Synthetic Sciences

Synthetic Sciences is a Y Combinator-backed AI research lab building foundation models for scientific discovery. Its flagship product, Atlas, is an agent-first research graph and infrastructure layer for autonomous science: durable typed nodes (hypotheses, experiment plans, recorded runs, evidence, decisions) linked into a shared, forkable map that humans and AI agents read and write directly over a REST API and the @synsci/atlas CLI. Atlas bundles a Library for indexing and grounded search over repos, docs, papers, and datasets, a cross-provider GPU compute marketplace, GEPA-based optimization and refereed autoresearch, and nine agent Skills. The lab also ships two open-source (Apache 2.0) projects: OpenScience, a model-agnostic AI workbench that runs the full research loop, and Delphi, a self-hosted MCP server that gives coding agents semantic context over code, papers, and HuggingFace datasets.

Synthetic Sciences is listed with 1 API on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Learning, Scientific Research, and Developer Tools.

Synthetic Sciences’ developer surface includes documentation, API reference, getting-started guide, pricing, signup flow, support, CLI, and 17 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

16.3/100 emerging ▬ flat Agent 10/100 agent aware Front door AI 4/6 moderate Heartbeat 100/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

1 API 1 MCP Servers 3 Agent Skills

Company Artificial Intelligence Machine Learning Scientific Research Developer Tools Agents Foundation Models Knowledge Graph MCP CLI Research Infrastructure

Kin Score

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

Composite quality — 16.3/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 7.2 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.5 / 10

Discoverability 6.2 / 9

Regulatory Posture 2.3 / 15

Agent readiness — 10/100 · 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 9 / 9

Stable Error Semantics 8 / 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 AI & Biometrics 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
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Rate-Limit Signaling +7 pts — OpenAPI responses document X-RateLimit-* / RateLimit headers (verified), else a rate_limits artifact or RateLimits link (documented)
Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link

9 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 0.0 — up to +20.0 composite points
Operational Transparency scores 2.6 — up to +12.7 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/synthetic-sciences/apis.yml: open an issue on github.com/api-evangelist/synthetic-sciences 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
Agent Skills (SKILL.md packages)	undeclared	-	-	2

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.

Model Context Protocol (MCP)

tagged — the provider's own APIs.json carries the standard as a tag; implementation not verified

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

Atlas REST API

The direct HTTP API behind the Atlas CLI — the research graph, Library, runs, evidence, compute, and research surfaces. Bearer (thk_*) auth; JSON responses; Idempotency-Key on w...

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Synthetic Sciences MCP Server

Local (stdio) MCP server; 29 tools listed.

MCP SERVER

Agent Skills 3

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

atlas

AGENT SKILL

atlas-research-loop

AGENT SKILL

atlas-source-to-map

AGENT SKILL

Security Posture 2

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

Synthetic Sciences Authentication

http/oauth2 · 2 schemes

SECURITY

Synthetic Sciences Domain Security

TLSv1.3 · HSTS

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

SignUp

SignUp

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

MCPServer

MCPServer

AgentSkill

AgentSkill

Design & Contract 6

Pagination, idempotency, versioning, errors, and events

Conventions

Idempotency

ErrorCatalog

Lifecycle

DataModel

Conformance

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 2

Authentication, authorization, and security posture

Authentication

Authentication

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 1

Pricing, plans, and the legal terms of use

Pricing

Pricing

Company 1

The organization behind the API

Website

Website

Other 1

Properties that don't map to a standard resource type

ProductSite

ProductSite

Source (apis.yml)

aid: synthetic-sciences
name: Synthetic Sciences
description: 'Synthetic Sciences is a Y Combinator-backed AI research lab building foundation models for scientific discovery.
  Its flagship product, Atlas, is an agent-first research graph and infrastructure layer for autonomous science: durable typed
  nodes (hypotheses, experiment plans, recorded runs, evidence, decisions) linked into a shared, forkable map that humans
  and AI agents read and write directly over a REST API and the @synsci/atlas CLI. Atlas bundles a Library for indexing and
  grounded search over repos, docs, papers, and datasets, a cross-provider GPU compute marketplace, GEPA-based optimization
  and refereed autoresearch, and nine agent Skills. The lab also ships two open-source (Apache 2.0) projects: OpenScience,
  a model-agnostic AI workbench that runs the full research loop, and Delphi, a self-hosted MCP server that gives coding agents
  semantic context over code, papers, and HuggingFace datasets.'
url: https://raw.githubusercontent.com/api-evangelist/synthetic-sciences/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: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://app.syntheticsciences.ai/synsc-logo.png
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
- a16z
- firestreak-ventures
- pareto-holdings
x-tier: profiled
x-tier-reason: enriched-from-public-surface
x-enrichment:
  date: '2026-07-21'
  status: enriched
  artifacts_added: 16
  pass: local-v1
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- Scientific Research
- Developer Tools
- Agents
- Foundation Models
- Knowledge Graph
- MCP
- CLI
- Research Infrastructure
tags_raw:
- Company
- Artificial Intelligence
- Machine Learning
- Scientific Research
- Developer Tools
- Agents
- Foundation Models
- Knowledge Graph
- Model Context Protocol
- CLI
- Research Infrastructure
- Machine-Learning
apis:
- name: Atlas REST API
  description: The direct HTTP API behind the Atlas CLI — the research graph, Library, runs, evidence, compute, and research
    surfaces. Bearer (thk_*) auth; JSON responses; Idempotency-Key on writes. Base https://app.syntheticsciences.ai/api/v1.
  humanURL: https://docs.syntheticsciences.ai/atlas/rest-api
  baseURL: https://app.syntheticsciences.ai/api/v1
  tags:
  - Research
  - Agents
  - Knowledge Graph
  properties:
  - type: Authentication
    url: authentication/synthetic-sciences-authentication.yml
  - type: Conventions
    url: conventions/synthetic-sciences-conventions.yml
  - type: ErrorCatalog
    url: errors/synthetic-sciences-problem-types.yml
  - type: DataModel
    url: data-model/synthetic-sciences-data-model.yml
common:
- type: Website
  url: https://syntheticsciences.ai
- type: DeveloperPortal
  url: https://docs.syntheticsciences.ai
- type: Documentation
  url: https://docs.syntheticsciences.ai/atlas
- type: APIReference
  url: https://docs.syntheticsciences.ai/atlas/commands
- type: GettingStarted
  url: https://docs.syntheticsciences.ai/atlas/quickstart
- type: Pricing
  url: https://docs.syntheticsciences.ai/atlas/billing
- type: SignUp
  url: https://app.syntheticsciences.ai
- type: Support
  url: mailto:team@syntheticsciences.ai
- type: GitHubOrganization
  url: https://github.com/synthetic-sciences
- type: ProductSite
  url: https://tryatlas.sh
- type: LLMsTxt
  url: llms/synthetic-sciences-llms.txt
- type: Packages
  url: packages/synthetic-sciences-packages.yml
- type: SDKs
  url: packages/synthetic-sciences-packages.yml
- type: CLI
  url: cli/synthetic-sciences-cli.yml
- type: MCPServer
  url: mcp/synthetic-sciences-mcp.yml
- type: Authentication
  url: authentication/synthetic-sciences-authentication.yml
- type: Conventions
  url: conventions/synthetic-sciences-conventions.yml
- type: Idempotency
  url: conventions/synthetic-sciences-conventions.yml
- type: ErrorCatalog
  url: errors/synthetic-sciences-problem-types.yml
- type: Lifecycle
  url: lifecycle/synthetic-sciences-lifecycle.yml
- type: DataModel
  url: data-model/synthetic-sciences-data-model.yml
- type: Conformance
  url: conformance/synthetic-sciences-conformance.yml
- type: AgentSkill
  url: skills/_index.yml
- type: DomainSecurity
  url: security/synthetic-sciences-domain-security.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io

Synthetic Sciences API — frequently asked questions

Does Synthetic Sciences have an API?

Yes. APIs.io lists 1 API from Synthetic Sciences, including Atlas REST API.

How do I authenticate with the Synthetic Sciences API?

Synthetic Sciences uses bearer-token authentication (http/oauth2 · 2 schemes). The details are profiled on APIs.io as Synthetic Sciences Authentication.

Does Synthetic Sciences have an MCP server?

Yes. APIs.io lists 1 MCP server for Synthetic Sciences: Local (stdio) MCP server; 29 tools listed.

Is the Synthetic Sciences API ready for AI agents?

Synthetic Sciences has a Kin Score of 16.3 out of 100 (emerging) and an Agent Readiness score of 9.5 (agent-aware), scored by API Evangelist on 2026-11-06.

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