High-VelocityAgent IDE
Structured coding runs through multi-agent review. Draft, challenge, vote, and audit before the result lands.
Every model. Every stage. One equal vote each.
These are structured review phases, not public personas. Every selected model moves through the same workflow, casts a binary yes/no vote, and must explain a no. A blocking no returns to council discussion until it resolves into a genuine yes.
Backend-sourced access, no hardcoded caps
The live plan catalog controls which model access is available. Public pages avoid fixed model counts or static prices; checkout and billing read the backend catalog.
Live catalog
Available plans, checkout price keys, and entitlements come from the backend.
Managed routing
The public app describes capability while the runtime keeps provider details opaque.
Binary review
Every consensus step resolves to yes or no with a written reason for any no.
See pricing for the live plan catalog.
Clarify
Interactive requirements Q&A — all models clarify scope together
"What is the exact success criterion for this task?"
Plan
Planning — the council aligns on approach and trade-offs
"Here are three viable approaches with their trade-offs."
Architect
System design — interfaces, modules, and data contracts
"The interface contract must be established before implementation begins."
Draft
Code draft — the first implementation is produced
"Draft written. Submitting for council review."
Refine
Refine and challenge — the full council critiques the draft
"The draft is correct but this path can be O(n log n) instead of O(n²)."
Safety Review
Red-team and security audit — adversarial review by all models
"Input is not sanitized here — this is an injection vector."
Debugger
Final validation — the council verifies correctness end-to-end
"Root cause traced three layers deep. Fix confirmed, regression covered."
Review duties stay internal
The desktop runtime assigns coordination and authoring duties internally while public copy stays focused on the outcome: multi-model review, binary consensus, and verified artifacts. This keeps marketing stable while backend and IDE contracts evolve.
Core Features
The technology behind the desktop workflow
SAMI Context Engine
A multi-layered retrieval pipeline that understands code structure. It intelligently switches between vector retrieval for semantics and keyword search for exact symbol matches.
- Semantic Graph Retrieval
- Token Usage Optimization
- Repo-Aware Indexing
Adversarial Consensus
Every selected model reviews with equal vote weight across the structured workflow. Voting is binary — yes or no — and a blocking no must carry a written rationale. A no is never dropped or rubber-stamped: the council deliberates until the objection resolves into a genuine yes.
- Equal-weight binary voting: yes / no
- A no must carry a written reason and is resolved through discussion, never silently dropped.
- The council re-deliberates on every no until it becomes a genuine yes
Execution-Verify Gate
A council that doesn't just agree — it runs the code. Most AI tools vote on plausibility; SAMI's council votes on proven behaviour. It actually runs the project's build, tests, lint, and typecheck and feeds the real result back before any verdict is reached. A passing run is proven, not assumed. A failing run is put in front of the council with the real failure in view — the council either accepts it or blocks on it, which flows into the same never-abort resolution loop as a veto.
- Votes on proven run output, not plausibility
- Failing runs surface the real error to the council
- A failing run is resolved by the council to a genuine yes, never silently stopped
Security-First Pipeline
Industrial-grade security is baked into the development lifecycle. Automated red-teaming and dependency auditing occur before a single line of code is merged.
- Automated Red-Teaming
- Vulnerability Scanning
- Audit-Ready Logs
Multi-Project Context
Manage complex microservices or mono-repos with ease. SAMI maintains independent context pools for each project while allowing cross-repo knowledge sharing.
- Isolation by Design
- Cross-Project Referencing
- Context Hot-Swapping
One shared context, every stage
The context engine grounds the whole run. A shared baseline context flows under every stage, each stage pulls its own slice on top, and any model can look back at what another said earlier in the run.
Hybrid retrieval blends semantic, keyword and code intelligence into one project-context stream the council can draw on.
- Shared baseline context — every stage reads it
- Per-stage context slice — pulled on top into each stage
- Cross-stage recall — look back at earlier council context
The IDE You Already Know
Built on a familiar editor foundation — same shortcuts and keybindings, with an entire AI team working for you.
Curated access to leading models without requiring individual API keys.
Unified billing across all provider models used within your workspace.
Automatic failover routing to ensure high availability for all agents.
Security-controlled access layers for sensitive enterprise environments.
Familiar Editor Base
Same editor foundation you already know — same shortcuts, same keybindings, no relearning curve
Industry-Standard Code Editor
Familiar coding surface with full IntelliSense support
Live Multi-Agent Chat
Watch agents discuss and debate in real-time
Live Diff View
Real-time streaming of code changes
Semantic Search
Find code by meaning, not just text
External Tool Integration
Connect approved tools and data sources explicitly when a run needs them
Managed Model Access
SAMI uses a curated model catalog. You choose the model that fits your plan and we handle routing, billing, and safety controls behind a single managed runtime.
Curated Model Catalog
Pick from the models SAMI currently offers. No provider setup, no key management, no separate billing.
Centralized Routing
Requests are routed through SAMI's managed provider layer for consistent latency, failover, and billing behavior.
Managed Security
Provider credentials stay inside SAMI infrastructure while your plan controls access to the available models.
How SAMI Compares
Other tools give you one AI assistant. SAMI runs a council of models that debate, vote, and must reach consensus before any result is accepted.
| Feature | SAMI (IDE) | GitHub Copilot | Cursor (IDE) | Windsurf (IDE) | v0 (Vercel) | Bolt (Netlify) | Lovable |
|---|---|---|---|---|---|---|---|
| Primary AI Logic | Configurable AI Review | Single Chat Agent | Composer / Solo Agent | Cascade Flow | UI Generation | Scaffold Bot | App Logic Gen |
| Cognitive Architecture | Multi-Stage Pipeline | Reactive Chat | Reactive Edits | Sequential Cascade | Component Gen | App Generation | App Generation |
| Validation Strategy | Peer Review & Veto | Manual | Iterative Fixes | Agentic Fixes | Visual Feedback | Terminal Errors | Terminal Errors |
| Multi-Agent Debate | |||||||
| Autonomous Debugging | Deep Audit Pass | Manual | Agentic Assist | Cascade Fix | UI Refiner | Self-Heal (Basic) | One-Click Fix |
| Integrated Browser | Full Subagent | Limited View | Live View | Web-Container | Live Preview | ||
| Data Sovereignty | Managed Model Access | Enterprise Cloud | SaaS Privacy Mode | Windsurf Cloud | Vercel Ecosystem | Netlify Cloud | Lovable Cloud |
| Code Ownership | Local Filesystem | Local Filesystem | Local Filesystem | Local Filesystem | Git Sync | Git Sync | Export to GitHub |
| Agent Manager | Multi-Project Control | Global Settings | Single Context | Single Context | Project View | Project View | Project Focus |
Start with a free account.
Start with the desktop workflow. Manage account and billing from the web dashboard.