langchain4j
LangChain4j is an open-source Java library that simplifies building applications powered by large language models (LLMs). It provides unified APIs to work with 20+ LLM providers and 30+ vector databases, with built-in support for agents, RAG, and tool calling—all designed for Java enterprise frameworks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | langchain4j/langchain4j |
| Owner | langchain4j |
| Primary language | Java |
| License | Apache-2.0 — OSI-approved |
| Stars | 12.5k |
| Forks | 2.4k |
| Open issues | 785 |
| Latest release | 1.17.2 (2026-07-06) |
| Last updated | 2026-07-08 |
| Source | https://github.com/langchain4j/langchain4j |
What langchain4j is
LangChain4j offers type-safe, idiomatic Java abstractions over heterogeneous LLM and embedding store APIs, including prompt templating, chat memory, function calling, agents, and RAG pipelines. It integrates natively with Quarkus, Spring Boot, Helidon, and Micronaut via dependency injection and fluent builder patterns.
Get the langchain4j source
Clone the repository and explore it locally.
git clone https://github.com/langchain4j/langchain4j.gitcd langchain4j# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Dependency management: Library uses Maven Central; audit transitive dependencies for security and license compliance in enterprise environments.
- Provider credentials: Manage API keys securely (environment variables, vaults) outside code; no built-in credential storage or rotation.
- Integration testing: Test LLM provider calls against real endpoints or mocks early; costs and rate limits apply to development/staging.
- JVM version: Latest release targets JDK 17+; verify compatibility with your production Java runtime before adoption.
- Feature maturity: 785 open issues indicate active development; core RAG/agent/tool-calling features are stable, but newer integrations may be experimental.
When to avoid it — and what to weigh
- Python-first ML/AI teams — If your team standardizes on Python and LangChain (Python), adopting LangChain4j introduces Java runtime complexity and diverges from the broader ecosystem.
- Real-time, low-latency constraints — LLM calls are inherently latent; LangChain4j is not suited for sub-second response time requirements. Assess SLA viability before committing.
- Minimal JVM footprint required — LangChain4j and Java runtime overhead may conflict with serverless or embedded deployment targets with strict memory/startup budgets.
- Cutting-edge model research — This is a framework for application building, not a research or model training platform. New model architectures may lag provider SDK support.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability disclaimers.
Apache 2.0 permits commercial use without restrictions. No commercial license required. However, you are responsible for compliance with third-party provider terms (OpenAI, Anthropic, etc.) and any transitive dependencies; review those licenses before deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security incidents disclosed in data. Practices to evaluate: API key management (use secrets manager, never commit credentials), input validation on LLM prompts (prompt injection risk), rate limiting and quota enforcement, audit logging of LLM calls, and transitive dependency vulnerability scanning. No built-in encryption, intrusion detection, or compliance certifications mentioned; enforce via infrastructure/framework layers.
Alternatives to consider
LangChain (Python)
Industry standard with broader ecosystem, but requires Python runtime; not suitable for Java-first teams.
Semantic Kernel (C#/.NET)
Similar unified API abstraction for .NET/C#; better fit if your backend is .NET, but smaller LLM provider support than LangChain4j.
Bedrock/native provider SDKs
Direct AWS Bedrock or provider-native APIs offer tighter control and no abstraction overhead, but require learning multiple APIs and lock you to specific providers.
Build on langchain4j with DEV.co software developers
LangChain4j provides production-ready abstractions for enterprise Java teams. Start with the getting-started guide, explore examples, and join the Discord community for support.
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langchain4j FAQ
Does LangChain4j work with my existing Spring Boot app?
Can I switch LLM providers without rewriting code?
Is there a cost to using LangChain4j?
What are the data privacy implications?
Software developers & web developers for hire
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Ready to Build LLM Applications in Java?
LangChain4j provides production-ready abstractions for enterprise Java teams. Start with the getting-started guide, explore examples, and join the Discord community for support.