AGNT Agent OS
A local-first runtime for durable AI agents, repeatable workflows, and long-running goals.
- Source repo
- agnt-gg/agnt
- Stars
- ★ 564
- Last updated
- 9d ago
- License
- NOASSERTION
- Primary language
- JavaScript
- FA score
- 52/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformCodex · Claude Code · OpenAI API · Claude API
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An automation builder who wants timer, webhook, Slack, or Discord events to start traceable AI workflows.
- Not a fit if
- Teams needing public multi-tenant hosting or zero-trust tenant isolation
- Large enterprises with 50+ concurrent users on one shared instance
- Users wanting a zero-config cloud chatbot with no local setup
- Source review
- 52/100 · Major gaps
What does this agent do, and when should you use it?
AGNT is a local-first agent operating system composed of an Electron desktop shell, a Vue 3 frontend, and an Express backend on port 3333. It organizes work around agents, visual DAG workflows, and long-running goals, with SQLite and the filesystem providing local persistence. The runtime includes workflow nodes, triggers, plugins, MCP, skills, evaluations, execution records, and real-time streaming interfaces. It can run from source, as a headless self-hosted service, or in Docker; the Full image includes browser automation while Lite omits it. It fits individuals, small trusted teams, and developers who want local control while combining judgment-driven chat with repeatable automation, rather than public multi-tenant SaaS deployments.
Users create and run agents, workflows, and goals through the desktop interface or local API. The agent runtime handles persistent chat, memory, tool calls, skills, streaming output, traces, and token and cost accounting; the workflow engine executes visual DAGs with branches, checkpoints, nested workflows, and triggers such as timers and webhooks; and the goal engine plans, decomposes tasks, executes, evaluates, replans, pauses, resumes, and reverts work. AGNT can call its listed model providers, MCP tools, plugin tools, custom HTTP APIs, and integrations including Slack, Discord, Gmail, and Telegram. Messages, tool calls, errors, outputs, and evaluations can become insights, memory, and Skills. Its backend exposes route families including /api/agents, /api/workflows, /api/goals, /api/plugins, /api/mcp, and /api/skills, with live updates over SSE and Socket.IO.
- An automation builder who wants timer, webhook, Slack, or Discord events to start traceable AI workflows.
- A small product team that needs research, copy, engineering, and evaluation work delegated in parallel while retaining execution records and human approval steps.
- A self-hosting user who wants agents, workflows, credentials, and execution data to remain on a personal machine, homelab server, VPS, or Raspberry Pi.
- A developer combining visual process automation with OpenAI, Anthropic, Gemini, or a custom OpenAI-compatible endpoint.
- An integration developer who needs to extend a local workspace with MCP or packaged
.agntplugins that add tools, triggers, widgets, or workflow nodes.
How do you install or deploy this agent?
Source installation requires Node.js 18+, npm 9+, and Git:
git clone https://github.com/agnt-gg/agnt.git
cd agnt
npm installcd frontend && npm install && cd ..
npm startThe backend runs at http://localhost:3333. For frontend hot reload, run cd frontend && npm run dev in one terminal and npm start in another. For a production web build, run cd frontend && npm run build && cd .., then npm start. Remote models and external services require the relevant provider API key, CLI authorization, or integration credentials in AGNT. For Docker or production setup, create .env from .env.example and set JWT_SECRET, SESSION_SECRET, and ENCRYPTION_KEY.
How do you use this agent?
After startup, open http://localhost:3333 and choose among the Agents, Workflows, and Goals execution modes. Use the workflow canvas to combine nodes, branches, checkpoints, and triggers for repeatable work; create a goal for work that needs planning, evaluation, and resumption; and run an agent where judgment and tool use are needed. Other local programs can access resources through the /api surface, including /api/agents, /api/workflows, /api/executions, and /api/goals; no copyable API request example is provided. To create a plugin, use node build-plugin.js my-awesome-plugin from backend/plugins, then install the resulting .agnt package through Marketplace or place it in <data-dir>/plugins/installed/.
What are this agent's strengths and limitations?
- Combines persistent agents, visual workflows, and long-running goals in one runtime instead of delivering only one execution model.
- Its local Express API, SQLite and filesystem storage, SSE, and Socket.IO provide a scriptable and observable automation boundary.
- Ships with 60+ workflow nodes and tool actions, then extends through
.agntplugins, MCP, plugin tools, and custom HTTP APIs. - Supports desktop, Docker, headless servers, VPS deployments, homelabs, and Raspberry Pi, with Full and Lite images that make the browser-automation tradeoff explicit.
- The documented target is a trusted local workspace, not public multi-tenant SaaS, zero-trust tenant isolation, or large enterprises with 50+ concurrent users.
- Source deployment requires Node.js, npm, and Git; production Docker deployments also require the operator to manage persistent volumes, an access layer, and generated secrets.
- Remote models, messaging, and SaaS integrations depend on user-supplied API keys, OAuth or CLI authorization, and external services, whose network use and costs vary by provider.
- Lite removes Chromium, Puppeteer, and Playwright browser automation, so web scraping or browser automation requires the larger Full variant.
- Repository metadata states NOASSERTION while the README says Custom License; adopters should inspect LICENSE.md before use.
How does this agent compare with similar options?
Against LangChain, AutoGen, or CrewAI, AGNT presents itself as more than a framework by adding a desktop UI, local backend, workflow canvas, goals, storage, and runtime APIs. Against n8n or Zapier, it emphasizes persistent agents, memory, SkillForge, MCP, provider authentication, and local traces; against OpenWebUI-style chat apps, chat is only one execution mode. The README also compares it with Hermes Agent, OpenClaw, and Claude Code, positioning AGNT around visual workflows, long-running goals, plugins, evaluations, dashboards, and durable local state.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| AGNT Agent OS This agent | 52 · Major gaps | Desktop appFree + model costs | ★ 564 | 9d ago | JavaScript | Codex · Claude Code · OpenAI API · Claude API |
| Auto Company | 70 · Some gaps | CLIFree + model costs | ★ 3.2k | 6d ago | Python | Codex · Claude Code |
| LobeHub — Chief Agent Operator for Multi-Agent Work | 48 · Major gaps | Self-hosted serviceFree + model costs | ★ 84k | today | TypeScript | ChatGPT · OpenAI API · Claude API |
| Sim AI Workspace | 71 · Some gaps | CLIFreemium | ★ 30k | 1d ago | TypeScript | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows plugin installer has permission declarations, integrity checks, permission-diff gating, and rollback, but data flow transparency and sensitive data handling are only partially covered. Deductions: no full data flow diagram or sensitive data encryption details.
Self-consistency is good, tests cover install, update, rollback scenarios, but dependency availability is unverified, failure messages are present in tests. Deductions: dependency versions not locked, may affect reproducibility.
Targets multiple scenarios (desktop, Docker, VPS), capability boundaries clear, but trigger precision and specific environment fit not fully documented. Deductions: lack of detailed documentation for specific triggers.
Information architecture clear, install notes detailed, naming stable, examples and FAQ present, but known limitations and changelog incomplete. Deductions: missing explicit known limitations list and detailed changelog.
Output usability high, marginal value evident, but cost-benefit not quantified. Deductions: no performance benchmarks or cost analysis.
Claim traceability moderate, cross-source corroboration limited, fact-inference separation unclear. Deductions: some claims lack independent verification.
- Plugin installer has permission gates, but user confirmation flow may not be prominent enough; ensure explicit user consent.
- Dependency versions are not locked, which may lead to security vulnerabilities or compatibility issues.
- Sensitive data handling details are insufficient; recommend encrypting API keys and similar data.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
What's Changed * Make a run visible to every client, and survive a restart by @rimusz in https://github.com/agnt-gg/agnt/pull/62 * fix(skills): a by-id route must check who owns the row by @rimusz in https://github.com/agnt-gg/agnt/pull/68 * fix(models): a save must not erase the columns it does not name by @rimusz in https://github.com/agnt-gg/agnt/pull/67 * test(plugins): the update-status test raced the millisecond clock by @rimusz in https://github.com/agnt-gg/agnt/pull/74 …View on GitHub ↗ -
v0.6.6 0.6.6