10xGraphv0.9.2Search

    How to use prebuilt tools

    In shortUse the prebuilt tools in tenxgraph.prebuilt.tools: fetch_url, file tools, safe_calculator, web search, memory tools, and create_handoff_tool.

    • 4 min read
    • 11 sections
    • Updated
    • v0.9.2
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    10xGraph ships a set of production-ready tools in tenxgraph.prebuilt.tools. Drop them into any ToolNode or pass them directly to a prebuilt agent’s tools list.

    Python
    from tenxgraph.prebuilt.tools import (
    fetch_url,
    file_read,
    file_write,
    file_search,
    safe_calculator,
    google_web_search,
    vertex_ai_search,
    memory_tool,
    make_user_memory_tool,
    make_agent_memory_tool,
    create_handoff_tool,
)

    fetch_url

    Fetches the text content of any public HTTP/HTTPS URL. Blocks private/loopback IP addresses, enforces a configurable timeout, and truncates long responses.

    Python
    from tenxgraph.core.graph import Agent, ToolNode
from tenxgraph.prebuilt.tools import fetch_url

tool_node = ToolNode([fetch_url])

agent = Agent(
    model="gpt-4o",
    tool_node=tool_node,
    system_prompt=[{"role": "system", "content": "You are a research assistant."}],
)

    Tool schema (what the LLM sees):

    Parameter Type Default Description
    url str required Public HTTP/HTTPS URL to fetch.
    timeout float 10.0 Request timeout in seconds (max 30).
    max_chars int 20000 Maximum characters to return.

    The tool returns a JSON string:

    JSON
    {
  "url": "https://example.com",
  "status_code": 200,
  "content_type": "text/html",
  "content": "...",
  "truncated": false
}

    Tags: ["web", "fetch", "network"]

    safe_calculator

    Evaluates arithmetic expressions without exposing __builtins__. Safe for production.

    Python
    from tenxgraph.prebuilt.tools import safe_calculator

tool_node = ToolNode([safe_calculator])

    Tool schema:

    Parameter Type Description
    expression str Arithmetic expression to evaluate, e.g. "123 * 456 + 789".

    Returns the result as a string, or an error message if evaluation fails.

    Local filesystem tools. All three enforce that the path is within the current working directory or an explicit allowed root.

    Python
    from tenxgraph.prebuilt.tools import file_read, file_write, file_search

tool_node = ToolNode([file_read, file_write, file_search])

    file_read

    Parameter Type Description
    path str Relative or absolute path to the file.
    encoding str File encoding (default "utf-8").

    Returns file content as a string.

    file_write

    Parameter Type Description
    path str Path to write to.
    content str Text content to write.
    mode str "w" (overwrite, default) or "a" (append).

    Returns a success or error message.

    Parameter Type Description
    pattern str Glob pattern, e.g. "*.py" or "**/*.md".
    root str Root directory to search from (default: current working directory).

    Returns a JSON list of matching file paths.

    Calls the Google Custom Search API. Requires GOOGLE_API_KEY and GOOGLE_CSE_ID environment variables.

    Terminal
    export GOOGLE_API_KEY=your-google-api-key
export GOOGLE_CSE_ID=your-custom-search-engine-id
    Python
    from tenxgraph.prebuilt.tools import google_web_search

tool_node = ToolNode([google_web_search])

    Tool schema:

    Parameter Type Default Description
    query str required Search query string.
    num_results int 5 Number of results to return (max 10).

    Returns a JSON list of {"title": ..., "url": ..., "snippet": ...} objects.

    Calls Google Vertex AI Search. Requires Google Cloud credentials and a Vertex AI data store ID.

    Python
    from tenxgraph.prebuilt.tools import vertex_ai_search

tool_node = ToolNode([vertex_ai_search])

    Configure via environment variables:

    Terminal
    export GOOGLE_CLOUD_PROJECT=your-gcp-project
export VERTEX_AI_DATA_STORE_ID=your-data-store-id

    Memory tools

    Memory tools let the LLM search and write long-term user or agent memories. They are designed to be used with MemoryConfig.

    memory_tool

    A general-purpose memory search and write tool for use without MemoryConfig.

    Python
    from tenxgraph.prebuilt.tools import memory_tool
from tenxgraph.storage.store import create_local_qdrant_store, OpenAIEmbedding

store = create_local_qdrant_store("./qdrant_data", OpenAIEmbedding())
tool = memory_tool(store)

tool_node = ToolNode([tool])

    make_user_memory_tool and make_agent_memory_tool

    These are used internally by MemoryConfig; you can also call them directly if you need more control.

    Python
    from tenxgraph.prebuilt.tools import make_user_memory_tool, make_agent_memory_tool
from tenxgraph.storage.store import MemoryConfig, UserMemoryConfig

config = MemoryConfig(store=store)
user_tool = make_user_memory_tool(config)
agent_tool = make_agent_memory_tool(config)

tool_node = ToolNode([user_tool, agent_tool])

    The typical pattern is to let Agent(..., memory=MemoryConfig(...)) inject these tools automatically rather than registering them manually.

    create_handoff_tool

    Creates a handoff tool that transfers control from one agent to another in multi-agent graphs (swarm or supervisor patterns). See how-to/python/handoff-between-agents for the full handoff guide.

    Python
    from tenxgraph.prebuilt.tools import create_handoff_tool

handoff_to_billing = create_handoff_tool(
    agent_name="billing",
    description="Transfer the user to the billing agent for payment questions.",
)

tool_node = ToolNode([handoff_to_billing])

    Parameters:

    Parameter Type Description
    agent_name str Name of the target agent node in the graph.
    description str Description shown to the LLM to help it decide when to hand off.

    Composing tools

    All prebuilt tools can be mixed with custom tools in a single ToolNode:

    Python
    from tenxgraph.core.graph import Agent, StateGraph, ToolNode
from tenxgraph.prebuilt.tools import fetch_url, safe_calculator
from tenxgraph.utils.decorators import tool

@tool(name="get_exchange_rate", tags=["finance"])
async def get_exchange_rate(from_currency: str, to_currency: str) -> str:
    """Get the current exchange rate between two currencies."""
    return f"1 {from_currency} = 1.08 {to_currency}"

tool_node = ToolNode([fetch_url, safe_calculator, get_exchange_rate])

agent = Agent(
    model="gpt-4o",
    tool_node=tool_node,
)

    Prebuilt tool tags reference

    Tool Tags
    fetch_url ["web", "fetch", "network"]
    safe_calculator ["math", "calculator"]
    file_read ["file", "read"]
    file_write ["file", "write"]
    file_search ["file", "search"]
    google_web_search ["search", "web", "google"]
    vertex_ai_search ["search", "vertex", "google"]

    Use Agent(..., tools_tags={"search"}) to expose only search-tagged tools to a particular agent.

    What you learned

    • fetch_url fetches public URLs safely with timeout and size limits.
    • safe_calculator evaluates arithmetic without exposing Python builtins.
    • file_read, file_write, file_search provide controlled filesystem access.
    • google_web_search and vertex_ai_search enable live web search capabilities.
    • Memory tools are best used via Agent(..., memory=MemoryConfig(...)) rather than added manually.
    • create_handoff_tool enables agent-to-agent handoffs in multi-agent workflows.

    Next steps

    Last updated for v0.9.2Edit this page on GitHubReport an issue