Khanmigo

https://www.khanmigo.ai/

Stores full chat transcripts per student (visible to learner and teacher via dashboard). Layers over Khan Academy's existing mastery graph (exercise completion, skill progress) for cross-session topic awareness; cross-session memory is derived from structured mastery state rather than free-text recall.

Active · Verified 2026-10-22 · fresh as of 2026-10-22 · 36 cited facts, 7 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Tier
T2
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo 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
relational
retrieval
injection
persistence
cross-session
update
append-only
unit
episode
governance
auditable
conflict
append

When to use

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

Anti-fit: not for non-educational use cases

Pros & cons

Pros

Uniquely integrates AI tutoring with a structured curriculum graph — memory is mapped to a known knowledge domain, not just chat history.

Cons

Adoption is shallow — only ~15% of students with access actually use it; cross-session memory is bounded by mastery taxonomy rather than open-ended learning goals.

Claims & capabilities

2M students/educators/parents in 2024–25 school year. K–12 student usage 40k → 700k YoY (Khan Academy SY24-25 Annual Report).

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

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 (35 cited facts, 7 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” → “Teachers free; learners/families $4/mo or $44/yr; district pricing on request” source
    #219