SandBase Harness

SandBase Harness

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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.

[SandBase Harness architecture]

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 metadata by default for agents, sessions, environments, credential vaults, memory stores, files, skills, and API keys — local file/skill bytes 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
• MCP toolsets, permission policies, built-in tools, and skill packages
• TypeScript SDK at managed-agents/sdk
• Release gate: npm run release:check

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.gitcd sandbase-harnessnpm cinpm run buildmkdir ../my-agents && cd ../my-agentsnode ../sandbase-harness/dist/index.js initnode ../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.

Screenshots

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.

CMA compatibility

Coverage of the published Claude Managed Agents contract is declared entry by entry in src/core/capabilities/matrix.ts: of the official SDK's route surface (110 method/path pairs as of SDK 0.131.0), 90 routes are mounted and 20 refuse by name; no route is deferred. Partial and Unsupported entries always name their reason.

The table below is generated by npm run docs:compat, and a contract-honesty test fails when it drifts from the matrix.

CLI

managed-agents initmanaged-agents start [--host 127.0.0.1] [--port 3000]managed-agents apply <file...>   # create or update agents/environments/deployments from manifestsmanaged-agents listmanaged-agents reloadmanaged-agents chat <agent-id> --message "hello" [--tool-approval ask|allow|deny]managed-agents template list | install <name> | create <name>

apply is the declarative form of the same API: a Markdown file whose frontmatter is the agent definition (the body below --- becomes the system prompt), a .yaml environment document, or a Markdown deployment manifest (frontmatter carries name, agent, environment, schedule; the body becomes the initial user.message). The resolved ids land in managed-agents-lock.json beside the files, so re-running apply after an edit updates the same resource instead of creating a duplicate — commit the lock file with the manifests. A later file may reference an earlier one by name, so apply coding-assistant.md environment.yaml deployment.md wires the deployment to the rows the first two files created.

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.

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 environmentexport MANAGED_AGENTS_API_KEY=sk-local-example
# Or create a managed keycurl -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>.

Integrations and examples

• DeepSeek Harness plugin — run this runtime as a DSH plugin over MCP stdio: install, preflight, tool list, and troubleshooting live in examples/deepseek-harness. A DSH project can also take a portable Skill from GitHub source — npx --yes github:sandbaseai/sandbase-skills add multi-source-search installs into .dsh/skills/multi-source-search.
• Agent Plugins 1.0 clients (Copilot CLI, VS Code) and the standalone MCP bridge container — see agent-plugin/PLUGIN.md.
• Use cases — the Showcase walks through an auditable coding agent, DSH as an interactive front end, and controlled code execution across Local, Docker, Kubernetes, and self-hosted sandboxes.
• Agent configuration — the YAML agent definition, config.yaml, and the workspace layout live in the usage guide; curl walkthroughs for every resource are in docs/api.md.

Documentation

• Machine-readable project metadata
• Agent / MCP installation guide
• Agent Plugin marketplace manifest
• Installation
• Usage Guide
• API Reference
• Skills
• DeepSeek V4
• MiniMax
• Deployment
• Architecture
• DeepSeek Harness integration
• Contributing
• Citation metadata
• Changelog

Development

npm cinpm run typecheck    # src + tests + Consolenpm test             # vitestnpm run build        # runtime + console + SDKnpm 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. Community use-case discussions: memory migration between Codex, Claude Code, and DSH, sandbox and filesystem protection for third-party plugins.

License

Apache-2.0