EvolutionaryScale

EvolutionaryScale is a New York-based biology foundation model lab spun out of Meta AI's ESM team that develops AI to deepen scientific understanding of biology. Its flagship ESM3 model is a multimodal generative protein language model that reasons jointly across sequence, structure, and function, scaling to 98B parameters trained on 771B tokens from 2.78B natural proteins. The companion ESM Cambrian (ESM C) family provides protein representation learning at 300M–6B parameters as a performant ESM2 replacement. Models are accessible via the hosted Forge inference API (forge.evolutionaryscale.ai), an open-source Python SDK (`pip install esm`), open weights on Hugging Face, and AWS Marketplace (SageMaker, NVIDIA BioNeMo and NIM). EvolutionaryScale was integrated into the Biohub organization in 2025; the ESM SDK now lives at github.com/Biohub/esm.

EvolutionaryScale publishes 7 API contracts indexed on the APIs.io network, including Embeddings API, Encoding API, Generation API, and 4 more. Tagged areas include Artificial Intelligence, Biology, Bioinformatics, Computational Biology, and Drug Discovery.

The EvolutionaryScale catalog on APIs.io includes 1 JSON-LD context and 2 Spectral governance rulesets.

EvolutionaryScale’s developer surface includes authentication and 8 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

34.4/100 thin ▬ flat Agent 22/100 agent aware Front door AI 4/6 moderate Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessFreeSelf serve⚡ Free to try

3 published contracts

Artificial Intelligence Biology Bioinformatics Computational Biology Drug Discovery ESMESM3ESM CambrianFoundation Models Generative Biology Life Sciences Machine Learning Protein DesignProtein FoldingProtein Language ModelsProteinsRepresentation LearningStructure Prediction

Kin Score

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

Composite quality — 34.4/100 · thin

Contract Quality 12.3 / 19

Developer Ergonomics 2.9 / 15

Access Clarity 2.4 / 15

Operational Transparency 2.8 / 10

Contract Governance 1.2 / 9

Discoverability 4.7 / 8

Regulatory Posture 1.9 / 15

Create-or-Update Ergonomics 0.0 / 10

Agent readiness — 22/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 10 / 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 7 / 7

Rate-Limit Signaling 7 / 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

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Health 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 applies to this provider. This API accepts writes, so it carries 10 points of the composite. It is scored from the published contracts themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in front of it, and the first time that check is skipped a duplicate record is created. Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet, not penalised by it.

The six quality facets above are damped to 75 points between them, because the conditional facet above carries the other 25. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 75% 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):

MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
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
Registration Without a Human +6 pts — a `registration_endpoint` in the served RFC 8414 / OIDC discovery document
Delegated User Identity +6 pts — a served discovery document listing authorization_code in grant_types_supported (served), else an oauth2 authorizationCode flow or openIdConnect declared in an OpenAPI (documented)

8 more unmet checks in the Agent Readiness table above.

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

Access Clarity scores 15.8 — up to +16.8 composite points
Developer Ergonomics scores 19.0 — up to +16.2 composite points
Contract Governance scores 13.6 — up to +10.4 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/evolutionaryscale/apis.yml: open an issue on github.com/api-evangelist/evolutionaryscale 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
GA4GH Genomics Standards (htsget, DRS, TES, WES, Beacon, Passports)	undeclared	-	-	-

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.

OpenAPI

measured — observed by APIs.io (published)

APIs 7

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

EvolutionaryScale ESM Python SDK

Official Python SDK packaging ESM3 and ESM Cambrian model loaders, the `ESMProtein` multi-track data model, generation/sampling configurations, structure tokenization utilities,...

EvolutionaryScale Embeddings API

Sequence-only tokenization and representation extraction.

EvolutionaryScale Encoding API

Tokenize ESMProtein objects into ESMProteinTensor inputs (and back).

EvolutionaryScale Generation API

Generate proteins across sequence, structure, and function tracks.

EvolutionaryScale MSA API

Fetch multiple sequence alignments used by structure predictors.

EvolutionaryScale Sampling API

Low-level forward passes with logits and sampling control.

EvolutionaryScale Structure API

Predict protein backbone coordinates from sequence and back.

Scroll for all 7

Open Collections 9

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

EvolutionaryScale Forge ESM3 Embeddings API

OPEN COLLECTION

EvolutionaryScale Forge ESM3 Embeddings Encoding API

OPEN COLLECTION

EvolutionaryScale Forge ESM3 API

OPEN COLLECTION

EvolutionaryScale Forge ESM Cambrian API

OPEN COLLECTION

EvolutionaryScale Forge Folding API

OPEN COLLECTION

EvolutionaryScale Forge ESM3 Embeddings Generation API

OPEN COLLECTION

EvolutionaryScale Forge ESM3 Embeddings MSA API

OPEN COLLECTION

EvolutionaryScale Forge ESM3 Embeddings Sampling API

OPEN COLLECTION

EvolutionaryScale Forge ESM3 Embeddings Structure API

OPEN COLLECTION

Scroll for all 9

EvolutionaryScale API pricing

Not yet checked against EvolutionaryScale's own pricing page. Compiled by API Evangelist on 2026-05-24 from forge.evolutionaryscale.ai; confirm before you rely on a figure. A free plan or tier is listed.

Plan	Price
Forge Beta contact-sales	—
Forge Academic gratis	Free
Open Weights (ESM3-open / ESM C) gratis	Free
AWS Marketplace (SageMaker / BioNeMo / NIM) usage-based	—

Plan details, limits and sources →

Billed by: request · FinOps profile →

Pricing Plans 1

Published pricing tiers and plan structures.

Evolutionaryscale Plans Pricing

4 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Evolutionaryscale Rate Limits

7 limits

RATE LIMITS

Service Levels 1

Uptime the provider promises or reports, as SLA4OAI. Read from its own pages; APIs.io runs no monitors.

EvolutionaryScale

SLA

FinOps 1

Cost, billing, and metering signals for API financial operations.

Evolutionaryscale Finops

FINOPS

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Evolutionaryscale Context

0 classes · 5 properties

JSON-LD

Spectral Rules 2

Spectral governance rulesets for linting and validating these APIs.

EvolutionaryScale — jsonschema rules

5 rules · 4 warnings 1 info

SPECTRAL

EvolutionaryScale API Rules

7 rules · 3 errors 4 warnings

SPECTRAL

JSON Schema 3

Standalone JSON Schema definitions for this provider's data models.

EvolutionaryScale ESMProtein

7 properties

JSON SCHEMA

EvolutionaryScale GenerationConfig

7 properties

JSON SCHEMA

EvolutionaryScale LogitsOutput

3 properties

JSON SCHEMA

Examples 3

Example request and response payloads for these APIs.

Evolutionaryscale Forge Esmc Logits Example

2 fields

EXAMPLE

Evolutionaryscale Forge Fold Example

2 fields

EXAMPLE

Evolutionaryscale Forge Generate Example

2 fields

EXAMPLE

Security Posture 2

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

Evolutionaryscale Authentication

http · 1 scheme

SECURITY

Evolutionaryscale Domain Security

TLSv1.2 · HSTS · DMARC

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Evolutionaryscale Agentic Access

11 operations · 10 acting

AGENTIC

Resources

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess

AgenticAccess

Access & Security 2

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Authentication

Authentication

Operate 3

Status, limits, changes, and where to get help

RateLimits

RateLimits

IssueTracker

IssueTracker

Releases

Releases

Commercial 2

Pricing, plans, and the legal terms of use

Plans

Plans

FinOps

FinOps

Company 1

The organization behind the API

Website

Website

Source (apis.yml)

aid: evolutionaryscale
deliveryModel:
  model: unknown
  license_evidence:
    not_product:
    - biohub/esm
  open_source: unknown
  commercial: false
  callable_host: true
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - openapi
  - repository-not-product
  generated: '2026-09-25'
  method: derived
accessModel:
  pricing: free
  onboarding: self-serve
  trial: false
  try_now: true
  public: false
  gated: none
  gated_source:
  - onboarding
  label: Free · Self-serve signup
  confidence: medium
  source:
  - plans
  - authentication
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/evolutionaryscale.png
url: https://raw.githubusercontent.com/api-evangelist/evolutionaryscale/refs/heads/main/apis.yml
apis:
- aid: evolutionaryscale:esm-python-sdk
  name: EvolutionaryScale ESM Python SDK
  tags:
  - Artificial Intelligence
  - Biology
  - SDK
  - Python
  - Open Source
  - ESM3
  - ESM Cambrian
  tags_raw:
  - AI
  - Biology
  - SDK
  - Python
  - Open Source
  - ESM3
  - ESM Cambrian
  - Open-Source
  humanURL: https://github.com/Biohub/esm
  properties:
  - url: https://github.com/Biohub/esm
    type: SourceCode
  - url: https://pypi.org/project/esm/
    type: SDKs
  - url: https://huggingface.co/biohub/esm3-sm-open-v1
    type: Documentation
  - url: https://huggingface.co/biohub/esmc-300m-2024-12
    type: Documentation
  - url: https://huggingface.co/biohub/esmc-600m-2024-12
    type: Documentation
  - url: https://github.com/Biohub/esm/tree/main/cookbook
    type: CodeExamples
  description: Official Python SDK packaging ESM3 and ESM Cambrian model loaders, the `ESMProtein` multi-track data model,
    generation/sampling configurations, structure tokenization utilities, and a `forge.client()` factory that swaps local
    checkpoints for Forge-hosted inference without code changes. Installable from PyPI as `esm`. Mixed commercial / non-commercial
    licenses.
- aid: evolutionaryscale:evolutionaryscale-embeddings-api
  name: EvolutionaryScale Embeddings API
  description: Sequence-only tokenization and representation extraction.
  humanURL: https://forge.evolutionaryscale.ai
  tags:
  - Embeddings
  properties:
  - type: OpenAPI
    url: openapi/evolutionaryscale-embeddings-api-openapi.yml
  - type: Documentation
    url: https://forge.evolutionaryscale.ai
  - type: SourceCode
    url: https://github.com/Biohub/esm
  - type: JSONSchema
    url: json-schema/evolutionaryscale-esm-protein-schema.json
  - type: JSONSchema
    url: json-schema/evolutionaryscale-generation-config-schema.json
  - type: JSONLD
    url: json-ld/evolutionaryscale-context.jsonld
  - type: JSONSchema
    url: json-schema/evolutionaryscale-logits-output-schema.json
- aid: evolutionaryscale:evolutionaryscale-encoding-api
  name: EvolutionaryScale Encoding API
  description: Tokenize ESMProtein objects into ESMProteinTensor inputs (and back).
  humanURL: https://forge.evolutionaryscale.ai
  tags:
  - Encoding
  properties:
  - type: OpenAPI
    url: openapi/evolutionaryscale-encoding-api-openapi.yml
  - type: Documentation
    url: https://forge.evolutionaryscale.ai
  - type: SourceCode
    url: https://github.com/Biohub/esm
  - type: JSONSchema
    url: json-schema/evolutionaryscale-esm-protein-schema.json
  - type: JSONSchema
    url: json-schema/evolutionaryscale-generation-config-schema.json
  - type: JSONLD
    url: json-ld/evolutionaryscale-context.jsonld
  - type: JSONSchema
    url: json-schema/evolutionaryscale-logits-output-schema.json
- aid: evolutionaryscale:evolutionaryscale-generation-api
  name: EvolutionaryScale Generation API
  description: Generate proteins across sequence, structure, and function tracks.
  humanURL: https://forge.evolutionaryscale.ai
  tags:
  - Generation
  properties:
  - type: OpenAPI
    url: openapi/evolutionaryscale-generation-api-openapi.yml
  - type: Documentation
    url: https://forge.evolutionaryscale.ai
  - type: SourceCode
    url: https://github.com/Biohub/esm
  - type: JSONSchema
    url: json-schema/evolutionaryscale-esm-protein-schema.json
  - type: JSONSchema
    url: json-schema/evolutionaryscale-generation-config-schema.json
  - type: JSONLD
    url: json-ld/evolutionaryscale-context.jsonld
  - type: JSONSchema
    url: json-schema/evolutionaryscale-logits-output-schema.json
- aid: evolutionaryscale:evolutionaryscale-msa-api
  name: EvolutionaryScale MSA API
  description: Fetch multiple sequence alignments used by structure predictors.
  humanURL: https://forge.evolutionaryscale.ai
  tags:
  - MSA
  properties:
  - type: OpenAPI
    url: openapi/evolutionaryscale-msa-api-openapi.yml
  - type: Documentation
    url: https://forge.evolutionaryscale.ai
  - type: SourceCode
    url: https://github.com/Biohub/esm
  - type: JSONSchema
    url: json-schema/evolutionaryscale-esm-protein-schema.json
  - type: JSONSchema
    url: json-schema/evolutionaryscale-generation-config-schema.json
  - type: JSONLD
    url: json-ld/evolutionaryscale-context.jsonld
  - type: JSONSchema
    url: json-schema/evolutionaryscale-logits-output-schema.json
- aid: evolutionaryscale:evolutionaryscale-sampling-api
  name: EvolutionaryScale Sampling API
  description: Low-level forward passes with logits and sampling control.
  humanURL: https://forge.evolutionaryscale.ai
  tags:
  - Sampling
  properties:
  - type: OpenAPI
    url: openapi/evolutionaryscale-sampling-api-openapi.yml
  - type: Documentation
    url: https://forge.evolutionaryscale.ai
  - type: SourceCode
    url: https://github.com/Biohub/esm
  - type: JSONSchema
    url: json-schema/evolutionaryscale-esm-protein-schema.json
  - type: JSONSchema
    url: json-schema/evolutionaryscale-generation-config-schema.json
  - type: JSONLD
    url: json-ld/evolutionaryscale-context.jsonld
  - type: JSONSchema
    url: json-schema/evolutionaryscale-logits-output-schema.json
- aid: evolutionaryscale:evolutionaryscale-structure-api
  name: EvolutionaryScale Structure API
  description: Predict protein backbone coordinates from sequence and back.
  humanURL: https://forge.evolutionaryscale.ai
  tags:
  - Structure
  properties:
  - type: OpenAPI
    url: openapi/evolutionaryscale-structure-api-openapi.yml
  - type: Documentation
    url: https://forge.evolutionaryscale.ai
  - type: SourceCode
    url: https://github.com/Biohub/esm
  - type: JSONSchema
    url: json-schema/evolutionaryscale-esm-protein-schema.json
  - type: JSONSchema
    url: json-schema/evolutionaryscale-generation-config-schema.json
  - type: JSONLD
    url: json-ld/evolutionaryscale-context.jsonld
  - type: JSONSchema
    url: json-schema/evolutionaryscale-logits-output-schema.json
name: EvolutionaryScale
tags:
- Artificial Intelligence
- Biology
- Bioinformatics
- Computational Biology
- Drug Discovery
- ESM
- ESM3
- ESM Cambrian
- Foundation Models
- Generative Biology
- Life Sciences
- Machine Learning
- Protein Design
- Protein Folding
- Protein Language Models
- Proteins
- Representation Learning
- Structure Prediction
tags_raw:
- AI
- Artificial Intelligence
- Biology
- Bioinformatics
- Computational Biology
- Drug Discovery
- ESM
- ESM3
- ESM Cambrian
- Foundation Models
- Generative Biology
- Life Sciences
- Machine Learning
- Protein Design
- Protein Folding
- Protein Language Models
- Proteins
- Representation Learning
- Structure Prediction
- Machine-Learning
commonProperties:
- url: https://www.evolutionaryscale.ai
  type: Portal
- url: https://forge.evolutionaryscale.ai
  name: EvolutionaryScale Forge
  type: Signup
- url: https://github.com/Biohub/esm
  name: ESM SDK on GitHub
  type: SourceCode
- url: https://pypi.org/project/esm/
  name: esm package on PyPI
  type: SDKs
- url: https://huggingface.co/biohub
  name: Biohub on Hugging Face
  type: Documentation
- url: https://huggingface.co/biohub/esm3-sm-open-v1
  name: ESM3-open (1.4B) on Hugging Face
  type: Models
- url: https://huggingface.co/biohub/esmc-300m-2024-12
  name: ESM C 300M on Hugging Face
  type: Models
- url: https://huggingface.co/biohub/esmc-600m-2024-12
  name: ESM C 600M on Hugging Face
  type: Models
- url: https://github.com/Biohub/esm/tree/main/cookbook
  name: ESM Cookbook
  type: CodeExamples
- url: https://github.com/Biohub/esm/tree/main/cookbook/tutorials
  name: ESM Tutorials
  type: Tutorials
- url: https://www.science.org/doi/10.1126/science.ads0018
  name: ESM3 — Science paper (Hayes et al. 2025)
  type: Documentation
- url: https://www.evolutionaryscale.ai/blog/esm3-release
  name: ESM3 release announcement
  type: Blog
- url: https://www.evolutionaryscale.ai/blog/esm-cambrian
  name: ESM Cambrian announcement
  type: Blog
- url: https://www.evolutionaryscale.ai/blog
  type: Blog
- url: https://aws.amazon.com/marketplace/seller-profile?id=seller-iw2nbscescndm
  name: EvolutionaryScale on AWS Marketplace (SageMaker)
  type: Marketplace
- url: https://github.com/evolutionaryscale/esm-sagemaker
  name: ESM on Amazon SageMaker examples
  type: CodeExamples
- url: https://github.com/evolutionaryscale/esm-partner
  name: Partner integrations
  type: CodeExamples
- url: https://www.evolutionaryscale.ai/policies/cambrian-inference-clickthrough-license-agreement
  name: Cambrian Inference Clickthrough License Agreement
  type: TermsOfService
- url: https://responsiblebiodesign.ai
  name: Responsible Biodesign Framework
  type: Documentation
- url: https://bit.ly/esm-slack
  name: ESM Community Slack
  type: Forums
- url: https://github.com/evolutionaryscale
  type: GitHubOrganization
- url: https://github.com/Biohub
  name: Biohub GitHub Organization (ESM home)
  type: GitHubOrganization
- url: plans/evolutionaryscale-plans-pricing.yml
  type: Plans
- url: rate-limits/evolutionaryscale-rate-limits.yml
  type: RateLimits
- url: finops/evolutionaryscale-finops.yml
  type: FinOps
- url: vocabulary/evolutionaryscale-vocabulary.yml
  type: Vocabulary
- type: Models
  data:
  - name: esm3-large-2024-03
    parameters: 98B
    description: Largest ESM3 checkpoint, trained on 771B tokens from 2.78B natural proteins; 1e24 FLOPs.
  - name: esm3-medium-2024-08
    parameters: 7B
    description: Mid-size ESM3 checkpoint suitable for most Forge inference workloads.
  - name: esm3-small-2024-08
    parameters: 1.4B
    description: Small ESM3 checkpoint; open weights as esm3-sm-open-v1 (non-commercial use).
  - name: esm3-open
    parameters: 1.4B
    description: Open weights of esm3-small (biohub/esm3-sm-open-v1 on Hugging Face).
  - name: esmc-6b-2024-12
    parameters: 6B
    description: Largest ESM Cambrian representation model.
  - name: esmc-600m-2024-12
    parameters: 600M
    description: Mid-size ESM Cambrian representation model.
  - name: esmc-300m-2024-12
    parameters: 300M
    description: Small ESM Cambrian model; ESM2 650M-class quality with reduced memory footprint.
- type: Features
  data:
  - ESM3 — multimodal generative model jointly conditioning on protein sequence, structure, and function
  - 98B-parameter ESM3 trained on 771B tokens from 2.78B natural proteins (1e24 FLOPs)
  - ESM Cambrian (ESM C) representation models at 300M, 600M, and 6B parameters
  - Forge API providing generate, batch_generate, encode, decode, forward_and_sample, and logits operations
  - Fold and inverse-fold endpoints for structure prediction and structure-conditioned sequence design
  - MSA endpoint for fetching multiple sequence alignments used by structure prediction
  - Iterative masked sampling with configurable num_steps, temperature, top_p, and decoding schedules
  - Per-track generation across sequence, structure, secondary_structure, sasa, and function tracks
  - Structure tokenizer converting PDB / atom37 coordinates to and from discrete tokens
  - ESMProtein and ESMProteinTensor data model unifying raw and tokenized representations
  - Async/sync client surface (`async_generate`, `async_fold`, `async_encode`, ...) for high-throughput jobs
  - Drop-in Forge client (`esm.sdk.client(model, token=...)`) replaces local checkpoints with hosted inference
  - Open-weights ESM3-open (1.4B) and ESM Cambrian distributions on Hugging Face under research license
  - AWS Marketplace deployment via SageMaker, NVIDIA BioNeMo, and NVIDIA NIM microservice
  - Cookbook tutorials covering protein generation, embedding workflows, and esmGFP-style design
  - Responsible Biodesign Framework governing model release and biosecurity review
  sources:
  - https://www.evolutionaryscale.ai
  - https://github.com/Biohub/esm
  - https://forge.evolutionaryscale.ai
  - https://www.science.org/doi/10.1126/science.ads0018
  - https://www.evolutionaryscale.ai/blog/esm3-release
  - https://www.evolutionaryscale.ai/blog/esm-cambrian
  updated: '2026-05-24'
created: '2026-05-24'
modified: '2026-09-16'
position: Consuming
description: EvolutionaryScale is a New York-based biology foundation model lab spun out of Meta AI's ESM team that develops
  AI to deepen scientific understanding of biology. Its flagship ESM3 model is a multimodal generative protein language model
  that reasons jointly across sequence, structure, and function, scaling to 98B parameters trained on 771B tokens from 2.78B
  natural proteins. The companion ESM Cambrian (ESM C) family provides protein representation learning at 300M–6B parameters
  as a performant ESM2 replacement. Models are accessible via the hosted Forge inference API (forge.evolutionaryscale.ai),
  an open-source Python SDK (`pip install esm`), open weights on Hugging Face, and AWS Marketplace (SageMaker, NVIDIA BioNeMo
  and NIM). EvolutionaryScale was integrated into the Biohub organization in 2025; the ESM SDK now lives at github.com/Biohub/esm.
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com
  X: apievangelist
  url: https://apievangelist.com
specificationVersion: '0.23'
common:
- type: Plans
  url: plans/evolutionaryscale-plans-pricing.yml
- type: RateLimits
  url: rate-limits/evolutionaryscale-rate-limits.yml
- type: FinOps
  url: finops/evolutionaryscale-finops.yml
- type: Website
  url: https://evolutionaryscale.ai
- type: IssueTracker
  url: https://github.com/Biohub/esm/issues
- type: Releases
  url: https://github.com/Biohub/esm/releases
- type: AgenticAccess
  url: agentic-access/evolutionaryscale-agentic-access.yml
- type: DomainSecurity
  url: security/evolutionaryscale-domain-security.yml
- type: Authentication
  url: authentication/evolutionaryscale-authentication.yml

EvolutionaryScale API — frequently asked questions

Does EvolutionaryScale have an API?

Yes. APIs.io lists 7 APIs from EvolutionaryScale, including EvolutionaryScale ESM Python SDK, EvolutionaryScale Embeddings API and EvolutionaryScale Encoding API, and 4 more.

Is there an OpenAPI specification for the EvolutionaryScale API?

Yes. APIs.io indexes 6 OpenAPI specifications for EvolutionaryScale, each linked for download from this page.

How do I authenticate with the EvolutionaryScale API?

EvolutionaryScale uses bearer-token authentication (http · 1 scheme). The details are profiled on APIs.io as Evolutionaryscale Authentication.

How much does the EvolutionaryScale API cost?

APIs.io lists 4 pricing plans for EvolutionaryScale, with each plan's price, limits and inclusions under Pricing on this page.

Is the EvolutionaryScale API ready for AI agents?

EvolutionaryScale has a Kin Score of 34.4 out of 100 (thin) and an Agent Readiness score of 22.1 (agent-aware), scored by API Evangelist on 2026-11-06.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

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https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

All 92 tools →

Call it yourself

curl for this page

This provider

curl "https://apis.io/api/v1/providers/evolutionaryscale"

All providers

curl "https://apis.io/api/v1/providers?limit=25"

Every operation they expose

curl "https://apis.io/api/v1/providers/evolutionaryscale/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/evolutionaryscale/evidence"

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