Agnt vs Two-tier-memory

Agnt and Two-tier-memory 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.

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.

Two-tier-memory

The library implements what the repo calls the 'two-tier fix': structured storage in a local SQLite database, with semantic or keyword queries pulling back only the relevant rows instead of the entire memory corpus. The core workflow is a single Python file and a SQL schema — add a memory, query a memory, done. It runs entirely on-device with no external API calls. The wall you hit is expressiveness: the schema is fixed, so teams with complex memory taxonomies end up forking the schema or layering their own abstraction on top. At that point you are maintaining a fork.

Attribute	Agnt	Two-tier-memory
Pricing	Paid	Free
Price	$0 or $333/year per additional user for hosted version	—
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Desktop (Windows, macOS, Linux), Docker, Kubernetes, headless server, VPS, homelab, Raspberry Pi	Python, SQLite
Pros	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.	Queries the SQLite store for only the relevant memory entries rather than loading the full history into context, so the agent's effective memory scales with the size of the database rather than the size of the context window. Entirely local and dependency-light — no API keys, no network calls, no managed service — which means the memory layer cannot go down because a third-party endpoint is unavailable. MIT license with full source in a single Python file, so you can read exactly what happens to your stored data and modify the retrieval logic without waiting on a vendor. CLI-driven interface means you can add or query memories from shell scripts, editor plugins, or agent tool calls without importing a framework. Persistent across sessions by default via SQLite, so a solved problem recorded in one session is available in every subsequent session without any additional configuration.
Cons	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.	The schema ships with a fixed structure targeting solved coding problems and project decisions. Teams whose memory needs include different record types — hierarchical documentation, multi-entity relationships, or domain-specific metadata — hit the schema ceiling immediately and must fork and migrate, at which point they own all future schema evolution. There is no server, no sync layer, and no multi-agent access model. A team with more than one agent process, or a developer working across multiple machines, gets no shared state — each environment has its own isolated database, and keeping them consistent is a manual problem. At the point where a team needs semantic vector search rather than keyword or structured queries — typical once the memory corpus grows large and queries become fuzzy — this library provides no embedding or vector retrieval path. That is the condition under which teams move to a dedicated vector database or a memory framework like Mem0 instead.

Bottom line

Agnt is paid while Two-tier-memory is free; only Agnt exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Agnt and Two-tier-memory?

Agnt is Paid and open source, while Two-tier-memory is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Agnt better than Two-tier-memory?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.