Using the GitHub Copilot SDK for Java

Microsoft and GitHub released the GitHub Copilot SDK for Java, version 1.0.7-preview.1, a framework-agnostic client library that lets enterprise Java developers drive AI from idiomatic Java code using annotations, virtual threads, and CompletableFuture. The SDK supports any direct model provider, including OpenAI, Azure, Anthropic, or OpenAI-compatible endpoints, via a provider/ProviderConfig with a custom baseUrl and apiKey, and requires no Copilot subscription. A sample Jakarta EE 11 application demonstrates a real-estate lead-management agent pipeline using Open Liberty 26.0.0.5, PrimeFaces 15.0.16, and H2 in-memory database with 10 seed property listings.

Edward Burns https://github.blog/author/edburns/ Ed Burns is a Principal Software Engineer working to bring Java idiomatic experiences to Microsoft and GitHub technologies. Ed's been working with Java since 1997 in all aspects from client to server to cloud and AI. Enterprise Java developers have a new superpower—drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. Java developers no longer have to rely on Java framework-specific approaches to drive AI from their enterprise apps. While it is true that Langchain4j empowered developers by disintermediating specific AI vendors, you still had a dependency on Langchain4j. And with Spring AI, well, of course you had a dependency on design choices made by Spring, if not on Spring itself. Now, GitHub Copilot SDK for Java is the first truly framework agnostic way to drive AI from Java. And with its BYOK support, GitHub Copilot SDK for Java is also AI vendor neutral. 💡 Even though it’s called GitHub Copilot SDK, you can use it with any direct model provider, such as OpenAI, Azure, Anthropic, or OpenAI-compatible endpoints, by passing a provider / ProviderConfig with your own baseUrl + apiKey or bearer token . No Copilot subscription required. | The GitHub Copilot SDK for Java is a client library that empowers your server-side Java code to create Copilot agent sessions, register tools, send prompts, and receive structured responses—all programmatically. It works in server environments, including Jakarta EE and Spring. If you’ve been building enterprise Java for any length of time, this SDK will feel like home: CompletableFuture , annotations, lambdas, virtual threads, it’s all here. This post shows you how to use the SDK, walks through a complete Jakarta EE 11 sample application, and leaves you with concrete next steps to try it yourself. I chose Jakarta EE 11 for my demo because I was the lead release coordinator for that release. I believe in open standards as the best way to empower developers. For more on Jakarta EE 11 see this InfoQ article https://www.infoq.com/news/2025/07/jakarta-ee-11-updates/ . This sample app is an agent harness using Jakarta EE 11. But, of course, developers can build their own agent harness using the well-known Java frameworks and libraries of their choice. Clone the sample app and try it yourself https://github.com/microsoft/Build26-BRK206-your-agent-anywhere-multiclient-multidevice-with-github-copilot-sdk The SDK is available as a Maven dependency: <dependency <groupId com.github</groupId <artifactId copilot-sdk-java</artifactId <version 1.0.7-preview.1</version </dependency Prerequisites: The best way to see the SDK in action is to run this sample application https://github.com/microsoft/Build26-BRK206-your-agent-anywhere-multiclient-multidevice-with-github-copilot-sdk . git clone https://github.com/microsoft/Build26-BRK206-your-agent-anywhere-multiclient-multidevice-with-github-copilot-sdk.git cd Build26-BRK206-your-agent-anywhere-multiclient-multidevice-with-github-copilot-sdk/src/java-agent-orchestrator mvn clean package liberty:run Open http://localhost:9080/index.xhtml The Java demo is built on: | Concern | Technology | |---|---| | Runtime | Open Liberty 26.0.0.5 | | Platform | Jakarta EE 11 Faces 4.1, CDI 4.1, WebSocket 2.2, Data 1.0, Persistence 3.2 | | UI | PrimeFaces 15.0.16 | | AI orchestration | Copilot SDK for Java 1.0.7-preview.1 | | Database | H2 in-memory 10 seed property listings | The application is a real-estate lead-management agent pipeline. A customer submits an enquiry “I’m looking for a 3-bedroom house in London under £800,000” , and the system spins up an isolated Copilot Agent on a virtual thread to process it through a pipeline: The architecture uses Jakarta WebSocket to push real-time status updates from the server to the browser, so you can watch agents progress through phases as the model calls tools: Submit multiple inquiries simultaneously to see concurrent virtual-thread agents in action. Each one processes independently with its own Copilot session. Let’s walk through the key SDK features as they appear in the sample code. @CopilotTool This is the headline API. If you’ve ever written a @GET endpoint in JAX-RS or an @MessageDriven bean, this will feel instantly familiar: @CopilotTool value = "Sets the current phase of the agent. Use this to report progress.", name = "set current phase" public String setCurrentPhase @CopilotToolParam "The phase to transition to VALIDATING, SEARCHING, " + "WRITING REPORT, REJECTED GARBAGE, REJECTED NO MATCHES, or DONE " String phaseName { phase = Phase.valueOf phaseName.trim .toUpperCase Locale.ROOT ; notifyUi ; return "Phase set to " + phase.getLabel ; } The @CopilotTool annotation declares the method as a tool the model can call. The @CopilotToolParam annotation describes each parameter so the model knows what to pass. The SDK handles all the JSON Schema generation, argument parsing, and dispatch. You just write a normal Java method. Two build prerequisites for @CopilotTool. The annotation-based tool API is currently an experimental feature of the SDK, so you need to configure two things in your Maven build: -Acopilot.experimental.allowed=true to the compiler. Without this flag, the annotation processor will refuse to generate the tool metadata. For more details on the experimental APIs see annotationProcessorPath so the compiler can find the @CopilotTool processor and generate the $$CopilotToolMeta classes at compile time.Both are configured in the maven-compiler-plugin : <plugin <groupId org.apache.maven.plugins</groupId <artifactId maven-compiler-plugin</artifactId <version 3.15.0</version <configuration <compilerArgs <arg -Acopilot.experimental.allowed=true</arg </compilerArgs <annotationProcessorPaths <path <groupId com.github</groupId <artifactId copilot-sdk-java</artifactId <version 1.0.7-preview.1</version </path </annotationProcessorPaths </configuration </plugin To register all annotated tools from an object: List<ToolDefinition annotatedTools = ToolDefinition.fromObject this ; ToolDefinition.from ... When you want a tool defined at the call site without a dedicated method, use the lambda style: ToolDefinition reportIntentTool = ToolDefinition .from "report intent", "Reports the current intent of the agent", Param.of String.class, "intent", "Intent in max 4 words" , String intent - { currentIntent = intent; addEvent Instant.now , "intent", "Intent updated", intent ; notifyUi ; return "ok"; } .overridesBuiltInTool true ; Notice .overridesBuiltInTool true . This tells the SDK that our report intent tool deliberately replaces a built-in tool of the same name. This is useful when you need custom behaviour for a tool the model already knows about. Tools don’t have to live in the same class as your agent logic. Here’s searchProperties defined in a separate CDI bean: @ApplicationScoped public class PropertyDatabase { @CopilotTool value = "Searches the real estate listings database. " + "Returns up to 10 matching properties.", name = "search properties" public List<Property searchProperties @CopilotToolParam "Property type substring e.g. 'flat', 'house' " String type, @CopilotToolParam "City substring e.g. 'London', 'Bristol' " String city, @CopilotToolParam "Minimum number of bedrooms 0 for no minimum " int minBedrooms, @CopilotToolParam "Maximum price in GBP 0 for no maximum " double maxPriceGbp { // ... filter and return matching properties ... } } You would normally register these with ToolDefinition.fromObject propertyDatabase . In the sample app, we use a lambda wrapper instead, because CDI client proxies can obscure the annotation metadata. The SDK gives you fine-grained control over the system message. Use SystemMessageMode.CUSTOMIZE to replace specific sections while preserving the rest: SystemMessageConfig systemMessage = new SystemMessageConfig .setMode SystemMessageMode.CUSTOMIZE .setSections Map.of SystemMessageSections.IDENTITY, new SectionOverride .setAction SectionOverrideAction.REPLACE .setContent """ You are part of a real estate recommendation system. You will receive enquiries from customers, and you must carry out the following workflow... """ ; The text block """...""" makes multi-line prompts readable without string concatenation. The IDENTITY section override replaces only the model’s self-description while leaving safety guardrails intact. If you prefer a simpler approach, SystemMessageMode.APPEND adds your content after the default system message without replacing anything. sendAndWait ... One line kicks off the full agentic loop: session = client.createSession sessionConfig .get ; // ... AssistantMessageEvent result = session.sendAndWait escapedEnquiry .get ; Behind .get , the model reasons, calls your tools potentially multiple times , and returns its final response. On a virtual thread, .get is cheap. No platform thread is consumed while waiting. The SDK dispatches tool calls to your registered handlers automatically and feeds results back to the model until it’s done. session.on ... Subscribe to session events to build responsive UIs: php sessionSubscription = session.on event - { captureSessionEvent event ; uiUpdateSocket.pushDetailUpdate id ; } ; Every tool call, every result, every assistant message fires an event. The sample app captures these events and pushes them to the browser via Jakarta WebSocket, so the pipeline dashboard updates in real time. You can use pattern matching to handle specific event types: if event instanceof AssistantMessageEvent msg { finalReport = msg.getData .content ; } else if event instanceof ToolExecutionStartEvent start { // Tool is being invoked... } The client is configured for server-side operation: copilotClient = new CopilotClient new CopilotClientOptions .setMode CopilotClientMode.EMPTY .setCopilotHome copilotHome .setExecutor contextualVirtualThreadExecutor ; CopilotClientMode.EMPTY means no IDE integration — the client talks directly to the Copilot CLI. The custom Executor discussed below ensures tool callbacks run with container context. For permission handling, the sample uses: sessionConfig.setOnPermissionRequest PermissionHandler.APPROVE ALL ; APPROVE ALL is appropriate for demos and development. In production, implement a real permission policy that validates which tools the model is allowed to invoke. The SDK is not a framework island. It composes naturally with Jakarta EE — and of course also with proprietary frameworks such as Spring. The Executor parameter is the key integration point. Jakarta Concurrency §5.2 in the 3.1 spec requires that application-created threads be obtained from a ManagedThreadFactory so the container can: @PreDestroy / server stop contextualRunnable Open Liberty 26.x supports virtual-thread ManagedThreadFactory via the virtual attribute in server.xml . <managedThreadFactory jndiName="concurrent/virtualThreadFactory" virtual="true" / Then, in AppState.java we inject the factory: @Resource lookup = "concurrent/virtualThreadFactory" private ManagedThreadFactory virtualThreadFactory; And use it to create the Executor we pass to the Copilot SDK. // The ManagedThreadFactory virtual=true creates container-managed virtual // threads that automatically propagate CDI, JNDI, and transaction context. Executor managedVirtualExecutor = runnable - virtualThreadFactory.newThread runnable .start String copilotHome = Path.of System.getProperty "user.home" , ".copilot" .toString ; CopilotClientOptions copilotClientOptions = new CopilotClientOptions .setMode CopilotClientMode.EMPTY .setCopilotHome copilotHome .setExecutor managedVirtualExecutor ; copilotClient = new CopilotClient copilotClientOptions ; This creates virtual threads that carry the container’s context. When the SDK dispatches a tool call to searchProperties , that method can @Inject a JPA repository and query the database, because the container context is present on the callback thread. Other integration patterns in the sample: @ApplicationScoped CopilotClient one client per application lifecycle . f:websocket push PushContext . @Repository Fine-grained tool access control with ToolSet. The SessionConfig lets you specify exactly which tools each session can access: sessionConfig.setAvailableTools new ToolSet .addCustom " " // all registered custom tools .addBuiltIn "web fetch" ; // only the web fetch built-in This is an important production concern. Rather than exposing every built-in tool file system access, shell execution, etc. , you explicitly opt in to only what the agent needs. In the sample app, we allow all custom tools plus web fetch so the agent can look up real-time property information during the Search phase. Here’s what we covered: CompletableFuture , annotations, lambdas, and virtual threads make the SDK feel like idiomatic Java, not a ported-from-another-language afterthought. sendAndWait ... handles the full tool-calling loop automatically. session.on ... enables responsive UIs and observability. Executor integration point. provider / ProviderConfig with your own baseUrl + apiKey or bearer token . No Copilot subscription required. session.setModel ... to experiment with different Copilot models. @CopilotTool method a mortgage calculator, a school-district lookup and watch the agent discover and use it.The Copilot SDK for Java puts the full power of GitHub Copilot behind your Java code with no IDE required and no framework lock-in. Go beyond chat in the GitHub Copilot app with these slash commands. They’ll help you plan, collaborate, automate, and customize your dev workflow. Learn how to build tools to simplify how you work—without writing a single line of code. Instead of one huge, un-reviewable pull request, teach coding agents to decompose work into a clean, ordered stack with GitHub stacked pull requests.