deepseek-harness vs docker-agent
Two of the top projects in Agent backends, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
deepseek-harness
Plugin-based agent harness from DeepSeek with a local Web UI
docker-agent
Docker CLI plugin that runs multi-agent teams defined in YAML
Verdict
deepseek-harness ranks higher, 70 to 59. Of the 5 score parts we measure for both, deepseek-harness leads on 3, docker-agent on 1 and 1 is even.
- Adoptiondeepseek-harness leads by 51 points, 100 to 49: a wide gap.
- FreshnessEven at 100.
- Maintenancedeepseek-harness leads by 7 points, 100 to 93.
- Easy to rundocker-agent leads by 17 points, 17 to 0: a wide gap.
- Agent-readydeepseek-harness leads by 55 points, 85 to 30: a wide gap.
Pick deepseek-harness if you are
Developers building agents through plugins, tolerant of breaking changes
- Starts with one npx command; no Docker or database mentioned
- Everything-is-a-plugin design on Cordis, with a dsh-plugin topic for discovery
Pick docker-agent if you are
Developers building multi-agent setups from YAML with MCP tools
- Agents, tools and models declared in versionable YAML, no code required
- Works with local, remote or Docker-based MCP servers as toolsets
| What we compare | deepseek-harness | docker-agent |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 100, widely used | 49, known |
| Freshness | 100, active | 100, active |
| Maintenance | 100, healthy | 93, healthy |
| Easy to run | 0, hard | 17, hard |
| Agent-ready | 85, ready | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 250.4k | 4.5k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | None published | Oct 2026 |
| Language | TypeScript | Go |
| Docker | No | Yes |
| GPU | Not stated | Optional |
| arm64 or Apple Silicon | Not stated | Mentioned |
deepseek-harness
DeepSeek Harness (`dsh`) is a TypeScript agent harness built on the Cordis plugin framework, where every component is a plugin. It runs via `npx @deepseek-ai/dsh web` or from a source checkout with pnpm, and serves a Web UI at 127.0.0.1:3080 by default. It is a developer preview, and the README warns of compatibility-breaking changes.
Who it is for: Developers building agents through plugins, tolerant of breaking changes
Strengths
- Starts with one npx command; no Docker or database mentioned
- Everything-is-a-plugin design on Cordis, with a dsh-plugin topic for discovery
- MIT license, documentation site and third-party license notices
- Web UI binds to 127.0.0.1 by default; --no-open skips the browser
Weaknesses
- Developer preview; README warns of compatibility-breaking changes
- No tagged release published
- README does not list supported models, providers or system requirements
- Safety notice must be read before running; its contents are not in the README
- TypeScript
- Needs Node.js, pnpm (source build)
docker-agent
docker-agent is a Go CLI plugin (`docker agent run agent.yaml`) that runs agents declared in YAML: model, instructions, and toolsets, including any MCP server. Agents can delegate to other agents, and configs can be pushed to and pulled from OCI registries. It supports OpenAI, Anthropic, Gemini, AWS Bedrock, Mistral, xAI and Docker Model Runner, plus built-in RAG with BM25, embeddings and reranking.
Who it is for: Developers building multi-agent setups from YAML with MCP tools
Strengths
- Agents, tools and models declared in versionable YAML, no code required
- Works with local, remote or Docker-based MCP servers as toolsets
- Agent configs can be shared through any OCI registry
- Many providers supported, plus local models via Docker Model Runner
Weaknesses
- Collects anonymous usage telemetry; opt-out details are only in the docs
- Needs a cloud provider API key or Docker Model Runner to do anything
- Configuration is YAML-centric; custom logic needs MCP tools rather than code hooks
- RAM and hardware requirements are not documented in the README
- Go, OpenAI, Anthropic, Gemini, AWS Bedrock
- Needs Docker CLI (plugin), LLM provider API key or Docker Model Runner, MCP servers (optional), OCI registry (optional)
- Docker
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