SandBase Harness

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A local-first runtime for AI agents. Sessions, sandboxed tools, memory, credentials, audit trails, and a built-in Console — all running on your machine or in your own infrastructure.

git clone --branch v0.3.2 --depth 1 https://github.com/sandbaseai/sandbase-harness.git
cd sandbase-harness
npm ci
npm run build
mkdir ../my-agents && cd ../my-agents
node ../sandbase-harness/dist/index.js init
node ../sandbase-harness/dist/index.js start
# open http://127.0.0.1:3000/dashboard

Choose SandBase Harness when you need more than a model loop:

Need	What Harness provides
Run generated code safely	Local, Docker, Kubernetes, and self-hosted worker sandboxes
Inspect long-running agents	Persistent sessions, resumable event streams, audit, and replay
Control tool access	MCP toolsets, credential vaults, permission policies, and approvals
Operate any model	OpenAI, Anthropic, and OpenAI-compatible providers, including DeepSeek V4
Keep infrastructure yours	Local-first SQLite and file storage with no required hosted control plane

Why

Agent SDKs handle the model loop. Production agents need more: persistent sessions, tool governance, sandbox boundaries, credential handling, memory, auditability, and a UI for humans to inspect what happened. managed-agents is that runtime layer — not a visual workflow builder and not another model SDK.

Features

• Claude Managed Agents-style /v1 API and local Console
• SQLite-backed agents, sessions, environments, credential vaults, memory stores, files, skills, and API keys — SQLite metadata by default
• local file/skill bytes stored in the workspace state directory
• Resumable Server-Sent Events for session replay and debugging
• One active model provider boundary configured through Settings V2
• Sandbox backends: local process, Docker (per-session containers), Kubernetes (kubectl exec/cp), self-hosted worker queue
• Settings V2: one workspace model vendor, loop engine, storage, memory, sandbox — with validation, form/JSON modes, and restart flow
• MCP toolsets, permission policies, built-in tools, and skill packages
• DeepSeek Harness bridge over MCP stdio for agents, sessions, streamed turns, artifacts, and cancellation
• TypeScript SDK at managed-agents/sdk
• Release gate: npm run release:check

Screenshots

Console overview	Settings	API reference

Requirements

• Node.js 22+
• npm 10+
• A model provider API key (OpenAI, Anthropic, or OpenAI-compatible endpoint)
• Docker (optional, for Docker-backed sandboxes)

DeepSeek Harness

Run this project as a DSH plugin instead of treating dsh-plugin as discovery metadata only. Install the bundle into a DSH profile, start managed-agents, then boot that profile:

export MANAGED_AGENTS_URL=http://127.0.0.1:3000
dsh plugin --profile web add managed-agents
dsh web

The patch starts managed-agents-mcp over stdio. DSH can then list agents, create and run sessions, inspect results and artifacts, and stop work through native mcp__sandbase__* tools. See examples/deepseek-harness for the full tool list and authenticated-runtime configuration.

Pair the plugin with SandBase Skills to give the same DSH project a portable, source-verifiable research workflow:

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