Agent Development Kit (ADK) vs Agnt

Agent Development Kit (ADK) and Agnt are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Agent Development Kit (ADK)

ADK is the open-source agent development framework that lets you build, debug, and deploy reliable AI agents at enterprise scale.

Agnt

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.

Attribute	Agent Development Kit (ADK)	Agnt
Pricing	Free	Paid
Price	—	$0 or $333/year per additional user for hosted version
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	Python, TypeScript, Go, and Java	Desktop (Windows, macOS, Linux), Docker, Kubernetes, headless server, VPS, homelab, Raspberry Pi
Languages	Python, TypeScript, Go, and Java	—
Released	2025-04	—
Pros	Context is treated like source code with structured assembly of sessions, memory, tool outputs, and artifacts, automatic filtering of irrelevant events, summarization of older turns, lazy-loading of artifacts, and token usage tracking to keep agents fast, efficient, and reliable by default Multi-language support with Python, TypeScript, Go, and Java implementations Model-agnostic and compatible with other frameworks while optimized for Gemini Built-in development UI for testing, evaluating, debugging, and showcasing agents When deploying to Google Cloud, agents inherit managed infrastructure, built-in authentication, Cloud Trace observability, and enterprise-grade security without code changes	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	Optimized primarily for Google Cloud deployment and Gemini models, though model-agnostic capabilities exist Development version builds directly from latest code commits may contain experimental changes or bugs not present in stable release	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.

Bottom line

Agent Development Kit (ADK) is free while Agnt is paid; Agnt is open source. Choose based on which difference matters most for your workflow.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.