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Agnt
Summary
Most agent frameworks give you a canvas, a few nodes, and a billing meter that starts ticking the moment you scale — then leave you debugging why your agent forgot what it did three steps ago.
AGNT is a local-first agent operating system built around an AGI loop: the agent executes a step, evaluates the result, and re-plans before moving forward — without you steering each decision. Persistent memory and skill layers mean context survives across sessions, not just within a single run. The visual workflow designer handles repeatable paths; goal-mode hands the agent an objective and lets it figure out the steps. Self-hosted deployment with Docker keeps data on your own infrastructure, which matters when your legal team has opinions about where prompts and outputs live. The custom license — not OSI-standard — is the detail that stops procurement at some organizations before the first demo.
Bottom line: AGNT earns its place when you need agents that remember, re-plan, and run on your own hardware with no per-execution bill — but the non-OSI license forces a legal review before any commercial deployment, and that review kills the timeline for teams at larger organizations.
Pricing Plans
Subscription- Price
- $0 or $333/year per additional user for hosted version
Open-Source Self-Hosted
Full-featured local-first agent OS for desktop and self-hosted deployment
- Unlimited agents, workflows, and executions
- Full SDK and API access
- Visual workflow designer
- SkillForge self-improvement
- 60+ built-in tools
- 15+ AI providers
- Plugin marketplace
- Desktop app and Docker deployment
Cloud Sync (Optional)
Optional encrypted cloud sync with 99.9% uptime for local-first AGNT instances
- Optional encrypted cloud sync
- 99.9% uptime SLA
- Zero telemetry
View full pricing on github.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- AGI loop (execute → evaluate → re-plan) means the agent adapts when a step returns an unexpected result, so you aren't rebuilding the workflow every time real data doesn't match the demo assumption.
- Persistent memory across sessions, so an agent working a multi-step task over hours or days carries context forward — without this, every run starts from zero and you hand-manage state yourself.
- Local-first Docker deployment with no execution-based billing, which means compliance-sensitive teams can run agents on internal data without renegotiating data processing agreements or watching a cost meter.
- Goal-mode lets you set an objective and let the agent sequence its own steps, so you aren't manually building every branch for tasks where the path depends on intermediate results.
- Plugin and subagent architecture allows parallel delegation, so work that can happen simultaneously doesn't queue behind a single-threaded pipeline.
Cons
Sign in to edit- The license is a custom non-OSI-standard document — not MIT, Apache, or GPL. Teams at enterprises or funded startups with formal open-source review processes cannot deploy to production until legal clears it, and that process adds weeks to any timeline. Some teams skip the review entirely and move to a competitor with a standard license.
- Community support is thin: a few hundred stars and a handful of open issues means when you hit an edge case in the re-planning loop or a plugin integration, there is precious little in forums or Stack Overflow to guide you. You are reading source code.
- The visual workflow designer handles linear and moderately branched paths well; deeply conditional logic — branching based on what the third or fourth agent returned — pushes against what a canvas can express cleanly. Teams building that complexity end up extending with code outside the visual layer, at which point they are maintaining two systems.
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About
- Platforms
- Desktop (Windows, macOS, Linux), Docker, Kubernetes, headless server, VPS, homelab, Raspberry Pi
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-09T07:44:48.078Z
Best For
Who it's for
- Teams needing autonomous agents with end-to-end self-improvement
- Organizations requiring data sovereignty and local-first architecture
- Developers building complex workflow automation without usage-based billing
- Businesses with strict compliance requirements
- Multi-agent systems requiring persistent state and coordination
What it does well
- Building autonomous AI agents with persistent memory and skills
- Automating complex multi-step workflows with visual workflow designer
- Running goal-oriented agents that execute and re-plan autonomously
- Creating and distributing custom plugins and integrations
- Local-first automation for compliance-sensitive environments (SOC2, HIPAA, GDPR)
Integrations
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Frequently Asked Questions
- Is Agnt free?
- Agnt is a paid tool ($0 or $333/year per additional user for hosted version). No permanent free tier is offered.
- Is Agnt open source?
- Yes. Agnt is open source.
- Does Agnt have an API?
- Yes. Agnt exposes a developer API. See the official documentation at https://github.com/agnt-gg/agnt for details.
- Can I self-host Agnt?
- Yes. Agnt supports self-hosting on your own infrastructure.
- What platforms does Agnt support?
- Agnt is available on: Desktop (Windows, macOS, Linux), Docker, Kubernetes, headless server, VPS, homelab, Raspberry Pi.
Hours Saved & ROI Stories Community
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Curated lists that include this category
Agent pipelines that run clean in development tend to break on the third real-world task, when the agent hits an unexpected result and has no mechanism to adapt. AGNT addresses this with what the repo describes as an AGI loop — execute, evaluate, re-plan — so the agent doesn’t halt or silently fail when a step returns something unexpected. The visual workflow designer handles structured, repeatable paths; a separate goal-mode lets you hand the agent an objective and let it sequence its own steps. Persistent memory and a skills layer carry context across sessions, so an agent working a multi-day task doesn’t start blank each time.
The differentiating architectural choice is local-first deployment. Everything runs via Docker on your own infrastructure — the vendor states this explicitly as targeting SOC2, HIPAA, and GDPR environments where data leaving your perimeter is not an option. There is no usage-based billing for the open-source build; the execution model is yours to scale. Plugin and subagent support means you can delegate parallel workstreams or distribute custom integrations across a team.
This fits teams that need agents running autonomously on sensitive data, or developers who want a fixed-cost architecture with no API billing surprise at month-end. It breaks — or at least stalls — at the procurement stage: the GitHub repo states a custom license rather than a standard OSI-approved one, which the validator notes creates genuine uncertainty about commercial use rights. Teams inside organizations with formal open-source review processes will spend time in legal before writing a line of production code. Community adoption is early-stage by the star count, which means sparse third-party tutorials and community support thin out fast once you leave the happy path.