Resource library
Start with product documentation and security evidence, then explore implementation guides, comparisons, troubleshooting, and regulated engineering workflows.
Review deployment boundaries, verified controls, telemetry facts, and assurance status.
See connection methods, limitations, implementation evidence, and vendor-recognition status.
Install SeaShell, configure models and permissions, and learn the terminal workflow.
Search common tasks and troubleshooting material, or contact the SeaShell team.
Topic hubs
206 guides
Run AI coding workflows without giving client code, notes, or internal context to hosted training systems.
14 guides
Keep a durable, git-tracked record of what was decided, promised, and delivered, so AI-assisted work can be reviewed, audited, and proven months after the session ended.
4 guides
Coordinate Ollama, open-source models, embeddings, model routing, and private inference flows.
138 guides
Use coordinated agents, repeatable workflows, review passes, and task decomposition inside engineering work.
11 guides
Search, map, and reason across projects from small repos to large codebases with durable context.
21 guides
Control permissions, audit trails, safety gates, and approval flows for AI-assisted engineering.
34 guides
Connect SeaShell to developer tools, notes, docs, MCP servers, and team knowledge systems.
100 guides
Compare SeaShell against other coding tools and decide when private, terminal-native workflows fit better.
105 guides
Diagnose and fix SeaShell errors: authentication, MCP servers, model providers, permissions, git, and tool failures.
272 guides
Pick and configure the model behind SeaShell: frontier models, fast models, open weights, and fully local inference.
1383 guides
Learn terminal-native setup, command workflows, rendering, sessions, and day-to-day SeaShell usage across languages and frameworks.