CodeWhale
[live]
ISS#2749Support project-level `.codewhale/mcp.json` auto-merge· 1hPR#2737fix(skills): union configured skills_dir with workspace skills instead of or_e· 1hPR#2750fix(tui): price Xiaomi MiMo primary models· 1hPR#2747fix(tui): preserve underscored MCP server names· 1hPR#2746fix: parse MCP server names with underscores· 2hPR#2732Phase 3: pausable command lifecycle (pause / resume / cancel)· 2hPR#2745feat(init): LLM-powered codebase analysis for AGENTS.md generation· 2hISS#2744MCP tool name parsing breaks when server name contains underscores· 2hISS#2743FR:适配Claude Code的技能生态· 2hPR#2742 fix(tui): use Ollama default model in completions· 3hPR#2733feat(plan): richer PlanArtifact schema for v0.9.0 (#2691)· 4hPR#2741feat(config): add HarnessPosture data model for per-model behavior profiles (#· 4hISS#2749Support project-level `.codewhale/mcp.json` auto-merge· 1hPR#2737fix(skills): union configured skills_dir with workspace skills instead of or_e· 1hPR#2750fix(tui): price Xiaomi MiMo primary models· 1hPR#2747fix(tui): preserve underscored MCP server names· 1hPR#2746fix: parse MCP server names with underscores· 2hPR#2732Phase 3: pausable command lifecycle (pause / resume / cancel)· 2hPR#2745feat(init): LLM-powered codebase analysis for AGENTS.md generation· 2hISS#2744MCP tool name parsing breaks when server name contains underscores· 2hISS#2743FR:适配Claude Code的技能生态· 2hPR#2742 fix(tui): use Ollama default model in completions· 3hPR#2733feat(plan): richer PlanArtifact schema for v0.9.0 (#2691)· 4hPR#2741feat(config): add HarnessPosture data model for per-model behavior profiles (#· 4h
v0.8.53·MIT·DeepSeek V4 native

Terminal coding agent for DeepSeek V4.

CodeWhale wraps DeepSeek V4 in a harness — a written Constitution that ranks every source of authority for each turn, live tool output fed back as evidence between turns, and V4's prefix cache making that Constitution cheap to reference recursively, so the model stays oriented through long tool-using sessions instead of drifting.

Planread-only·Agentwith approval·YOLOauto-approve
# Recommended: npm — no Rust toolchain
$ npm install -g codewhale
$ codewhale --model auto

# Or Cargo / Homebrew / direct download — see /install
config lives at ~/.codewhale/all methods →
How it works

A written Constitution makes authority arbitrable, and a feedback loop makes drift correctable.

Every turn, the agent has to arbitrate between the user's intent, the project's rules, system defaults, live tool output, and stale memory. CodeWhale answers that with a written Constitution that ranks nine sources of authority explicitly (current user message above stale project instructions, live tool output above assumptions, verification above confidence), and uses V4's prefix cache to keep that Constitution almost free to reference recursively — roughly 100× cheaper per turn than a cold read — so the model spends a long session reading an open book rather than guessing from memory.

The feedback half closes itself: non-zero exit codes, type errors that rust-analyzer reports between edits, and sandbox denials come back into the context as correction vectors, so the model uses its own drift to self-correct. When you run with --model auto, CodeWhale spends a cheap Flash call at the start of each turn to route — keeping short conversations on Flash, and escalating coding, debugging, and architecture work to Pro at higher thinking depth.