AgentRecall vs Skillier.ai

AgentRecall and Skillier.ai are both inference engines & infra 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.

AgentRecall

AgentRecall is a memory layer that gives AI agents persistent context across sessions — so a support agent recalls a customer's past issue, a sales agent remembers where a deal stalled, and a coding assistant doesn't ask you to re-explain your architecture for the third time. The vendor describes a retrieval-and-storage infrastructure that indexes memories and surfaces relevant ones at query time, rather than stuffing the full conversation history into every prompt. The cloud tier caps at 1,000 stored memories, which is adequate for prototyping but a ceiling teams hit in production. Self-hosting under the MIT license removes that ceiling and keeps data inside your own infrastructure — the tradeoff is that you own the ops. API access covers JavaScript and Python environments.

Skillier.ai

Skillier sits between you and your AI client, detecting what domain you're working in and loading the relevant skill — finance modeling, legal reasoning, DevOps runbooks — into the context without you leaving the interface. The Lite version is MIT-licensed and runs offline, which matters for air-gapped environments where cloud-dependent tooling is a non-starter. The routing model hands control back through an AskUserQuestion prompt, so you confirm the skill selection rather than having it decided for you. That model works cleanly for single-domain sessions. Blended workflows — writing copy while checking financial assumptions, for instance — require you to manually re-route between skills, and the seams show.

Attribute	AgentRecall	Skillier.ai
Pricing	Paid	Paid
Price	$9/month for Pro (cloud); self-hosted is free	—
Free trial	No	No
Open source	No	No
Has API	Yes	No
Self-hosted option	Yes	Yes
Platforms	Cloud (hosted API), Self-hosted (Docker/bare metal on user infrastructure)	Claude Desktop, Claude Web, Claude Code CLI, OpenClaw
Pros	Persistent memory across sessions, so a support or sales agent can reference a customer's prior context without the user having to repeat themselves — which is the difference between an agent that feels useful and one that feels like a fresh chatbot every time. Self-hosted MIT-licensed deployment, so teams with data residency requirements can keep every stored memory inside their own infrastructure without negotiating a custom data agreement. API-first design with JavaScript and Python SDKs, which means the memory layer drops into an existing agent stack without a rewrite — teams avoid building and maintaining a bespoke retrieval system from scratch. Retrieval-at-query-time architecture, so only relevant memories surface per session rather than inflating every prompt with full history — which keeps token costs and latency from compounding as memory volume grows. Claude Desktop integration documented by the vendor, so teams already in that environment get memory persistence without standing up separate infrastructure.	Offline skill access via the self-hostable Lite version, so air-gapped teams and low-connectivity environments can load domain expertise without a live API call — something cloud-only tools in this category cannot offer. Skill routing that triggers without leaving the chat interface, which means the context window you've built up in a session doesn't get abandoned every time you need to shift to a different domain. MIT-licensed Lite version with no paid tier required, so teams that need to audit, fork, or self-host the code have a legal path to do that without a procurement conversation. Explicit AskUserQuestion confirmation before a skill loads, so you stay in control of what gets injected into context — preventing the silent prompt stuffing that degrades output quality when auto-routing guesses wrong.
Cons	The cloud tier caps at 1,000 stored memories — a solo developer's prototype fits, but a customer support deployment with hundreds of users hits that ceiling within days. Teams either move to the paid-only cloud tier or take on self-hosting, neither of which is free in time or money. Self-hosting transfers all ops responsibility to your team: infrastructure provisioning, uptime, upgrades, and any debugging when retrieval quality degrades. Teams without dedicated DevOps capacity discover this is not a one-afternoon setup. The scraped page content does not confirm a native vector database or specify retrieval ranking logic, which means teams with precision recall requirements — where surfacing the wrong memory is worse than surfacing none — have no documented way to audit or tune retrieval quality before they hit that problem in production. Teams that need memory scoped by user, tenant, or access role in a multi-tenant SaaS product will find no documented isolation model in available sources. When that requirement surfaces mid-build, the path forward is custom middleware or a competitor that ships tenant-aware memory out of the box.	Multi-domain sessions hit the routing model's friction ceiling fast: each skill switch requires a confirmation prompt, so a workflow that blends financial modeling with technical writing generates repeated interruptions — teams doing this regularly report falling back to manual context pasting because it's faster. No API surface is described, which means teams who want to embed skill routing inside a pipeline, a CI step, or any system outside Claude Desktop and Claude Web have no integration path — at that point they are looking at building their own context-injection layer or switching to a tool that exposes programmatic control. Scoped exclusively to Claude Desktop and Claude Web at time of review, so organizations standardized on other AI clients — GPT-4 via ChatGPT, Gemini, or internal models — get no benefit and need a different solution entirely.

Bottom line

Only AgentRecall exposes a public API. 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.