hive

Overview

Build autonomous, reliable, self-improving AI agents without hardcoding workflows. Define your goal through conversation with a coding agent, and the framework generates a node graph with dynamically created connection code. When things break, the framework captures failure data, evolves the agent through the coding agent, and redeploys. Built-in human-in-the-loop nodes, credential management, and real-time monitoring give you control without sacrificing adaptability.

Visit adenhq.com for complete documentation, examples, and guides.

https://github.com/user-attachments/assets/846c0cc7-ffd6-47fa-b4b7-495494857a55

Who Is Hive For?

Hive is designed for developers and teams who want to build production-grade AI agents without manually wiring complex workflows.

Hive is a good fit if you:

• Want AI agents that execute real business processes, not demos
• Prefer goal-driven development over hardcoded workflows
• 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 environments

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 you need:

• Long-running, autonomous agents
• Strong guardrails, process, and controls
• Continuous improvement based on failures
• Multi-agent coordination
• A framework that evolves with your goals

Quick Links

• Documentation - Complete guides and API reference
• Self-Hosting Guide - Deploy Hive on your infrastructure
• Changelog - Latest updates and releases
• Roadmap - Upcoming features and plans
• Report Issues - Bug reports and feature requests
• Contributing - How to contribute and submit PRs

Quick Start

Prerequisites

• Python 3.11+ for agent development
• Claude Code, Codex CLI, or Cursor for utilizing agent skills

Note for Windows Users: It is strongly recommended to use WSL (Windows Subsystem for Linux) or Git Bash to run this framework. Some core automation scripts may not execute correctly in standard Command Prompt or PowerShell.

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.

# Clone the repository
git clone https://github.com/adenhq/hive.git
cd hive

# Run quickstart setup
./quickstart.sh

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
• All required Python dependencies with uv

Build Your First Agent

# Build an agent using Claude Code
claude> /hive

# Test your agent
claude> /hive-debugger

# (at separate terminal) Launch the interactive dashboard
hive tui

# Or run directly
hive run exports/your_agent_name --input '{"key": "value"}'

Coding Agent Support

Codex CLI

Hive includes native support for OpenAI Codex CLI (v0.101.0+).

1. Config: .codex/config.toml with agent-builder MCP server (tracked in git)
2. Skills: .agents/skills/ symlinks to Hive skills (tracked in git)
3. Launch: Run codex in the repo root, then type use hive

Example:

codex> use hive

Opencode

Hive includes native support for Opencode.

1. Setup: Run the quickstart script
2. Launch: Open Opencode in the project root.
3. Activate: Type /hive in the chat to switch to the Hive Agent.
4. Verify: Ask the agent "List your tools" to confirm the connection.

The agent has access to all Hive skills and can scaffold agents, add tools, and debug workflows directly from the chat.

📖 Complete Setup Guide - Detailed instructions for agent development

Antigravity IDE Support

Skills and MCP servers are also available in Antigravity IDE (Google's AI-powered IDE). Easiest: open a terminal in the hive repo folder and run (use ./ — the script is inside the repo):

./scripts/setup-antigravity-mcp.sh

Important: Always restart/refresh Antigravity IDE after running the setup script—MCP servers only load on startup. After restart, agent-builder and tools MCP servers should connect. Skills are under .agent/skills/ (symlinks to .claude/skills/). See docs/antigravity-setup.md for manual setup and troubleshooting.

Features

• Goal-Driven Development - Define objectives in natural language; the coding agent generates the agent graph and connection code to achieve them
• Adaptiveness - Framework captures failures, calibrates according to the objectives, and evolves the agent graph
• Dynamic Node Connections - No predefined edges; connection code is generated by any capable LLM based on your goals
• SDK-Wrapped Nodes - Every node gets shared memory, local RLM memory, monitoring, tools, and LLM access out of the box
• 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
• Interactive TUI Dashboard - Terminal-based dashboard with live graph view, event log, and chat interface for agent interaction
• Cost & Budget Control - Set spending limits, throttles, and automatic model degradation policies
• Production-Ready - Self-hostable, built for scale and reliability

Integration

Hive is built to be model-agnostic and system-agnostic.

• LLM flexibility - Hive Framework is designed to support various types of LLMs, including hosted and 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 Aden

Hive focuses on generating agents that run real business processes rather than generic agents. Instead of requiring you to manually design workflows, define agent interactions, and handle failures reactively, Hive flips the paradigm: you describe outcomes, and the system builds itself—delivering an outcome-driven, adaptive experience with an easy-to-use set of tools and integrations.

flowchart LR
    GOAL["Define Goal"] --> GEN["Auto-Generate Graph"]
    GEN --> EXEC["Execute Agents"]
    EXEC --> MON["Monitor & Observe"]
    MON --> CHECK{{"Pass?"}}
    CHECK -- "Yes" --> DONE["Deliver Result"]
    CHECK -- "No