# Manas AI

**Canonical:** https://apis.io/providers/manas-ai/  
**APIs profiled:** 0

Manas AI is an artificial-intelligence drug-discovery company co-founded by Reid Hoffman and physician-scientist Siddhartha Mukherjee, launched in late 2024 and backed by General Catalyst and Greylock. The company applies machine learning and computational biology to accelerate the discovery and design of new medicines, with an initial focus on hard-to-treat cancers such as breast and prostate cancer and lymphoma. Manas AI operates as a private biotech / therapeutics research organization rather than a developer-facing platform: as of this profile it publishes no public API, developer portal, SDKs, or technical documentation. This entry is maintained in the API Evangelist network as a company profile surfaced from the General Catalyst portfolio.

## Kin Score — 2.2 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 2.2).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 0.0 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

Regulatory layer — **Health**: 0.0 (matched via tags).

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Tags

Company, Artificial Intelligence, Drug Discovery, Biotechnology, Healthcare, Machine-Learning, Oncology, Life Sciences

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/manas-ai/). Scores are computed from the provider's own public artifacts under a published rubric.