Hive — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Hive. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: May 5, 2026 • Sentiment updated: Apr 24, 2026

GitHub Statistics

Community Sentiment

Community Buzz: HIVE Digital secures $115 million for massive AI expansion, according to TheStreet on Twitter, and 'Beyond the Hive' free update is out now on Reddit

Pros & Cons

What People Love

Innovative AI solutions, HIVE Digital's funding news on Twitter and Reddit

Common Complaints

High cost, No significant complaints

Biggest Positive: Innovative AI

Biggest Negative: Expensive

Why Hive Stands Out

Hive stands out for its ability to bridge the gap between AI model development and production deployment. By providing a runtime harness for AI agents, Hive enables teams to move agents from prototype to production while addressing the bottlenecks of state management, failure recovery, cost control, and observability. Hive's unique approach to self-healing and adaptive agents through failure capture and graph evolution sets it apart from other solutions.

Built With

Build a production-ready AI agent that handles state persistence and crash recovery — Hive provides a runtime harness for AI agents that manage state isolation, checkpoint-based crash recovery, and cost enforcement, Build a self-healing and adaptive agent that improves over time — Hive captures failure data, evolves the agent graph through the coding agent, and redeploys automatically, Build a multi-agent coordination system with session isolation and shared buffers — Hive scales with model improvements and allows for parallel execution of agents, Build a human-in-the-loop system with real-time observability and cost limits — Hive integrates human oversight, audit trails, and cost enforcement, Build a scalable and reliable agent framework that adapts to model improvements — Hive provides a runtime that handles state, recovery, and parallel execution at scale

Getting Started

  1. Install Python 3.11+ for agent development
  2. Set up an LLM provider that powers the agents
  3. Install ripgrep (optional) for faster file search
  4. Generate a new Hive project using `hive init <project_name>`
  5. Configure Hive by editing `config.yaml` and setting up your LLM provider, try running `hive run --help` to verify it works

About

Multi-Agent Harness for Production AI

Category & Tags

Category: multi-agent

Tags: agent, agent-framework, agent-skills, anthropic, automation, autonomous-agents, claude, harness, harness-engineering, human-in-the-loop, openai, python, self-hosted, self-improving

Market Context

Competitive AI market