DeepSeek HarnessvsRaven
Generated from the two spec rows. Green marks the better value where a spec has a clear direction. Everything else is just different.
DeepSeek Harness
DeepSeek · Agent harness
OSSMCP
Panel
6.22 spec wins
- Reliability
- 5.3
- Usefulness
- 6.5
- Cost
- 6.8
- Longevity
- 6.0
“Ships a PowerShell tool plugin, so the agent can now break things on Windows with the same confidence.”
Raven
EverMind AI · Agent harness
OSS
Panel
6.22 spec wins
- Reliability
- 5.3
- Usefulness
- 6.3
- Cost
- 8.0
- Longevity
- 5.2
“It has a Sentinel that sends you proactive nudges, so the agent now has opinions about your afternoon.”
Spec by spec
| Spec | DeepSeek Harness | Raven |
|---|---|---|
| Architecture | ||
| Category | Agent harness | Agent harness |
| Runs | local, sandbox | local, sandbox |
| Platforms | macos, linux, windows | macos, linux, windows |
| Context window | not documented | not documented |
| Protocols | ||
| MCP client | Yes | No |
| MCP server | No | No |
| Capabilities | ||
| Runs terminal commands | Yes | Yes |
| Multi-file edits | Yes | Yes |
| Git operations | No | No |
| Browser control | No | YesDeep Research performs web search and page reading via MiroThinker and returns a cited report; there is no general browser-automation tool documented. |
| Sandboxed execution | Yes | YesOnboarding asks for a sandbox or execution location, documented separately under docs/sandbox/usage.md. |
| Multi-agent orchestration | Yes | YesThe agent loop spawns subagents, and tracing records parent-child subagent runs. |
| Headless / CI mode | Yes | Yes |
| Models | ||
| Backbone | DeepSeek, providers via pi-ai route | OpenAI, Anthropic, Gemini, OpenRouter, DeepSeek, MiniMax, Moonshot, Groq, Azure OpenAI, GitHub Copilot (OAuth), Codex (OAuth), Ollama, vLLM |
| Bring your own model | Yes | Yes |
| Local models | No | Yes |
| Cost | ||
| Pricing model | byok | byok |
| Starts at | $0/mo | $0/mo |
| Free tier | Yes | Yes |
| Bring your own key | Yes | Yes |
| Openness | ||
| Open source | Yes | Yes |
| License | MIT | Apache-2.0 |
| GitHub stars | 241,869 | 5,061 |
Which one would each critic pick
| Critic | DeepSeek Harness | Raven | Pick |
|---|---|---|---|
| El Juez | — | — | not enough reviews |
| El Amigo | 6.5 | 6.5 | no preference |
| El Crítico | 5.8 | 5.8 | no preference |
| El Profesor | 6.0 | 6.3 | Raven — The context engine budgets tokens explicitly rather than truncating when it runs out, and a separate component runs evaluation loops the authors describe as reproducible. |
| La Inversora | 6.3 | 6.0 | DeepSeek Harness — A lab-owned harness with no price tag is a distribution channel for DeepSeek tokens, and the pi-ai route is the hedge that lets it survive if the lab's own model falls behind. |
| La Jefa | 5.8 | 5.0 | DeepSeek Harness — Free software and a DeepSeek invoice for sixty engineers, but no SSO or audit log in the config catalog, an experimental agent-team mode, and a local web UI per laptop. |
| El Hacker | 6.8 | 7.8 | Raven — Apache-2.0, Ollama and vLLM among the supported providers, and tracing switched on by default but written to my own disk instead of a vendor's. |
Picks are derived from each critic's own scores. Humans vote on matchups on the duels page.