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aden-hive/

hive

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Hive is a Python-based multi-agent harness for production AI, offering graph-based execution, memory, observability, and human-in-the-loop controls. It supports numerous LLM providers and self-improving agent graphs.

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5.7kforks
1.3kissues
Apache-2.0license
2026since
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Reviewgenerated from repository data · Aug 5, 2026

What it is

Hive is a multi-agent harness for production workloads that dynamically generates multi-agent topologies to tackle complex, long-running business workflows. It emphasizes state management, fault tolerance, observability, and human oversight.

How it works

  • Define a goal in plain English.
  • The agent graph is auto-generated, including connection code and test cases.
  • Workers execute using an SDK-wrapped node structure with observability and tool access.
  • A control plane provides real-time metrics and policy management.
  • If failures occur, the graph can evolve and redeploy.

Getting started

Installation guidance emphasizes a quickstart workflow rather than a standard pip install. The repository recommends cloning and running a quickstart script:

# 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 setup creates framework and tools virtual environments, a credential store, and an LLM configuration, then opens the Hive interface in a 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 assists in building the solution.

Use Template Agents

Choose a sample agent template to run or extend.

Run Agents

Select an agent and use the Run button or interact with the queen to execute the agent.

Integration

Hive is model-agnostic and supports various providers via LiteLLM, including Anthropic, OpenAI, OpenRouter, Hive LLM, and local models. It also connects to business systems (CRM, support, data, APIs) via MCP.

Documentation

Provides links to developer guides, getting started, and configuration.

Licensing and contribution

Open-source under the Apache License 2.0; contribution guidelines and issue-based workflow are described.

Agent skills inside · 16
All skills →
browser-edge-casesSOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.Otherscriptshive.terminal-tools-fs-searchUse terminal_rg / terminal_find when you need raw filesystem search outside the project tree — system configs, /var/log, /etc, archive contents — or when files-tools.search_files is too project-scoped. Teaches the rg vs find vs terminal_exec("ls/du/tree") split, common rg flag combos for code/logs/configs, find predicates for mtime/size/type queries, and the rule that for tree views or single-file stat info you should just use terminal_exec instead of inventing a tool. Read before reaching for raw shell to grep or find anything.Otherhive.terminal-tools-job-controlUse when launching anything that runs longer than a minute, anything that streams logs, anything you want to keep running while doing other work — or when terminal_exec auto-backgrounded on you and returned a job_id. Teaches the start→poll→wait pattern with terminal_job_logs offset bookkeeping, the `wait_until_exit=True` blocking-poll idiom, the truncated_bytes_dropped resumption signal, the merge_stderr decision, the SIGINT→SIGTERM→SIGKILL escalation ladder via terminal_job_manage, and the hard rule that jobs die when the terminal-tools server restarts. Read before calling terminal_job_start,Backend & APIhive.terminal-tools-foundationsRequired reading whenever any shell_* tool is available. Teaches the foreground/background dichotomy (terminal_exec auto-promotes past 30s, returns a job_id you poll with terminal_job_logs), the standard envelope shape (exit_code, stdout, stdout_truncated_bytes, output_handle, semantic_status, warning, auto_backgrounded, job_id), output handle pagination via terminal_output_get, when to read semantic_status instead of raw exit_code (grep/rg/find/diff/test exit 1 is NOT an error), the destructive-warning surface (rm -rf, git push --force, DROP TABLE), tool preference (use files-tools / gcu-toolTesting & QAhive.context-preservationProactively extract critical values from tool results into working notes before automatic context pruning destroys them.Otherhive.terminal-tools-troubleshootingRead when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.AI & Agents
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