sandbase-harnessDeepSeek Harness plugin

Local-first, self-hosted AI agent runtime and MCP bridge with sandboxed sessions, memory, credentials, audit/replay, and a local Console.

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Stars
641
Forks
62
License
Apache-2.0
Last commit
Aug 31, 2026
Latest release
v0.3.8

Overview

Local-first, self-hosted AI agent runtime and MCP bridge with sandboxed sessions, memory, credentials, audit/replay, and a local Console.

Original README

Cached from the project repository on Sep 3, 2026. This is source content, separate from the Agents.md review above.

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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

Looking for a lightweight bridge instead of a full runtime? SandBase CLI connects 25 AI client targets to 2,000+ models and APIs through a local stdio MCP bridge. If it fits your workflow, star SandBase CLI so other agent users can discover it.

Need hosted model and media APIs instead? SandBase provides one interface for LLM, image, and video generation APIs, with the API quickstart covering keys and first calls.

bash
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
# open http://127.0.0.1:3000/dashboard

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

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

Find SandBase Harness

The project is also discoverable through these independent ecosystem directories:

Recently verified community references:

Pending community review:

These listings are independent directories; the repository and its release metadata remain the source of truth.

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:

bash
node dist/index.js start --host 0.0.0.0

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

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 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:

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:

bash
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:

bash
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:

yaml
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:

bash
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.

Quick Start

bash
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

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:

Container package: GitHub Container Registry

bash
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.

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:

bash
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:

bash
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

1my-agents/
2├── agents/                  # Seed agent definitions (YAML)
3│   └── assistant.yaml
4├── skills/                  # Seed skill packages
5│   └── example-skill/
6│       └── SKILL.md
7└── .managed-agents/         # Runtime state (gitignored)
8    ├── config.yaml          # Workspace configuration
9    ├── data.db              # SQLite metadata
10    ├── logs/runtime.log
11    ├── files/               # Uploaded file bytes
12    ├── skills/              # Uploaded skill packages
13    ├── snapshots/           # Session workspace snapshots
14    └── sandbox/             # Local session sandboxes

Configuration

.managed-agents/config.yaml:

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.

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

CLI

bash
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:

bash
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):

bash
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:

bash
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:

bash
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:

bash
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:

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

SDK

typescript
1import { ManagedAgentsClient } from 'managed-agents/sdk';
2
3const client = new ManagedAgentsClient({
4  baseUrl: 'http://127.0.0.1:3000',
5});
6
7const session = await client.sessions.create({
8  agent: 'agent_...',
9  environment_id: 'env_...',
10});
11
12for await (const event of client.sessions.chat(session.id, 'Hello')) {
13  if (event.type === 'agent.message_chunk') {
14    process.stdout.write(event.delta ?? '');
15  }
16}

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

typescript
1import Anthropic from '@anthropic-ai/sdk';
2
3const client = new Anthropic({
4  apiKey: process.env.MANAGED_AGENTS_API_KEY ?? 'local-dev-key',
5  baseURL: 'http://127.0.0.1:3000',
6});
7
8const session = await client.beta.sessions.create({
9  agent: 'agent_...',
10  environment_id: 'env_...',
11});

Authentication

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

bash
# 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/:

yaml
1name: Incident commander
2description: Triages alerts and coordinates response.
3model: gpt-4o
4system: |-
5  You are an on-call incident commander.
6mcp_servers:
7  - name: sentry
8    type: url
9    url: https://mcp.sentry.dev/mcp
10tools:
11  - type: agent_toolset_20260401
12    default_config:
13      permission_policy: { type: always_ask }
14    configs:
15      - name: bash
16        permission_policy: { type: always_ask }
17  - type: mcp_toolset
18    mcp_server_name: sentry
19skills:
20  - type: custom
21    skill_id: skill_...
22metadata:
23  template: incident-commander

Development

bash
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 and APIs with one onboarding command.
  • DSH Plugin Store — discover, filter, install, and manage community DeepSeek Harness plugins from the native Settings experience.
  • SandBase — hosted agent infrastructure, model access, tools, and managed sandboxes.

Documentation

Community Guides

  • Build an Auditable Research Agent — a reproducible guide combining evidence ledgers, sandboxed execution, credentials, audit, and replay with SandBase Harness.
  • Self-host the SandBase agent runtime by SSD Nodes — an independent VPS walkthrough covering installation, agent configuration, MCP servers, sandbox modes, and reverse-proxy deployment. The article demonstrates v0.3.2; use the current release command above for v0.3.8.

License

Apache-2.0