Insika

Your agent is the idea. Insika is what holds it up in production.

Insika is Zulu for the pillar that carries a structure — the part nobody admires and everything rests on. A turn that survives a crash, tools that cannot wander off, limits that hold under load, and an API your clients already speak. Build the agent; the scaffolding is already here.

Concretely: a Ruby runtime for LLM agents in production — a durable, resumable turn pipeline behind an OpenAI-Responses-compatible HTTP API (POST /v1/responses), with tools, skills, cross-session memory, per-agent policy, content-safety guardrails, and a web control UI. Point an existing Responses client at it and serve many agents from one deployment.

Your first agent

Ruby >= 3.3 and a provider key (the demo uses DeepSeek). The whole program:

require "insika"

assistant = Insika.agent("assistant") do
  model "deepseek-v4-flash"
  provider :deepseek
  instructions "You are a concise and friendly assistant. Answer briefly."
end

puts assistant.reply("hi, what can you do?")   # one turn, in-process

Swap reply for serve and the same agent is a server — the control UI at /studio plus the drop-in API, on :9292. → Running locally

Or let your coding agent read the docs

A running instance serves this same documentation as raw markdown, plus a skill-structured prompt that walks a coding agent through building your first agent:

Read http://localhost:9292/start.md then help me build my first agent

Alongside it, GET /models.json (configured providers, model ids, defaults — no secrets), GET /docs and GET /docs/<name>.md. Public and on by default when you serve; opt-in in production (INSIKA_ONBOARDING=1).

Where to go next

Start here — why a runtime, getting one running, and what a turn actually does.
Core concepts — agents, limits, tools, skills, context, workflows.
Integrate — the API, channels, media, embedding, plugins, templates.
Ship it — security, confined execution, deployment, scaling past one worker.
Operate — observability, schedules, artifacts, load and soak testing.
Improve — evals, refinement, outcomes, and the three learning loops.

Pre-release: APIs may still change and nothing is tagged yet. Licensed MIT.