Onepot

Onepot (onepot.ai) is a San Francisco-based startup building an AI-powered, fully automated custom synthesis platform for small-molecule drug discovery. It combines ultra-high-throughput lab automation in its POT-1 lab with an AI organic chemist ("Phil") and reaction foundation models to search, quote, and synthesize make-on-demand molecules end to end, aiming to replace slow, error-prone CRO workflows. Onepot exposes a developer API and a Python client (published on PyPI as "onepot") that lets teams run Tanimoto similarity and SMILES/SMARTS substructure search over the full onepot CORE compound space, perform retrosynthetic decomposition with building-block and price/risk filters, stream real-time results over server-sent events, and place synthesis orders programmatically from their own pipelines using an API key.

Onepot publishes 1 API on the APIs.io network. Tagged areas include Company, Chemistry, Cheminformatics, Drug Discovery, and Small Molecule Synthesis.

Onepot’s developer surface includes documentation, API reference, getting-started guide, engineering blog, authentication, and 10 more developer resources.

🌐 Visit website 📡 Source on GitHub

19.3/100 emerging ▬ flat Agent 10/100 agent aware Full breakdown ↓
scored 2026-08-20 · rubric v0.12.0

AccessSelf serve

1 APIs 1 MCP Servers

CompanyChemistryCheminformaticsDrug DiscoverySmall Molecule SynthesisContract Research OrganizationArtificial IntelligenceMachine-LearningLab AutomationLife Sciences

Kin Score

Kin Score How this is scored →
scored 2026-08-20 · rubric v0.12.0

Composite quality — 19.3/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 10.4 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 6.5 / 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 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

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.

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/.

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/onepot: 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 1

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

Onepot API

Programmatic access to onepot CORE for make-on-demand molecule discovery and synthesis. Run similarity (Tanimoto) and substructure (SMILES/SMARTS) search, optional retrosyntheti...

MCP Servers 1

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

Get Started 2

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

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

AgentSkill

AgentSkill

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Conventions

Conventions

Build 2

SDKs, sample code, and the tooling you integrate with

Packages

Packages

SDKs

SDKs

Access & Security 2

Authentication, authorization, and security posture

Authentication

Authentication

DomainSecurity

DomainSecurity

Company 2

The organization behind the API

Website

Website

Blog

Blog

Other 1

Properties that don't map to a standard resource type

Research

Research

Source (apis.yml)

aid: onepot
name: Onepot
description: Onepot (onepot.ai) is a San Francisco-based startup building an AI-powered, fully automated custom synthesis
  platform for small-molecule drug discovery. It combines ultra-high-throughput lab automation in its POT-1 lab with an AI
  organic chemist ("Phil") and reaction foundation models to search, quote, and synthesize make-on-demand molecules end to
  end, aiming to replace slow, error-prone CRO workflows. Onepot exposes a developer API and a Python client (published on
  PyPI as "onepot") that lets teams run Tanimoto similarity and SMILES/SMARTS substructure search over the full onepot CORE
  compound space, perform retrosynthetic decomposition with building-block and price/risk filters, stream real-time results
  over server-sent events, and place synthesis orders programmatically from their own pipelines using an API key.
accessModel:
  pricing: unknown
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  label: Self-serve signup
  confidence: medium
  source:
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://www.onepot.ai/onepot_logo_color.svg
url: https://raw.githubusercontent.com/api-evangelist/onepot/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- speedinvest
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Chemistry
- Cheminformatics
- Drug Discovery
- Small Molecule Synthesis
- Contract Research Organization
- Artificial Intelligence
- Machine-Learning
- Lab Automation
- Life Sciences
tags_raw:
- Company
- Chemistry
- Cheminformatics
- Drug Discovery
- Small Molecule Synthesis
- Contract Research Organization
- Artificial Intelligence
- Machine Learning
- Lab Automation
- Life Sciences
- API
common:
- type: Website
  url: https://www.onepot.ai
- type: DeveloperPortal
  url: https://www.onepot.ai/api
- type: Documentation
  url: https://www.onepot.ai/api
- type: APIReference
  url: https://www.onepot.ai/api
- type: GettingStarted
  url: https://www.onepot.ai/api
- type: Blog
  url: https://www.onepot.ai/blog
- type: Research
  url: https://www.onepot.ai/research
- type: Packages
  url: packages/onepot-packages.yml
- type: SDKs
  url: packages/onepot-packages.yml
- type: Authentication
  url: authentication/onepot-authentication.yml
- type: Conventions
  url: conventions/onepot-conventions.yml
- type: MCPServer
  url: mcp/onepot-mcp.yml
- type: LLMsTxt
  url: llms/onepot-llms.txt
- type: AgentSkill
  url: skills/_index.yml
- type: DomainSecurity
  url: security/onepot-domain-security.yml
apis:
- name: Onepot API
  description: Programmatic access to onepot CORE for make-on-demand molecule discovery and synthesis. Run similarity (Tanimoto)
    and substructure (SMILES/SMARTS) search, optional retrosynthetic decomposition with building-block/price/risk filters,
    stream progress and results over server-sent events, and place synthesis orders. Accessed via the first-party Python client
    (pip install onepot) using an API key; access is gated ("Request API access").
  humanURL: https://www.onepot.ai/api
  baseURL: https://www.onepot.ai
  tags:
  - Chemistry
  - Cheminformatics
  - Molecule Search
  - Retrosynthesis
  - Synthesis
  properties:
  - type: Documentation
    url: https://www.onepot.ai/api
  - type: GettingStarted
    url: https://www.onepot.ai/api
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 8
  pass: local-v1