DeepSeek-TUI - an open-source terminal programming intelligent agent, adapted for DeepSeek V4.

What is DeepSeek-TUI?

DeepSeek-TUI is a programmable intelligent agent that runs entirely in the terminal, built by Hayden Brown in Rust, and specifically designed for the DeepSeek V4 series of models.deepseek-v4-pro / deepseek-v4-flashNative design. The tool directly integrates DeepSeek's 1M token ultra-long context window and Chain-of-Thought mode into the developer workspace, supporting full-stack operations such as file reading and writing, shell command execution, Git management, web page search, and sub-agent scheduling, all through a keyboard-driven TUI interface.

Main functions of DeepSeek-TUI

• File operations and editingSupports file reading and writing, applying patches, batch modifications, and integrates LSP diagnostics (rust-analyzer, pyright, gopls, clangd, etc.) for real-time error feedback.
• Shell and Git executionRun shell commands and manage Git repositories directly within the terminal without switching contexts.
• Web search and browsingBuilt-in web search and page browsing capabilities to assist in research and data collection.
• Sub-agent schedulingSupports spawning child agents to process complex tasks in parallel, in conjunction with RLM (rlm_queryIt can fan out 1–16 deepseek-v4-flash Batch analysis of instances
• MCP protocol supportsNatively integrates the Model Context Protocol client, enabling connection to external MCP servers to extend the toolchain.
• Thinking mode streaming output: Real-time display of the model's inference chain (Chain-of-Thought), observing how it analyzes code issues step by step.
• Session ManagementSupports session saving/restoring (checkpoint), working directory rollback (side-git snapshot, without polluting the main repository). .gitPersistent task queue (background tasks can survive restarts)
• Skills SystemSupports installing community skill packs from GitHub.SKILL.mdWorkflows can be scaled without backend services.
• Real-time cost trackingStatistics on token usage, cache hit/miss ratio, and cost estimates by round and session.
• HTTP/SSE Runtime API:pass deepseek serve --http Provides a headless agent workflow interface
• FIM editing toolsvia DeepSeek /beta Fill-in-the-middle endpoints enable precise code modification
• Vim Modal EditingComposer supports normal/insert modes and standard Vim keyboard shortcuts.
• User MemoryOptional persistent note files are injected with system prompts, and preference settings are retained across sessions.

How to use DeepSeek-TUI

• Installation tools:pass npm install -g deepseek-tui or cargo install One-click installation.
• Configuration keySet the DeepSeek API Key or select a backend such as NVIDIA NIM / Fireworks.
• Startup screenEnter in the terminal deepseek-tui Enter the interactive programming environment.
• Describe the taskEnter your natural language requirements into Composer, and the Agent will automatically analyze and execute them.
• Select modeSwitch between Plan (produce a solution first), Agent (confirm step by step), or YOLO (fully automatic) according to the scenario.
• Monitoring costsThe bottom right corner of the interface displays real-time information on token consumption and estimated costs for each round.
• Compressed contextManual input during long sessions /compact Or wait for automatic compression.
• Rollback operationIf the result is not satisfactory, use /restore or revert_turn Revert to the previous snapshot.
• Save SessionSave automatically or manually before exiting, and resume from where you left off the next time you start the app.

Key information and usage requirements for DeepSeek-TUI

• System RequirementsIt can run on Linux, macOS, and Windows (including ARM64).
• Model dependencyYou need to provide your own DeepSeek V4 API Key, or connect to NVIDIA NIM / Fireworks / SGLang.
• Network environmentFor domestic users, it is recommended to configure the TUNA Cargo image to accelerate installation.
• Fee NoteAfter opening multiple sub-agents, the cache hit rate decreases, and the price of a missed token is 10 times that of a hit token. It is recommended to pay attention to real-time fees.
• Version StatusThe current version is v0.8.8, which is undergoing frequent iterations. It is recommended to keep it updated.

DeepSeek-TUI's core advantages

• Single binary zero dependencyIt is compiled into a single binary based on Rust, requires no Node.js or Python environment to run, and has an extremely lightweight deployment.
• DeepSeek V4 native adaptationFrom the tool calling protocol and prompt word encapsulation to the streaming model, everything is a native DeepSeek design, not a generic OpenAI wrapper.
• 1M Token with Extremely Long Context: Use DeepSeek V4's million-level context windows to process large codebases, and combine them with prefix caching to reduce redundant calculations and API costs.
• Intelligent Context CompressionWhen the context is close to the limit, it automatically summarizes the history of the conversation, retains the recent original content, and injects the summary as a prefix to avoid information truncation.
• Three risk control modesPlan (read-only analysis), Agent (interactive with approval required), and YOLO (automatic approval execution) are available to meet the security needs of different scenarios.
• Reasoning Intensity Grading:pass Shift+Tab exist off → high → max Switch between model inference depths to flexibly balance speed and quality; v0.8.12 adds: auto The mode automatically selects the inference intensity based on the task type.
• Multilingual UISupports English, Japanese, Simplified Chinese (zh-Hans), and Brazilian Portuguese; automatically detects system language.

DeepSeek-TUI project address

• GitHub repository: https://github.com/Hmbown/DeepSeek-TUI

Comparison of DeepSeek-TUI with similar competitors

Application scenarios of DeepSeek-TUI

• Large-scale project analysis and restructuring: Load the entire codebase at once with a 1M context for global refactoring or module migration.
• Automated code review and bug fixingScan for code issues in Plan mode, then switch to Agent/YOLO mode to fix them in batches.
• Document generation and maintenanceAutomatically update README, API documentation, or comments based on code changes.
• CI cleanup and dependency updatesSchedule periodic tasks via a durable task queue, such as cleaning up expired CI configurations and upgrading dependency versions.
• Batch Parallel Inference: Use RLM to analyze multiple files or execute multiple test strategies simultaneously to accelerate decision-making.
• Headless automated workflow:pass deepseek serve --http Integrate TUI as a backend service into the internal toolchain.