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sandbase-harness

sandbaseai/sandbase-harness

Connects dsh to a local SandBase Harness runtime over stdio MCP for managing agents and sessions, streaming turns, inspecting artifacts and cancelling work.

Install

dsh plugin --profile web add github:sandbaseai/sandbase-harness

README

SandBase Harness

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GitHub stars Listed on deepseek-plugin.org Release Official MCP Registry Discussions CodeQL License

AI-readable project metadata: llms.txt · installation guide

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.

Building with DeepSeek Harness? The independent DeepSeek Harness Handbook provides source-backed runtime guides, multilingual troubleshooting, and a regularly updated Agent-first resource map.

SandBase Harness architecture

Quick Start

Requirements: Node.js 22+, npm 10+, and a model provider API key (OpenAI, Anthropic, MiniMax, or any OpenAI-compatible endpoint). Docker is optional and only needed for Docker-backed sandboxes.

git clone --branch v0.3.8 --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

init writes a workspace into the directory you run it from: an agent, a skills folder, and config.yaml, whose provider reference is the ${OPENAI_API_KEY} environment variable. start serves the API and the Console on http://127.0.0.1:3000.

Two steps finish the setup, both on Settings > Setup at http://127.0.0.1:3000/dashboard:

  1. The provider. Paste your API key into the provider form and save. The page then reports that the saved configuration is not active yet, so restart the runtime — stop it with Ctrl+C and run the start command again, or use the restart button. A saved setting only takes effect at startup. If your provider is not in the list, choose the OpenAI-compatible vendor and set its base URL.
  2. The model. In the Agent models panel, set the model ID your provider actually serves — deepseek-chat for DeepSeek, for example. An agent carries its own model ID, so the gpt-4o that init writes is not valid for every provider, and a wrong ID fails the turn with model_not_found.

Send the first message from the Console: open Sessions, create a session for the agent, and type into the composer. From a terminal it is one command:

node ../sandbase-harness/dist/index.js chat agent_assistant --message "hello" --tool-approval allow

chat sends that one message and exits once the turn settles; without --message it keeps the session open and streams until you interrupt it. --tool-approval allow preauthorizes the tool calls the agent may make, which the init template otherwise parks for approval and waits for a person to answer; see CLI.

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.

Try it in Codespaces

Open in GitHub Codespaces

The included development container installs dependencies and builds the runtime. When the terminal is ready, start the server on the forwarded port:

node dist/index.js start --host 0.0.0.0

Open the forwarded SandBase Harness Console port, then configure a model in Settings > Setup. Codespaces usage may be billed by GitHub; the local quick start above remains free and keeps all runtime data on your machine.

Use the Official SDK

The runtime answers its own /v1 API on that same port, and an official Anthropic TypeScript SDK client drives it unchanged: point the client's baseURL at the runtime, give it the runtime API key, and the quickstart in examples/official-sdk runs a whole turn — message, tool call, tool result, final reply — against it. That example is executed on every pull request by tests/conformance/official-sdk-quickstart.test.ts, so the compatibility it describes is compatibility that is tested rather than claimed.

The same surface is specified in docs/api.md, and this repository's own TypeScript SDK is documented under SDK below.

Documentation

Portable Agent Plugin

Copilot CLI, VS Code, and other Agent Plugins 1.0 clients can install the same OCI-backed MCP bridge directly from this repository. Start the Harness API and Docker first, then expose its URL to the plugin process:

export MANAGED_AGENTS_URL=http://host.docker.internal:3000
# Optional when the runtime requires authentication:
export MANAGED_AGENTS_API_KEY=your-runtime-key

copilot plugin install sandbaseai/sandbase-harness:agent-plugin

The plugin passes these environment variables through to the pinned ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.8 image. It does not store a key in plugin.json, mcp.json, or the installed plugin files. On Linux, the plugin's Docker command maps host.docker.internal through host-gateway.

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

MCP Bridge Container

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:

Container package: GitHub Container Registry

docker pull ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.8
docker run --rm -i \
  -e MANAGED_AGENTS_URL=http://host.docker.internal:3000 \
  ghcr.io/sandbaseai/sandbase-harness-mcp:0.3.8

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.

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.

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

NeedWhat Harness provides
Run generated code safelyLocal, Docker, Kubernetes, and self-hosted worker sandboxes
Inspect long-running agentsPersistent sessions, resumable event streams, audit, and replay
Control tool accessMCP toolsets, credential vaults, permission policies, and approvals
Operate any modelOpenAI, Anthropic, MiniMax, and OpenAI-compatible providers, including DeepSeek V4
Keep infrastructure yoursLocal-first SQLite and file storage with no required hosted control plane

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 overviewSettingsAPI reference
overviewsettingsapi-ref

Start with a use case

See the Showcase for three practical paths: an auditable coding agent, DeepSeek Harness as an interactive front end, and controlled code execution across Local, Docker, Kubernetes, and self-hosted sandboxes.

For client-specific setup, see the installation guide, including the pinned Cline CLI command and the Docker MCP Bridge configuration.

Community use-case discussions:

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
# Preferred: install a local source checkout after `npm run build`.
dsh plugin --profile web add -w ../sandbase-harness
# Git URL fallback. Keep HTTPS; do not convert the spec to SSH.
# dsh plugin --profile web add git+https://github.com/sandbaseai/sandbase-harness.git
dsh web

If Plugin Hub reports already installed: managed-agents after a partial or repeated install, update the Hub first, then remove only the displayed managed-agents plugin entry and retry from the tagged HTTPS Git source:

dsh plugin --profile web update dsh-plugin
dsh plugin --profile web remove managed-agents
dsh plugin --profile web add git+https://github.com/sandbaseai/sandbase-harness.git

This is a Plugin Hub duplicate-install path, not an npm installation path. If the installed view shows a different target identifier, remove that exact identifier instead. Keep the profile directory and its evidence until the runtime starts successfully; see the reported recovery issue.

The profile installs the verified source checkout directly; it does not resolve the unrelated unscoped npm package. A git-hosted install runs prepare only when dist/ is missing. Keep the HTTPS git spec; converting it to SSH fails on Windows hosts without GitHub SSH access.

A git-hosted install needs one extra step for pnpm's build allowlist. The first dsh plugin --profile web add fails with ERR_PNPM_GIT_DEP_PREPARE_NOT_ALLOWED and prints the exact key. Add that key under allowBuilds: in the profile's pnpm-workspace.yaml, then re-run the same add command; a plain package name does not match a git-hosted resolution:

allowBuilds:
  "managed-agents@https://codeload.github.com/sandbaseai/sandbase-harness/tar.gz/<commit>": true

The second run builds dist/ through prepare, creates the managed-agents / managed-agents-mcp bins, and joins the bundle layer. The patch starts the bundled MCP entry 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.

For a walkthrough that starts with DSH and adds this runtime as a real third-party plugin, read the DeepSeek Harness developer guide. The Chinese edition is available as well; both articles are maintained against the pinned SandBase Harness v0.3.8 integration.

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.

For a complete, reproducible workflow that combines the evidence ledger with sandboxed execution, credentials, audit, and replay, read Build an Auditable Research Agent.

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. Read its SandBase Harness bridge guide for the DSH-specific contract, then start with the local-browser Install Doctor for installation evidence, or use the Failure Router to identify the first broken runtime boundary.

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.

${OPENAI_API_KEY} is read from the environment the runtime was started with. If it is not set, the first turn fails with a message naming that variable and sends no request: set it before starting the runtime, or paste a literal key under Settings > Setup, which also lists every agent's model so the ID can be set without editing these files.

For DeepSeek V4 Pro/Flash configuration, including maximum reasoning effort, see DeepSeek V4.

For first-class MiniMax configuration, regional endpoints, and the supported MiniMax-M3 and MiniMax-M2.7 model IDs, see MiniMax.

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" [--tool-approval ask|allow|deny]
managed-agents template list | install <name> | create <name>

A turn whose tool needs approval parks instead of failing, and chat asks before running it, then lets the runtime continue the same turn. --tool-approval allow decides every such call in advance, which is what a script or a CI job uses, and deny refuses them. With no terminal to prompt, the default ask answers nothing and exits non-zero with the calls that are waiting named, so a script states its policy rather than inheriting one. A custom tool is the exception: only your own client can produce its result, and chat says so and exits non-zero. See usage.

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"

A stream opened without a cursor carries live events only; read the event log first (GET /v1/sessions/SESSION_ID/events) and resume from the last seq you saw.

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 + Console
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.

Star and share

If this runtime solves a real agent-infrastructure problem for you, star the repository so other builders can find it.

Ecosystem directories, community guides, and related projects are in docs/ecosystem.md.

License

Apache-2.0

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