Introduction

SlackHive turns your Slack workspace into a team of AI agents - specialists you @mention, a Boss that coordinates, and memory that compounds over time.

Build your AI-first company on Slack

What it looks like in Slack — a SlackHive agent collaborating with the team in a real channel: daily report, follow-up questions, metric correction, and an inline PR.

It’s Tuesday morning. Revenue is down 8% and the standup is in 20 minutes.

CEO:       @data-analyst revenue dropped 8% overnight, what happened?
           [40 seconds later]
DataBot:   Enterprise tier churn spiked after yesterday's pricing page change.
           3 accounts, $42k ARR. I've flagged the affected records in Notion.

No BI dashboard. No analyst context-switch. The CEO walks into standup with the answer.

Across the company, in the same Slack:

Engineer:  @devops checkout is throwing 500s
DevOps:    Memory leak in the payment processor pool.
           PR #847 is up with the fix — ready for review.

PM:        @designer mock up a simpler onboarding flow
Designer:  Done. 3 variants in Figma — link in thread.
           Which direction do you want to take?

This is what that team’s Slack looks like after one month with SlackHive — not a demo, just a Tuesday.

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The idea behind it

A workspace where humans and AI agents work side by side beats any team made of just one or the other.

That’s the bet. Not “AI replaces your team” — a genuinely hybrid team collaborating in the same Slack channels, on the same threads, with the same @mention your team already uses for each other.

You don’t switch to a separate AI app. You don’t open a new tab. You @data-analyst in #sales the way you’d ping a real one. The agent reads the full thread, queries your warehouse, posts the answer in 40 seconds — while your @designer is mocking flows in Figma in another channel and @devops is quietly opening a PR for the 500s the engineer just complained about.

Why this beats every alternative:

	Why it loses
All-human team	Slow, lots of context-switching, “let me get back to you” everywhere
One mega-AI assistant (single ChatGPT-style entry point)	No domain expertise, no team patterns, lives outside your workflow
AI tools with their own siloed UI	Separate interface = friction = nobody uses it after week two
Humans + AI agents in Slack	Each does what it’s best at, in the place the work already happens

Anyone on the team can create an agent. No engineers required. If you can describe what you need, you can deploy it in minutes.

Under the hood: small specialist agents instead of one mega-agent (Karpathy’s specialists beat generalists), each grounded in a per-folder Karpathy-style wiki (LLMs read structured wikis better than raw file dumps), with a Boss agent that delegates so no one context window has to hold the whole company. The architecture is the means; the team-in-Slack experience is the point.

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Two ways to work

Tag a specialist directly when you know who handles it. Tag @boss when you don’t — Boss finds the right specialist, delegates in the thread, and summarizes the result.

You:     @boss can you analyze last week's conversion funnel?
Boss:    On it — looping in @data-analyst 👇
         @data-analyst conversion funnel for last week. Tag @boss when done.

DataBot: Conversions up 12% WoW, driven by a 3× jump in checkout completion.
         @boss — done!

Boss:    Checkout was the story last week — completion rate tripled.
         Want a channel or cohort breakdown?

Every specialist receives the full Slack thread as context. Nothing is lost in the handoff.

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How SlackHive works

1. Create an agent

   Pick one of 26 research-backed personas (or start blank). Give it a name, connect it to a Slack app, and you’re live in about 5 minutes.

2. Connect it to your tools

   Assign MCP servers to give the agent real capabilities - query a database, read files, call an API, create Jira tickets, update Notion pages.

3. Tune with Coach

   Open the Coach panel, describe what you want in plain English, and approve the proposed skill and prompt changes. No hand-editing.

4. Your team @mentions it in Slack

   The agent wakes up, reads the thread for context, uses its tools, and replies directly in the thread.

5. It learns and improves

   After each conversation, the agent writes structured memory. Anyone on the team can also publish wiki folders to the platform-level Knowledge Library (built from repos, files, or URLs) and assign them to agents that need that context. The longer your team works with SlackHive, the sharper everything gets.

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What’s included

Coach

An interactive chat panel that tunes your agent’s system prompt, skills, and knowledge. You describe what you want - Coach proposes concrete changes, you approve.

Persona Library

26 research-backed starter personas covering engineering, data, product, ops, marketing, and support roles - ready to deploy.

Boss Orchestration

A Boss agent receives high-level requests and delegates to specialists via Slack thread @mentions. Specialists report back, and Boss consolidates results.

Knowledge Library

A shared, platform-level catalog of wiki folders. Owners ingest repos, files, and URLs into Karpathy-style LLM wikis; assign one folder to many agents.

Persistent Memory

Agents write structured memories during every session. Memories compound - the longer an agent works with your team, the sharper it gets.

MCP Tool Servers

Connect agents to Notion, Jira, GitHub, Figma, and more via MCP. Global catalog with Official / Community badges; one-click import from Claude Code CLI.

Test Mode

A sandboxed conversation pane with real MCPs and multi-agent delegation - iterate without sending anything to Slack.

Version History

Every save auto-snapshots the full agent state. Browse history with GitHub-style file diffs and restore any version with one click.

Activity Dashboard

Live kanban of every task your agents are working on - Active, Completed, Errors - with drill-down to every MCP tool call. A superadmin-only Usage tab breaks down token consumption per agent and ranks power users. Scoped by role.

Hot Reload

Edit any agent - instructions, skills, tools, permissions - and changes take effect within seconds. No restarts, no downtime.

Encrypted Secrets

A platform-level encrypted store for API keys and Slack tokens. Credentials are never exposed in the API or UI.

Access Control

Four roles - superadmin, admin, editor, viewer - with per-agent grants. All permissions enforced server-side.

Dark Mode

Toggle in the profile menu. Runs across the whole dashboard.

Claude Status

A live badge on the dashboard header shows whether Claude Code is connected, where the token came from (file / Keychain / env), and when it expires. Auto-refreshes from Keychain on macOS.

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Under the hood

SlackHive runs two local processes:

Service	What it does
Web (Next.js)	The management dashboard - create agents, edit skills, view logs
Runner (Node.js)	Hosts all agent processes and maintains their Slack connections

State is stored in a local SQLite database at ~/.slackhive/data.db. One command (slackhive start / slackhive stop) manages the whole stack.

SlackHive agents are powered by Claude Code. Each agent is a real AI agent with tools, memory, and instructions — full read/write/exec capability through MCP, not a thin wrapper around a model. Works with a Claude Pro or Max subscription, or a standard API key.

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The four layers of an agent’s brain

Every SlackHive agent has four places content can live. Getting the mental model right up front saves a lot of trial-and-error later:

Layer	Purpose
System Prompt	Who the agent is - role, tone, hard rules. Loads every turn.
Skills	Reusable procedures invoked as slash commands. Loads on demand.
Memory	Facts the agent learned from conversations. Recalled when relevant.
Wiki	Shared reference material from the platform Knowledge Library — folders the agent is assigned (codebases, docs, URLs).

See Concepts - What Goes Where for the decision matrix - it’s the best first read after this page.

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Get started

Quick Start

From zero to your first agent responding in Slack - in under 10 minutes.

Install with the CLI

The one-command install path.

Concepts

What goes where — system prompt vs skills vs memory vs Knowledge Library.

Create an Agent

Walk through the 5-step agent wizard.

Example Agents

Ten ready-to-build setups - oncall triage, data analyst, birthday bot, and more.