AgentConfig
Base classes¶
Name | Children | Inherits |
---|---|---|
BaseModel pydantic.main Usage docs: https://docs.pydantic.dev/2.10/concepts/models/ |
⋔ Inheritance diagram¶
graph TD
94350417678112["agents.AgentConfig"]
94350366564672["main.BaseModel"]
140709601677504["builtins.object"]
94350366564672 --> 94350417678112
140709601677504 --> 94350366564672
🛈 DocStrings¶
Bases: BaseModel
Configuration for a single agent in the system.
Defines an agent's complete configuration including its model, environment, capabilities, and behavior settings. Each agent can have its own: - Language model configuration - Environment setup (tools and resources) - Response type definitions - System prompts and default user prompts - Role-based capabilities
The configuration can be loaded from YAML or created programmatically.
Source code in src/llmling_agent/models/agents.py
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avatar
class-attribute
instance-attribute
¶
avatar: str | None = None
URL or path to agent's avatar image
capabilities
class-attribute
instance-attribute
¶
capabilities: Capabilities = Field(default_factory=Capabilities)
Current agent's capabilities.
config_file_path
class-attribute
instance-attribute
¶
config_file_path: str | None = None
Config file path for resolving environment.
description
class-attribute
instance-attribute
¶
description: str | None = None
Optional description of the agent's purpose
enable_db_logging
class-attribute
instance-attribute
¶
enable_db_logging: bool = True
Enable session database logging.
end_strategy
class-attribute
instance-attribute
¶
end_strategy: EndStrategy = 'early'
The strategy for handling multiple tool calls when a final result is found
environment
class-attribute
instance-attribute
¶
environment: str | AgentEnvironment | None = None
Environment configuration (path or object)
forward_to
class-attribute
instance-attribute
¶
Targets to forward results to.
include_role_prompts
class-attribute
instance-attribute
¶
include_role_prompts: bool = True
Whether to include default prompts based on the agent's role.
inherits
class-attribute
instance-attribute
¶
inherits: str | None = None
Name of agent config to inherit from
knowledge
class-attribute
instance-attribute
¶
knowledge: Knowledge | None = None
Knowledge sources for this agent.
mcp_servers
class-attribute
instance-attribute
¶
List of MCP server configurations: - str entries are converted to StdioMCPServer - MCPServerConfig for full server configuration
model
class-attribute
instance-attribute
¶
model: str | AnyModel | None = None
The model to use for this agent. Can be either a simple model name string (e.g. 'openai:gpt-4') or a structured model definition.
model_settings
class-attribute
instance-attribute
¶
Additional settings to pass to the model
result_retries
class-attribute
instance-attribute
¶
result_retries: int | None = None
Max retries for result validation
result_tool_description
class-attribute
instance-attribute
¶
result_tool_description: str | None = None
Custom description for the result tool
result_tool_name
class-attribute
instance-attribute
¶
result_tool_name: str = 'final_result'
Name of the tool used for structured responses
result_type
class-attribute
instance-attribute
¶
result_type: str | ResponseDefinition | None = None
Name of the response definition to use
retries
class-attribute
instance-attribute
¶
retries: int = 1
Number of retries for failed operations (maps to pydantic-ai's retries)
session
class-attribute
instance-attribute
¶
session: str | SessionQuery | None = None
Session configuration for conversation recovery.
system_prompts
class-attribute
instance-attribute
¶
System prompts for the agent
triggers
class-attribute
instance-attribute
¶
Event sources that activate this agent
type
class-attribute
instance-attribute
¶
type: ProviderConfig | Literal['ai', 'human', 'litellm'] = 'ai'
Provider configuration or shorthand type
user_prompts
class-attribute
instance-attribute
¶
Default user prompts for the agent
workers
class-attribute
instance-attribute
¶
Worker agents which will be available as tools.
get_agent_kwargs
¶
Get kwargs for Agent constructor.
Returns:
Type | Description |
---|---|
dict[str, Any]
|
dict[str, Any]: Kwargs to pass to Agent |
Source code in src/llmling_agent/models/agents.py
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get_config
¶
get_config() -> Config
Get configuration for this agent.
Source code in src/llmling_agent/models/agents.py
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get_environment_display
¶
get_environment_display() -> str
Get human-readable environment description.
Source code in src/llmling_agent/models/agents.py
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get_environment_path
¶
get_environment_path() -> str | None
Get environment file path if available.
Source code in src/llmling_agent/models/agents.py
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get_mcp_servers
¶
get_mcp_servers() -> list[MCPServerConfig]
Get processed MCP server configurations.
Converts string entries to StdioMCPServer configs by splitting into command and arguments.
Returns:
Type | Description |
---|---|
list[MCPServerConfig]
|
List of MCPServerConfig instances |
Raises:
Type | Description |
---|---|
ValueError
|
If string entry is empty |
Source code in src/llmling_agent/models/agents.py
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get_provider
¶
get_provider() -> AgentProvider
Get resolved provider instance.
Creates provider instance based on configuration: - Full provider config: Use as-is - Shorthand type: Create default provider config
Source code in src/llmling_agent/models/agents.py
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get_session_query
¶
get_session_query() -> SessionQuery | None
Get session query from config.
Source code in src/llmling_agent/models/agents.py
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handle_model_types
classmethod
¶
Convert model inputs to appropriate format.
Source code in src/llmling_agent/models/agents.py
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is_structured
¶
is_structured() -> bool
Check if this config defines a structured agent.
Source code in src/llmling_agent/models/agents.py
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normalize_workers
classmethod
¶
Convert string workers to WorkerConfig.
Source code in src/llmling_agent/models/agents.py
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render_system_prompts
¶
Render system prompts with context.
Source code in src/llmling_agent/models/agents.py
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resolve_paths
classmethod
¶
Store config file path for later use.
Source code in src/llmling_agent/models/agents.py
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validate_result_type
classmethod
¶
Convert result type and apply its settings.
Source code in src/llmling_agent/models/agents.py
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