Catalog Systems Vertical / domain-specific AI memory MagicSchool

MagicSchool

https://www.magicschool.ai/

Teacher productivity platform (80+ teacher tools, 50+ student tools) for planning, differentiation, IEP generation, feedback. Does not maintain a cross-session learner model. Teachers input student context manually each session; district deployments can pre-populate fields via RAG over uploaded curriculum/policy documents.

Active · Verified 2026-10-22 · fresh as of 2026-10-22 · 33 cited facts, 4 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type
Teacher tooling — no student-facing memory by design vendor fix
Tier
T1
Section
Vertical / domain-specific AI memory
Created
2023 (launched March-May 2023 by Adeel Khan; Denver CO) third-party fix
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Pricing
Free (teachers); Plus $12.99/mo or $99.96/yr; Enterprise (districts) custom vendor fix
Funding
$45M Series B (Valor Equity Partners, 2025-02) on top of $15M Series A vendor fix
Freshness
2026-10-22 fix
Layer
agent-infra repo fix
Licensing
commercial vendor fix
Deployment locus
cloud vendor fix
Platform vs product
product vendor fix

Memory taxonomy

storage
file
retrieval
injection
persistence
session
update
read-only
unit
document
governance
opaque
conflict
n/a

When to use

Optimised for: learner profile + spaced repetition + assessment-driven adaptation

Anti-fit: not for non-educational use cases

Pros & cons

Pros

Largest educator user base in this set; district-level RAG (curriculum + policy docs) is a practical and architecturally honest form of context persistence that does not require a student profile.

Cons

No student-facing memory or adaptive personalization — context is ephemeral and teacher-supplied each session; deliberate product choice but disqualifies meaningful student-memory classification.

Claims & capabilities

~6M educators. 40% of US public schools. 13,000+ schools/districts in 160+ countries. 77% of teachers report significantly improved quality of life.

Technical surface

API surface
searched not found
Backend storage
searched not found
Deployment
Managed-only
Embedding model
searched not found
Multi-tenancy
searched not found
MCP
no MCP support advertised — vertical product, no MCP server / client integration documented
A2A
no A2A protocol support advertised — vertical product, no A2A integration documented
OpenTelemetry
no OpenTelemetry integration advertised — vendor logs/observability not publicly documented

Alternatives to MagicSchool → live options ranked by shared design, licence, hosting and MCP support

Similar systems

Other vertical / domain-specific ai memory in the catalog, ranked by inbound references.

  • Inworld AI T1

    Formerly an NPC character engine with named Long-Term Memory; now sells realtime voice models (TTS-2, STT-1, speech-to-speech) and OpenAI-compatible open-model inference. Character/NPC memory is no longer the headline product.

  • NVIDIA ReMEmbR T3

    Builds long-horizon memory by captioning video segments with VILA, storing captions with timestamps + 3D position coordinates in MilvusDB. At query time, LLM iterates retrieval across text, time, and position modalities. Deployed on Nova Carter robot (Jetson Orin).

  • Abridge T1

    Clinician-assist ambient documentation. Source mapping: every AI-generated summary element traced back to the source utterance. Audit-and-trust layer over episodic memory. Built on proprietary 1.5M+ medical-encounter dataset.

  • ASAPP GenerativeAgent T1

    Treats memory as first-class architecture. Captures the digital footprint of every interaction; retrieves preference and history at engagement time. Public example: airline knowing a frequent flyer wants aisle seats with her son — preference-aware, not just history-lookup.

  • BenevolentAI T1

    Taken private: merged into Osaka Holdings (renamed BenevolentAI S.A.) and delisted from Euronext Amsterdam 2025-03-13. Target identification / drug repurposing / mechanism tracing. 85+ data sources, petabyte-scale, rebuilt every few weeks. Wet-lab results re-enter the graph and shift downstream predictions — institutional experimental memory closing a feedback loop. As of Oct 2026 benevolent.com leads with Qor, powered by BenevolentAI, a life-science intelligence service for biopharma decision makers and consultancies. Qor (qor.one) is a continuously updated decision-intelligence service built on BenevolentAI's knowledge graph and reasoning methods: it tracks evidence on an asset or indication (competitive position, rival readouts, comparable deals), source-verifies each claim and flags when an earlier conclusion stops holding. It targets biotech teams and life-science consultancies.

  • Causaly T1

    Agentic AI platform for life sciences: orchestrated scientific agents over a curated biomedical knowledge graph (plus Bio Graph and Pipeline Graph), an evidence-retrieval system and an enterprise data fabric over literature, trials and patents.

Sources (32 cited facts, 4 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-22
    • Pricing “Free + paid” → “Free (teachers); Plus $12.99/mo or $99.96/yr; Enterprise (districts) custom” source
    • Funding / ownership “$15M total Series A · 2024-06” → “$45M Series B (Valor Equity Partners, 2025-02) on top of $15M Series A” source
    #219