Slack connector for AI agents

Tools your Slack agent reaches for, scoped per user.

CALL ANY TOOL

Read channels, send messages, look up members, and upload files. Same toolkit, every framework, no auth plumbing to maintain.

slack_channels_list

List channels

List all channels in the workspace with optional filters for type and archived status. Returns channel ID, name, topic, purpose, and member count.

Parameters

Name

Type

Required

Description

types

string

Optional

Comma-separated channel types: public_channel, private_channel, im, mpim

exclude_archived

boolean

Optional

Exclude archived channels from results

limit

integer

Optional

Max channels to return (default 100)

slack_channel_history

Get channel messages

slack_messages_send

Send message

slack_thread_replies

Get thread replies

slack_users_list

List users

slack_users_lookup

Look up user

slack_files_upload

Upload file

Build your Agent

Drop the toolkit in, point it at the user, and your agent can read Slack messages, send replies, and look up team members from the first run.

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import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

// list slack tools scoped to a specific user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["slack"], toolNames: ["slack_channels_list", "slack_channel_history", "slack_messages_send"] },
  pageSize: 100,
});

// wrap each Scalekit tool as a LangChain DynamicStructuredTool
const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: t.tool.definition.input_schema,
  func: async (args) => sk.tools.executeTool({
    toolName: t.tool.definition.name, identifier: "user_123", toolInput: args,
  }),
}));

const agent = createReactAgent({ llm, tools: lcTools });

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import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["slack"], toolNames: ["slack_channels_list", "slack_channel_history", "slack_messages_send"] },
  pageSize: 100,
});

// shape tools as OpenAI function definitions
const llmTools = tools.map((t) => ({
  type: "function" as const,
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["slack"], toolNames: ["slack_channels_list", "slack_channel_history", "slack_messages_send"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

Copied

import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/slack",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

These are the questions teams ask Slack every day. Copy any prompt, paste into your agent, watch it route to the right channel with the member's scope.

Search & recall

Copy the prompt

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What was decided in #engineering this week?

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What did [person] say about [topic] in [channel]?

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Find all messages mentioning [keyword] in #general.

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What threads are unresolved in #customer-support?

Action & outreach

Copy the prompt

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Send a message to #announcements: [message text].

Copy the prompt

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Post a follow-up to the thread in #sales from yesterday.

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DM [user] with these meeting notes.

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Upload this summary to #weekly-updates.

Team & context

Copy the prompt

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Who is on the #design team?

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List all active channels in this workspace.

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What was the last message from [person] in #general?

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Find [user] by their email address.

SEE HOW AUTH WORKS

Your users authorize Slack once. Their workspace credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

Slack

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

A’s meetings only

Store

Their

Slack

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

Slack

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

Slack

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

See the same per-member auth pattern across other messaging and productivity connectors. Working code, live demos, fork what fits.

ENGINEERING

Slack workflow agent (LangGraph)

LangGraph agent that drives multi-step Slack workflows: triggers, approvals, and follow-up actions per user identity.

Slack

View agent

ENGINEERING

DevOps assistant agent

Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.

SUPPORT

Support triage agent

Read Zendesk tickets, fetch runbooks from Notion, and route to the right Slack channel with a drafted response.

SALES

Deal intelligence agent

Combine Gong call recordings, Attio contact data, and Slack signals to surface deal risks and next-best actions. Updated after every call.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with member identity, scope enforcement, and audit so production deploys hold up.

01.

Shared bot tokens break user-level attribution

A shared Slack bot token looks fine in a demo. In production, every message looks like it came from a bot account. Audit logs lose user context. Per-user scoping breaks. Scalekit resolves the credential of the actual workspace member who triggered the agent, never a shared bot.

// shared bot token
 token = "xoxb-shared-xxx"
 audit → slack_bot_service
 user_filter → broken


 // scalekit: per-user
 token = resolve(user_id)
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Slack today. Notion, GitHub, Salesforce tomorrow.

Capability

DIY

Scalekit AgentKit

Token storage

Build + maintain yourself

AES-256 vault, managed

Per-user isolation

Custom credential map

Per-tenant namespace, default

Scope enforcement

Manual checks or none

Per-request, pre-API call

Token refresh

Cron job you maintain

Automatic

Audit trail

Build your own logging

90-day, SIEM-exportable

New connector

New OAuth implementation

Same pattern, one config

Multi-framework

Per-framework adapter code

8 adapters included

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit

Start in your coding agent

Up and running in one command

Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up

Claude Code REPL

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/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack

Cursor Code REPL

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# ~/.cursor/mcp.json
{
""mcpServers"": {
""slack"": {
""url"": ""https://mcp.scalekit.com/slack"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}

Codex Code REPL

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# ~/.codex/config.toml
[mcp_servers.slack]
url = ""https://mcp.scalekit.com/slack""
auth_env = ""SCALEKIT_TOKEN""

Copilot Code REPL

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# .vscode/mcp.json
{
""servers"": {
""slack"": {
""url"": ""https://mcp.scalekit.com/slack"",
""type"": ""http""
}
}
}