provider	Config Class	Environment Variable
OpenAI	`OpenAIConfig`	`OPENAI_API_KEY`
Anthropic	`AnthropicConfig`	`ANTHROPIC_API_KEY`
Google Gemini	`GeminiConfig`	`GEMINI_API_KEY`
DeepSeek	`DeepSeekConfig`	`DEEPSEEK_API_KEY`
OpenRouter	`OpenRouterConfig`	`OPENROUTER_API_KEY`
MiniMax	`MiniMaxConfig`	`MINIMAX_API_KEY`
DashScope (Qwen)	`DashScopeConfig`	`DASHSCOPE_API_KEY`
Zhipu AI	`ZAIConfig`	`ZAI_API_KEY`
Moonshot	`MoonshotConfig`	`MOONSHOT_API_KEY`
xAI	`XAIConfig`	`XAI_API_KEY`

Custom OpenAI-Compatible Endpoints

Use CustomEndpointConfig to connect to any OpenAI-compatible API. Custom endpoints take priority over other providers for text generation (but not embeddings).

from reflexio.models.config_schema import APIKeyConfig, CustomEndpointConfig

config.api_key_config = APIKeyConfig(
    custom_endpoint=CustomEndpointConfig(
        model="my-model",
        api_key="your-key",
        api_base="http://localhost:8000/v1"
    )
)
client.set_config(config)

curl -X POST "${REFLEXIO_URL:-https://www.reflexio.ai}/api/set_config" \
  -H "User-Agent: my-agent-reflexio" \
  -H "Authorization: Bearer $REFLEXIO_API_KEY" \
  -H "Content-Type: application/json" \
  --data @- <<'JSON'
{
  "...": "updated full config object"
}
JSON

Azure OpenAI

Use AzureOpenAIConfig nested inside OpenAIConfig to connect to Azure OpenAI:

from reflexio.models.config_schema import (
    APIKeyConfig, OpenAIConfig, AzureOpenAIConfig
)

config.api_key_config = APIKeyConfig(
    openai=OpenAIConfig(
        azure_config=AzureOpenAIConfig(
            api_key="your-azure-key",
            endpoint="https://your-resource.openai.azure.com/",
            api_version="2024-02-15-preview",
            deployment_name="gpt-4o"
        )
    )
)
client.set_config(config)

curl -X POST "${REFLEXIO_URL:-https://www.reflexio.ai}/api/set_config" \
  -H "User-Agent: my-agent-reflexio" \
  -H "Authorization: Bearer $REFLEXIO_API_KEY" \
  -H "Content-Type: application/json" \
  --data @- <<'JSON'
{
  "...": "updated full config object"
}
JSON

Model Selection

Reflexio uses different models for different tasks. Sensible defaults are provided, but you can override them via LLMConfig. Only set fields you want to override — None fields keep the defaults.

Field	Default	Purpose
`should_run_model_name`	`minimax/MiniMax-M2.5`	Fast check to decide if extraction should run on a given interaction
`generation_model_name`	`minimax/MiniMax-M2.5`	Profile extraction, playbook generation, and evaluation
`embedding_model_name`	OSS: `local/minilm-l6-v2`; Enterprise: `local/nomic-embed-text-v1.5`	Vector embeddings for semantic search
`pre_retrieval_model_name`	`minimax/MiniMax-M2.5`	Model for pre-retrieval query reformulation

from reflexio.models.config_schema import LLMConfig

config = client.get_config()
config.llm_config = LLMConfig(
    generation_model_name="openai/gpt-4o",
    embedding_model_name="text-embedding-3-small",
)
client.set_config(config)

curl -X GET "${REFLEXIO_URL:-https://www.reflexio.ai}/api/get_config" \
  -H "User-Agent: my-agent-reflexio" \
  -H "Authorization: Bearer $REFLEXIO_API_KEY"

curl -X POST "${REFLEXIO_URL:-https://www.reflexio.ai}/api/set_config" \
  -H "User-Agent: my-agent-reflexio" \
  -H "Authorization: Bearer $REFLEXIO_API_KEY" \
  -H "Content-Type: application/json" \
  --data @- <<'JSON'
{
  "...": "updated full config object"
}
JSON

Model names use LiteLLM's "provider/model-name" format (e.g., "openai/gpt-4o", "anthropic/claude-3-5-sonnet", "deepseek/deepseek-chat").

Enterprise

Enterprise settings include known embedding choices plus a custom model field. After changing embedding_model_name, save the configuration first, then use Regenerate embeddings in Advanced Settings. Regeneration is manually triggered and runs server-side for the current organization using the saved model. Cloud embedding models such as text-embedding-3-small and gemini/gemini-embedding-001 are supported when the corresponding provider credentials are configured and the model produces 512-dimensional vectors — regeneration validates the vector width and fails the job if a model returns a different dimension.

Changing embedding models puts new query vectors and existing stored vectors in different vector spaces until regeneration finishes, so semantic search quality may be temporarily degraded during the job.