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Hive is a Python-based multi-agent orchestration framework designed to move AI agents from prototype to production. It handles state management, fault recovery, parallel execution, and human oversight across multiple LLM providers without requiring orchestration boilerplate.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | aden-hive/hive |
| Owner | aden-hive |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 10.6k |
| Forks | 5.6k |
| Open issues | 1.3k |
| Latest release | v0.11.0 (2026-05-02) |
| Last updated | 2026-05-29 |
| Source | https://github.com/aden-hive/hive |
What hive is
A model-agnostic agent harness that compiles objectives into strict, graph-based execution DAGs with persistent role-based memory, deterministic fault tolerance, and asynchronous multi-agent coordination. Supports OpenAI, Anthropic, Google Gemini, and LiteLLM-compatible providers via MCP tool integrations.
Get the hive source
Clone the repository and explore it locally.
git clone https://github.com/aden-hive/hive.gitcd hive# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Python 3.11+ required; uses uv workspace layout—avoid standard pip install. Follow quickstart.sh/quickstart.ps1 scripts for proper environment setup.
- Requires explicit LLM provider configuration (OpenAI, Anthropic, Gemini, or OpenRouter); no built-in model included. API keys stored encrypted in ~/.hive/credentials.
- Graph-based DAG compilation means workflows must be designed as strict, composable task graphs; highly dynamic or unstructured agent interactions may be difficult to express.
- MCP tool integration is core to capability expansion; plan to map business system APIs (CRM, support, data) as MCP tools for agent access.
- Human-in-the-loop features and observability dashboard are built-in; factor in UI/UX training and monitoring overhead for your ops team.
When to avoid it — and what to weigh
- Simple Agent Chains or One-Off Scripts — If your use case is quick experimentation or single-agent tasks, Hive's state management and multi-agent runtime will introduce unnecessary overhead.
- Minimal Observability or Audit Requirements — Projects without production-grade demands for logging, cost limits, and human-in-the-loop control will find Hive overengineered.
- Self-Hosted Infrastructure Unavailable — Hive requires deployment on your infrastructure; there is no clear mention of managed hosting. If on-premises deployment is not feasible, evaluate alternatives.
- Strict Determinism Without Graph Compilation — If your workflow requires arbitrary, ad-hoc agent choreography outside of compiled DAGs, Hive's rigid graph model may conflict with your architecture.
License & commercial use
Apache License 2.0 (OSI-approved, permissive open-source license). Permits commercial use, modification, and distribution with attribution and liability disclaimers.
Apache 2.0 is a permissive, OSI-approved license that explicitly permits commercial use. No ambiguity in commercial rights; you may use Hive in production and proprietary applications. Verify compliance with attribution requirements and any dependencies' licenses.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Encrypted credential storage (~/.hive/credentials) for API keys indicated but implementation details unknown. Self-hosted deployment means you control network boundaries and access control. No security audit or vulnerability disclosure policy mentioned in provided data. Requires review of authentication, RBAC, audit logging, and data isolation for production workloads.
Alternatives to consider
Anthropic/OpenAI API + Custom Orchestration
Direct LLM API calls with custom Python orchestration offer maximum flexibility but require building state, fault recovery, and multi-agent coordination from scratch.
LangChain / LlamaIndex
Lightweight agent frameworks with broader ecosystem support and lower barrier to entry; better for prototyping but lack Hive's production-grade state management and fault tolerance.
Temporal / Apache Airflow
Mature workflow orchestration platforms with broader language support and battle-tested reliability; require more manual agent implementation but offer proven DevOps integrations.
Build on hive with DEV.co software developers
Hive handles the orchestration, state, and fault recovery so your agents actually run at scale. Explore the docs, try the quickstart, and join the Discord community to get started.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
hive FAQ
Can I use Hive with models other than OpenAI or Anthropic?
Is Hive suitable for prototyping, or is it only for production?
Do I need to host Hive myself?
How does Hive handle state and fault recovery?
Software development & web development with DEV.co
Pairing hive with the rest of your stack is where most open-source devops projects stall. DEV.co is a software development agency that does the integration work — plus AI development when models are in scope.
Ready to Move AI Agents to Production?
Hive handles the orchestration, state, and fault recovery so your agents actually run at scale. Explore the docs, try the quickstart, and join the Discord community to get started.