Chalk

Chalk is a real-time AI/ML data platform ("Context Engine") that lets teams define features, embeddings, LLM outputs, and prompts once in Python and serve them everywhere — training, real-time inference, and agents — computed on infrastructure the customer controls. Instead of stitching together a feature store, vector database, retrieval and prompt tooling, orchestration, and a sandbox runtime, Chalk unifies them: features are point-in-time correct, served in single-digit milliseconds, and deployed via a branch-based model inside the customer's own cloud. It exposes a REST API and gRPC client libraries (Python, TypeScript, Go, Java, C#), a first-party CLI, OAuth 2.0 authentication, and MCP-scoped agent access. Backed by Felicis and General Catalyst.

Chalk publishes 1 API on the APIs.io network. Tagged areas include Company, Machine Learning, Feature Store, Artificial Intelligence, and Data Platform.

Chalk’s developer surface includes documentation, API reference, getting-started guide, engineering blog, signup flow, changelog, authentication, and 23 more developer resources.

🌐 Visit website 📡 Source on GitHub
39.1/100 thin Agent 25/100 agent aware Full breakdown ↓
scored 2026-07-20 · rubric v0.4
1 APIs 1 MCP Servers 0 Features
CompanyMachine LearningFeature StoreArtificial IntelligenceData PlatformMLOpsReal-Time DataLLMAgentsFeature Engineering

API Rating

API Evangelist API Evangelist Rating How this is scored →
scored 2026-07-20 · rubric v0.4
Composite quality — 39.1/100 · thin
Contract Quality 0.0 / 25
Developer Ergonomics 15.2 / 20
Commercial Clarity 8.4 / 20
Operational Transparency 6.2 / 13
Governance 0.0 / 12
Discoverability 9.3 / 10
Agent readiness — 25/100 · agent aware
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 12 / 12
Machine-Readable Auth 10 / 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 4 / 4
Consent & Bot Identity 0 / 3
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/chalk: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

APIs

Chalk API

REST + gRPC API for querying features from the Chalk Context Engine — online single-row queries, bulk (feather/Arrow) queries, and asynchronous offline dataset generation — plus...

MCP Servers

chalk-mcp.yml

MCP SERVER

Resources

Get Started 5

Portal, sign-up, and the first successful call

DeveloperPortal
DeveloperPortal
 GettingStarted
GettingStarted
 Login
Login
 SignUp
SignUp
 Sandbox
Sandbox

Documentation 2

Reference material describing how the API behaves

Documentation
Documentation
 APIReference
APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

MCPServer
MCPServer
 LLMsTxt
LLMsTxt
 WellKnown
WellKnown

Design & Contract 3

Pagination, idempotency, versioning, errors, and events

Conventions
Conventions
 Lifecycle
Lifecycle
 Conformance
Conformance

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization
GitHubOrganization
 Packages
Packages
 SDKs
SDKs
 CLI
CLI

Access & Security 6

Authentication, authorization, and security posture

Authentication
Authentication
 OAuthScopes
OAuthScopes
 Compliance
Compliance
 DomainSecurity
DomainSecurity
 VulnerabilityDisclosure
VulnerabilityDisclosure
 Security
Security

Operate 3

Status, limits, changes, and where to get help

StatusPage
StatusPage
 ChangeLog
ChangeLog
 ChangeLog
ChangeLog

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService
TermsOfService
 PrivacyPolicy
PrivacyPolicy

Company 2

The organization behind the API

Website
Website
 Blog
Blog

Source (apis.yml)

apis.yml Raw ↑ 
aid: chalk
name: Chalk
description: 'Chalk is a real-time AI/ML data platform ("Context Engine") that lets teams define features, embeddings, LLM
  outputs, and prompts once in Python and serve them everywhere — training, real-time inference, and agents — computed on
  infrastructure the customer controls. Instead of stitching together a feature store, vector database, retrieval and prompt
  tooling, orchestration, and a sandbox runtime, Chalk unifies them: features are point-in-time correct, served in single-digit
  milliseconds, and deployed via a branch-based model inside the customer''s own cloud. It exposes a REST API and gRPC client
  libraries (Python, TypeScript, Go, Java, C#), a first-party CLI, OAuth 2.0 authentication, and MCP-scoped agent access.
  Backed by Felicis and General Catalyst.'
url: https://raw.githubusercontent.com/api-evangelist/chalk/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- felicis
- general-catalyst
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-18'
image: https://chalk.ai/opengraph.png
tags:
- Company
- Machine Learning
- Feature Store
- Artificial Intelligence
- Data Platform
- MLOps
- Real-Time Data
- LLM
- Agents
- Feature Engineering
apis:
- name: Chalk API
  description: REST + gRPC API for querying features from the Chalk Context Engine — online single-row queries, bulk (feather/Arrow)
    queries, and asynchronous offline dataset generation — plus deployment of feature and resolver definitions. OAuth 2.0
    (client_credentials) Bearer authentication.
  humanURL: https://docs.chalk.ai/api-docs
  baseURL: https://api.chalk.ai
  tags:
  - Machine Learning
  - Feature Store
  - Real-Time Data
  properties:
  - type: Authentication
    url: authentication/chalk-authentication.yml
  - type: OAuthScopes
    url: scopes/chalk-scopes.yml
  - type: Conventions
    url: conventions/chalk-conventions.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://chalk.ai
- type: DeveloperPortal
  url: https://docs.chalk.ai
- type: Documentation
  url: https://docs.chalk.ai/docs/overview
- type: APIReference
  url: https://docs.chalk.ai/api-docs
- type: GettingStarted
  url: https://docs.chalk.ai/docs/getting-started
- type: Blog
  url: https://chalk.ai/blog
- type: GitHubOrganization
  url: https://github.com/chalk-ai
- type: Login
  url: https://chalk.ai/login
- type: SignUp
  url: https://chalk.ai/book-demo
- type: TermsOfService
  url: https://chalk.ai/legal/terms
- type: PrivacyPolicy
  url: https://chalk.ai/legal/privacy
- type: StatusPage
  url: https://chalk.statuspage.io
- type: ChangeLog
  url: https://docs.chalk.ai/docs/changelog
- type: ChangeLog
  url: changelog/chalk-changelog.yml
- type: Authentication
  url: authentication/chalk-authentication.yml
- type: OAuthScopes
  url: scopes/chalk-scopes.yml
- type: Packages
  url: packages/chalk-packages.yml
- type: SDKs
  url: packages/chalk-packages.yml
- type: CLI
  url: cli/chalk-cli.yml
- type: MCPServer
  url: mcp/chalk-mcp.yml
- type: LLMsTxt
  url: llms/chalk-llms.txt
- type: WellKnown
  url: well-known/chalk-well-known.yml
- type: Conventions
  url: conventions/chalk-conventions.yml
- type: Lifecycle
  url: lifecycle/chalk-lifecycle.yml
- type: Sandbox
  url: sandbox/chalk-sandbox.yml
- type: Conformance
  url: conformance/chalk-conformance.yml
- type: Compliance
  url: https://docs.chalk.ai/docs/security
- type: DomainSecurity
  url: security/chalk-domain-security.yml
- type: VulnerabilityDisclosure
  url: security/chalk-vulnerability-disclosure.yml
- type: Security
  url: https://docs.chalk.ai/docs/security
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence