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.

> Looking for a lightweight bridge instead of a full runtime? SandBase CLI > connects 25 AI client targets to 2,000+ models through a local stdio MCP bridge.

git clone --branch v0.3.4 --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, MiniMax, 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
![overview](docs/assets/dashboard-overview.png)	![settings](docs/assets/dashboard-settings-models.png)	![api-ref](docs/assets/dashboard-api-reference.png)

Requirements

Node.js 22+
npm 10+
A model provider API key (OpenAI, Anthropic, MiniMax, or an 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](examples/deepseek-harness/README.md) 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:

npx --yes github:sandbaseai/sandbase-skills add multi-source-search
dsh web

This installs the complete Skill into .dsh/skills/multi-source-search, DSH's project-scoped discovery directory. It runs from GitHub source and needs no SandBase account when DSH already provides web/search tools.

New to DSH profiles, plugin composition, tool policy, or session semantics? The independent DeepSeek Harness Handbook provides source-backed quickstarts, architecture maps, and troubleshooting for the runtime layers used by this integration.

Quick Start

git clone --branch v0.3.4 --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, go to Settings > Models, paste your API key, and you're running.

The unscoped managed-agents name on npm is not this project. Until an official scoped package is announced in this repository, install only from the tagged GitHub source release shown above. Do not run npx managed-agents or npm install managed-agents.

The six-tool MCP bridge is published as a multi-architecture OCI image. Start the Harness API, then add this stdio command to an MCP client:

docker pull ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.4
docker run --rm -i \
  -e MANAGED_AGENTS_URL=http://host.docker.internal:3000 \
  ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.4

For an authenticated remote runtime, also pass MANAGED_AGENTS_API_KEY. The container image contains only the MCP bridge; agent sessions and sandbox work remain in the connected Harness runtime. Every release image is built from the matching Git tag for linux/amd64 and linux/arm64, includes OCI source and MCP ownership metadata, and receives a GitHub build-provenance attestation.

For development from the latest main branch:

git clone https://github.com/sandbaseai/sandbase-harness.git
cd sandbase-harness && npm ci && npm run build
cd .. && mkdir my-agents-dev && cd my-agents-dev
node ../sandbase-harness/dist/index.js init
node ../sandbase-harness/dist/index.js start

Workspace Layout

my-agents/
├── agents/                  # Seed agent definitions (YAML)
│   └── assistant.yaml
├── skills/                  # Seed skill packages
│   └── example-skill/
│       └── SKILL.md
└── .managed-agents/         # Runtime state (gitignored)
    ├── config.yaml          # Workspace configuration
    ├── data.db              # SQLite metadata
    ├── logs/runtime.log
    ├── files/               # Uploaded file bytes
    ├── skills/              # Uploaded skill packages
    ├── snapshots/           # Session workspace snapshots
    └── sandbox/             # Local session sandboxes

Configuration

.managed-agents/config.yaml:

model:
  provider: openai
  api_key: ${OPENAI_API_KEY}

storage:
  metadata: { provider: sqlite, options: {} }
  artifacts: { provider: local, options: { base_path: files } }

Agents pick concrete model IDs (gpt-4o, claude-sonnet-4-20250514, openai/gpt-5.5). The workspace config only says how to reach the model service.

For DeepSeek V4 Pro/Flash configuration, including maximum reasoning effort, see [DeepSeek V4](docs/deepseek-v4.md).

For first-class MiniMax configuration, regional endpoints, and the supported MiniMax-M3 and MiniMax-M2.7 model IDs, see [MiniMax](docs/minimax.md).

CLI

managed-agents init
managed-agents start [--host 127.0.0.1] [--port 3000]
managed-agents list
managed-agents reload
managed-agents chat <agent-id> --message "hello"
managed-agents template list | install <name> | create <name>

API Examples

Create an agent:

curl -X POST http://127.0.0.1:3000/v1/agents \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Incident commander",
    "model": "gpt-4o",
    "system": "You are an on-call incident commander.",
    "tools": [{ "type": "agent_toolset_20260401" }]
  }'

Create an environment (local sandbox):

curl -X POST http://127.0.0.1:3000/v1/environments \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Default local",
    "config": { "hosting_type": "local", "sandbox_provider": "local" }
  }'

Create a Docker-isolated environment:

curl -X POST http://127.0.0.1:3000/v1/environments \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Docker sandbox",
    "config": {
      "sandbox_provider": "docker",
      "image": "node:22-slim",
      "resources": { "memory": "1g", "cpu": 1 }
    }
  }'

Start a session:

curl -X POST http://127.0.0.1:3000/v1/sessions \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "agent_...",
    "environment_id": "env_...",
    "title": "Triage SENTRY-123"
  }'

Send a message:

curl -X POST http://127.0.0.1:3000/v1/sessions/SESSION_ID/messages \
  -H "Content-Type: application/json" \
  -d '{ "content": "Investigate the alert." }'

Resume the event stream:

curl -N http://127.0.0.1:3000/v1/sessions/SESSION_ID/events/stream \
  -H "Last-Event-ID: 42"

SDK

import { ManagedAgentsClient } from 'managed-agents/sdk';

const client = new ManagedAgentsClient({
  baseUrl: 'http://127.0.0.1:3000',
});

const session = await client.sessions.create({
  agent: 'agent_...',
  environment_id: 'env_...',
});

for await (const event of client.sessions.chat(session.id, 'Hello')) {
  if (event.type === 'agent.message_chunk') {
    process.stdout.write(event.delta ?? '');
  }
}

The /v1 API follows Claude Managed Agents resource shapes, so you can also point the Anthropic SDK at the local runtime:

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: process.env.MANAGED_AGENTS_API_KEY ?? 'local-dev-key',
  baseURL: 'http://127.0.0.1:3000',
});

const session = await client.beta.sessions.create({
  agent: 'agent_...',
  environment_id: 'env_...',
});

Authentication

Open by default. Authentication activates when at least one API key exists:

# Static key via environment
export MANAGED_AGENTS_API_KEY=sk-local-example

# Or create a managed key
curl -X POST http://127.0.0.1:3000/v1/api-keys \
  -H "Content-Type: application/json" \
  -d '{ "name": "Local Console" }'

Clients send Authorization: Bearer <key>.

Agent Definition

Agents are YAML files in agents/:

name: Incident commander
description: Triages alerts and coordinates response.
model: gpt-4o
system: |-
  You are an on-call incident commander.
mcp_servers:
  - name: sentry
    type: url
    url: https://mcp.sentry.dev/mcp
tools:
  - type: agent_toolset_20260401
    default_config:
      permission_policy: { type: always_ask }
    configs:
      - name: bash
        permission_policy: { type: always_ask }
  - type: mcp_toolset
    mcp_server_name: sentry
skills:
  - type: custom
    skill_id: skill_...
metadata:
  template: incident-commander

Development

npm ci
npm run typecheck    # src + tests
npm test             # vitest
npm run build        # runtime + console + SDK
npm run release:check  # full local release gate

release:check runs typecheck, tests, both builds, npm pack --dry-run, CLI init smoke, and examples/basic startup smoke.

SandBase Ecosystem

SandBase Skills — 88 installable

Agent Skills for research, social intelligence, marketing, and business workflows across Codex, Claude Code, Cursor, Gemini CLI, and other clients.

SandBase CLI — connect Cursor, Claude Code,

Codex, Windsurf, Gemini CLI, OpenCode, and other MCP clients to 2,000+ AI models with one onboarding command.

SandBase — hosted agent infrastructure, model access,

tools, and managed sandboxes.

Documentation

[Installation](docs/installation.md)
[Usage Guide](docs/usage.md)
[API Reference](docs/api.md)
[Skills](docs/skills.md)
[Deployment](docs/deployment.md)
[Architecture](docs/spec/architecture.md)
[Contributing](CONTRIBUTING.md)
[Changelog](CHANGELOG.md)

License

[Apache-2.0](LICENSE)

sandbase-harness

安装

检查来源文件

SandBase Harness

Why

Features

Screenshots

Requirements

DeepSeek Harness

Quick Start

Workspace Layout

Configuration

CLI

API Examples

SDK

Authentication

Agent Definition

Development

SandBase Ecosystem

Documentation

License

来源信息

安装

检查来源文件

SandBase Harness

Why

Features

Screenshots

Requirements

DeepSeek Harness

Quick Start

Workspace Layout

Configuration

CLI

API Examples

SDK

Authentication

Agent Definition

Development

SandBase Ecosystem

Documentation

License

更多相关插件

sandbase-skills

ouroboros#integrations/dsh-plugin

tongflow#dsh-tongflow