hive

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<p align="center"><em>The agent harness for production workloads — state management, failure recovery, observability, and human oversight so your agents actually run.</em></p>

## Overview

Hive is a runtime harness for AI agents in production. You describe your goal in natural language; a coding agent (the queen) generates the agent graph and connection code to achieve it. During execution, the harness manages state isolation, checkpoint-based crash recovery, cost enforcement, and real-time observability. When agents fail, the framework captures failure data, evolves the graph through the coding agent, and redeploys automatically. Built-in human-in-the-loop nodes, browser control, credential management, and parallel execution give you production reliability without sacrificing adaptability.

Visit [adenhq.com](https://adenhq.com) for complete documentation, examples, and guides.

Visit [HoneyComb](http://honeycomb.open-hive.com/) to see what jobs are being automated by AI. It’s a stock market for jobs, driven by our community’s AI agent progress. You can long and short jobs (with no real money but compute token)based on how much you think a job is going to be replaced by AI.

https://github.com/user-attachments/assets/bf10edc3-06ba-48b6-98ba-d069b15fb69d


## Who Is Hive For?

Hive is the multi-agent harness layer for teams moving AI agents from prototype to production. Single agents like Openclaw and Cowork can finish personal jobs pretty well but lack the rigor to fulfil business processes. 

Hive is a good fit if you:

- Want AI agents that **execute real business processes**, not demos
- Need a **runtime that handles state, recovery, and parallel execution** at scale
- Need **self-healing and adaptive agents** that improve over time
- Require **human-in-the-loop control**, observability, and cost limits
- Plan to run agents in **production** where uptime, cost, and auditability matter

Hive may not be the best fit if you’re only experimenting with simple agent chains or one-off scripts.

## When Should You Use Hive?

Use Hive when the bottleneck is no longer the model but the harness around it:

- Long-running agents that need **state persistence and crash recovery**
- Production workloads requiring **cost enforcement, observability, and audit trails**
- Agents that **self-heal** through failure capture and graph evolution
- Multi-agent coordination with **session isolation and shared buffers**
- A framework that **scales with model improvements** rather than fighting them

## Quick Links

- **[Documentation](https://docs.adenhq.com/)** - Complete guides and API reference
- **[Self-Hosting Guide](https://docs.adenhq.com/getting-started/quickstart)** - Deploy Hive on your infrastructure
- **[Changelog](https://github.com/aden-hive/hive/releases)** - Latest updates and releases
- **[Roadmap](docs/roadmap.md)** - Upcoming features and plans
- **[Report Issues](https://github.com/aden-hive/hive/issues)** - Bug reports and feature requests
- **[Contributing](CONTRIBUTING.md)** - How to contribute and submit PRs

## Quick Start

### Prerequisites

- Python 3.11+ for agent development
- An LLM provider that powers the agents
- **ripgrep (optional, recommended on Windows):** The `search_files` tool uses ripgrep for faster file search. If not installed, a Python fallback is used. On Windows: `winget install BurntSushi.ripgrep` or `scoop install ripgrep`

> **Windows Users:** Native Windows is supported via `quickstart.ps1` and `hive.ps1`. Run these in PowerShell 5.1+. WSL is also an option but not required.

### Installation

> **Note**
> Hive uses a `uv` workspace layout and is not installed with `pip install`.
> Running `pip install -e .` from the repository root will create a placeholder package and Hive will not function correctly.
> Please use the quickstart script below to set up the environment.

```bash
# Clone the repository
git clone https://github.com/aden-hive/hive.git
cd hive

# Run quickstart setup (macOS/Linux)
./quickstart.sh

# Windows (PowerShell)
.\quickstart.ps1
```

This sets up:

- **framework** - Core agent runtime and graph executor (in `core/.venv`)
- **aden_tools** - MCP tools for agent capabilities (in `tools/.venv`)
- **credential store** - Encrypted API key storage (`~/.hive/credentials`)
- **LLM provider** - Interactive default model configuration, including Hive LLM and OpenRouter
- All required Python dependencies with `uv`

- Finally, it will open the Hive interface in your browser

> **Tip:** To reopen the dashboard later, run `hive open` from the project directory.

### Build Your First Agent

Type the agent you want to build in the home input box. The queen is going to ask you questions and work out a solution with you.

<img width="2500" height="1214" alt="Image" src="https://github.com/user-attachments/assets/1ce19141-a78b-46f5-8d64-dbf987e048f4" />

### Use Template Agents

Click "Try a sample agent" and check the templates. You can run a template directly or choose to build your version on top of the existing template.

### Run Agents

Now you can run an agent by selecting the agent (either an existing agent or example agent). You can click the Run button on the top left, or talk to the queen agent and it can run the agent for you.

<img width="2549" height="1174" alt="Screenshot 2026-03-12 at 9 27 36 PM" src="https://github.com/user-attachments/assets/7c7d30fa-9ceb-4c23-95af-b1caa405547d" />

## Features

- **Browser-Use** - Control the browser on your computer to achieve hard tasks
- **Parallel Execution** - Execute the generated graph in parallel. This way you can have multiple agents completing the jobs for you
- **[Goal-Driven Generation](docs/key_concepts/goals_outcome.md)** - Define objectives in natural language; the coding agent generates the agent graph and connection code to achieve them
- **[Adaptiveness](docs/key_concepts/evolution.md)** - Framework captures failures, calibrates according to the objectives, and evolves the agent graph
- **[Dynamic Node Connections](docs/key_concepts/graph.md)** - No predefined edges; connection code is generated by any capable LLM based on your goals
- **SDK-Wrapped Nodes** - Every node gets a shared data buffer, local RLM memory, monitoring, tools, and LLM access out of the box
- **[Human-in-the-Loop](docs/key_concepts/graph.md#human-in-the-loop)** - Intervention nodes that pause execution for human input with configurable timeouts and escalation
- **Real-time Observability** - WebSocket streaming for live monitoring of agent execution, decisions, and node-to-node communication

## Integration

<a href="https://github.com/aden-hive/hive/tree/main/tools/src/aden_tools/tools"><img width="100%" alt="Integration" src="https://github.com/user-attachments/assets/a1573f93-cf02-4bb8-b3d5-b305b05b1e51" /></a>
Hive is built to be model-agnostic and system-agnostic.

- **LLM flexibility** - Hive Framework supports Anthropic, OpenAI, OpenRouter, Hive LLM, and other hosted or local models through LiteLLM-compatible providers.
- **Business system connectivity** - Hive Framework is designed to connect to all kinds of business systems as tools, such as CRM, support, messaging, data, file, and internal APIs via MCP.

## Why Hive

As models improve, the upper bound of what agents can do rises — but their reliability and production value are determined by the harness. Hive focuses on generating agents that run real business processes rather than generic agents. Instead of requiring you to manually des