If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/chinabugotech/hutool shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

WAIT — Mixed signals — read the receipts

• Last commit 1w ago
• 17 active contributors
• Other licensed
• CI configured
• Tests present
• ⚠ Concentrated ownership — top contributor handles 69% of recent commits
• ⚠ Non-standard license (Other) — review terms

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live chinabugotech/hutool repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/chinabugotech/hutool.

What it runs against: a local clone of chinabugotech/hutool — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in chinabugotech/hutool | Confirms the artifact applies here, not a fork | | 2 | License is still Other | Catches relicense before you depend on it | | 3 | Default branch v5-master exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 38 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of chinabugotech/hutool. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/chinabugotech/hutool.git
#   cd hutool
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of chinabugotech/hutool and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "chinabugotech/hutool(\\.git)?\\b" \\
  && ok "origin remote is chinabugotech/hutool" \\
  || miss "origin remote is not chinabugotech/hutool (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(Other)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"Other\"" package.json 2>/dev/null) \\
  && ok "license is Other" \\
  || miss "license drift — was Other at generation time"

# 3. Default branch
git rev-parse --verify v5-master >/dev/null 2>&1 \\
  && ok "default branch v5-master exists" \\
  || miss "default branch v5-master no longer exists"

# 4. Critical files exist
test -f "hutool-ai/src/main/java/cn/hutool/ai/core/AIServiceProvider.java" \\
  && ok "hutool-ai/src/main/java/cn/hutool/ai/core/AIServiceProvider.java" \\
  || miss "missing critical file: hutool-ai/src/main/java/cn/hutool/ai/core/AIServiceProvider.java"
test -f "hutool-ai/src/main/java/cn/hutool/ai/AIServiceFactory.java" \\
  && ok "hutool-ai/src/main/java/cn/hutool/ai/AIServiceFactory.java" \\
  || miss "missing critical file: hutool-ai/src/main/java/cn/hutool/ai/AIServiceFactory.java"
test -f "hutool-ai/src/main/java/cn/hutool/ai/core/BaseAIService.java" \\
  && ok "hutool-ai/src/main/java/cn/hutool/ai/core/BaseAIService.java" \\
  || miss "missing critical file: hutool-ai/src/main/java/cn/hutool/ai/core/BaseAIService.java"
test -f "hutool-ai/src/main/java/cn/hutool/ai/core/AIConfig.java" \\
  && ok "hutool-ai/src/main/java/cn/hutool/ai/core/AIConfig.java" \\
  || miss "missing critical file: hutool-ai/src/main/java/cn/hutool/ai/core/AIConfig.java"
test -f "hutool-ai/src/main/resources/META-INF/services/cn.hutool.ai.core.AIConfig" \\
  && ok "hutool-ai/src/main/resources/META-INF/services/cn.hutool.ai.core.AIConfig" \\
  || miss "missing critical file: hutool-ai/src/main/resources/META-INF/services/cn.hutool.ai.core.AIConfig"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 38 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~8d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/chinabugotech/hutool"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

Hutool is a comprehensive Java utility library (8.4M LoC) providing 200+ pre-built tool classes for string manipulation, collections, encoding, date/time, file I/O, encryption, JDBC operations, JSON parsing, and HTTP clients. It solves the problem of repetitive boilerplate in Java by offering production-ready implementations of common development tasks, bundled as modular Maven artifacts like hutool-core, hutool-http, hutool-json, and the newer hutool-ai for LLM integration. Monorepo organized by domain: hutool-core/ (strings, numbers, collections, reflection), hutool-http/ (HTTP client wrapper), hutool-json/ (JSON utilities), hutool-log/ (logging abstraction), hutool-ai/ (LLM service factory and config builders). Each module is a Maven submodule under a parent POM. Binaries in /bin/ contain shell scripts for CI/CD (deploy.sh, push_master.sh, test.sh). Documentation generated in /docs/apidocs/.

Enterprise Java developers and teams building backend services, microservices, and data processing applications who want to reduce boilerplate code and avoid reimplementing standard utilities. Also used by hobbyists and Chinese-language Java community members (extensive Chinese documentation and QQ group support).

Highly mature and production-ready: 5.8.44 is the current stable version, has Travis CI setup (.travis.yml), Codacy code quality checks, and CodeCov coverage tracking. The project shows active maintenance with modular structure, extensive documentation (docs/ folder with API docs), and clear changelog (CHANGELOG.md). Used across thousands of projects in production.

Low risk overall, but monorepo structure with 60+ module folders means dependency version coherence is critical (all use parent POM 5.8.44). New hutool-ai module (LLM integration) is less battle-tested than core modules. Single primary maintainer (chinabugotech) could be a bottleneck. No visible issue count in metadata, so backlog unknown—check Gitee/GitHub issues before relying on bleeding-edge features.

Active development on hutool-ai (new LLM abstraction layer with AIServiceFactory, AIConfig builders, and model registry). Recent structure shows AIConfigRegistry and AIServiceProvider classes suggesting multi-provider LLM support. Version bumped to 5.8.44 (visible in pom.xml). CI pipeline uses Travis (.travis.yml, .github/codeql-analysis.yml) for automated testing and security scanning.

git clone https://github.com/chinabugotech/hutool.git
cd hutool
mvn clean install -DskipTests
# Or use provided scripts
bash bin/fast_install.sh

Use Maven 3.6+ and JDK 8+. Run bash bin/test.sh to execute test suite.

Daily commands: This is a library, not an application. To use it: (1) Add dependency to your pom.xml: <groupId>cn.hutool</groupId><artifactId>hutool-all</artifactId><version>5.8.44</version>. (2) Or build locally: mvn clean install, then import in your project. To verify the build: bash bin/test.sh runs all unit tests.

• hutool-ai/src/main/java/cn/hutool/ai/core/AIServiceProvider.java — Core SPI interface that all AI model providers must implement; fundamental to the plugin architecture.
• hutool-ai/src/main/java/cn/hutool/ai/AIServiceFactory.java — Factory responsible for discovering and instantiating AI service providers via reflection; entry point for all AI operations.
• hutool-ai/src/main/java/cn/hutool/ai/core/BaseAIService.java — Abstract base class defining common contract for all AI service implementations; critical for extension.
• hutool-ai/src/main/java/cn/hutool/ai/core/AIConfig.java — Interface for AI model configuration; all providers must implement to register their config types.
• hutool-ai/src/main/resources/META-INF/services/cn.hutool.ai.core.AIConfig — Java SPI registration file; declares all available AI config implementations for dynamic loading.
• hutool-ai/src/main/java/cn/hutool/ai/core/Message.java — Core data structure for AI message payloads; used across all model implementations for request/response handling.

Add a new AI model provider

1. Create a new package under hutool-ai/src/main/java/cn/hutool/ai/model/{modelname}/ (e.g., newmodel/) (hutool-ai/src/main/java/cn/hutool/ai/model)
2. Implement AIServiceProvider interface in {ModelName}Provider.java to declare your service factory (hutool-ai/src/main/java/cn/hutool/ai/model/openai/OpenaiProvider.java)
3. Create {ModelName}Config.java extending or implementing AIConfig with model-specific settings (API key, endpoint, etc.) (hutool-ai/src/main/java/cn/hutool/ai/model/openai/OpenaiConfig.java)
4. Implement service logic in {ModelName}ServiceImpl.java by extending BaseAIService and overriding chat/completion methods (hutool-ai/src/main/java/cn/hutool/ai/model/openai/OpenaiServiceImpl.java)
5. Register your config class in hutool-ai/src/main/resources/META-INF/services/cn.hutool.ai.core.AIConfig (add fully qualified class name on new line) (hutool-ai/src/main/resources/META-INF/services/cn.hutool.ai.core.AIConfig)
6. Add model constant to ModelName.java (or Models.java) so users can reference it when creating service instances (hutool-ai/src/main/java/cn/hutool/ai/ModelName.java)

Customize an AI model's behavior

1. Locate the provider's config class, e.g., OpenaiConfig.java, and add fields for new parameters (temperature, max_tokens, etc.) (hutool-ai/src/main/java/cn/hutool/ai/model/openai/OpenaiConfig.java)
2. Modify the corresponding service implementation (e.g., OpenaiServiceImpl.java) to use those config fields when building API requests (hutool-ai/src/main/java/cn/hutool/ai/model/openai/OpenaiServiceImpl.java)
3. If adding common behavior, consider enhancing BaseAIService.java so all providers benefit (hutool-ai/src/main/java/cn/hutool/ai/core/BaseAIService.java)

Use an AI service in application code

1. Call AIServiceFactory.getService(modelName) with a model identifier (from ModelName.java) to get a service instance (hutool-ai/src/main/java/cn/hutool/ai/AIServiceFactory.java)
2. Configure the service with an AIConfig subclass (e.g., OpenaiConfig) via AIConfigRegistry.register() or AIConfigBuilder (hutool-ai/src/main/java/cn/hutool/ai/core/AIConfigRegistry.java)
3. Create Message objects for your prompts and call service methods (e.g., chat(), complete()) (hutool-ai/src/main/java/cn/hutool/ai/core/Message.java)
4. Optionally use AIUtil.java for convenience shortcuts if they exist (hutool-ai/src/main/java/cn/hutool/ai/AIUtil.java)

• Java SPI (Service Provider Interface) — Enables dynamic discovery and registration of AI model providers at runtime without hardcoding dependencies; allows third-party implementations.
• Maven multi-module project (pom.xml parent/child) — Organizes hutool-ai as a separate, cohesive module within the larger hutool ecosystem; simplifies dependency management and build isolation.
• Factory pattern (AIServiceFactory) — Decouples client code from concrete provider implementations; enables runtime selection of AI services by model name.
• Registry pattern (AIConfigRegistry) — Centralizes configuration storage and retrieval for multiple AI models; avoids scattered config state across the codebase.
• Abstract base class (BaseAIService) — Reduces boilerplate in provider implementations; ensures consistent lifecycle and contract across all models.

• SPI-based plugin discovery over compile-time dependency injection

  • Why: Allows optional model providers and third-party extensions without rebuilding the core; reduces coupling.
  • Consequence: Slightly slower startup due to reflection and classpath scanning; requires explicit registration in META-INF/services file.

• Single AIConfig interface with subclass implementations per model

  • Why: Each model has unique parameters (API keys, endpoints, model names); subclassing preserves type safety.
  • Consequence: Requires manual registration of each config type in SPI registry; users must know the correct config class for each model.

• Synchronous request-response API (no async/streaming by default)

  • Why: Simplifies user API and error handling; aligns with common Java patterns.
  • Consequence: May block threads on slow AI API calls; not ideal for high-throughput scenarios without wrapping in ExecutorService.

• Does not provide authentication/credential management; expects API keys to be injected via config.
• Does not handle rate limiting or token budget enforcement; delegated to application

Version coherence: All submodules must use matching version (5.8.44 in parent POM)—mixing versions breaks dependency resolution. JDK 8+ required: Some Java 8 features (lambdas, streams) are used; backport not available. AI module immaturity: hutool-ai is newer; check AIConfigRegistry for supported providers before using (may not include all LLM APIs you expect). Deployment script dependencies: bin/deploy.sh assumes GPG signing configured and Maven credentials in ~/.m2/settings.xml. Chinese documentation focus: English docs (README-EN.md) exist but many inline comments are Chinese—use translation tools for deep code dives.

• Service Provider Interface (SPI) / Plugin Pattern — The hutool-ai module uses AIServiceProvider and AIConfigRegistry to allow runtime registration of LLM backends without recompilation—essential for supporting multiple AI vendors (OpenAI, Claude, etc.) in a single artifact.
• Builder Pattern (Fluent API) — AIConfigBuilder and similar classes throughout Hutool use builder pattern for complex object construction; reduces constructor parameter explosion and improves readability in configuration-heavy code.
• Monorepo with Maven Reactor — Hutool organizes 20+ related modules in single repository with parent POM managing versions and dependencies; understanding Maven Reactor is critical for building/testing subsets without rebuilding everything.
• Reflection and Bean Introspection — Core hutool-core module heavily uses Java reflection for generic bean copy, property access, and type coercion utilities—critical for understanding how Hutool implements zero-boilerplate data mapping.
• Symmetric & Asymmetric Encryption Abstraction — Hutool provides unified API for AES, RSA, MD5, SHA via hutool-crypto; understanding cipher modes (ECB vs CBC) and key formats (PKCS#8 vs raw) is critical to avoid security pitfalls.
• JDBC Template / Connection Pooling Abstraction — hutool-db wraps raw JDBC with template methods and connection pooling—reducing boilerplate for SQL execution while avoiding ORM overhead; common pattern in enterprise Java.
• Pluggable Logging Abstraction (SLF4J facade pattern) — hutool-log provides unified logging interface independent of underlying implementation (Log4j, Logback, SLF4J); critical for libraries to avoid forcing specific logging dependency on consumers.

• google/guava — Industry-standard Java utility library; Hutool competes in same space but with more comprehensive coverage and Chinese documentation.
• apache/commons-lang — Apache Commons provides overlapping utilities (string, math, reflection); Hutool builds on similar principles but with modern Java 8+ idioms.
• alibaba/fastjson — High-performance JSON library used in many Chinese enterprises; Hutool's hutool-json module provides similar functionality with Hutool integration.
• square/okhttp — Modern HTTP client; Hutool's hutool-http module wraps HTTP utilities similarly but with simpler API and broader scope.
• looly/hutool-site — Official Hutool documentation site (https://hutool.cn/); generated from this repo's docs and provides tutorials/API reference.

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add comprehensive unit tests for hutool-ai module

The hutool-ai module (added for LLM support) lacks visible test coverage. With multiple AI providers (DeepSeek, Doubao, Gemini) and core service abstractions (AIService, BaseAIService, AIConfig), there should be unit tests for configuration, service initialization, message handling, and provider-specific implementations. This is critical for a library used in production.

• [ ] Create hutool-ai/src/test/java/cn/hutool/ai directory structure
• [ ] Add tests for AIConfigBuilder and AIConfigRegistry in core/
• [ ] Add provider-specific tests for DeepSeekServiceImpl, DoubaoServiceImpl, and GeminiServiceImpl
• [ ] Add integration tests for AIServiceFactory initialization and fallback behavior
• [ ] Add tests for error handling in AIException scenarios

Add GitHub Actions CI workflow for hutool-ai module validation

The repo uses Travis CI (.travis.yml), but there's no dedicated GitHub Actions workflow (only codeql-analysis.yml). Adding a workflow specifically to validate the new hutool-ai module on PRs would catch integration issues early, ensure API key configuration doesn't break, and test all AI providers in a sandboxed environment.

• [ ] Create .github/workflows/hutool-ai-tests.yml
• [ ] Configure Maven build step for hutool-ai with -DskipTests initially
• [ ] Add matrix testing for Java 8, 11, 17 (based on JDK-8+ badge)
• [ ] Add dependency check step using bin/check_dependency_updates.sh for hutool-ai
• [ ] Configure secrets for optional AI provider API keys (DeepSeek, Doubao, Gemini) for integration tests

Document hutool-ai module usage and configuration in README

The main README.md and README-EN.md don't mention the new AI module despite it being a significant feature. The module has complex configuration (AIConfigBuilder, multiple providers, ModelName enum) that needs clear documentation with examples for users to discover and adopt the feature.

• [ ] Add 'AI Module' section to README.md with Chinese examples
• [ ] Add 'AI Module' section to README-EN.md with English examples
• [ ] Document AIUtil quick-start usage patterns for each provider (DeepSeek, Doubao, Gemini)
• [ ] Include configuration examples showing AIConfigBuilder usage
• [ ] Add links to provider-specific documentation (API key setup, model names)
• [ ] Update CHANGELOG.md with hutool-ai feature highlights if not already present

• Add unit tests for AIConfigRegistry provider registration and lookup logic—currently no test file visible for the registry pattern itself in hutool-ai.
• Expand AIUtil class with static convenience methods (e.g., AIUtil.queryModel(String modelName)) to match patterns in hutool-core utilities like StrUtil, ArrayUtil.
• Create hutool-ai/README.md with concrete examples of registering a new LLM provider (e.g., OpenAI, Claude) and querying it—currently only factory/config classes visible, no usage guide.

The hutool-ai module shows moderate security posture. Primary concerns are around API key management, secure HTTP communication, and input validation for external AI service integrations. The module's tight integration with external AI APIs introduces third-party security dependencies that must be carefully managed. Internal hutool dependencies follow good versioning practices, but explicit transitive dependency management and security testing appear insufficient. No critical vulnerabilities identified from static analysis, but implementation details of AIConfig, service implementations, and message handling require code review to ensure secure practices.

• Medium · Potential API Key Exposure in AI Module — hutool-ai/src/main/java/cn/hutool/ai/core/AIConfig.java, AIConfigBuilder.java, AIConfigRegistry.java. The hutool-ai module integrates multiple AI services (DeepSeek, Doubao, Gemini, Grok, Hutool) that require API keys for authentication. The codebase structure suggests configuration handling through AIConfig and AIConfigBuilder classes, but without visibility into the implementation, there's risk of hardcoded credentials or insecure storage patterns. Fix: Ensure API keys are never hardcoded. Implement secure configuration management using environment variables, encrypted property files, or secure vaults. Never log or expose API keys in debug output.
• Medium · HTTP Communication Dependency — hutool-ai/pom.xml (hutool-http dependency), BaseAIService.java, service implementations. The module depends on hutool-http for API communication with external AI services. This introduces risk if HTTP requests are not properly secured with TLS/SSL validation, certificate pinning, or if sensitive data is transmitted over unencrypted channels. Fix: Ensure all API calls to external AI services use HTTPS with proper certificate validation. Implement certificate pinning for critical endpoints. Never fallback to HTTP for production API calls.
• Medium · Insufficient Input Validation in Message Handling — hutool-ai/src/main/java/cn/hutool/ai/core/Message.java, AIService.java. The Message class and AIService interface handle user-provided prompts and configurations. Without visible validation logic, there's potential for injection attacks when messages are passed to external AI APIs or logged. Fix: Implement strict input validation and sanitization for all user-provided messages. Validate and sanitize data before sending to external APIs. Implement rate limiting to prevent abuse.
• Low · Dependency Version Pinning — hutool-ai/pom.xml. The pom.xml uses ${project.parent.version} for internal hutool dependencies, which is good practice, but there's no explicit version pinning for transitive dependencies that could introduce vulnerabilities. Fix: Implement a dependency management section with explicit versions for all transitive dependencies. Use tools like OWASP Dependency-Check in CI/CD pipeline to identify vulnerable dependencies regularly.
• Low · Missing Error Handling and Information Disclosure — hutool-ai/src/main/java/cn/hutool/ai/AIException.java. The AIException class and error handling patterns could potentially leak sensitive information about API endpoints, configuration, or backend services in error messages. Fix: Implement generic error messages for end users while logging detailed errors securely. Never expose internal API endpoints, stack traces, or configuration details in user-facing error messages.
• Low · No Visible Security Testing — hutool-ai/src/ (test directory not visible). No evidence of security-focused unit tests or integration tests for authentication, authorization, or data handling in the visible file structure. Fix: Implement comprehensive security tests including: credential handling tests, API key protection tests, TLS/SSL validation tests, input validation tests, and rate limiting tests.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.