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What is swarm?#

The one-line: an opinionated multi-agent ML platform for regulated industries.

The one-paragraph: swarm takes a problem statement in plain English, routes it through a team of specialized AI agents (problem-classifier, algorithm-selector, trainer, evaluator, deployer, auditor…), picks a model from an internal algorithm registry, trains it, runs compliance checks appropriate to your industry, and produces a signed audit PDF ready to hand to a regulator — in one afternoon instead of a two-week cross-functional ticket.

What swarm is not#

  • Not a framework you build an MLOps stack out of. If you want that, look at Kubeflow or MLflow directly.
  • Not a general-purpose agent orchestrator. LangGraph, CrewAI, AutoGen cover that. We borrow ideas; we're not trying to replace them.
  • Not a chat assistant. The LLM is infrastructure, not the product.

What swarm is#

  • A specialized, opinionated orchestrator for ML workflows with a compliance-first posture.
  • A platform — REST + CLI + dashboard + plugin ecosystem.
  • A certified-vertical-first play: RBI FREE-AI, HIPAA, EU AI Act conformity packs (each with regulator-format audit PDFs).

Where we fit in the landscape#

Tool Strength Different from swarm
Kubeflow / MLflow Pipeline orchestration at scale No agent layer; DIY compliance story
LangChain / LangGraph Generic LLM app framework No ML training loop; no regulator-facing compliance bundle
CrewAI / AutoGen Multi-agent orchestration No ML training / deployment; no compliance
Databricks / SageMaker End-to-end MLOps on cloud Proprietary, expensive, not regulator-customizable
Anthropic Claude Code Agentic coding assistant Code-focused, not ML-focused; we're compatible with their plugins

swarm sits at the intersection: specialized multi-agent orchestration + production ML training + regulatory evidence as first-class output.

Why regulated industries specifically?#

Three compounding reasons:

  1. High willingness to pay. A BFSI or healthcare org will pay $100K+/year for something that saves the compliance team a 2-week ticket per model.
  2. Regulation as moat. RBI directives, HIPAA clauses, EU AI Act Annex III are stable targets we can certify against. Competitors need to rebuild that certification path from scratch.
  3. Concrete buyers. Unlike "AI platform," a compliance-pack pitch has a named buyer (CRO / Head of Model Risk) with an explicit budget line.

Core design decisions#

These show up everywhere in the code; knowing them helps you navigate the rest of the docs.

  • Composable monolith. One Python process runs the API + agent loop + scheduler. One Next.js process runs the dashboard. Event-log as the decomposition seam for later. Details.
  • Permission engine is the mediator. Every tool dispatch, every HTTP route, every HITL gate resolves through one pipeline. Details.
  • Plugins are CC-format compatible. A plugin that works in Claude Code works in swarm unchanged. Details.
  • Compliance profiles are pluggable. RBI FREE-AI today; HIPAA and EU AI Act on the same extension surface. Details.
  • Audit PDFs are tamper-evident. SHA-256 pinned, regulator-format layout. Details.

Status (April 2026)#

  • Version: v0.11.0
  • Tests: 628 passing, 0 failing
  • License: Apache-2.0 (core) / commercial (compliance packs, managed hosting, SSO extras)
  • Customers: design-partner program (pre-launch)
  • Deployment modes: Docker Compose (dev), Kubernetes + Helm (v0.12), customer-VPC (v0.12), air-gapped (v0.13)

Next#