Expected Parrot

Q: How do I authenticate with the Expected Parrot API?

Expected Parrot uses bearer-token authentication (apiKey · 2 schemes). The details are profiled on APIs.io as Expected Parrot Authentication.

Q: How much does the Expected Parrot API cost?

Expected Parrot publishes its prices; its anthropic claude-haiku-4-5-20251001 (input) plan is $1.00 per 1M input tokens as of 2026-10-07. APIs.io lists 8 pricing plans for Expected Parrot, with each plan's price, limits and inclusions under Pricing on this page.

Q: Is the Expected Parrot API ready for AI agents?

Expected Parrot has a Kin Score of 24.5 out of 100 (emerging) and an Agent Readiness score of 6.1 (agent-aware), scored by API Evangelist on 2026-11-06.

Expected Parrot is a research-technology company building tools for AI-powered social science. Its open-source Python library EDSL (Expected Parrot Domain-Specific Language) lets researchers design, conduct, and analyze surveys and experiments in which large language models power simulated AI agents that answer questions, alongside or in place of human respondents. The Expected Parrot platform adds remote inference across many model providers (Anthropic, OpenAI, Google, Mistral, and more) via a single Expected Parrot API key, plus cloud storage, sharing, and collaboration for surveys, agents, scenarios, and results. It is used for market research, data labeling, digital twins, synthetic-user UX studies, and reproducible computational social science.

Expected Parrot is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, LLM, Surveys, and Research.

Expected Parrot’s developer surface includes documentation, getting-started guide, pricing, authentication, changelog, and 10 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

24.5/100 emerging ▬ flat Agent 6/100 agent aware Heartbeat 88/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessPaid

0 APIs 11 Agent Skills

Company Artificial Intelligence LLM Surveys ResearchSocial ScienceSynthetic Data AI Agents Market Research Python

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 24.5/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 8.1 / 17

Access Clarity 6.5 / 17

Operational Transparency 2.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 9

Regulatory Posture 1.6 / 15

Agent readiness — 6/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 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 5 / 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

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

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.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0

11 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Access Clarity scores 38.2 — up to +12.4 composite points
Contract Governance scores 0.0 — up to +12.0 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

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 the provider profile at artifacts.apis.io/expected-parrot/apis.yml: open an issue on github.com/api-evangelist/expected-parrot to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
Surveys, Market Research and Experience Management	undeclared	-	-	1

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Standards implemented 1

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Agent Skills

measured — observed by APIs.io (published)

Agent Skills 11

Packaged agent skills for driving this provider's APIs from an AI assistant.

analyze-results

AGENT SKILL

answer-question

AGENT SKILL

create-agent-list

AGENT SKILL

create-study

AGENT SKILL

create-survey

AGENT SKILL

design-experiment

AGENT SKILL

edsl-agent-reference

AGENT SKILL

edsl-persistence-reference

AGENT SKILL

edsl-survey-reference

AGENT SKILL

publish-study

AGENT SKILL

search-objects

AGENT SKILL

Scroll for all 11

Expected Parrot API pricing

Prices published by Expected Parrot, read 2026-10-07 from www.expectedparrot.com/models.

Plan	Price
anthropic claude-haiku-4-5-20251001 (input)	$1.00 per 1M input tokens
anthropic claude-haiku-4-5-20251001 (output)	$5.00 per 1M output tokens
openai gpt-5 (input)	$1.25 per 1M input tokens
openai gpt-5 (output)	$10.00 per 1M output tokens
openai_v2 o1-pro (input)	$150.00 per 1M input tokens
openai_v2 o1-pro (output)	$600.00 per 1M output tokens
google gemini-2.5-flash-image (image output)	$30.00 per 1M image output tokens
deep_infra MiniMaxAI/MiniMax-M2.7-Turbo (input)	< $0.01 per 1M input tokens

Plan details, limits and sources →

Pricing Plans 1

Published pricing tiers and plan structures.

Expected Parrot Plans Pricing

8 plans

PLANS

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Expected Parrot Authentication

apiKey · 2 schemes

SECURITY

Expected Parrot Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

GettingStarted

GettingStarted

Login

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

AgentSkill

AgentSkill

Build 3

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Packages

Packages

SDKs

SDKs

Access & Security 2

Authentication, authorization, and security posture

Authentication

Authentication

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

ChangeLog

ChangeLog

Commercial 2

Pricing, plans, and the legal terms of use

Plans

Plans

Pricing

Pricing

Company 2

The organization behind the API

BlogRSS

BlogRSS

Website

Website

Source (apis.yml)

aid: expected-parrot
name: Expected Parrot
description: Expected Parrot is a research-technology company building tools for AI-powered social science. Its open-source
  Python library EDSL (Expected Parrot Domain-Specific Language) lets researchers design, conduct, and analyze surveys and
  experiments in which large language models power simulated AI agents that answer questions, alongside or in place of human
  respondents. The Expected Parrot platform adds remote inference across many model providers (Anthropic, OpenAI, Google,
  Mistral, and more) via a single Expected Parrot API key, plus cloud storage, sharing, and collaboration for surveys, agents,
  scenarios, and results. It is used for market research, data labeling, digital twins, synthetic-user UX studies, and reproducible
  computational social science.
url: https://raw.githubusercontent.com/api-evangelist/expected-parrot/refs/heads/main/apis.yml
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: paid
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Paid
  confidence: medium
  source:
  - plans
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://github.com/expectedparrot.png
x-type: company
x-source: vc-portfolio
x-backed-by:
- bloomberg-beta
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-10-06'
x-docs-platform: mintlify
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-10-03
tags:
- Company
- Artificial Intelligence
- LLM
- Surveys
- Research
- Social Science
- Synthetic Data
- AI Agents
- Market Research
- Python
tags_raw:
- Company
- Artificial Intelligence
- Large Language Models
- Surveys
- Research
- Social Science
- Synthetic Data
- AI Agents
- Market Research
- Python
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Plans
  url: plans/expected-parrot-plans-pricing.yml
- url: https://blog.expectedparrot.com/feed
  type: BlogRSS
- type: Website
  url: https://www.expectedparrot.com/
- type: Documentation
  url: https://docs.expectedparrot.com/
- type: GettingStarted
  url: https://docs.expectedparrot.com/en/latest/getting_started.html
- type: GitHubOrganization
  url: https://github.com/expectedparrot
- type: Pricing
  url: https://www.expectedparrot.com/models
- type: Login
  url: https://www.expectedparrot.com/login
- type: Packages
  url: packages/expected-parrot-packages.yml
- type: SDKs
  url: packages/expected-parrot-packages.yml
- type: Authentication
  url: authentication/expected-parrot-authentication.yml
- type: ChangeLog
  url: changelog/expected-parrot-changelog.yml
- type: LLMsTxt
  url: llms/expected-parrot-llms.txt
- type: AgentSkill
  url: skills/_index.yml
- type: DomainSecurity
  url: security/expected-parrot-domain-security.yml
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

Expected Parrot API — frequently asked questions

How do I authenticate with the Expected Parrot API?

Expected Parrot uses bearer-token authentication (apiKey · 2 schemes). The details are profiled on APIs.io as Expected Parrot Authentication.

How much does the Expected Parrot API cost?

Expected Parrot publishes its prices; its anthropic claude-haiku-4-5-20251001 (input) plan is $1.00 per 1M input tokens as of 2026-10-07. APIs.io lists 8 pricing plans for Expected Parrot, with each plan's price, limits and inclusions under Pricing on this page.

Is the Expected Parrot API ready for AI agents?

Expected Parrot has a Kin Score of 24.5 out of 100 (emerging) and an Agent Readiness score of 6.1 (agent-aware), scored by API Evangelist on 2026-11-06.

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