codewhale/README.md

DeepSeek TUI

A terminal-native coding agent for DeepSeek V4 models — with 1M-token context, thinking-mode reasoning, and full tool-use.

npm i -g deepseek-tui

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What is it?

DeepSeek TUI is a coding agent that runs entirely in your terminal. It gives DeepSeek's frontier models direct access to your workspace — reading and editing files, running shell commands, searching the web, managing git, and orchestrating sub-agents — all through a fast, keyboard-driven TUI.

Built for DeepSeek V4 (deepseek-v4-pro / deepseek-v4-flash) with 1M-token context windows and native thinking-mode (chain-of-thought) streaming. See the model's reasoning unfold in real time as it works through your tasks.

Key Features

• Native RLM (rlm_query tool) — fans out 1–16 cheap deepseek-v4-flash children in parallel against the existing DeepSeek client for batched analysis, decomposition, or parallel reasoning
• Thinking-mode streaming — shows DeepSeek's chain-of-thought as it reasons about your code
• Full tool suite — file ops, shell execution, git, web search/browse, apply-patch, sub-agents, MCP servers
• 1M-token context — automatic intelligent compaction when context fills up
• Three interaction modes — Plan (read-only explore), Agent (interactive with approval), YOLO (auto-approved). Decomposition-first system prompts teach the model to checklist_write, update_plan, and spawn sub-agents before acting
• Reasoning-effort tiers — cycle through off → high → max with Shift+Tab
• Session save/resume — checkpoint and resume long sessions
• Workspace rollback — side-git pre/post-turn snapshots with /restore and revert_turn, without touching your repo's .git
• HTTP/SSE runtime API — deepseek serve --http for headless agent workflows
• MCP protocol — connect to Model Context Protocol servers for extended tooling; see docs/MCP.md
• Live cost tracking — per-turn and session-level token usage and cost estimates
• Dark theme — DeepSeek-blue palette

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Quickstart

npm install -g deepseek-tui
deepseek

On first launch you'll be prompted for your DeepSeek API key. You can also set it ahead of time:

# via CLI
deepseek login --api-key "YOUR_DEEPSEEK_API_KEY"

# via env var
export DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY"
deepseek

Using NVIDIA NIM

deepseek auth set --provider nvidia-nim --api-key "YOUR_NVIDIA_API_KEY"
deepseek --provider nvidia-nim

# or per-process:
DEEPSEEK_PROVIDER=nvidia-nim NVIDIA_API_KEY="..." deepseek

Other DeepSeek V4 providers

deepseek auth set --provider fireworks --api-key "YOUR_FIREWORKS_API_KEY"
deepseek --provider fireworks --model deepseek-v4-pro

# SGLang is self-hosted; auth is optional for localhost deployments.
SGLANG_BASE_URL="http://localhost:30000/v1" deepseek --provider sglang --model deepseek-v4-flash

Install from source

git clone https://github.com/Hmbown/DeepSeek-TUI.git
cd DeepSeek-TUI
cargo install --path crates/tui --bin deepseek-tui --locked   # requires Rust 1.85+
cargo install --path crates/cli --bin deepseek --locked

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What's new in v0.6.0

🌊 rlm_query — recursive language models as a first-class tool

The model now has direct access to a native recursive-LLM primitive. Inspired by Alex Zhang's RLM work and Sakana AI's published research on novelty search, but trimmed to what an agent loop actually needs: one tool, structured args, no DSL.

// Single child:
rlm_query({ "prompt": "Summarise this 4k-line log: ..." })

// 8 parallel children, indexed result:
rlm_query({
  "prompts": [
    "Review src/foo.rs for race conditions: ...",
    "Review src/foo.rs for input validation: ...",
    "Review src/foo.rs for error-handling gaps: ...",
    "..."
  ]
})

// Promote one call to Pro:
rlm_query({ "prompt": "Hard reasoning here", "model": "deepseek-v4-pro" })

Children run concurrently against the existing DeepSeek client via tokio — no external binary, no Python sandbox, no fenced-block DSL. Returns a single string for one prompt or [i] ... indexed blocks for many. Available in Plan / Agent / YOLO. The cost is folded into the session's running total automatically.

Other changes

• Scroll position survives content rewrites — anchor fallback now clamps to the nearest surviving cell instead of teleporting to the bottom (#56)
• Looser command-safety chains — cargo build && cargo test is no longer blocked outright; chains of known-safe commands escalate to RequiresApproval instead of Dangerous (#57)
• Multi-turn tool calls no longer 400 on thinking-mode models — reasoning_content is replayed across user-message boundaries with a safe placeholder when the round produced none

Full history: CHANGELOG.md.

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Models & Pricing

DeepSeek TUI targets DeepSeek V4 models with 1M-token context windows by default.

Model	Context	Input (cache hit)	Input (cache miss)	Output
deepseek-v4-pro	1M	$0.003625 / 1M*	$0.435 / 1M*	$0.87 / 1M*
deepseek-v4-flash	1M	$0.0028 / 1M	$0.14 / 1M	$0.28 / 1M

Legacy aliases deepseek-chat and deepseek-reasoner silently map to deepseek-v4-flash.

NVIDIA NIM hosted variants (deepseek-ai/deepseek-v4-pro, deepseek-ai/deepseek-v4-flash) use your NVIDIA account terms — no DeepSeek platform billing.

*DeepSeek lists the Pro rates above as a limited-time 75% discount valid until 2026-05-05 15:59 UTC; the TUI cost estimator falls back to base Pro rates after that timestamp.

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Usage

deepseek                                      # interactive TUI
deepseek "explain this function"              # one-shot prompt
deepseek --model deepseek-v4-flash "summarize" # model override
deepseek --yolo                               # YOLO mode (auto-approve tools)
deepseek login --api-key "..."                # save API key
deepseek doctor                               # check setup & connectivity
deepseek doctor --json                        # machine-readable diagnostics
deepseek setup --status                       # read-only setup status
deepseek setup --tools --plugins              # scaffold local tool/plugin dirs
deepseek models                               # list live API models
deepseek sessions                             # list saved sessions
deepseek resume --last                        # resume latest session
deepseek serve --http                         # HTTP/SSE API server
deepseek mcp list                             # list configured MCP servers
deepseek mcp validate                         # validate MCP config/connectivity
deepseek mcp-server                           # run dispatcher MCP stdio server

Keyboard shortcuts

Key	Action
Tab	Complete / or @ entries; while a turn is running, queue the draft as a follow-up; otherwise cycle mode
Shift+Tab	Cycle reasoning-effort: off → high → max
F1	Help
Esc	Back / dismiss
Ctrl+K	Command palette
Ctrl+R	Resume an earlier session
Alt+R	Search prompt history and recover cleared drafts
@path	Attach file/directory context in composer
/attach <path>	Attach image/video media references; select the row with ↑ at composer start and remove with Backspace/Delete

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Modes

Mode	Behavior
Plan 🔍	Read-only investigation — model explores and proposes a decomposition plan (update_plan + checklist_write) before making changes
Agent 🤖	Default interactive mode — multi-step tool use with approval gates; model outlines work via checklist_write before requesting writes
YOLO ⚡	Auto-approve all tools in a trusted workspace; model still creates checklist_write/update_plan to keep work visible and trackable

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Configuration

~/.deepseek/config.toml — see config.example.toml for every option.

Key environment overrides:

Variable	Purpose
DEEPSEEK_API_KEY	API key
DEEPSEEK_BASE_URL	API base URL
DEEPSEEK_MODEL	Default model
DEEPSEEK_PROVIDER	Provider: deepseek (default), nvidia-nim, fireworks, or sglang
DEEPSEEK_PROFILE	Config profile name
NVIDIA_API_KEY	NVIDIA NIM API key
FIREWORKS_API_KEY	Fireworks AI API key
SGLANG_BASE_URL	Self-hosted SGLang endpoint
SGLANG_API_KEY	Optional SGLang bearer token

Quick diagnostics: deepseek setup --status checks API key, MCP, sandbox, and .env state without network calls; deepseek doctor --json is suitable for CI; deepseek setup --tools --plugins scaffolds local tool and plugin directories.

DeepSeek context caching is automatic — when the API returns cache hit/miss token fields, the TUI includes them in usage and cost tracking.

Full reference: docs/CONFIGURATION.md and docs/MCP.md.

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Publishing your own skill

DeepSeek-TUI can install community skills directly from a GitHub repo, with no backend service in the loop:

1. Create a public GitHub repo with a SKILL.md at the root containing the usual --- frontmatter (name, description).
2. Multi-skill bundles use skills/<name>/SKILL.md instead — the installer picks the first match and names the install after the frontmatter name.
3. Push to main (or master); the installer fetches archive/refs/heads/main.tar.gz and falls back to master.tar.gz.
4. Users install via /skill install github:<owner>/<repo> — installs are gated by the [network] policy, validated for path traversal and size, and placed under ~/.deepseek/skills/<name>/.
5. Submit a PR to the curated index.json (default registry) to make the skill installable by name (/skill install <name>) instead of the GitHub spec.

Documentation

Doc	Topic
ARCHITECTURE.md	Codebase internals
CONFIGURATION.md	Full config reference
MODES.md	Plan / Agent / YOLO modes
MCP.md	Model Context Protocol integration
RUNTIME_API.md	HTTP/SSE API server
RELEASE_RUNBOOK.md	Release process
OPERATIONS_RUNBOOK.md	Ops & recovery

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Contributing

See CONTRIBUTING.md. Pull requests welcome!

Not affiliated with DeepSeek Inc.

License

MIT