Engram

Engram is an early-stage AI company building models that learn from an organization's unique context and data, with the pitch that they can match or beat frontier models while using only 1%-10% of the tokens ("AI has learned the world. Now it should learn yours."). Positioned as a learned AI memory layer and backed by Kleiner Perkins, Engram is at a pre-product / research stage: as of this enrichment pass the company publishes no public API, SDK, developer portal, documentation, pricing, blog, sign-up, or GitHub organization. This profile captures the company's public identity only; there is no developer or API surface available to enrich.

Engram is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine-Learning, LLM, and Model Training.

🌐 Visit website 📡 Source on GitHub

5.0/100 minimal ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs

Company Artificial Intelligence Machine-Learning LLM Model Training Context Memory Fine-Tuning

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 5.0/100 · minimal

Contract Quality 0.0 / 25

Developer Ergonomics 0.0 / 20

Access Clarity 0.0 / 20

Operational Transparency 0.0 / 13

Contract Governance 0.0 / 12

Discoverability 5.0 / 10

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/engram: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Resources

Company 3

The organization behind the API

Website

Website

Careers

Careers

LinkedIn

Other 1

Properties that don't map to a standard resource type

Source (apis.yml)

aid: engram
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/engram.png
name: Engram
description: 'Engram is an early-stage AI company building models that learn from an organization''s unique context and data,
  with the pitch that they can match or beat frontier models while using only 1%-10% of the tokens ("AI has learned the world.
  Now it should learn yours."). Positioned as a learned AI memory layer and backed by Kleiner Perkins, Engram is at a pre-product
  / research stage: as of this enrichment pass the company publishes no public API, SDK, developer portal, documentation,
  pricing, blog, sign-up, or GitHub organization. This profile captures the company''s public identity only; there is no developer
  or API surface available to enrich.'
url: https://raw.githubusercontent.com/api-evangelist/engram/refs/heads/main/apis.yml
x-type: company
x-source: kleiner-perkins-portfolio
x-tier: stub
x-tier-reason: portfolio-lead
x-api-provider: false
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Artificial Intelligence
- Machine-Learning
- LLM
- Model Training
- Context
- Memory
- Fine-Tuning
tags_raw:
- Company
- AI
- Machine Learning
- LLM
- Model Training
- Context
- Memory
- Fine-Tuning
apis: []
common:
- type: Website
  url: https://engram.com
- type: Careers
  url: https://engram.com/careers
- type: X
  url: https://x.com/EngramLab
- type: LinkedIn
  url: https://www.linkedin.com/company/engram-lab/
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
- FN: Engram
  email: hello@engram.com
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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