Apache-2.0 local-first agent runtime installed into DeepSeek Harness as a plugin — a bundled MCP bridge over stdio exposes sandboxed sessions, audit, replay and a local Console to any DSH profile.
DSH integration
Compatible
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
Apache-2.0
01What can it help you accomplish?
Run sandboxed, auditable agent sessions from inside DeepSeek Harness
DSH can list agents, create and run sessions, stream turns, inspect results and artifacts, and stop work through native mcp__sandbase__* tools
DSH users who want a self-hosted agent runtime with sandboxing, audit trails and replay behind their DSH profile
Execute generated agent code safely in isolated sandboxes
Session sandboxes on local process, Docker (per-session containers), Kubernetes (kubectl exec/cp), or a self-hosted worker queue, plus audit trails and replay
Teams running untrusted or generated code who need sandbox boundaries, credential handling and auditability
Operate any model provider, including DeepSeek V4, from one local-first runtime
A single configured model provider boundary (OpenAI, Anthropic, MiniMax, or OpenAI-compatible endpoints) driving agents and sessions, with SQLite metadata kept local
Developers who want model-agnostic agents without a required hosted control plane
02How to install into DeepSeek Harness
Prerequisites
- DeepSeek Harness (dsh) installed with a profile available (the README example uses `--profile web`)
- Node.js 22+ and npm 10+
- A model provider API key (OpenAI, Anthropic, MiniMax, or an OpenAI-compatible endpoint)
- Docker (optional, for Docker-backed sandboxes)
Installation steps
- 01
Clone the tagged release and build: `git clone --branch v0.3.7 --depth 1 https://github.com/sandbaseai/sandbase-harness.git`, `cd sandbase-harness`, `npm ci`, `npm run build`
$ git clone --branch v0.3.7 --depth 1 https://github.com/sandbaseai/sandbase-harness.git
- 02
Create a workspace and start the runtime: `mkdir ../my-agents && cd ../my-agents`, `node ../sandbase-harness/dist/index.js init`, `node ../sandbase-harness/dist/index.js start`
- 03
Point DSH at the running runtime: `export MANAGED_AGENTS_URL=http://127.0.0.1:3000`
- 04
Install the bundle into a DSH profile and boot it: `dsh plugin --profile web add -w ../sandbase-harness`, then `dsh web`
$ dsh plugin --profile web add -w ../sandbase-harness
Verify the integration
- Open `http://127.0.0.1:3000/dashboard`, go to Settings > Models, paste your API key — the README says "you're running"
03DSH integration and capability boundaries
Installed into a DSH profile as a third-party plugin (`dsh plugin --profile web add -w ../sandbase-harness`); a bundled MCP bridge over stdio exposes agents, sessions, streamed turns, artifacts and cancellation as native mcp__sandbase__* tools
DeepSeek Harness MCP bridge
a running SandBase Harness runtime reachable at MANAGED_AGENTS_URL→agents, sessions, streamed turns, artifacts and cancellation exposed to DSH as native mcp__sandbase__* tools over stdio
Sandboxed session execution
agent sessions with generated code or tool calls→isolated execution on local process, Docker (per-session containers), Kubernetes (kubectl exec/cp), or self-hosted worker queue
Docker-backed mode starts per-session containers; local mode writes session sandboxes and workspace snapshots to the .managed-agents state directoryPersistent sessions with audit and replay
running agent sessions→persistent sessions, resumable Server-Sent Events for replay and debugging, and audit trails stored in SQLite
writes SQLite metadata (data.db), logs, uploaded files and skill bytes to the workspace state directoryMulti-provider model operation
one configured provider boundary via Settings (OpenAI, Anthropic, MiniMax, or OpenAI-compatible endpoints, including DeepSeek V4)→agents and sessions served against the chosen model provider
model requests go over the network to the configured provider; an API key is required
04Who is it for? When not to use it?
Good for
- DSH users who want a self-hosted agent runtime with sandboxing, audit trails and replay behind their DSH profile
- Teams running untrusted or generated code who need sandbox boundaries, credential handling and auditability
- Developers who want model-agnostic agents without a required hosted control plane
Not for
- The unscoped `managed-agents` name on npm is not this project. Install only from the tagged GitHub source release; do not run `npx managed-agents` or `npm install managed-agents`.
- The runtime API is open by default; authentication only activates once at least one API key exists (static MANAGED_AGENTS_API_KEY or a managed key), so keys should be configured before exposing the port beyond localhost.
05Compatibility, maintenance and safety notes
- The unscoped `managed-agents` name on npm is not this project. Install only from the tagged GitHub source release; do not run `npx managed-agents` or `npm install managed-agents`.
- The runtime API is open by default; authentication only activates once at least one API key exists (static MANAGED_AGENTS_API_KEY or a managed key), so keys should be configured before exposing the port beyond localhost.
- Requires Node.js 22+, npm 10+ and a model provider API key to run; Docker is additionally required for Docker-backed sandboxes, and Codespaces usage may be billed by GitHub.
Apache-2.0 · actively maintained (latest release v0.3.7, 2026-08-20)
06Frequently asked questions
How do I install SandBase Harness into DeepSeek Harness?
Clone the tagged release, build it with npm, init and start the runtime from a workspace, export MANAGED_AGENTS_URL=http://127.0.0.1:3000, then run `dsh plugin --profile web add -w ../sandbase-harness` and `dsh web`. The profile installs the verified source checkout directly — not an npm package.
Is the integration native or MCP?
It is a DSH plugin: installing the bundle makes DSH start the bundled MCP entry over stdio, exposing agents, sessions, streamed turns, artifacts and cancellation as native mcp__sandbase__* tools.
What do I need before installing?
Node.js 22+, npm 10+, a model provider API key (OpenAI, Anthropic, MiniMax, or an OpenAI-compatible endpoint), and optionally Docker for Docker-backed sandboxes. The runtime must be running so MANAGED_AGENTS_URL is reachable.
Where does my data go?
Local-first: SQLite metadata, uploaded files, skills, snapshots and session sandboxes live in the workspace `.managed-agents` state directory on your machine. Model calls go to the provider you configure; there is no required hosted control plane.
Do I need a SandBase account or the npm package?
No SandBase account is needed when DSH already provides web/search tools. And the unscoped `managed-agents` name on npm is not this project — install only from the tagged GitHub source release.
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08Data and sources
Run this project as a DSH plugin instead of treating `dsh-plugin` as discovery metadata only. Install the bundle into a…
DeepSeek Harness bridge over MCP stdio for agents, sessions, streamed turns, artifacts, and cancellation
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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