Anam.ai
Anam.ai is a real-time interactive AI avatar platform for building conversational video AI agents with photorealistic personas. Its proprietary CARA-4 avatar model streams over WebRTC with ultra-low latency (around 180ms) across 70+ languages, powering customer support, sales, tutoring, medical, and training use cases. The REST API (https://api.anam.ai/v1/) lets teams create and manage personas, avatars, voices, LLM configs, RAG knowledge groups, tools, share links, and sessions, and invite personas into Google Meet, Zoom, and Microsoft Teams calls via the Meetings API. Auth is a two-step Bearer flow (API key exchanged for a short-lived session token). Anam is HIPAA and SOC 2 certified. Surfaced as a Techstars portfolio company and enriched into the API Evangelist network.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Anam.ai the way a machine reads it — 17 machine-readable artifacts across 10 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Anam.ai scores 61.7/100 (strong), with a separate agent-readiness read of 79/100 (agent native). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Anam.ai
Each block below is one kind of artifact we hold for Anam.ai. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 10
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Anam.ai Avatars API
The Avatars API from Anam.ai — 2 operation(s) for avatars.
Anam.ai Engine API
The Engine API from Anam.ai — 1 operation(s) for engine.
Anam.ai Knowledge API
The Knowledge API from Anam.ai — 6 operation(s) for knowledge.
Anam.ai LLMs API
The LLMs API from Anam.ai — 2 operation(s) for llms.
Anam.ai Meetings API
The Meetings API from Anam.ai — 2 operation(s) for meetings.
Anam.ai Personas API
The Personas API from Anam.ai — 2 operation(s) for personas.
Anam.ai Sessions API
The Sessions API from Anam.ai — 9 operation(s) for sessions.
Anam.ai Share Links API
The Share Links API from Anam.ai — 2 operation(s) for share links.
Anam.ai Tools API
The Tools API from Anam.ai — 2 operation(s) for tools.
Anam.ai Voices API
The Voices API from Anam.ai — 3 operation(s) for voices.
Scroll within the panel for all 10 ·
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
anamai-mcp.yml
MCP SERVERRate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Anamai Rate Limits
RATE LIMITSSecurity Posture 4
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Anam.ai — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 4
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 5
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Build 3
SDKs, sample code, and the tooling you integrate with
Access & Security 8
Authentication, authorization, and security posture
Scroll within the panel for all 8 ·
Operate 4
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/anamai · machine-readable index on apis.io