Osmosis

Osmosis (Gulp AI Inc.) is a forward-deployed reinforcement-learning platform for post-training large language models. Teams implement an AgentWorkflow and a Grader in Python, then use the Osmosis CLI and web platform to submit evaluation and training runs (GRPO/DAPO, multi-turn tool training) that produce task-specific LoRA models which beat foundation models at a fraction of the cost. Osmosis also ships an Agent Improvement REST API for storing agent interaction knowledge and enhancing agent tasks from past interactions, an open-source Python SDK/CLI (osmosis-ai), a TypeScript logging SDK, an open-source MCP server (Osmosis-Apply), webhooks, and hosted documentation. Backed by CRV, Felicis, and Paradigm.

Kin Score

This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.

How we profile Osmosis

Each block below is one kind of artifact we hold for Osmosis. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.

APIs 3

Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.

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

Osmosis agent API

Operations for enhancing agent interactions and decisions

Osmosis knowledge API

Operations for managing the knowledge base

Osmosis Osmosis Agent Improvement API API

The Osmosis Agent Improvement API API from Osmosis — 1 operation(s) for osmosis agent improvement api.

MCP Servers 1

Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.

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

osmosis-mcp.yml

MCP SERVER

Event Specifications 1

Not every API is request/response. AsyncAPI describes the event-driven and streaming side — the webhooks and channels — so the asynchronous half of the interface is documented the same way the synchronous half is.

AsyncAPI definitions for this provider's event-driven and streaming APIs.

Osmosis Webhooks

ASYNCAPI

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.

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

Osmosis Authentication

apiKey · 1 scheme

SECURITY

Osmosis Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Agentic Access 1

An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.

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

Osmosis Agentic Access

5 operations · 3 acting

5 operations · 3 acting

AGENTIC

Resources

Every other property we hold for Osmosis — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.

← All providers · Data indexed from github.com/api-evangelist/osmosis · machine-readable index on apis.io