Configurations

Shared Configurations

The shared_config.yaml file is used to define configurations that can be shared across multiple agents or components in Solace Agent Mesh. This allows for centralized management of common settings like broker connections and language model configurations.

Using Shared Configurations

All agents and gateways require access to a shared_config object. You can provide configuration in the following ways:

1. Hard-coding values: You can directly embed the shared_config values within your agent or gateway YAML files.
2. Using !include: For better project consistency and management, you can use the !include directive to load a shared configuration file.

When a plugin is installed, it may come with hard-coded default values. It is a best practice to remove this section and use !include to point to the centralized shared_config file. This ensures that all components are using the same base configuration.

Managing Multiple Shared Configuration Files

You can use multiple shared configuration files to manage different environments or setups (e.g., for different cloud providers). You must follow these rules:

1. Filename: The filename must always start with shared_config (e.g., shared_config_aws.yaml, shared_config_gcp.yaml).
2. Sub-directories: You can organize these files into sub-directories (e.g., configs/agents/shared_config.yaml). When you do this, you must update the !include path in your agent or gateway configurations to point to the correct location.

The file uses YAML anchors (&anchor_name) to create reusable configuration blocks, which can then be referenced in agent configuration files.

Parameters

Example shared_config.yaml

shared_config:
  - broker_connection: &broker_connection
      dev_mode: ${SOLACE_DEV_MODE, false}
      broker_url: ${SOLACE_BROKER_URL, ws://localhost:8080}
      broker_username: ${SOLACE_BROKER_USERNAME, default}
      broker_password: ${SOLACE_BROKER_PASSWORD, default}
      broker_vpn: ${SOLACE_BROKER_VPN, default}
      temporary_queue: ${USE_TEMPORARY_QUEUES, true}
      # Ensure high enough limits if many agents are running
      # max_connection_retries: -1 # Retry forever

  - models:
    planning: &planning_model
      # This dictionary structure tells ADK to use the LiteLlm wrapper.
      # 'model' uses the specific model identifier your endpoint expects.
      model: ${LLM_SERVICE_PLANNING_MODEL_NAME} # Use env var for model name
      # 'api_base' tells LiteLLM where to send the request.
      api_base: ${LLM_SERVICE_ENDPOINT} # Use env var for endpoint URL
      # 'api_key' provides authentication.
      api_key: ${LLM_SERVICE_API_KEY} # Use env var for API key
      # Enable parallel tool calls for planning model
      parallel_tool_calls: true 
      # max_tokens: ${MAX_TOKENS, 16000} # Set a reasonable max token limit for planning
      # temperature: 0.1 # Lower temperature for more deterministic planning
    
    general: &general_model
      # This dictionary structure tells ADK to use the LiteLlm wrapper.
      # 'model' uses the specific model identifier your endpoint expects.
      model: ${LLM_SERVICE_GENERAL_MODEL_NAME} # Use env var for model name
      # 'api_base' tells LiteLLM where to send the request.
      api_base: ${LLM_SERVICE_ENDPOINT} # Use env var for endpoint URL
      # 'api_key' provides authentication.
      api_key: ${LLM_SERVICE_API_KEY} # Use env var for API key

      # ... (similar structure)

  - services:
    # Default session service configuration
    session_service: &default_session_service
      type: "memory"
      default_behavior: "PERSISTENT"
    
    # Default artifact service configuration
    artifact_service: &default_artifact_service
      type: "filesystem"
      base_path: "/tmp/samv2"
      artifact_scope: namespace
    
    # Default data tools configuration
    data_tools_config: &default_data_tools_config
      sqlite_memory_threshold_mb: 100
      max_result_preview_rows: 50
      max_result_preview_bytes: 4096

Broker Connection

The broker_connection section configures the connection to the Solace PubSub+ event broker. The connection parameters are described in the following table:

Parameter	Environment Variable	Description	Default
dev_mode	SOLACE_DEV_MODE	When set to true, uses an in-memory broker for testing.	false
broker_url	SOLACE_BROKER_URL	The URL of the Solace broker.	ws://localhost:8080
broker_username	SOLACE_BROKER_USERNAME	The username for authenticating with the broker.	default
broker_password	SOLACE_BROKER_PASSWORD	The password for authenticating with the broker.	default
broker_vpn	SOLACE_BROKER_VPN	The Message VPN to connect to on the broker.	default
temporary_queue	USE_TEMPORARY_QUEUES	Whether to use temporary queues for communication. If false, a durable queue will be created.	true
max_connection_retries	MAX_CONNECTION_RETRIES	The maximum number of times to retry connecting to the broker if the connection fails. A value of -1 means retry forever.	-1

tip

If you need to configure multiple brokers, you can do so by adding additional entries under shared_config with a unique name (For example, broker_connection_eu: &broker_connection_eu or broker_connection_us: &broker_connection_us) and then use the proper reference in your agent configurations. (Example: <<: *broker_connection_eu)

Models

The models section is used to configure the various Large Language Models (LLMs) and other generative models used by the agents. The configuration uses the LiteLLM library, which provides a standardized way to interact with different model providers.

Model Configuration Structure

The following table describes the parameters that tell the system how to interact with the model:

Parameter	Environment Variable	Description
model	LLM_SERVICE_<MODEL_NAME>_MODEL_NAME	The specific model identifier that the endpoint expects in the format of provider/model (e.g., openai/gpt-4, anthropic/claude-3-opus-20240229).
api_base	LLM_SERVICE_ENDPOINT	The base URL of the LLM provider's API endpoint.
api_key	LLM_SERVICE_API_KEY	The API key for authenticating with the service.
parallel_tool_calls	PARALLEL_TOOL_CALLS	Enable parallel tool calls for the model.
max_tokens	MAX_TOKENS	Set a reasonable max token limit for the model.
temperature	TEMPERATURE	Lower temperature for more deterministic planning.

Alternatively, you can use Gemini models directly through Google Studio AI or Vertex AI:

model: gemini-2.5-pro

See the documentation for details on setting the environment for Gemini models.

Pre-Defined Model Types

The shared_config.yaml example defines several models for different purposes. A pre-defined model serves as an alias for the model configuration. This alias allows you to refer to a configuration by its use case rather than its specific parameters.

• planning: Used by agents for planning and decision-making. It's configured for deterministic outputs (temperature: 0.1) and can use tools in parallel.
• general: A general-purpose model for various tasks.
• image_gen: A model for generating images.
• image_describe: A model for describing the content of images.
• audio_transcription: A model for transcribing audio files.
• report_gen: A model specialized for generating reports.
• multimodal: A simple string reference to a multimodal model (e.g., "gemini-1.5-flash-latest").

You can define any number of models in this section and reference them in your agent configurations. By default, the system only uses the planning and the general models. No need to fill the other fields

Services

The services section in shared_config.yaml is used to configure various services that are available to agents.

Session Service

The parameters are described in the following table:

Parameter	Options	Description	Default
type	memory, vertex_rag	Configuration for ADK Session Service	memory
default_behavior	PERSISTENT, RUN_BASED	The default behavior of keeping the session history	PERSISTENT

Artifact Service

The artifact_service is responsible for managing artifacts, which are files or data generated by agents.

Parameter	Options	Description	Default
type	memory, gcs, filesystem	Service type for artifact storage. Use memory for in-memory, gcs for Google Cloud Storage, or filesystem for local file storage.	memory
base_path	local path	Base directory path for storing artifacts. Required only if type is filesystem.	(none)
bucket_name	bucket name	Google Cloud Storage bucket name. Required only if type is gcs.	(none)
artifact_scope	namespace, app	Scope for artifact sharing. namespace: shared by all components in the namespace. app: isolated by agent/gateway name. Must be consistent for all components in the same process.	namespace
artifact_scope_value	custom scope id	Custom identifier for artifact scope. Required if artifact_scope is set to a custom value.	(none)

Data Tools Config

The data_tools_config section configures the behavior of data analysis tools.

Parameter	Type	Description	Default
sqlite_memory_threshold_mb	integer	The memory threshold in megabytes for using an in-memory SQLite database.	100
max_result_preview_rows	integer	The maximum number of rows to show in a result preview.	50
max_result_preview_bytes	integer	The maximum number of bytes to show in a result preview.	4096

System Logs

For details on how to configure system logging, including log rotation and verbosity levels, please see the System Logs section in the debugging documentation.