Dao Code 道

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A terminal coding agent built around cost, experience, and availability — squeezing the most capability and the lowest cost out of the high-value DeepSeek V4.

Dao Code (command dao) is a terminal-native AI coding assistant: it reads code, writes code, runs commands, and fixes bugs right in your terminal — streaming its reasoning and tool calls while executing safely behind an approval gate, until the task is done. It targets DeepSeek V4 (1M context), is Chinese-first, and is inspired by Claude Code — but takes a different road: rather than buying experience with an expensive model, it builds on DeepSeek's strong price-performance and ultra-cheap cache pricing, engineering byte-stable prefixes and cache-reusing forks so cross-session memory and a continuous self-correction layer add almost no token cost.

Why Dao Code?

🌐 Availability

A coding agent is only useful if you can actually run it.

• Claude Code needs an Anthropic account and network access — a high bar to use out of the box in mainland China;
• GLM's Coding Plan has scarce quota that's often hard to grab;
• Dao Code is fully open source (MIT), and its base DeepSeek is register-and-go, pay-as-you-go, directly reachable in mainland China — no network gymnastics, no quota grabbing, no waitlist.

💰 Cost

• Low unit price — DeepSeek sits at the lowest price tier among mainstream capable models; both input and output prices are far below the top-tier closed models.

• Cache cuts it further — DeepSeek's prefix-cache hit price is ≈ 1/120 of a miss (~two orders of magnitude). Dao Code keeps the system prefix / tool table / memory byte-stable, and runs reflection & memory on cache-reusing forks, so the hit rate keeps climbing.

• Measured on real OSS bug-fixes (not toy demos) — 7 SWE-bench-style tasks (valibot / date-fns / es-toolkit / sqlglot / hono), 3.89M input tokens total, 95.8% aggregate cache hit (85.4%–97.7% per task). At DeepSeek V4 Pro current pricing, a full feature (read + edit + test + self-review) runs ¥0.07–0.21, avg ¥0.15; all 7 total ¥1.07. Every figure traces back to evals/runs/<task>/run-1/agent.log; replay any time with /cost.

• Cost vs Claude Code — pricing the same token trace of these 7 tasks under each vendor's official rates (and crediting Dao Code's high hit rate to Claude too, in its favor), total cost is still ~30× cheaper than Claude Opus 4.8, ~18× cheaper than Sonnet 4.6.

  Task (real OSS repo)	Input tok	Hit %	DeepSeek Pro	vs Opus	vs Sonnet
  t7-sqlglot-sqlite-autoinc	1,218,385	97.7%	¥0.213	37×	22×
  t6-sqlglot-comment-on	625,772	96.3%	¥0.144	32×	19×
  t9-hono-compress	699,479	96.0%	¥0.209	31×	18×
  t8-hono-cookie-dup	502,866	94.9%	¥0.136	28×	17×
  t4-estoolkit-omitby	445,475	94.4%	¥0.159	28×	17×
  L1-nodeps-toolkit	289,989	93.2%	¥0.140	27×	16×
  t5-estoolkit-uniqwith	104,071	85.4%	¥0.068	21×	12×
  Total	3,886,037	95.8%	¥1.07	30×	18×

  _{Prices as of 2026-06 official rates: DeepSeek V4 Pro hit/miss/output = $0.003625 / $0.435 / $0.87 per 1M; Claude Opus 4.8 = $5 / $25 (hits at 0.1× cache-read), Sonnet 4.6 = $3 / $15. Multiples are USD-on-USD, exchange-rate-independent; ¥ converted at ¥7.1/$. Cross-check: re-pricing L1 at current rates gives ¥0.140 ≈ the ¥0.136 the in-log /cost reported.}

• Verify the cache mechanism live — npm run accept:cache runs a multi-turn conversation against the live API so you can watch the hit rate climb from cold start to steady state (mechanism demo; cost figures above come from the real eval suite).

🧠 Experience

• Memory you can trust + a reflection layer — remembers your preferences and project conventions across sessions, and re-verifies them against your live code on every startup: stale facts pruned, changed ones flagged, not blindly piled up (others remember, but misremember). Self-reviews when stuck and pulls back when drifting. All three run as forks that reuse the main prefix cache — better quality at almost no extra spend.
• Long tasks don't drift or hit the wall — auto-compaction carries context past the limit, and periodic refocusing curbs scope creep, so it stays on track even running autonomously for a long time.
• Constitution-style priority — safety & truth > your current instruction > Dao Code's core policy (model / cache discipline) > skills / memory. A third-party skill you install can change how work is done, but never the safety and cache bottom line.

✅ Verified

On a SWE-bench-style benchmark drawn from recent real-world open-source bug fixes (dual-track fail2pass + pass2pass judging, with test files hidden from the agent to prevent reward-hacking): 13/14 solved reliably. See Testing & evaluation.

✨ Features

🗜️ Context & cache engineering

A byte-stable system prefix rides DeepSeek's prefix cache to the max; reflection and memory run on forks that reuse the main cache (without breaking the prefix); near the limit it auto-compacts (reactive retry + in-place clearing of stale tool results + incremental summary + hard-truncate fallback if the summarizer fails); oversized output spills to disk, leaving only a pointer in context. /cost shows hit rate & spend; /audit cache pinpoints "what broke the cache" via a four-dimension fingerprint.

🧠 Cross-session memory (self-verifying)

At session end it distills your preferences, project conventions, and key facts; at startup it deterministically verifies them against the current code — stale ones dropped, changed ones flagged, rather than blindly piling up history. A decay GC clears dead memories; the model can memory_read on demand.

🔍 Reflection layer (self-correct when stuck / drifting)

Challenger: on a failure streak or recurring error, a skeptical independent review that questions the premise. Refocuser: every N turns on a long task, restate the original goal and catch scope creep. Reply-challenger: kicks in when you re-raise the same problem. All three run as cache-reusing forks — at almost no extra spend.

🪢 Long-task robustness

Session log + crash recovery (dao -c); shadow-git checkpoints (/restore /rewind, a separate snapshot that never touches your .git); todo list survives compaction to prevent goal drift; Definition-of-Done verification (/dod + verify_done); stuck detection with a circuit breaker; parallel / background / worktree-isolated subagents with two-way child↔parent messaging; --goal autonomous long-task mode.

🎐 A Taoist-aesthetic terminal experience

Rich Ink rendering + a Taiji splash + light/dark adaptation; @ file references, slash-command Tab completion, steering (type while a turn is running, queued), diffs with line numbers + syntax highlighting, thinking blocks, todo checklists, a Taoist-verb spinner; ESC interrupts (model stream and shell stop together); non-TTY auto-fallback to a plain-text REPL.

Basics (mirror CC, all shipped): 24 tools · layered allow/ask/deny permissions + auto smart approval + defense-in-depth (secret scanning / SSRF / sandbox / keychain) · Skills (incl. auto-adapting foreign skills' tool names & model tiers) · MCP (stdio + HTTP/SSE, tools/resources/prompts) · Hooks (5 lifecycle events) · custom subagents / slash commands / plugins · multi-account profiles (/account) · OS cron scheduling (/schedule). See Extension system and the tool overview below.

📦 Install

A. One-line install (no Node):

curl -fsSL https://raw.githubusercontent.com/tigicion/dao-code/master/install.sh | sh

Or download manually from Releases: macOS dao-darwin-arm64 (Apple silicon) / dao-darwin-x64 (Intel), Linux dao-linux-arm64/dao-linux-x64, Windows dao-windows-x64.exe. On Unix chmod +x then run; on Windows just double-click the .exe.

B. npm (Node ≥ 20, all platforms):

npx dao-code        # zero-install trial
npm i -g dao-code   # global install, command name dao

C. From source:

git clone https://github.com/tigicion/dao-code.git && cd dao-code
npm install && npm run build && npm link   # then dao is global
# or run directly in dev: npm run dev

🚀 Quick start

1. Get a DeepSeek API key: https://platform.deepseek.com/api_keys

2. Launch → follow the prompt to enter your key:

   dao                # installed (binary / global); or npx dao-code

   On first run with no key detected, it walks you through pasting one and saves it to ~/.dao/config.json (auto-read next time — no env setup needed).

3. Or use headless one-shot with a key:

   dao --api-key sk-xxx --provider deepseek "tell me a joke"

4. Light terminals: type /theme at runtime, or set DAO_THEME=light before launch.

Common slash commands (full list via /help):

Add --yolo at launch (e.g. dao --yolo / dao --yolo "task") to start in auto-approve; toggle anytime with /yolo. dao --verbose (or --debug) enters verbose mode at startup: full tool results, full thinking, and raw tool arguments. Plain dao truncates by default; at runtime press Ctrl+O to expand/collapse full output (mirrors CC). History already printed to the scroll region can't be edited in place, so expanding re-prints the most recent collapsed block once.

⌨️ Usage

Interactive mode (default):

dao

• Type a message and press Enter; ↑/↓ browse history; Esc interrupts the current turn; lines starting with / are slash commands (with completion hints).
• Inline editing: ←/→ move cursor, Ctrl-A/E line start/end, Ctrl-W delete word, Backspace/Delete delete at cursor; paste supported (no auto-submit).
• @ to reference a file: type @ + a path fragment to list matches, Tab to complete.
• Write/exec operations go through the approval gate ([y] once [a] remember (write allow rule) [n] deny); you can also pre-allow or block with allow/ask/deny rules in .dao/settings.json (see "Extension system · Permissions"); /yolo or --yolo auto-approves everything (deny still blocks).

One-shot mode (task as an argument, exits when done, no memory distillation, good for scripts):

dao "make formatDate in src/utils.ts timezone-aware"

🧠 How it works

You ──▶ Ink TUI ──▶ agent loop ──▶ DeepSeek V4
                       │  streamChat (streaming reasoning + answer)
                       │  ▶ model requests a tool call
                       │  ▶ approval gate (write/exec needs clearance)
                       │  ▶ run tool → feed result back
                       └─ loop until the model stops requesting tools

• agent loop (src/agent/loop.ts): each turn calls streamChat to stream reasoning + answer; if the model requests a tool, it runs through the approval gate, feeds the result back, and loops until done or max turns; AbortSignal is threaded through the model stream and tools for ESC interrupts.
• Modes: in plan mode, even a write/exec tool request is denied for the turn by the per-turn allow table (read-only + propose); normal mode runs as usual.
• Memory (src/memory/): at startup migrate → load → deterministically verify against live code → inject into the fixed prefix; on exit distill new facts with the cheap flash model and upsert after dedup.
• Cache & compaction: the system prefix is pinned to ride DeepSeek's prefix cache; near the 1M context limit early messages are auto-compacted into a summary.

🪢 Long-task robustness

Built for long tasks that "run autonomously for a long time without drifting, are recoverable, and can be verified":

• Session log + crash recovery: each turn writes events to .dao/sessions/<id>/events.jsonl and a state snapshot to state.json; after a crash/abnormal exit, dao -c resumes the last session (src/session/log.ts).
• Shadow git checkpoints: a separate .dao/shadow.git snapshots the working tree (never touches your .git / never rewrites your history); /restore reverts the last turn's changes in one step (src/session/checkpoint.ts).
• Todo list survives compaction: the list maintained by todo_write is re-injected as a system message after compaction, preventing goal drift on long tasks.
• Definition-of-Done verification: /dod <command> (or DAO_VERIFY_CMD) sets an executable acceptance command; verify_done runs it — only success (exit 0) counts as done; if unset, the model self-judges from evidence.
• Stuck detection + circuit breaker: repeating the same tool call / hitting the same error past a threshold → first a nudge to change approach, then a stop, so it doesn't spin and burn budget (src/agent/stuck.ts).
• Large output spilled to disk: when tool output exceeds a threshold it's spilled in full to .dao/spill/; the context keeps only a truncation + pointer, fetched back on demand via read_file.
• Parallel / background subagents + notification queue: pass tasks[] to run in parallel, or background:true to run in the background (returns immediately, doesn't block the main loop); on completion the result is auto-injected as a <task-notification> to continue (src/agent/tasks.ts).
• On-demand memory retrieval: memory_read lets the model actively retrieve cross-session memory (startup injects only top-K; truncated or just-written entries are still findable).
• Long-task autonomous mode: dao --goal (legacy --task / --coordinator still accepted) or `/