a2a-utils

A2A Utils Python

This package is a comprehensive set of utility functions for using A2A servers (remote agents), it powers the A2A MCP Server.

A2ASession is at the core of the package, it takes an A2AAgents (for connecting to agents, viewing Agent Cards, etc.), and optionally a TaskStore (for saving Tasks) and FileStore (for saving files). It has two methods, send_message and get_task.

These methods are more sophisticated versions of the same methods in the A2A SDKs. For example, send_message abstracts retrieving Agent Cards, sending headers, etc., allowing you to send a message with an agent's ID, e.g. send_message("research-bot", "Find recent papers on quantum computing"). It also sends the message as non-blocking and streams the response until the Task reaches a terminal state or times out. If send_message times out, get_task can be called with the Task ID to start streaming the response again. If TaskStore and FileStore are set, the Task, Artifacts, and files will automatically be saved.

A2AAgents stores user-defined agent IDs that link to Agent Card URLs and headers. Agents are stored this way so that Agent Card URLs and headers are not exposed to the agent and because Agent Cards can be dynamic (i.e. change depending on the headers). It has five methods, get_agents, get_agents_for_llm, get_agent, get_agent_for_llm, and add_agent. See the API reference below for more information.

Lastly, A2ATools is the most sophisticated class in the package, it takes an A2ASession and provides six LLM-friendly tools that can be used out-of-the-box with agent frameworks: get_agents, get_agent, send_message, get_task, view_text_artifact, and view_data_artifact. The tools are based on Writing effective tools for AI agents: they have LLM-friendly docstrings, return JSON-serialisable objects, and return actionable error messages.

Tool outputs are also optimised for LLMs. For example, get_agents returns a list of agent names and descriptions, whereas get_agent also returns an agent's skill names and descriptions. send_message and get_task return LLM-friendly types that are subsets of A2A types (e.g. TaskForLLM, MessageForLLM, and ArtifactForLLM) and automatically minimise large Artifacts, which can be viewed with view_text_artifact and view_data_artifact.

✨ Features

• 6 LLM-friendly tools — get_agents, get_agent, send_message, get_task, view_text_artifact, and view_data_artifact, designed with clear docstrings, JSON-serialisable outputs, and actionable error messages
• Simple message sending — send messages with just an agent ID and message string; Agent Card fetching, headers, and non-blocking streaming are handled automatically
• Multi-turn conversations — continue conversations across multiple messages using context_id
• Long-running task support — if send_message times out, use get_task to resume streaming until the task reaches a terminal state
• Automatic artifact minimization — large text and data artifacts are automatically minimized for LLM context windows, with view_text_artifact and view_data_artifact for detailed navigation
• Task persistence — save and load tasks as JSON files with JSONTaskStore
• File artifact storage — save file artifacts to the local filesystem with LocalFileStore, or implement FileStore for custom backends (S3, GCS, etc.)
• Agent Card management — A2AAgents stores agent IDs, URLs, and custom headers, with multiple detail levels for LLM summaries (name, basic, skills, full)
• Runtime agent registration — add new agents at runtime with add_agent
• Configurable timeouts and polling — customisable timeouts for sending messages, monitoring tasks, and polling intervals

📦 Installation

To install with pip:

pip install a2a-utils

To install with uv:

uv add a2a-utils

🚀 Quick Start

Create an A2ASession, then A2ATools to get LLM-friendly tools that can be used out-of-the-box with agent frameworks.

from pathlib import Path

from a2a_utils import A2ATools, A2ASession, A2AAgents, JSONTaskStore, LocalFileStore
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI

a2a_agents = A2AAgents({
    "weather": {"url": "https://weather.example.com/.well-known/agent-card.json"},
    "research-bot": {
        "url": "https://research.example.com/.well-known/agent-card.json",
        "custom_headers": {"X-API-Key": "key_123"},
    },
})

a2a_session = A2ASession(
    agents=a2a_agents,
    task_store=JSONTaskStore(Path("./storage/tasks")),
    file_store=LocalFileStore(Path("./storage/files"))
)

a2a_tools = A2ATools(a2a_session)

model = ChatOpenAI(model="gpt-5.1", reasoning={"effort": "medium"})

agent = create_agent(model, tools=a2a_tools.tools)

📖 API Reference

A2ATools

Ready-made tools for agents to communicate with A2A servers. Every method has LLM-friendly docstrings, returns JSON-serialisable objects, and returns actionable error messages. Use the tools property to pass the bound methods directly to agent frameworks such as LangChain.

from a2a_utils import A2ATools, A2ASession, A2AAgents

tools = A2ATools(session)

artifact_settings determines how Artifacts are minimized and viewed:

from a2a_utils import ArtifactSettings

settings = ArtifactSettings(
    send_message_character_limit=100_000,
    minimized_object_string_length=10_000,
    view_artifact_character_limit=100_000,
)
tools = A2ATools(session, artifact_settings=settings)

async get_agents() -> dict[str, Any]

List all available agents with their names and descriptions.

result = await tools.get_agents()

Example result:

{
  "research-bot": {
    "name": "Research Bot",
    "description": "Find and summarize research papers"
  },
  "weather": {
    "name": "Weather Agent",
    "description": "Get weather forecasts for any location"
  }
}

async get_agent(agent_id: str) -> dict[str, Any]

Get detailed information about a specific agent, including its skills.

result = await tools.get_agent("research-bot")

Example result:

{
  "name": "Research Bot",
  "description": "Find and summarize research papers",
  "skills": [
    {
      "name": "Search Papers",
      "description": "Search for papers by topic, author, or keyword"
    },
    {
      "name": "Summarize Paper",
      "description": "Generate a summary of a specific paper"
    }
  ]
}

async send_message(agent_id, message, context_id?, task_id?, timeout?) -> dict[str, Any]

Send a message to an agent and receive a structured response. The response includes the agent's reply and any generated Artifacts. Artifacts are automatically minimized to fit the context window.

result = await tools.send_message(
    "research-bot", "Find recent papers on quantum computing"
)

Example result:

{
    "id": "task-123",
    "context_id": "ctx-456",
    "kind": "task",
    "status": {
        "state": "completed",
        "message": "I found three recent papers on quantum computing and retrieved the abstract for the most recent one."
    },
    "artifacts": [
        {
            "artifact_id": "art-789",
            "description": "Search results for quantum computing papers",
            "name": "Search Results",
            "parts": [
                {
                    "kind": "data",
                    "data": [
                        {
                          "title": "Quantum Error Correction Advances",
                          "year": 2025,
                          "authors": "Chen et al."
                        },
                        {
                          "title": "Topological Quantum Computing Survey",
                          "year": 2024,
                          "authors": "Nakamura et al."
                        },
                        {
                          "title": "Fault-Tolerant Logical Qubits",
                          "year": 2024,
                          "authors": "Wang et al."
                        }
                    ]
                }
            ]
        },
        {
            "artifact_id": "art-790",
            "description": "Abstract of 'Quantum Error Correction Advances' by Chen et al.",
            "name": "Abstract",
            "parts": [
                {
                    "kind": "text",
                    "text": "Quantum computing has seen rapid advances in error correction.\nRecent work demonstrates fault-tolerant logical qubits at scale.\nThis paper surveys progress in quantum error correction from 2023-2025.\nWe review surface codes, color codes, and novel hybrid approaches.\nKey results include a 10x reduction in logical error rates.\nThese improvements bring practical quantum computing closer to reality.\nWe also discuss remaining challenges in qubit connectivity.\nFinally, we outline a roadmap for achieving fault-tolerant quantum computation."
                }
            ]
        }
    ]
}

Continue the conversation using context_id:

result_2 = await tools.send_message(
    "research-bot",
    "Summarize the most recent result",
    context_id="ctx-456",
)

async get_task(agent_id, task_id, timeout?, poll_interval?) -> dict[str, Any]

Check the progress of a task that is still in progress. Use this after send_message returns a task in a non-terminal state (e.g. "working").

result = await tools.get_task("research-bot", "task-123")

async view_text_artifact(agent_id, task_id, artifact_id, line_start?, line_end?, character_start?, character_end?) -> dict[str, Any]

View text content from an artifact, optionally selecting a range. Use this for artifacts containing text (documents, logs, code, etc.).

result = await tools.view_text_artifact(
    "research-bot", "task-123", "art-790", line_start=1, line_end=3
)

Example result:

{
    "artifact_id": "art-790",
    "description": "Abstract of 'Quantum Error Correction Advances' by Chen et al.",
    "name": "Abstract",
    "parts": [
        {
            "kind": "text",
            "text": "Quantum computing has seen rapid advances in error correction.\nRecent work demonstrates fault-tolerant logical qubits at scale.\nThis paper surveys progress in quantum error correction from 2023-2025."
        }
    ]
}

async view_data_artifact(agent_id, task_id, artifact_id, json_path?, rows?, columns?) -> dict[str, Any]

View structured data from an artifact with optional filtering. Use this for artifacts containing JSON data (objects, arrays, tables).

result = await tools.view_data_artifact(
    "research-bot", "task-123", "art-789",
    rows="0-1", columns="title,year",
)

Example result:

{
    "artifact_id": "art-789",
    "description": "Search results for quantum computing papers",
    "name": "Search Results",
    "parts": [
        {
            "kind": "data",
            "data": [
                {"title": "Quantum Error Correction Advances", "year": 2025},
                {"title": "Topological Quantum Computing Survey", "year": 2024}
            ]
        }
    ]
}

A2ASession

Programmatic interface for sending messages to A2A agents. Returns full A2A SDK types (Task, Message) for direct use.

from pathlib import Path
from a2a_utils import A2ASession, A2AAgents, JSONTaskStore, LocalFileStore

a2a_agents = A2AAgents({
    "research-bot": {"url": "https://research-bot.example.com/.well-known/agent-card.json"}
})

session = A2ASession(
    agents=a2a_agents,
    task_store=JSONTaskStore(Path("./storage/tasks")),
    file_store=LocalFileStore(Path("./storage/files")),
)

async send_message(agent_id: str, message: str, *, context_id: str | None = None, task_id: str | None = None, timeout: float | None = None) -> Task | Message

Send a message to an A2A agent. The returned task is automatically saved to the task store. File artifacts are saved via the file store.

from a2a.types import Task, Message

response = await session.send_message(
    "research-bot", "Find recent papers on quantum computing"
)

Continue the conversation using context_id:

response_2 = await session.send_message(
    "research-bot",
    "Summarize the most recent result",
    context_id=response.context_id,
)

Returns: Task | Message (from a2a.types)

async get_task(agent_id: str, task_id: str, *, timeout: float | None = None, poll_interval: float | None = None) -> Task

Get the current state of a task. Monitors until a terminal state (completed, canceled, failed, rejected) or actionable state (input_required, auth_required) is reached, or until timeout. Uses SSE resubscription if the agent supports streaming, otherwise polls at regular intervals.

On monitoring timeout, returns the current task state (which may still be non-terminal, e.g. working). The only errors from get_task are failed HTTP requests (agent down, network error).

task = await session.get_task("research-bot", "task-123")

Returns: Task (from a2a.types)

A2AAgents

Manages A2A agent cards keyed by user-defined agent IDs.

from a2a_utils import A2AAgents

# From dict
a2a_agents = A2AAgents({
    "language-translator": {
        "url": "https://example.com/language-translator/agent-card.json",
        "custom_headers": {"Authorization": "Bearer tok_123"},
    }
})

# From JSON file
a2a_agents_from_file = A2AAgents("./agents.json")

# Empty — add agents later
empty_a2a_agents = A2AAgents()

async get_agents() -> dict[str, AgentURLAndCustomHeaders]

Get all registered agents. Note: this should NOT be added to the LLM's context, use get_agents_for_llm instead.

Returns: dict[str, AgentURLAndCustomHeaders]

a2a_agent_configs = await a2a_agents.get_agents()

Example result:

{
    "language-translator": AgentURLAndCustomHeaders(
        agent_card=AgentCard(
            name="Universal Translator",
            description="Translate text and audio between 50+ languages",
            ...
        ),
        custom_headers={"Authorization": "Bearer tok_123"},
    ),
    "code-reviewer": AgentURLAndCustomHeaders(
        agent_card=AgentCard(
            name="Code Reviewer",
            description="Review code for best practices",
            ...
        ),
        custom_headers={"X-API-Key": "key_123"},
    ),
}

async get_agents_for_llm(detail: str = "basic") -> dict[str, dict[str, Any]]

Generate summary of all agents, sorted by agent_id.

Returns: dict[str, dict[str, Any]]

"name":

summaries = await a2a_agents.get_agents_for_llm("name")

{
  "code-reviewer": {"name": "Code Reviewer"},
  "language-translator": {"name": "Universal Translator"}
}

"basic" (default):

summaries = await a2a_agents.get_agents_for_llm()

{
  "code-reviewer": {
    "name": "Code Reviewer",
    "description": "Review code for best practices"
  },
  "language-translator": {
    "name": "Universal Translator",
    "description": "Translate text and audio between 50+ languages"
  }
}

"skills":

summaries = await a2a_agents.get_agents_for_llm("skills")

{
  "code-reviewer": {
    "name": "Code Reviewer",
    "description": "Review code for best practices",
    "skills": ["Review Code"]
  },
  "language-translator": {
    "name": "Universal Translator",
    "description": "Translate text between 50+ languages",
    "skills": ["Translate Text", "Translate Audio"]
  }
}

"full":

summaries = await a2a_agents.get_agents_for_llm("full")

{
  "code-reviewer": {
    "name": "Code Reviewer",
    "description": "Review code for best practices",
    "skills": [
      {
        "name": "Review Code",
        "description": "Review code for best practices, identify bugs, and suggest improvements"
      }
    ]
  },
  "language-translator": {
    "name": "Universal Translator",
    "description": "Translate text between 50+ languages",
    "skills": [
      {
        "name": "Translate Text",
        "description": "Translate text between any supported language pair"
      },
      {
        "name": "Translate Audio",
        "description": "Translate audio between any supported language pair"
      }
    ]
  }
}

async get_agent(agent_id: str) -> AgentURLAndCustomHeaders | None

Retrieve agent by ID. Note: this should NOT be added to the LLM's context, use get_agent_for_llm instead.

Returns: AgentURLAndCustomHeaders | None

agent = await a2a_agents.get_agent("language-translator")

Example result:

AgentURLAndCustomHeaders(
    agent_card=AgentCard(
        name="Universal Translator",
        description="Translate text and audio between 50+ languages",
        url="https://translate.example.com",
        version="1.0.0",
        capabilities=AgentCapabilities(streaming=False, pushNotifications=False),
        skills=[
            AgentSkill(
                id="translate-text",
                name="Translate Text",
                description="Translate text between any supported language pair",
                tags=["translate", "text", "language"],
                examples=["Translate 'hello' to French"],
            ),
            AgentSkill(
                id="translate-audio",
                name="Translate Audio",
                description="Translate audio between any supported language pair",
                tags=["translate", "audio", "language"],
            )
        ],
        defaultInputModes=["text", "audio/mpeg"],
        defaultOutputModes=["text", "audio/mpeg"],
    ),
    custom_headers={"Authorization": "Bearer tok_123"},
)

Returns None if the agent ID is not registered.

async get_agent_for_llm(agent_id: str, detail: str = "basic") -> dict[str, Any] | None

Generate summary for a single agent.

Returns: dict[str, Any] | None — summary dict or None if not found.

summary = await a2a_agents.get_agent_for_llm("language-translator")

{
  "name": "Universal Translator",
  "description": "Translate text and audio between 50+ languages"
}

async add_agent(agent_id: str, url: str, custom_headers: dict[str, str] | None = None) -> None

Register a new agent at runtime.

Raises: ValueError if agent_id is already registered.

await a2a_agents.add_agent(
    "code-reviewer",
    "https://review.example.com/.well-known/agent-card.json",
    custom_headers={"X-API-Key": "key_123"},
)

💾 JSONTaskStore

Persists Task objects as individual JSON files. Implements the A2A SDK TaskStore ABC.

from pathlib import Path
from a2a_utils import JSONTaskStore

task_store = JSONTaskStore(Path("./storage/tasks"))

async save(task: Task) -> None

Save a task to disk. Creates ./storage/tasks/{task_id}.json.

async get(task_id: str) -> Task | None

Load a task from disk.

Returns None if the task file does not exist.

async delete(task_id: str) -> None

Delete a task from disk.

📁 Files

FileStore

Abstract base class for file storage. Implement this to use custom storage backends (S3, GCS, etc.).

from abc import ABC, abstractmethod
from a2a.types import Artifact

class FileStore(ABC):
    @abstractmethod
    async def save(self, task_id: str, artifact: Artifact) -> list[str]:
        """Save file parts from an artifact. Returns list of storage locations."""

    @abstractmethod
    async def get(self, task_id: str, artifact_id: str) -> list[str]:
        """Get storage locations for a saved artifact's files. Returns empty list if not found."""

    @abstractmethod
    async def delete(self, task_id: str, artifact_id: str) -> None:
        """Delete saved files for an artifact."""

LocalFileStore

Saves artifact file parts to the local filesystem. Files are stored at storage_dir/task_id/artifact_id/filename.

from pathlib import Path
from a2a_utils import LocalFileStore

file_store = LocalFileStore(Path("./storage/files"))

async save(task_id: str, artifact: Artifact) -> list[str]

Save file parts from an artifact to disk.

saved_paths = await file_store.save("task-123", artifact)

Example result:

["./storage/files/task-123/art-789/quarterly_report.pdf"]

async get(task_id: str, artifact_id: str) -> list[str]

Get saved file paths for an artifact.

paths = await file_store.get("task-123", "art-789")

Example result:

["./storage/files/task-123/art-789/quarterly_report.pdf"]

Returns an empty list if no files are found.

async delete(task_id: str, artifact_id: str) -> None

Delete saved files for an artifact.

await file_store.delete("task-123", "art-789")

🎨 Artifacts

A2ATools uses the TextArtifacts and DataArtifacts classes to automatically minimize Artifacts returned from send_message and view Artifacts using view_text_artifact and view_data_artifact. They can also be used independently on raw data.

TextArtifacts

TextArtifacts.view(text, *, line_start=None, line_end=None, character_start=None, character_end=None, character_limit=50_000) -> str

View text content with optional line or character range selection. Supports line selection (1-based, inclusive) or character selection (0-based, Python slice semantics). These are mutually exclusive — providing both raises ValueError.

Returns: str

Line selection:

from a2a_utils import TextArtifacts

text = "[INFO] Server started\n[INFO] Connected to DB\n[WARN] Cache miss\n[INFO] Request OK"
TextArtifacts.view(text, line_start=1, line_end=2)

Example result:

"[INFO] Server started\n[INFO] Connected to DB"

Character selection:

TextArtifacts.view("Hello, World!", character_start=0, character_end=5)

Example result:

"Hello"

TextArtifacts.minimize(text, *, character_limit=50_000, tip=None) -> dict[str, Any]

Minimize text content for display. If text is within the character limit, returns it unchanged. If over the limit, shows first and last halves with metadata.

Returns: dict[str, Any]

Short text (under limit):

from a2a_utils import TextArtifacts

TextArtifacts.minimize("Hello, world!")

{"text": "Hello, world!"}

Long text (over limit):

from a2a_utils import TextArtifacts

TextArtifacts.minimize("x" * 60_000)

{
    "text": "xxxxxxx...\n\n[... 10,000 characters omitted ...]\n\nxxxxxxx...",
    "_total_lines": 1,
    "_total_characters": 60000,
    "_start_line_range": "1-1",
    "_end_line_range": "1-1",
    "_start_character_range": "0-25000",
    "_end_character_range": "35000-60000"
}

DataArtifacts

DataArtifacts.view(data, *, json_path=None, rows=None, columns=None, character_limit=50_000) -> Any

View structured data with optional filtering. Navigate with json_path, then filter with rows/columns.

Returns: Any (filtered data)

from a2a_utils import DataArtifacts

data = {
    "employees": [
        {"name": "Alice", "department": "Engineering", "level": 5},
        {"name": "Bob", "department": "Design", "level": 3},
        {"name": "Carol", "department": "Engineering", "level": 4},
    ]
}

DataArtifacts.view(data, json_path="employees", rows="0-2", columns=["name", "department"])

Example result:

[
    {"name": "Alice", "department": "Engineering"},
    {"name": "Bob", "department": "Design"}
]

DataArtifacts.minimize(data, *, character_limit=50_000, minimized_object_string_length=5_000, tip=None) -> dict[str, Any]

Minimize data content for display based on type. Automatically selects the best strategy: list-of-objects gets a table summary, dicts get string truncation, strings delegate to TextArtifacts.minimize.

Returns: dict[str, Any]

from a2a_utils import DataArtifacts

data = {
    "title": "Quarterly Report Q4 2025",
    "summary": "x" * 10_000,
    "metrics": {"revenue": 1_250_000, "growth": 12.5},
    "employees": [
        {
            "name": f"Employee {i}",
            "department": ["Eng", "Marketing", "Design", "Sales"][i % 4],
            "salary": 60_000 + i * 500,
        }
        for i in range(100)
    ],
    "tags": ["finance", "quarterly", "internal"],
}
DataArtifacts.minimize(data, character_limit=100, minimized_object_string_length=10)

{
    "data": {
        "title": "Quarterly ... [14 more chars]",
        "summary": "xxxxxxxxxx... [9,990 more chars]",
        "metrics": {
            "revenue": 1250000,
            "growth": 12.5
        },
        "employees": {
            "_total_rows": 100,
            "_columns": [
                {
                    "count": 100,
                    "unique_count": 100,
                    "types": [{
                        "name": "string",
                        "count": 100,
                        "percentage": 100.0,
                        "sample_value": "Employee 42",
                        "length_minimum": 10,
                        "length_maximum": 11,
                        "length_average": 10.9,
                        "length_stdev": 0.3
                    }],
                    "name": "name"
                },
                {
                    "count": 100,
                    "unique_count": 4,
                    "types": [{
                        "name": "string",
                        "count": 100,
                        "percentage": 100.0,
                        "sample_value": "Engineering",
                        "length_minimum": 5,
                        "length_maximum": 11,
                        "length_average": 7.75,
                        "length_stdev": 2.4
                    }],
                    "name": "department"
                },
                {
                    "count": 100,
                    "unique_count": 100,
                    "types": [{
                        "name": "int",
                        "count": 100,
                        "percentage": 100.0,
                        "sample_value": 75000,
                        "minimum": 60000,
                        "maximum": 109500,
                        "average": 84750,
                        "stdev": 14505.75
                    }],
                    "name": "salary"
                }
            ],
            "_json_path": "employees"
        },
        "tags": ["finance", "quarterly", "internal"]
    }
}

Lists of dictionaries are summarized as table summaries (see summarize_table) and lists of values as value summaries (see summarize_values).

DataArtifacts.summarize_table(data) -> list[dict[str, Any]]

Generate a summary of tabular data (list of dicts). Returns one summary dict per column with count, unique count, and per-type statistics.

Returns: list[dict[str, Any]]

from a2a_utils import DataArtifacts

# Same dataset as the minimize example above
data = [
    {
        "name": f"Employee {i}",
        "department": ["Eng", "Marketing", "Design", "Sales"][i % 4],
        "salary": 60_000 + i * 500,
    }
    for i in range(100)
]

DataArtifacts.summarize_table(data)

[
    {
        "count": 100,
        "unique_count": 100,
        "types": [
            {
                "name": "string",
                "count": 100,
                "percentage": 100.0,
                "sample_value": "Employee 42",
                "length_minimum": 10,
                "length_maximum": 11,
                "length_average": 10.9,
                "length_stdev": 0.3
            }
        ],
        "name": "name"
    },
    {
        "count": 100,
        "unique_count": 4,
        "types": [
            {
                "name": "string",
                "count": 100,
                "percentage": 100.0,
                "sample_value": "Engineering",
                "length_minimum": 5,
                "length_maximum": 11,
                "length_average": 7.75,
                "length_stdev": 2.4
            }
        ],
        "name": "department"
    },
    {
        "count": 100,
        "unique_count": 100,
        "types": [
            {
                "name": "int",
                "count": 100,
                "percentage": 100.0,
                "sample_value": 75000,
                "minimum": 60000,
                "maximum": 109500,
                "average": 84750,
                "stdev": 14505.75
            }
        ],
        "name": "salary"
    }
]

DataArtifacts.summarize_values(values) -> dict[str, Any] | list[Any]

Generate statistics for a list of values (like a single column). Includes count, unique count, and per-type statistics (min/max/avg/stdev for numbers, length stats for strings, etc.). If the summary would be larger than the original values, the original list is returned instead (inflation guard).

Returns: dict[str, Any] | list[Any]

from a2a_utils import DataArtifacts

salaries = [
    95000, 72000, 105000, 68000, 88000,
    # ... ~100 salary values total, with some nulls
    None, 115000, 92000, None, 78000,
]

DataArtifacts.summarize_values(salaries)

{
    "count": 100,
    "unique_count": 87,
    "types": [
        {
            "name": "int",
            "count": 92,
            "percentage": 92.0,
            "sample_value": 95000,
            "minimum": 45000,
            "maximum": 185000,
            "average": 87250.5,
            "stdev": 28430.12
        },
        {
            "name": "null",
            "count": 8,
            "percentage": 8.0,
            "sample_value": null
        }
    ]
}

minimize_artifacts(artifacts, *, character_limit=50_000, minimized_object_string_length=5_000, saved_file_paths=None, text_tip=None, data_tip=None) -> list[ArtifactForLLM]

Minimize a list of artifacts for LLM display. Called automatically by A2ATools.send_message. Combines all TextParts within each artifact into a single TextPartForLLM. Handles FileParts by including file metadata and saved paths.

Returns: list[ArtifactForLLM]

from a2a.types import Artifact, TextPart, DataPart, FilePart, FileWithBytes
from a2a_utils import minimize_artifacts

artifacts = [
    Artifact(
        artifactId="art-123",
        description="Full text of a research paper abstract spanning several pages.",
        name="Research Paper Abstract",
        parts=[TextPart(kind="text", text="x" * 60_000)],
    ),
    Artifact(
        artifactId="art-456",
        description="Company employee directory with names, departments, and salaries.",
        name="Employee Directory",
        parts=[DataPart(kind="data", data=[
            {
                "name": f"Employee {i}",
                "department": ["Eng", "Marketing", "Design", "Sales"][i % 4],
                "salary": 60_000 + i * 500,
            }
            for i in range(100)
        ])],
    ),
    Artifact(
        artifactId="art-789",
        description="Generated quarterly financial report in PDF format.",
        name="Quarterly Report",
        parts=[
            FilePart(
                kind="file",
                file=FileWithBytes(
                    name="q4-report.pdf",
                    mimeType="application/pdf",
                    bytes="base64encodeddata...",
                ),
            ),
        ],
    ),
]

minimized = minimize_artifacts(
    artifacts,
    text_tip="Text was minimized. Call view_text_artifact() to see specific line ranges.",
    data_tip="Data was minimized. Call view_data_artifact() to navigate to specific data.",
    saved_file_paths={"art-789": ["./storage/files/task-123/art-789/q4-report.pdf"]},
)

Example result:

[
    ArtifactForLLM(
        artifact_id="art-123",
        description="Full text of a research paper abstract spanning several pages.",
        name="Research Paper Abstract",
        parts=[
            TextPartForLLM(
                kind="text",
                text="xxxxxxx...\n\n[... 10,000 characters omitted ...]\n\nxxxxxxx...",
            ),
        ],
    ),
    ArtifactForLLM(
        artifact_id="art-456",
        description="Company employee directory with names, departments, and salaries.",
        name="Employee Directory",
        parts=[
            DataPartForLLM(
                kind="data",
                data={
                    "data": {
                        "_total_rows": 100,
                        "_columns": ["..."],
                        "_tip": "Data was minimized. Call view_data_artifact() to navigate to specific data.",
                    }
                },
            ),
        ],
    ),
    ArtifactForLLM(
        artifact_id="art-789",
        description="Generated quarterly financial report in PDF format.",
        name="Quarterly Report",
        parts=[
            FilePartForLLM(
                kind="file",
                name="q4-report.pdf",
                mime_type="application/pdf",
                uri=None,
                bytes={"_saved_to": ["./storage/files/task-123/art-789/q4-report.pdf"]},
            ),
        ],
    ),
]

📋 Types

All types are frozen dataclasses exported from a2a_utils.

AgentURLAndCustomHeaders

Returned by A2AAgents.get_agent() and A2AAgents.get_agents().

AgentURLAndCustomHeaders(
    agent_card=AgentCard(
        name="Universal Translator",
        description="Translate text and audio between 50+ languages",
        url="https://translate.example.com",
        version="1.0.0",
        capabilities=AgentCapabilities(streaming=False, pushNotifications=False),
        skills=[
            AgentSkill(
                id="translate-text",
                name="Translate Text",
                description="Translate text between any supported language pair",
                tags=["translate", "text", "language"],
                examples=["Translate 'hello' to French"],
            ),
            AgentSkill(
                id="translate-audio",
                name="Translate Audio",
                description="Translate audio between any supported language pair",
                tags=["translate", "audio", "language"],
            )
        ],
        defaultInputModes=["text", "audio/mpeg"],
        defaultOutputModes=["text", "audio/mpeg"],
    ),
    custom_headers={"Authorization": "Bearer tok_123"},
)

TaskForLLM

Returned by A2ATools.send_message() for task responses.

TaskForLLM(
    id="task-123",
    context_id="ctx-456",
    kind="task",
    status=TaskStatusForLLM(
        state=TaskState.completed,
        message=MessageForLLM(
            context_id="ctx-456",
            kind="message",
            parts=[
                TextPartForLLM(
                    kind="text",
                    text="I found three recent papers on quantum computing and retrieved the abstract for the most recent one.",
                ),
            ],
        ),
    ),
    artifacts=[
        ArtifactForLLM(
            artifact_id="art-789",
            description="Search results for quantum computing papers",
            name="Search Results",
            parts=[
                DataPartForLLM(kind="data", data=[
                    {
                        "title": "Quantum Error Correction Advances",
                        "year": 2025,
                        "authors": "Chen et al.",
                    },
                    {
                        "title": "Topological Quantum Computing Survey",
                        "year": 2024,
                        "authors": "Nakamura et al.",
                    },
                    {
                        "title": "Fault-Tolerant Logical Qubits",
                        "year": 2024,
                        "authors": "Wang et al.",
                    },
                ]),
            ],
        ),
        ArtifactForLLM(
            artifact_id="art-790",
            description="Abstract of 'Quantum Error Correction Advances' by Chen et al.",
            name="Abstract",
            parts=[
                TextPartForLLM(
                    kind="text",
                    text="Quantum computing has seen rapid advances in error correction.\nRecent work demonstrates fault-tolerant logical qubits at scale.\nThis paper surveys progress in quantum error correction from 2023-2025.\nWe review surface codes, color codes, and novel hybrid approaches.\nKey results include a 10x reduction in logical error rates.\nThese improvements bring practical quantum computing closer to reality.\nWe also discuss remaining challenges in qubit connectivity.\nFinally, we outline a roadmap for achieving fault-tolerant quantum computation.",
                ),
            ],
        ),
    ],
)

MessageForLLM

Returned by A2ATools.send_message() for message-only responses, or as TaskStatusForLLM.message.

MessageForLLM(
    context_id="ctx-456",
    kind="message",
    parts=[
        TextPartForLLM(
            kind="text",
            text="I found three recent papers on quantum computing and retrieved the abstract for the most recent one.",
        ),
    ],
)

TaskStatusForLLM

TaskStatusForLLM(
    state=TaskState.completed,
    message=MessageForLLM(
        context_id="ctx-456",
        kind="message",
        parts=[
            TextPartForLLM(
                kind="text",
                text="I found three recent papers on quantum computing and retrieved the abstract for the most recent one.",
            ),
        ],
    ),
)

ArtifactForLLM

Returned by view_text_artifact(), view_data_artifact(), and minimize_artifacts(). Used in TaskForLLM.artifacts.

ArtifactForLLM(
    artifact_id="art-790",
    description="Abstract of 'Quantum Error Correction Advances' by Chen et al.",
    name="Abstract",
    parts=[
        TextPartForLLM(
            kind="text",
            text="Quantum computing has seen rapid advances in error correction.\nRecent work demonstrates fault-tolerant logical qubits at scale.\nThis paper surveys progress in quantum error correction from 2023-2025.\nWe review surface codes, color codes, and novel hybrid approaches.\nKey results include a 10x reduction in logical error rates.\nThese improvements bring practical quantum computing closer to reality.\nWe also discuss remaining challenges in qubit connectivity.\nFinally, we outline a roadmap for achieving fault-tolerant quantum computation.",
        ),
    ],
)

TextPartForLLM

TextPartForLLM(
    kind="text",
    text="Quantum computing has seen rapid advances in error correction.\nRecent work demonstrates fault-tolerant logical qubits at scale.\nThis paper surveys progress in quantum error correction from 2023-2025.",
)

DataPartForLLM

DataPartForLLM(kind="data", data=[
    {
        "title": "Quantum Error Correction Advances",
        "year": 2025,
        "authors": "Chen et al.",
    },
    {
        "title": "Topological Quantum Computing Survey",
        "year": 2024,
        "authors": "Nakamura et al.",
    },
    {
        "title": "Fault-Tolerant Logical Qubits",
        "year": 2024,
        "authors": "Wang et al.",
    },
])

FilePartForLLM

Represents a file part in artifacts and messages. uri and bytes are mutually exclusive — at most one is set.

FilePartForLLM(
    kind="file",
    name="q4-report.pdf",
    mime_type="application/pdf",
    uri=None,
    bytes={
        "_saved_to": [
            "./storage/files/task-123/art-789/q4-report.pdf",
        ],
    },
)

📄 License

a2a-utils is distributed under the terms of the Apache-2.0 license.

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