LangChain - LithoBlocks

Use LithoBlocks with LangChain so your agent can send Slack messages via templates. The agent chooses a template and payload; a tool calls the LithoBlocks API. You get deterministic messages and far fewer tokens because the LLM does not generate Block Kit JSON. The resulting LLM savings help offset LithoBlocks cost, with net cost reduction at higher volumes.

Prerequisites

LithoBlocks API key and at least one template
Connecting Slack for your LithoBlocks workspace
LangChain installed (pip install langchain langchain-openai or equivalent)

Why use LithoBlocks with LangChain

Deterministic messages — The agent picks a template and data; the tool sends it. No Block Kit in the prompt.
Lower LLM cost — The agent outputs only template_id and a small payload; the tool does the HTTP call. LLM savings help offset LithoBlocks cost; at higher volumes, net cost reduction.
Consistency — Same layout every time; fewer malformed blocks.

Implementing the tool

Define a LangChain tool that accepts template_id and payload (mapped to API field data), then POSTs to the LithoBlocks API.

import os
from typing import Optional

import requests
from langchain_core.tools import tool

LITHOBLOCKS_API_KEY = os.environ.get("LITHOBLOCKS_API_KEY")
BASE_URL = "https://api.lithoblocks.com"

@tool
def send_slack_message(template_id: str, payload: dict, channel_id: Optional[str] = None) -> str:
    """Send a Slack message using a LithoBlocks template. Use when the user wants to notify someone in Slack.
    template_id: The LithoBlocks template ID (e.g. from your dashboard).
    payload: JSON object matching the template's placeholders (e.g. {"user": {"name": "Alice"}, "message": "Hello"}).
    channel_id: Optional Slack channel ID for send (see API reference for other destinations).
    """
    url = f"{BASE_URL}/v1/templates/send"
    headers = {
        "Authorization": f"Bearer {LITHOBLOCKS_API_KEY}",
        "Content-Type": "application/json",
    }
    body = {"template_id": template_id, "data": payload}
    if channel_id:
        body["channel_id"] = channel_id
    response = requests.post(url, json=body, headers=headers)
    if response.ok:
        return f"Message sent successfully: {response.json().get('message_ts', 'ok')}"
    return f"Failed to send message: {response.status_code} {response.text}"

For compile (get blocks without sending), POST to https://api.lithoblocks.com/v1/templates/compile (or your configured base URL plus /v1/templates/compile) with JSON {"template_id": "...", "data": {...}} and return the response to the agent or post blocks yourself. See the API reference for request/response shapes.

Using the tool in an agent

Add the tool to your agent’s tools list. The agent can then “send a Slack message” by invoking the tool with template_id and payload.

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tools = [send_slack_message]
prompt = ChatPromptTemplate.from_messages([
    ("system", "You can send Slack messages using the send_slack_message tool when the user asks to notify someone."),
    MessagesPlaceholder(variable_name="chat_history", optional=True),
    ("human", "{input}"),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
])
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "Send a Slack message to #general saying the report is ready."})

The agent will call send_slack_message with the appropriate template_id and payload for your template.

Next steps

AI agents overview

Why templates and agents fit together.

API reference

Endpoints, auth, and parameters.

Placeholders

Template data shape and syntax.

Documentation Index

​Prerequisites

​Why use LithoBlocks with LangChain

​Implementing the tool

​Using the tool in an agent

​Next steps