adk-python/src/google/adk/a2a/utils/agent_card_builder.py at main · google/adk-python · GitHub

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

from __future__ import annotations

import logging

from typing import Dict

from typing import List

from a2a.types import AgentCapabilities

from a2a.types import AgentCard

from a2a.types import AgentProvider

from a2a.types import AgentSkill

from a2a.types import SecurityScheme

from .. import _compat

from ...agents.base_agent import BaseAgent

from ...agents.llm_agent import LlmAgent

from ...agents.loop_agent import LoopAgent

from ...agents.parallel_agent import ParallelAgent

from ...agents.sequential_agent import SequentialAgent

from ...tools.example_tool import ExampleTool

from ...workflow import BaseNode

from ...workflow import START

from ...workflow import Workflow

from ..experimental import a2a_experimental

logger = logging.getLogger('google_adk.' + __name__)

@a2a_experimental

class AgentCardBuilder:

"""Builder class for creating agent cards from ADK agents.

This class provides functionality to convert ADK agents into A2A agent cards,

including extracting skills, capabilities, and metadata from various agent

types.

"""

def __init__(

self,

agent: BaseAgent | Workflow,

rpc_url: str | None = None,

capabilities: AgentCapabilities | None = None,

doc_url: str | None = None,

provider: AgentProvider | None = None,

agent_version: str | None = None,

security_schemes: Dict[str, SecurityScheme] | None = None,

if not agent:

raise ValueError('Agent cannot be None or empty.')

if not isinstance(agent, (BaseAgent, Workflow)):

raise TypeError(

'AgentCardBuilder requires a BaseAgent or Workflow, got '

f'{type(agent).__name__}.'

)

self._agent = agent

self._rpc_url = rpc_url or 'http://localhost:80/a2a'

self._capabilities = capabilities or AgentCapabilities(streaming=True)

self._doc_url = doc_url

self._provider = provider

self._security_schemes = security_schemes

self._agent_version = agent_version or '0.0.1'

async def build(self) -> AgentCard:

"""Build and return the complete agent card."""

try:

primary_skills = await _build_primary_skills(self._agent)

sub_agent_skills = await _build_sub_agent_skills(self._agent)

all_skills = primary_skills + sub_agent_skills

return _compat.build_agent_card(

name=self._agent.name,

description=self._agent.description or 'An ADK Agent',

version=self._agent_version,

url=self._rpc_url,

protocol_binding=getattr(

_compat.TP_JSONRPC, 'value', _compat.TP_JSONRPC

skills=all_skills,

capabilities=self._capabilities,

provider=self._provider,

security_schemes=self._security_schemes,

doc_url=self._doc_url,

default_input_modes=['text/plain'],

default_output_modes=['text/plain'],

supports_authenticated_extended_card=False,

)

except Exception as e:

raise RuntimeError(

f'Failed to build agent card for {self._agent.name}: {e}'

) from e

# Module-level helper functions

def _iter_child_nodes(agent: BaseNode) -> List[BaseNode]:

"""Returns the immediate child nodes of an agent or a workflow."""

if isinstance(agent, BaseAgent):

return list(agent.sub_agents)

if isinstance(agent, Workflow) and agent.graph is not None:

return [n for n in agent.graph.nodes if n.name != START.name]

return []

async def _build_primary_skills(agent: BaseNode) -> List[AgentSkill]:

"""Build skills for any node type."""

if isinstance(agent, LlmAgent):

return await _build_llm_agent_skills(agent)

else:

return await _build_non_llm_agent_skills(agent)

async def _build_llm_agent_skills(agent: LlmAgent) -> List[AgentSkill]:

"""Build skills for LLM agent."""

skills = []

# 1. Agent skill (main model skill). The card is a discovery document served

# without authentication, so the description comes from the agent's own

# public description and never from its instructions.

agent_description = _build_agent_description(agent)

agent_examples = await _extract_examples_from_agent(agent)

skills.append(

AgentSkill(

id=agent.name,

name='model',

description=agent_description,

examples=_extract_inputs_from_examples(agent_examples),

input_modes=_get_input_modes(agent),

output_modes=_get_output_modes(agent),

tags=['llm'],

)

# 2. Tool skills

if agent.tools:

tool_skills = await _build_tool_skills(agent)

skills.extend(tool_skills)

# 3. Planner skill

if agent.planner:

skills.append(_build_planner_skill(agent))

# 4. Code executor skill

if agent.code_executor:

skills.append(_build_code_executor_skill(agent))

return skills

async def _build_sub_agent_skills(agent: BaseNode) -> List[AgentSkill]:

"""Build skills for all child nodes (sub-agents or workflow nodes)."""

sub_agent_skills = []

for sub_agent in _iter_child_nodes(agent):

try:

sub_skills = await _build_primary_skills(sub_agent)

for skill in sub_skills:

# Create a new skill instance to avoid modifying original if shared

aggregated_skill = AgentSkill(

id=f'{sub_agent.name}_{skill.id}',

name=f'{sub_agent.name}: {skill.name}',

description=skill.description,

examples=skill.examples,

input_modes=skill.input_modes,

output_modes=skill.output_modes,

tags=[f'sub_agent:{sub_agent.name}'] + list(skill.tags or []),

)

sub_agent_skills.append(aggregated_skill)

except Exception as e:

# Log warning but continue with other sub-agents

logger.warning(

'Failed to build skills for sub-agent %s: %s', sub_agent.name, e

)

continue

return sub_agent_skills

async def _build_tool_skills(agent: LlmAgent) -> List[AgentSkill]:

"""Build skills for agent tools."""

tool_skills = []

canonical_tools = await agent.canonical_tools()

for tool in canonical_tools:

# Skip example tools as they're handled separately

if isinstance(tool, ExampleTool):

continue

tool_name = (

tool.name

if hasattr(tool, 'name') and tool.name

else tool.__class__.__name__

)

tool_skills.append(

AgentSkill(

id=f'{agent.name}-{tool_name}',

name=tool_name,

description=getattr(tool, 'description', f'Tool: {tool_name}'),

examples=None,

input_modes=None,

output_modes=None,

tags=['llm', 'tools'],

)

return tool_skills

def _build_planner_skill(agent: LlmAgent) -> AgentSkill:

"""Build planner skill for LLM agent."""

return AgentSkill(

id=f'{agent.name}-planner',

name='planning',

description='Can think about the tasks to do and make plans',

examples=None,

input_modes=None,

output_modes=None,

tags=['llm', 'planning'],

)

def _build_code_executor_skill(agent: LlmAgent) -> AgentSkill:

"""Build code executor skill for LLM agent."""

return AgentSkill(

id=f'{agent.name}-code-executor',

name='code-execution',

description='Can execute code',

examples=None,

input_modes=None,

output_modes=None,

tags=['llm', 'code_execution'],

)

async def _build_non_llm_agent_skills(agent: BaseNode) -> List[AgentSkill]:

"""Build skills for non-LLM agents and workflow nodes."""

skills = []

# 1. Agent skill (main agent skill)

agent_description = _build_agent_description(agent)

agent_examples = await _extract_examples_from_agent(agent)

# Determine agent type and name

agent_type = _get_agent_type(agent)

agent_name = _get_agent_skill_name(agent)

skills.append(

AgentSkill(

id=agent.name,

name=agent_name,

description=agent_description,

examples=_extract_inputs_from_examples(agent_examples),

input_modes=_get_input_modes(agent),

output_modes=_get_output_modes(agent),

tags=[agent_type],

)

# 2. Orchestration skill (for agents/workflows with child nodes)

if _iter_child_nodes(agent):

orchestration_skill = _build_orchestration_skill(agent, agent_type)

if orchestration_skill:

skills.append(orchestration_skill)

return skills

def _build_orchestration_skill(

agent: BaseNode, agent_type: str

) -> AgentSkill | None:

"""Build orchestration skill for agents/workflows with child nodes."""

sub_agent_descriptions = []

for sub_agent in _iter_child_nodes(agent):

description = sub_agent.description or 'No description'

sub_agent_descriptions.append(f'{sub_agent.name}: {description}')

if not sub_agent_descriptions:

return None

return AgentSkill(

id=f'{agent.name}-sub-agents',

name='sub-agents',

description='Orchestrates: ' + '; '.join(sub_agent_descriptions),

examples=None,

input_modes=None,

output_modes=None,

tags=[agent_type, 'orchestration'],

)

def _get_agent_type(agent: BaseNode) -> str:

"""Get the agent type for tagging."""

if isinstance(agent, LlmAgent):

return 'llm'

elif isinstance(agent, SequentialAgent):

return 'sequential_workflow'

elif isinstance(agent, ParallelAgent):

return 'parallel_workflow'

elif isinstance(agent, LoopAgent):

return 'loop_workflow'

elif isinstance(agent, Workflow):

return 'graph_workflow'

else:

return 'custom_agent'

def _get_agent_skill_name(agent: BaseNode) -> str:

"""Get the skill name based on agent type."""

if isinstance(agent, LlmAgent):

return 'model'

elif isinstance(agent, (SequentialAgent, ParallelAgent, LoopAgent, Workflow)):

return 'workflow'

else:

return 'custom'

def _build_agent_description(agent: BaseNode) -> str:

"""Build agent description from agent.description and workflow-specific descriptions."""

description_parts = []

# Add agent description

if agent.description:

description_parts.append(agent.description)

# Add workflow-specific descriptions for non-LLM agents

if not isinstance(agent, LlmAgent):

workflow_description = _get_workflow_description(agent)

if workflow_description:

description_parts.append(workflow_description)

return (

' '.join(description_parts)

if description_parts

else _get_default_description(agent)

)

def _get_workflow_description(agent: BaseNode) -> str | None:

"""Get workflow-specific description for non-LLM agents and workflows."""

if not _iter_child_nodes(agent):

return None

if isinstance(agent, SequentialAgent):

return _build_sequential_description(agent)

elif isinstance(agent, ParallelAgent):

return _build_parallel_description(agent)

elif isinstance(agent, LoopAgent):

return _build_loop_description(agent)

elif isinstance(agent, Workflow):

return _build_graph_workflow_description(agent)

return None

def _build_sequential_description(agent: SequentialAgent) -> str:

"""Build description for sequential workflow agent."""

descriptions = []

for i, sub_agent in enumerate(agent.sub_agents, 1):

sub_description = (

sub_agent.description or f'execute the {sub_agent.name} agent'

)

if i == 1:

descriptions.append(f'First, this agent will {sub_description}')

elif i == len(agent.sub_agents):

descriptions.append(f'Finally, this agent will {sub_description}')

else:

descriptions.append(f'Then, this agent will {sub_description}')

return ' '.join(descriptions) + '.'

def _build_parallel_description(agent: ParallelAgent) -> str:

"""Build description for parallel workflow agent."""

descriptions = []

for i, sub_agent in enumerate(agent.sub_agents):

sub_description = (

sub_agent.description or f'execute the {sub_agent.name} agent'

)

if i == 0:

descriptions.append(f'This agent will {sub_description}')

elif i == len(agent.sub_agents) - 1:

descriptions.append(f'and {sub_description}')

else:

descriptions.append(f', {sub_description}')

return ' '.join(descriptions) + ' simultaneously.'

def _build_loop_description(agent: LoopAgent) -> str:

"""Build description for loop workflow agent."""

max_iterations = (

'unlimited' if agent.max_iterations is None else agent.max_iterations

)

descriptions = []

for i, sub_agent in enumerate(agent.sub_agents):

sub_description = (

sub_agent.description or f'execute the {sub_agent.name} agent'

)

if i == 0:

descriptions.append(f'This agent will {sub_description}')

elif i == len(agent.sub_agents) - 1:

descriptions.append(f'and {sub_description}')

else:

descriptions.append(f', {sub_description}')

return (

f"{' '.join(descriptions)} in a loop (max {max_iterations} iterations)."

)

def _build_graph_workflow_description(workflow: Workflow) -> str:

"""Build description for a graph-based Workflow."""

child_nodes = _iter_child_nodes(workflow)

descriptions = []

for node in child_nodes:

node_description = (

node.description.rstrip('.')

if node.description

else f'execute the {node.name} node'

)

descriptions.append(f'{node.name}: {node_description}')

return (

'This workflow orchestrates the following nodes: '

+ '; '.join(descriptions)

+ '.'

)

def _get_default_description(agent: BaseNode) -> str:

"""Get default description based on agent type."""

agent_type_descriptions = {

LlmAgent: 'An LLM-based agent',

SequentialAgent: 'A sequential workflow agent',

ParallelAgent: 'A parallel workflow agent',

LoopAgent: 'A loop workflow agent',

Workflow: 'A graph-based workflow agent',

}

for agent_type, description in agent_type_descriptions.items():

if isinstance(agent, agent_type):

return description

return 'A custom agent'

def _extract_inputs_from_examples(

examples: list[dict[str, object]] | None,

) -> list[str]:

"""Extracts only the input strings so they can be added to an AgentSkill."""

if examples is None:

return []

extracted_inputs: list[str] = []

for example in examples:

example_input = example.get('input')

if not isinstance(example_input, dict):

continue

parts = example_input.get('parts')

if isinstance(parts, list):

part_texts: list[str] = []

for part in parts:

if not isinstance(part, dict):

continue

text = part.get('text')

if isinstance(text, str):

part_texts.append(text)

if part_texts:

extracted_inputs.append('\n'.join(part_texts))

else:

text = example_input.get('text')

if isinstance(text, str):

extracted_inputs.append(text)

return extracted_inputs

async def _extract_examples_from_agent(

agent: BaseNode,

) -> list[dict[str, object]] | None:

"""Extract examples from example_tool if configured, otherwise none."""

if not isinstance(agent, LlmAgent):

return None

# First, try to find example_tool in tools

try:

canonical_tools = await agent.canonical_tools()

for tool in canonical_tools:

if isinstance(tool, ExampleTool):

examples = _convert_example_tool_examples(tool)

if examples is not None:

return examples

except Exception as e:

logger.warning('Failed to extract examples from tools: %s', e)

# Examples come only from a declared example_tool, never mined out of the

# instruction, which is not publishable content.

return None

def _serialize_example_content(content: object) -> object:

model_dump = getattr(content, 'model_dump', None)

if callable(model_dump):

serialized: object = model_dump()

return serialized

return content

def _convert_example_tool_examples(

tool: ExampleTool,

) -> list[dict[str, object]] | None:

"""Convert ExampleTool examples to the expected format."""

if not isinstance(tool.examples, list):

logger.debug(

'Skipping dynamic ExampleTool provider when building an agent card'

)

return None

examples: list[dict[str, object]] = []

for example in tool.examples:

examples.append({

'input': _serialize_example_content(example.input),

'output': [

_serialize_example_content(output) for output in example.output

})

return examples

def _get_input_modes(agent: BaseNode) -> List[str] | None:

"""Get input modes based on agent model."""

if not isinstance(agent, LlmAgent):

return None

# This could be enhanced to check model capabilities

# For now, return None to use default_input_modes

return None

def _get_output_modes(agent: BaseNode) -> List[str] | None:

"""Get output modes from Agent.generate_content_config.response_modalities."""

if not isinstance(agent, LlmAgent):

return None

if (

hasattr(agent, 'generate_content_config')

and agent.generate_content_config

and hasattr(agent.generate_content_config, 'response_modalities')

return agent.generate_content_config.response_modalities

return None

adk-python/src/google/adk/a2a/utils/agent_card_builder.py at main · google/adk-python · GitHub

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