Build agents that improve themselves
Hive is a goal-driven agent framework that generates node graphs dynamically from natural language. When things break, the framework captures failure data, evolves the agent through a coding agent, and redeploys. Built-in human-in-the-loop nodes, credential management, and real-time monitoring give you control without sacrificing adaptability.Quickstart
Get up and running with your first agent in under 5 minutes
Installation
Detailed setup instructions for Python 3.11+, dependencies, and verification
Key concepts
Understand graphs, nodes, edges, and the self-correction loop
GitHub
Star the repo and contribute to the open-source framework
Why Hive?
Traditional agent frameworks require you to hardcode workflows, manually wire nodes, and reactively handle failures. Hive flips this paradigm: you describe outcomes, and the system builds itself.Hive uses a coding agent (“Queen”) to generate agent graphs from your goals. The framework then walks the graph, manages retries, and captures failures for continuous improvement.
Built for production
Hive is designed for developers and teams who need production-grade AI agents that execute real business processes:- Fast execution over open workflow
- Self-healing agents that improve over time
- Human-in-the-loop control and observability
- Cost limits and budget management
- Real-time monitoring via WebSocket streaming
How it works
Define your goal
Describe what you want to achieve in natural language. The coding agent generates an agent graph with nodes, edges, and connection code.
Workers execute
SDK-wrapped nodes run with full observability and tool access. Each node is an event loop where the LLM reasons, calls tools, and self-corrects until outputs meet criteria.
Framework monitors
Real-time metrics, cost tracking, and policy enforcement happen automatically. Human-in-the-loop nodes pause for approval at critical decision points.
Features at a glance
Goal-driven generation
Define objectives in natural language. The coding agent generates the agent graph and connection code to achieve them.
Dynamic node connections
No predefined edges. Connection code is generated by LLMs based on your goals and evolves when patterns fail.
Self-correction
Nodes use the reflexion pattern: try, evaluate, learn from the result, retry. Agents adapt in real time.
Human-in-the-loop
Pause execution for human input with configurable timeouts. Place approval gates at critical decision points.
Parallel execution
Execute graph branches in parallel. Multiple agents can complete jobs simultaneously for faster results.
Browser automation
Control browsers on your computer to navigate websites, fill forms, and scrape dynamic content.
Production-ready
Self-hostable with real-time observability, encrypted credential storage, and cost controls.
100+ integrations
Connect to business systems via MCP tools: CRM, support, messaging, databases, and internal APIs.
Hive vs. traditional frameworks
| Traditional frameworks | Hive |
|---|---|
| Hardcode agent workflows | Describe goals in natural language |
| Manual graph definition | Auto-generated agent graphs |
| Reactive error handling | Outcome-evaluation and adaptiveness |
| Static tool configurations | Dynamic SDK-wrapped nodes |
| Separate monitoring setup | Built-in real-time observability |
| DIY budget management | Integrated cost controls and degradation |
Supported LLM providers
Hive supports 100+ LLM providers through LiteLLM integration:- Anthropic (Claude Opus, Sonnet, Haiku) - Recommended
- OpenAI (GPT-4, GPT-4o, GPT-5)
- Google Gemini (Flash, Pro)
- DeepSeek, Mistral, Groq, Cerebras
- Local models via Ollama
Quick example
Here’s a minimal agent that greets users and converts messages to uppercase:Community and support
Join thousands of developers building production AI agents:- Discord community - Get help and share your agents
- GitHub issues - Report bugs and request features
- Twitter/X - Follow for updates
- Documentation - Complete guides and API reference
Next steps
Run the quickstart
Install Hive and build your first agent in 5 minutes
Learn the concepts
Understand how graphs, nodes, and self-correction work