Configuration
Ways to provide configuration
There are multiple ways to provide configuration to Erato.
All sources are configuration are merged together into one, so configuration can be specified via e.g. erato.toml, as well as via environment variables.
As of now, there is no specified precedence order when it comes to values provided for the same configuration key that are provided in different sources.
erato.toml files
The erato.toml file is the preferred way to provide configuration for Erato.
The file must be placed in the current working directory of the Erato process.
In the Helm chart, a secret from where the erato.toml file should be mounted can be specified via backend.configFile.
*.auto.erato.toml files
In addition to the main erato.toml file, Erato will also auto-discover all files matching the pattern *.auto.erato.toml in the current working directory.
This is useful if you e.g. want to split out all the secret values (LLM API keys, Database credentials) into a different file that is not checked into source control.
Environment variables
Configuration can also be provided via environment variables.
Though it is not recommended, values for nested configuration can also be provided via environment variables.
In that case, each nesting level is separated by double underscores (__). E.g. CHAT_PROVIDER__BASE_URL is equivalent to chat_provider.base_url.
Configuration reference
🚧Work in progress; Covers ~80% of available configuration options 🚧
frontend
frontend.theme
The name of the theme to use for the frontend.
When provided, the theme must be part of the frontend bundle (usually located in the public directory), and placed in the custom-theme directory, under the name provided here.
E.g. if frontend.theme is set to my-theme, the theme must be placed in public/custom-theme/my-theme.
See Theming for more information about themes and theme directory structure.
If not provided, the default bundled theme will be used.
Default value: None
Type: string | None
Example
[frontend]
theme = "my-theme"frontend.additional_environment
Additional values to inject into the frontend environment as global variables.
These will be made available to the frontend Javascript, and added to the window object.
This is a dictionary where each value can be a string or a map (string key, string value).
This may be useful if you are using a forked version of the frontend, which you need to pass some configuration to.
Default value: None
Type: object<string, any>
Example
[frontend]
additional_environment = { "FOO": "bar" }This will be inejcted into the frontend as:
window.FOO = "bar";chat_provider
Configuration for the chat provider (LLM) that Erato will use for generating responses.
chat_provider.provider_kind
The type of chat provider to use.
Type: string
Supported values: "openai", "azure_openai"
Example: "openai"
chat_provider.model_name
The name of the model to use with the chat provider.
Type: string
Example: "gpt-4o"
chat_provider.base_url
The base URL for the chat provider API. If not provided, will use the default for the provider.
Type: string | None
Example: "https://api.openai.com/v1/", "http://localhost:11434/v1/"
chat_provider.api_key
The API key for the chat provider.
Type: string | None
Example: "sk-..."
chat_provider.system_prompt
A static system prompt to use with the chat provider. This sets the behavior and personality of the AI assistant.
Type: string | None
Note: This option is mutually exclusive with system_prompt_langfuse.
Example:
[chat_provider]
provider_kind = "openai"
model_name = "gpt-4"
system_prompt = "You are a helpful assistant that provides concise and accurate answers."chat_provider.system_prompt_langfuse
Configuration for using a system prompt from Langfuse prompt management instead of a static prompt.
Type: object | None
Note: This option is mutually exclusive with system_prompt. Requires the Langfuse integration to be enabled.
Properties:
prompt_name(string): The name of the prompt in Langfuse to use
Example:
[chat_provider]
provider_kind = "openai"
model_name = "gpt-4"
system_prompt_langfuse = { prompt_name = "assistant-prompt-v1" }
[integrations.langfuse]
enabled = true
base_url = "https://cloud.langfuse.com"
public_key = "pk-lf-..."
secret_key = "sk-lf-..."integrations
Configuration for external service integrations.
integrations.langfuse
Configuration for the Langfuse observability and prompt management integration.
integrations.langfuse.enabled
Whether the Langfuse integration is enabled.
Default value: false
Type: boolean
integrations.langfuse.base_url
The base URL for your Langfuse instance. Use https://cloud.langfuse.com for Langfuse Cloud or your self-hosted URL.
Required when enabled: Yes
Type: string
Example: "https://cloud.langfuse.com" or "https://langfuse.yourcompany.com"
integrations.langfuse.public_key
Your Langfuse project’s public key. You can find this in your Langfuse project settings.
Required when enabled: Yes
Type: string
Example: "pk-lf-1234567890abcdef"
integrations.langfuse.secret_key
Your Langfuse project’s secret key. You can find this in your Langfuse project settings.
Required when enabled: Yes
Type: string
Example: "sk-lf-abcdef1234567890"
integrations.langfuse.tracing_enabled
Whether to enable detailed tracing of LLM interactions. When enabled, all chat completions will be logged to Langfuse.
Default value: false
Type: boolean
See the Langfuse Integration documentation for detailed usage instructions and feature descriptions.
integrations.sentry
Configuration for the Sentry error reporting and performance monitoring integration.
integrations.sentry.sentry_dsn
The Sentry DSN (Data Source Name) for your Sentry project. This enables error reporting and performance monitoring.
Default value: None
Type: string | None
Example:
[integrations.sentry]
sentry_dsn = "https://public@sentry.example.com/1"See the Sentry Integration documentation for detailed setup instructions.
mcp_servers
Configuration for Model Context Protocol (MCP) servers that extend Erato’s capabilities through custom tools and integrations.
Type: object<string, McpServerConfig>
Example:
[mcp_servers.file_provider]
transport_type = "sse"
url = "http://127.0.0.1:63490/sse"
[mcp_servers.filesystem]
transport_type = "sse"
url = "https://my-mcp-server.example.com/sse"
http_headers = { "Authorization" = "Bearer token123", "X-API-Key" = "key456" }mcp_servers.<server_id>.transport_type
The type of transport protocol used to communicate with the MCP server.
Type: string
Supported values: "sse" (Server-Sent Events)
Example: "sse"
mcp_servers.<server_id>.url
The URL endpoint for the MCP server. For SSE transport, this conventionally ends with /sse.
Type: string
Example: "http://127.0.0.1:63490/sse", "https://my-mcp-server.example.com/sse"
mcp_servers.<server_id>.http_headers
Optional static HTTP headers to include with every request to the MCP server. This is useful for authentication or API keys.
Type: object<string, string> | None
Example:
[mcp_servers.authenticated_server]
transport_type = "sse"
url = "https://secure-mcp-server.example.com/sse"
http_headers = { "Authorization" = "Bearer your-token", "X-API-Key" = "your-api-key" }See the MCP Servers documentation for more information about Model Context Protocol integration.