Zhanlu - AI Coding Assistant

Generates single-line and multi-line code completions during active editing by analyzing the current file, cross-file project context, and compilation state. Completions are surfaced inline with Tab-key acceptance, leveraging project-level architectural understanding to predict contextually relevant code patterns. The system maintains awareness of imported modules, class hierarchies, and function signatures across the entire codebase to ensure completions align with existing code structure.

Converts natural language descriptions (provided via in-editor prompts or chat interface) into executable code with auto-generated inline comments explaining logic. The system parses the natural language requirement, decomposes it into implementation steps, generates syntactically correct code in the target language, and annotates the code with method-level and inline comments. Supports code generation within the context of the current file or as standalone snippets.

Enables configuration and invocation of Model Context Protocol (MCP) tools to extend Zhanlu's capabilities with external integrations. Users can register custom MCP tools that interact with APIs, databases, file systems, or other services. The agent can invoke these tools as part of task execution, passing parameters and receiving results. Tool definitions include schema specifications, parameter validation, and error handling. Supports both built-in tools (file I/O, shell execution) and user-defined custom tools.

Provides enterprise-grade authentication supporting multiple identity providers (China Mobile Cloud, AK/SK credentials, SAML/SSO) and role-based access control (RBAC) for team environments. Users authenticate once and receive a session token valid across VS Code and web interfaces. RBAC controls which features and projects each user can access, with granular permissions for code review, test generation, and agent execution. Audit logging tracks all user actions for compliance and security monitoring.

Analyzes entire project codebase to identify code quality issues, performance bottlenecks, and optimization opportunities. Generates a comprehensive review report with specific recommendations for refactoring, performance improvement, and best-practice alignment. The system scans multiple files in parallel, builds a project-wide dependency graph, and surfaces issues ranked by severity and impact. Recommendations include before/after code examples and rationale for each suggested change.

Analyzes runtime exceptions and compilation errors (including stack traces) to diagnose root causes and suggest targeted repairs. The system parses error messages, traces execution paths through the codebase, identifies the problematic code section, and generates corrected code with explanation of the fix. Integrates with VS Code's error diagnostics to surface suggestions inline at error locations. Supports multi-step debugging by analyzing error chains and suggesting fixes that address root causes rather than symptoms.

Generates unit tests for specified functions or classes using framework-specific patterns and conventions. Supports batch test generation across multiple files, automatically selecting appropriate test frameworks (JUnit, Mockito, Spring Test for Java; pytest, unittest for Python) based on project configuration. Generated tests include setup/teardown logic, mock object creation, assertion statements, and edge case coverage. Tests are generated with proper naming conventions and documentation matching the target framework's idioms.

Translates source code from one programming language to another while preserving semantic meaning and adapting to target language idioms. Supports bidirectional translation between Java, Python, Go, JavaScript, TypeScript, C/C++, and C#. The system analyzes the source code's control flow, data structures, and algorithms, then reconstructs equivalent logic in the target language using idiomatic patterns (e.g., list comprehensions in Python, goroutines in Go). Maintains function signatures and class hierarchies where applicable, and generates comments explaining language-specific adaptations.

Operates as an autonomous agent that accepts high-level programming requirements, decomposes them into subtasks, generates implementation code, executes the code, detects failures, and iteratively fixes bugs until the task completes successfully. The agent maintains state across multiple steps, tracks completed subtasks, and can invoke MCP (Model Context Protocol) tools to interact with external systems (file I/O, API calls, database operations). Includes self-reflection capability to evaluate generated code quality and suggest improvements. Operates in a sandboxed environment with explicit user approval for each execution step.

Provides a chat interface for multi-turn dialogue about coding problems, technical questions, and implementation strategies. The system maintains conversation history and project context across turns, enabling follow-up questions and iterative refinement of solutions. Responses include code examples, explanations, and links to relevant project files. The chat interface integrates with the editor, allowing users to reference selected code snippets or error messages directly in questions.

Automatically generates descriptive commit messages and README documentation based on code changes detected in the working directory. Analyzes diffs to understand what was added, modified, or removed, then synthesizes natural language descriptions following conventional commit format (feat:, fix:, refactor:, etc.). For README generation, scans project structure, identifies key modules and functions, and produces documentation with usage examples and API descriptions. Integrates with version control workflows to suggest messages before commit.

Generates coding exercises and challenges organized by programming language and difficulty level. The system creates problem statements, test cases, and solution templates, then evaluates user submissions against the test cases. Provides hints and explanations when users request help, and tracks progress across multiple exercises. Exercises are generated dynamically based on selected language and difficulty, covering common algorithms, data structures, and language-specific idioms.