S T A G E N T

The world of agents is your stage.

A desktop-native, open-source harness that turns high-level goals into observable, long-running agent workflows — across any model, any tool, any timeline.

Read the Research Paper
The Problem

The agent landscape in 2026 is fragmented across four layers.

Infrastructure / Browser pools, compute sandboxes, LLM APIs

Browserbase, E2B, model providers deliver reliable primitives.

MISSING Task awareness — infrastructure does not know what the agent is trying to accomplish.

Application / Gateway control planes, messaging, tool connectors

Gateway patterns work. Routing, policy enforcement, observability.

MISSING Long-horizon persistence — sessions are reactive and ephemeral.

Orchestration / Workspace managers, multi-model routers

Parallel orchestration and multi-model routing are validated.

MISSING Goal decomposition — orchestrators manage workspaces, not objectives.

Harness / Coordinator-sub-agent patterns, desktop agents

Coordinator-sub-agent patterns work at 40x token overhead.

MISSING Memory-native architecture, cross-session persistence, graduated autonomy, agent-to-agent communication.

Stagent is the harness layer.

Every product analyzed excels at its layer but leaves a gap at the layer above. Gateway patterns work but remain reactive. Parallel orchestration works but stays session-scoped. Multi-model routing works but is cloud-only and opaque.

Stagent is the system that consumes infrastructure, adopts application patterns, orchestrates across them, and adds the capabilities no existing product provides.

Goal decomposition. Long-horizon persistence. Memory-native architecture. Graduated autonomy. Observable multi-agent execution.

Differentiation

Five pillars.

01

Long-Horizon Task Persistence

Tasks that span hours, days, or weeks.

Checkpoint/resume, progress tracking, failure recovery, and resource budgets. No existing product fully supports tasks beyond a single session. Stagent makes task persistence the architectural foundation, not a feature bolted onto chat.

02

Multi-Model Orchestration

Route subtasks to the best available model.

Claude for reasoning, GPT for long-context, Gemini for research, Grok for speed, open-source models for cost control and privacy. Transparent routing with measured performance — including local model support via Ollama for offline work.

03

Memory-Native Architecture

Memory as a core primitive, not a subsystem.

Four-tier hierarchical memory: working (active context), episodic (past interactions), semantic (distilled knowledge), and procedural (learned strategies). Agents curate their own memory — not just passively accumulate it.

04

Graduated Autonomy

Trust calibrated by observed performance.

New or risky tasks run in high-oversight mode. Well-understood tasks graduate to autonomous execution. Trust is per-agent-type, per-task-type, and per-risk-level — not global. Hard boundaries remain regardless of trust level.

05

Desktop-Native with Hybrid Execution

Local-first with cloud-optional elasticity.

Tauri-based desktop application for privacy, low latency, and full filesystem access. Cloud-optional for background execution and elastic compute. The same task graph runs locally or in the cloud — the orchestration layer is location-agnostic.

Architecture

Rust + TypeScript hybrid.

Rust owns the system — desktop shell, WASM sandboxing, persistent storage, IPC. TypeScript owns the intelligence — LLM interaction, agent logic, tool execution, protocol communication. Neither crosses into the other's domain.

The boundary is architectural, not arbitrary. Rust's safety and performance handle the system-level concerns that agents must never compromise. TypeScript's ecosystem breadth provides access to every major LLM SDK and the growing MCP connector ecosystem.

Six protocols form the communication backbone — from agent-to-tool (MCP) to agent-to-agent (A2A) to agent-to-website (WebMCP), with graceful fallback at every layer.

Rust TypeScript Tauri React SQLite WASM MCP A2A WebMCP CDP Ollama
Landscape

No competitor occupies the hybrid autonomous position.

Cloud-only platforms sacrifice local file access and privacy. Desktop-only tools lack cloud persistence for long-horizon tasks. Reactive systems wait for instructions. Supervised systems require approval for everything.

Stagent bridges both axes — hybrid execution with graduated autonomy. Local-first for privacy and speed, cloud-optional for background compute and elastic scaling. Supervised for new tasks, autonomous for proven workflows.

Execution Hybrid (WASM + Cloud)
Autonomy Graduated (Earned)
Models Any (Open)
License Apache 2.0
Portfolio

Built across three technology waves.

0K+
Lines of Code
0+
AI Agents Built
0
Production Systems
0+
Projects Shipped
2023

FinEdge

LLM APIs

Fintech decisioning engine

8K+ lines · React + TypeScript

2023

SuperCRM

Agent Frameworks

Social CRM with AI agents

6K+ lines · Full-stack TypeScript

2024

InfraWatch

RAG Systems

Infrastructure intelligence platform

10K+ lines · React + Python

2024

AgentKit

MCP Protocol

Multi-agent development toolkit

5K+ lines · TypeScript + MCP

2025

Canvas OS

Agent Orchestration

Visual workspace orchestrator

8K+ lines · React Flow + Zustand

2025

DeepResearch

Autonomous Agents

Autonomous research synthesis

4K+ lines · Claude SDK + MCP

2026

Stagent

The Harness Layer

Multi-agent autonomous harness

45K+ lines (projected) · Rust + TypeScript

Build what you own.
Consume what commoditizes.

Apache 2.0

The orchestration layer — task graphs, memory, graduated autonomy, checkpoint/resume — is model-independent. No model provider can revoke it. As models improve, the harness becomes more valuable because it enables more ambitious tasks.

Fully open source means full transparency. Users can inspect exactly what agents do with their data and credentials. Open source builds trust that closed-source agents cannot earn.

The framework-agnostic runtime naturally adapts to new models and modalities. The memory system compounds over time. The community grows the ecosystem through templates and connectors.

The harness layer is open.

Read the research. Explore the code. Join the community building the orchestration layer for autonomous agents.

Research Paper GitHub Community