DeepSeek Harness
An open-source, plugin-first agent harness with local Web UI, tools, sessions, approvals, and extensible model adapters.
By DeepSeek AI · reviewed 2026-08-20
Deep profile checked 2026-08-20 · DeepSeek Harness official repository, user guide, architecture documentation, and license
What DeepSeek Harness does
DeepSeek Harness (dsh) is DeepSeek AI's developer-preview framework for building and running agents. Its Cordis-based architecture treats model adapters, tools, session logs, sandboxes, approval policies, and the agent loop as replaceable plugins, with Web UI and headless profiles for different workflows.
Pricing model
Free and open source
Position in the stack
Where DeepSeek Harness fits
DeepSeek Harness belongs in the agent execution layer above model providers. It assembles model adapters, workspace tools, session history, approval policies, sandboxes, and agent loops into configurable plugin profiles; it does not itself provide model weights or GPU inference.
Typical deployment flow
A practical sequence for evaluating DeepSeek Harness before making it part of a permanent stack.
- STEP 1
Choose a model boundary
Configure the DeepSeek API or a reviewed OpenAI-compatible endpoint, and document which prompts, files, and tool results can leave the machine.
- STEP 2
Start an isolated profile
Run the Web UI from a dedicated workspace, inspect the active plugin tree, and begin with conservative filesystem, command, and network approvals.
- STEP 3
Validate before extending
Test representative tasks, session recovery, tool failures, and approval boundaries before adding third-party plugins or unattended execution.
Best for
- Building highly extensible agent workflows
- Running coding and workspace agents with approval controls
- Developing custom agent plugins and model adapters
Not the right layer for
- Teams requiring a compatibility-stable production platform today
- Direct model inference or GPU scheduling without a separate provider
System fit
Requirements and hardware notes
Application requirements are separate from the memory needed by the selected model and context window.
Check your hardwareMinimum baseline
- Node.js for the published npx package
- A DeepSeek API key or another supported model endpoint
- A selected local workspace for agent file operations
Recommended baseline
- An isolated test workspace with version control
- Pinned package and plugin versions during developer preview
- Conservative command, filesystem, network, and plugin approval policies
Hardware-specific notes
- The harness itself is an orchestration layer and does not require a GPU.
- Local inference memory and accelerator requirements come from the connected runtime and model.
- Long sessions, event logs, plugins, and subprocesses add CPU, memory, and storage overhead beyond model inference.
Installation and deployment paths
Choose one path that matches the number of users and the level of operations you can maintain.
Data boundary
What stays local and what may leave
- Local data
- Workspace files, session events, local configuration, and plugin state can remain on the host, subject to each installed plugin and configured storage adapter.
- Network dependencies
- Prompts, selected context, tool results, and file content can be transmitted to the configured model API or any network-capable plugin used by the active profile.
- Accounts and telemetry
- Review model credentials, telemetry plugins, third-party plugins, approval policies, and session logs before exposing confidential code or enabling unattended actions.
License checkpoints
- DeepSeek Harness is MIT-licensed, while bundled and third-party dependencies retain their own licenses.
- Installed plugins, connected models, and external APIs may impose separate use and redistribution terms.
Operational checkpoints
- Expect compatibility-breaking changes during developer preview and pin every deployed version.
- Start with an isolated repository and inspect diffs, commands, network calls, and approval prompts before using important workspaces.
- Back up profiles, patches, plugin configuration, credentials references, and session state together.
Capabilities
Capabilities refer to the tool's application layer. Hardware and model support can still depend on a connected inference engine.
Deployment and compatibility
- Deployment
- Desktop / localSelf-hosted
- Platforms
- macOS · Windows · Linux
- Hardware backends
- Uses connected model provider
- Install methods
- npx · Build from source
Models and integrations
- Model formats
- Uses connected model provider
- Common integrations
- DeepSeek APIOpenAI-compatible APIsdsh pluginsLocal workspace tools
Strengths
- Official DeepSeek project with a plugin-first architecture
- Replaceable agent loop, tools, model adapters, and policies
- Local Web UI plus headless and development paths
Limitations
- Developer preview may introduce breaking changes
- Requires a separate model provider and careful permission configuration
Planning checklist
Before you choose DeepSeek Harness
Answer these questions with the exact models, hardware, users, and data you expect to operate.
- Can the team tolerate breaking changes while the project remains in developer preview?
- Which model endpoint will provide inference, and what data will that provider receive?
- Which workspace, shell, network, and plugin actions require explicit approval?
- How will profiles, session state, plugins, and versions be backed up and reproduced?
DeepSeek Harness FAQ
Does DeepSeek Harness run DeepSeek models locally?
Not by itself. It is the agent harness layer and needs a configured model provider, such as the DeepSeek API or a compatible endpoint backed by a separate local or remote runtime.
Is DeepSeek Harness ready for production?
The official project labels the current release as developer preview and warns that compatibility-breaking changes will occur. Evaluate it with pinned versions and controlled workloads first.
What makes it different from a fixed coding agent?
Its Cordis architecture treats model adapters, tools, persistence, approval policy, telemetry, and even the agent loop as plugins that can be replaced or layered through profiles.
Official sources
Use these links to confirm current compatibility and installation requirements.
Related tools
Execution evidence
Known working recipes using DeepSeek Harness
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.