SandBase Harness
Local-first AI agent runtime and MCP bridge with sandboxed sessions, memory, credentials, and audit replay.
Evidence shows role-based access control (API keys, CORS, sandboxing) but lacks fine-grained least privilege. User confirmation exists but partial. Data flow transparency via audit logs but not explicit. Sensitive data handling: tokens encrypted in tests, but not comprehensive. Dependency security: no vulnerability scan evidence. External effects: sandboxing indicates control but not detailed. Rollback: replay exists but not explicit rollback. Source attribution: not clear.
Self-consistency: code and tests align but not fully verified. Dependency availability: versions not pinned, affecting reproducibility. Failure messages: good error handling in tests but not comprehensive.
Audience and scenarios: clear from README. Capability boundaries: documented but not exhaustive. Trigger precision: not specified. Environment fit: supports multiple backends but configuration details lacking.
Information architecture: clear. Install notes: provided. Naming stability: versioned. Examples and FAQ: examples but no FAQ. Known limitations: not listed. License: Apache-2.0. Versioning changelog: present. Maintenance responsibility: not explicit.
Output usability: clear API with tests. Marginal value: unique local-first runtime. Cost benefit: no performance or cost data.
Claim traceability: README claims not fully mapped to code. Cross-source corroboration: multiple directory listings but not independently verified. Fact-inference separation: mixed.
- Publisher identity unverified; treat with caution.
- Dependency versions not pinned, affecting reproducibility.
- User confirmation mechanisms are partial; sensitive operations may lack prompts.
- Numerous external directory links in README; verify independently.
What does this agent do, and when should you use it?
SandBase Harness is a local-first, self-hosted runtime for AI agents, offering session management, sandboxed execution, tool governance, memory, credential vaults, and audit/replay. It includes a built-in console and integrates with MCP, listed on the official MCP Registry. The runtime supports multiple sandbox backends including local, Docker, Kubernetes, and self-hosted workers. It is model-agnostic, supporting OpenAI, Anthropic, and OpenAI-compatible providers like DeepSeek V4. Installation is straightforward via npm, with data stored locally in SQLite and files, requiring no hosted control plane. The project is widely referenced across many community directories and actively submitted to awesome lists.
SandBase Harness provides a CLI to initialize and start the runtime; once running, it offers persistent sessions, resumable event streams, audit logging and replay, MCP toolsets, credential vaults, and permission policies, all accessible via a local console. It executes tool calls within sandboxed environments (local, Docker, Kubernetes, self-hosted workers) and connects to multiple model providers through adapters. It implements MCP server standards, exposing tools for agents to use.
- For developers needing to run generated code safely, use the local Docker sandbox to isolate tool executions.
- For teams managing long-running agents, leverage persistent sessions and audit replay to monitor and debug.
- For organizations controlling agent tool access, configure MCP toolsets, credential vaults, and approval policies.
- For users wanting to switch between multiple models like OpenAI, Anthropic, and DeepSeek, use the unified adapter interface.
- For privacy-conscious users, deploy fully on-premises with data staying within your own infrastructure.
What are this agent's strengths and limitations?
- Genuinely local-first, no hosted control plane required.
- Supports multiple execution sandboxes including Docker and Kubernetes.
- Includes audit logging and replay for traceability.
- Model-agnostic with adapters for multiple providers.
- Listed on the official MCP Registry, enhancing discoverability.
- Requires Node.js and npm for installation and build.
- Deploying to Kubernetes requires additional configuration.
- Isolation level depends on chosen backend; may need hardening.
- Community directory listings may lag; always refer to official sources.
How do you install or deploy this agent?
Clone the repository and enter the directory: git clone --branch v0.3.8 --depth 1 https://github.com/sandbaseai/sandbase-harness.git && cd sandbase-harness. Install dependencies and build: npm ci && npm run build.
How do you use this agent?
Create a new agent project directory and initialize it: mkdir ../my-agents && cd ../my-agents && node ../sandbase-harness/dist/index.js init. Start the runtime: node ../sandbase-harness/dist/index.js start. Then open the local console at http://127.0.0.1:3000/dashboard.
How does this agent compare with similar options?
Unlike SandBase CLI, which is a lightweight bridge for connecting many clients, Harness provides a full runtime with sandboxing and session management.