AI chat with a custom tool¶

Source: examples/panels/ai/chat_custom_tool.py Test: tests/panels/ai/examples/test_chat_custom_tool.py

How to hand a custom LangChain BaseTool to the chat so the model can call it.

The scenario¶

We give the LLM a simple in-memory key-value store with get / set / update / delete / list operations. The tool appears as a checkbox in the sidebar and becomes available to the model when toggled on.

The tool¶

from typing import Literal

from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field


class LocalStorageInput(BaseModel):
    action: Literal["get", "set", "update", "delete", "list"]
    key: str | None = None
    value: str | None = None


class LocalStorageTool(BaseTool):
    name: str = "local_storage"
    description: str = (
        "A simple key-value store. Supported actions: get, set, update, delete, list."
    )
    args_schema: type[BaseModel] = LocalStorageInput
    storage: dict[str, str] = Field(default_factory=dict)

    def _run(self, action, key=None, value=None) -> str:
        # routes to self._action_get / _set / _update / _delete / _list
        ...

    async def _arun(self, action, key=None, value=None) -> str:
        return self._run(action=action, key=key, value=value)

See the full source for the per-action handlers — they’re straightforward dict operations.

Wiring it into panelini¶

import panel as pn

from panelini import Panelini
from panelini.panels.ai import AiChat

chat = AiChat(
    system_message="You are a helpful assistant with access to a local storage tool.",
    tools=[LocalStorageTool()],
)

app = Panelini(title="AI Chat with Custom Tool", sidebar_enabled=True)
app.main_set(objects=[pn.Row(*chat.main_objects)])
app.sidebar_set(objects=chat.sidebar_objects)

Two things to notice:

We use AiChat directly rather than use_ai=True — that gives us control over the tools= list.
We stitch chat.main_objects and chat.sidebar_objects into the panelini layout by hand. AiChat is just a Panel component; you can compose it however you like.

What you’ll see¶

        graph TB
    subgraph sidebar [" Sidebar "]
        direction TB
        gen(["General Setup"])
        tools(["Basic Tools<br/>☐ get_current_time<br/>☐ update_preview<br/>☐ local_storage"])
    end
    subgraph main [" Main "]
        chat(["Chat"])
        preview(["Preview"])
    end

    classDef side fill:#6366f1,stroke:#4f46e5,color:#ffffff
    classDef main fill:#0d7377,stroke:#095c5f,color:#ffffff
    class gen,tools side
    class chat,preview main

When you tick the local_storage box, the chat posts a system message:

Tools updated — 2 tool(s) now available

From that point on, the model can decide to call local_storage when it fits — e.g. ”store this note under notes/today”.

Adding your own tool¶

Any LangChain BaseTool works — including:

HTTP fetchers
Database lookups
Vector-store retrievers
Your own domain APIs

The checkbox label comes from tool.name; the hover/tooltip text comes from tool.description. The checkbox state is wired through AiBackend.update_tools() — toggling it rebinds the tools on the underlying AiInterface without clearing history.