SeekCode: A Terminal-Native Intelligent Code Agent Based on DeepSeek

SeekCode is a terminal-native intelligent code agent tool centered on the DeepSeek model, designed to provide intelligent development assistance for real engineering repositories. Built around the deepseek-v4-pro and deepseek-v4-flash models, it integrates core capabilities such as long-context processing, tool calling, local validation, MCP protocol, skill system, and sub-agent workflow. Unlike traditional web-based AI coding tools, SeekCode runs entirely in the terminal environment, with TUI interface and command-line interaction, adapting to engineering scenarios that frequently operate the file system, execute command-line tools, and handle large codebases.

Project Background

SeekCode aims to provide developers with an immersive terminal coding experience, addressing the shortcomings of traditional web tools in handling large codebases and command-line operations.

DeepSeek Customization Design

• Model Adaptation: By default, it uses deepseek-v4-pro as the main model and deepseek-v4-flash as the fast companion model, with parameters such as context limits and inference intensity optimized for DeepSeek coding tasks.
• Multi-Provider Support: Compatible with official DeepSeek API and multiple inference service providers like deepseek-cn, NVIDIA NIM, OpenRouter, allowing flexible selection of service endpoints.
• Session Design: Designed specifically for real repository sessions, supporting complex engineering tasks with multi-turn interactions, tool calls, and context accumulation.

Usage Modes

• Interactive Mode: Run seek to enter the TUI interface, suitable for exploratory work and multi-turn conversations.
• One-Time Task Mode: Execute specific tasks (e.g., code review, architecture analysis, test fixing) via command-line parameters.
• Mode Switching: Supports three modes: plan (read-only planning), agent (approval-required agent), and yolo (high-trust execution), balancing security and efficiency.
• Local Server: seek serve can act as an HTTP/SSE service endpoint, supporting remote access and workflow integration.

Core Features

• Terminal Experience: Full-screen TUI, real-time tool activity display, operation approval, etc.
• Code Toolset: Automatic calling of file reading/writing, code search, git operations, etc.
• Shell & Validation: Command execution, test validation, LSP diagnostic integration.
• MCP Protocol: Access external capabilities and manage MCP servers.
• Skill System: Encapsulate and reuse workflows via SKILL.md.
• Session & Rollback: Save/load sessions, restore workspace snapshots.
• Sub-Agents: Generate parallel work processes to decompose tasks.

Configuration Management

• Hierarchical Configuration: CLI parameters > environment variables > project configuration > user configuration > default values.
• Path Management: User configuration (~/.seekcode/config.toml), project configuration (./.seekcode/config.toml), paths for session/artifact/task data, etc.
• Common Configurations: API keys, model selection, approval policies, sandbox mode, etc., compatible with legacy DEEPSEE_* environment variables.

Ecosystem Compatibility

• Supports AGENTS.md, Claude Code instruction format, and slash commands.
• Skill discovery is compatible with paths like .agents, .claude, .deepseek.
• Supports standard stdio/SSE MCP server configuration, and provides a migration tool for legacy configurations.

SeekCode适用于以下场景：

1. 大型代码库维护：借助长上下文能力和TUI界面，高效理解修改大型代码库，实现批量重构。
2. 自动化工作流：通过yolo模式集成到CI/CD管道，自动化代码审查、格式检查、测试修复。
3. 远程协作：seek serve模式共享实例，集成到内部工具。
4. 技能驱动开发：封装编码规范、审查checklist为可复用技能，提升团队开发质量。

SeekCode represents the development direction of terminal-native AI code agents. It fully utilizes the long-context and reasoning capabilities of the DeepSeek model, and through a well-designed TUI interface and tool integration, provides a powerful and controllable AI-assisted development experience. Amidst the abundance of AI coding tools today, its deep adaptation to the terminal environment and special optimization for the DeepSeek model offer an excellent choice for developers pursuing efficiency and control.

1. Choose the appropriate inference service provider (e.g., official DeepSeek API or NVIDIA NIM) based on cost and latency requirements.
2. Select the working mode according to task security: use plan mode for exploratory tasks, yolo mode for high-trust scenarios.
3. Use the skill system to encapsulate common team workflows to improve reuse efficiency.
4. Configure hierarchical parameters properly; prioritize CLI parameters or environment variables for temporary adjustments.
5. Save session snapshots regularly to facilitate task rollback and state recovery.