The Tool Desk
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The core difference in one view
Spring AI separates the portable model contract from the application-facing conversation API. A ChatModel represents a model implementation and accepts a Prompt, returning a ChatResponse. A ChatClient is a fluent facade constructed around that model; it builds the request, applies defaults and advisors, invokes the model, and exposes convenient response conversions.
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| Question | ChatModel |
ChatClient |
|---|---|---|
| Primary role | Direct, portable model abstraction | Application-facing fluent API |
| Input style | Explicit Prompt or String |
Fluent system, user, template, option, advisor, and tool configuration |
| Typical output | ChatResponse or reactive response stream |
String, ChatResponse, ChatClientResponse, typed entity, or reactive equivalents |
| Best fit | Provider adapters, shared infrastructure, explicit model control | Controllers and services implementing conversations, RAG, memory, tools, and structured responses |
| Construction | Provider-specific Spring bean | Built from ChatClient.Builder or a selected ChatModel |
Spring AI’s model abstraction is intended to let an application switch providers with minimal code changes. The current model reference lists implementations including OpenAI, Ollama, Amazon Bedrock, Mistral, and Anthropic, among others. ChatClient preserves that portability because it delegates execution to whichever ChatModel you provide.
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The direct contract
ChatModel extends Model<Prompt, ChatResponse> and StreamingChatModel. Its central operations are:
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call(String)for a simple text request.call(Prompt)when you need explicit messages and options.stream(Prompt)for a reactive stream of model output.
A Prompt contains the messages sent to the model and optional ChatOptions. The resulting ChatResponse contains one or more generations plus response metadata. That metadata and the complete generation structure are available without going through a convenience conversion to plain text.
When the direct API is the right boundary
- Provider adapters: code that implements or wraps a provider should depend on the model contract rather than on application-level prompt orchestration.
- Shared infrastructure: gateways, fallback services, retry layers, and policy components often need to construct a complete
Promptand inspect the full response. - Explicit options: code that chooses the model, temperature, token limit, penalties, stop sequences, top-k, or top-p per request can express those choices directly.
- Raw response handling: moderation, usage accounting, finish reasons, multiple generations, and provider metadata are easier to handle from
ChatResponse.
Direct call example
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.chat.prompt.ChatOptions;
import org.springframework.ai.chat.model.ChatResponse;
public final class ModelGateway {
private final ChatModel chatModel;
public ModelGateway(ChatModel chatModel) {
this.chatModel = chatModel;
}
public ChatResponse answer(String question) {
ChatOptions options = ChatOptions.builder()
.temperature(0.2)
.maxTokens(500)
.build();
Prompt prompt = new Prompt(question, options);
return chatModel.call(prompt);
}
}This style keeps request construction visible. It is useful when the caller must inspect the response object, but it also means the caller is responsible for assembling messages, applying conversation context, and handling any orchestration.
What ChatClient adds
A fluent request pipeline
ChatClient is created from a ChatClient.Builder. In a Spring Boot application, Boot can auto-configure a prototype-scoped builder when a ChatModel is available. A basic request looks like this:
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@Service
class AnswerService {
private final ChatClient chatClient;
AnswerService(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
String answer(String userInput) {
return chatClient.prompt()
.user(userInput)
.call()
.content();
}
}The fluent chain can set system and user content, expand prompt templates, provide per-request model options, add advisors, register tools, and choose the desired response representation. It is deliberately similar in style to Spring’s WebClient and RestClient APIs.
Terminal methods and lazy execution
call() selects synchronous execution, but the model is not necessarily contacted at that point. Actual execution occurs when a terminal result is requested, such as content(), chatResponse(), or responseEntity(). This allows the chain to finish specifying how the response should be converted before the request is sent.
For synchronous results, ChatClient can return:
- A plain
Stringthroughcontent(). - A full
ChatResponsethroughchatResponse(). - A
ChatClientResponsewhen application-level context and response details are needed. - A typed Java object through
responseEntity(YourType.class)or the corresponding conversion API.
For streaming, stream() can expose a reactive Flux<String>, Flux<ChatResponse>, or Flux<ChatClientResponse>.
Choosing the abstraction by task
| Your requirement | Preferred API | Why |
|---|---|---|
| Implement a provider adapter or model gateway | ChatModel |
The direct Prompt/ChatResponse contract keeps provider concerns explicit. |
| Answer one user prompt in a controller or service | ChatClient |
Fluent construction and direct content extraction reduce plumbing. |
| Apply one system prompt to most requests | ChatClient |
Builder defaults centralize shared instructions. |
| Use conversation memory, RAG, or advisors | ChatClient |
Advisors can add history, retrieved documents, and other context before execution. |
| Use tools or function calling | ChatClient |
Tool orchestration is integrated into the client pipeline. |
| Inspect generations and provider metadata | ChatModel, or ChatClient.chatResponse() |
Both expose ChatResponse; ChatModel is the lower-level contract. |
| Stream tokens reactively | ChatClient.stream() or ChatModel.stream() |
Both expose reactive streams; your application stack still determines whether streaming is practical. |
Many applications use both: a ChatClient at the feature boundary and a ChatModel behind it for infrastructure, tests, or a second workflow that needs unprocessed responses.
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Prompt construction, defaults, and option precedence
ChatModel startup options versus request options
Model configuration supplied when a ChatModel is initialized establishes its startup options. Portable ChatOptions fields include model, temperature, maximum tokens, frequency and presence penalties, stop sequences, top-k, and top-p. Provider implementations can add provider-specific options.
Options placed in a Prompt fully override the startup options for that request. Therefore, a request-level value is not merely merged with an identical startup value; it wins for the fields it supplies. Keep this precedence in mind when a model appears to ignore a setting configured at startup.
ChatClient defaults and incremental customizers
ChatClient is useful when most calls share a system instruction, user prefix, model option, advisor, or tool configuration. Builder defaults establish that common baseline. Request customizers then act as deltas over those defaults, so a single call can adjust one setting without rebuilding the complete prompt configuration.
A practical pattern is to put stable behavior in builder defaults and keep user-specific data in the request:
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ChatClient client = builder
.defaultSystem("Answer with concise, verifiable guidance.")
.build();
String result = client.prompt()
.user(u -> u.text("Explain {topic}").param("topic", topic))
.call()
.content();Use direct Prompt construction instead when a reusable infrastructure component must control every message and option explicitly.
Advisors, memory, RAG, and tools
Advisors are the orchestration hook
ChatClient advisors can modify the prompt and its context before the model runs. Common uses include adding conversation history, retrieving documents for retrieval-augmented generation, enforcing a policy, or attaching application context.
Advisor order matters. Each advisor receives the changes made by earlier advisors, so ordering can change the final prompt. For example, an advisor that retrieves documents may need to run before one that formats the final context, while a policy advisor may need to run after user and retrieval content have been assembled.
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Conversation memory requires an identifier
Memory advisors need ChatMemory.CONVERSATION_ID on every call. Omitting that identifier raises IllegalArgumentException; it is not treated as an anonymous new conversation. Pass a stable conversation key from your application, while keeping tenant and authorization boundaries in mind.
The default MessageWindowChatMemory window is 20 messages. That is an implementation default, not a guarantee that every provider or application should retain exactly 20 messages. Increase, reduce, or replace the memory strategy when your context budget, privacy policy, or conversation design requires a different window.
Tool calling has operational consequences
ChatClient supports tool calling, including automatic ToolCallingAdvisor registration. Automatic registration can be disabled globally with:
spring.ai.chat.client.tool-calling.enabled=falseIt can also be disabled for an individual call. Tool calling is imperative and can block while the application executes a tool. Do not assume that a reactive-looking client chain makes tool execution non-blocking; isolate blocking tools or adapt them deliberately to your runtime.
Streaming, blocking calls, and the Spring stack
Reactive streaming
Both APIs support streaming through reactive methods. With ChatModel, call stream(Prompt). With ChatClient, use stream() and select a reactive result such as Flux<String> or Flux<ChatResponse>. This is the appropriate path for incremental token delivery to a reactive endpoint or UI.
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Implementation details matter when choosing an application stack. Streaming is supported through the reactive stack, while non-streaming support is tied to the servlet stack and can block in a reactive application. A reactive controller that calls a blocking path directly can consume event-loop capacity and degrade unrelated requests. Use an appropriate bounded scheduler or a genuinely reactive integration rather than assuming that the return type alone makes the operation non-blocking.
HTTP client customization
When customizing HTTP clients, configure both RestClient and WebClient when the selected model implementation uses them. Configuring only one client can leave synchronous or streaming requests using an unintended default, producing inconsistent timeouts, proxies, interceptors, or observability behavior.
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Structured output and response handling
Choose the response terminal method based on what the feature needs. Use content() when the output is intentionally plain text. Use chatResponse() when you need generations or model metadata. Use typed response conversion when the model is expected to produce a schema represented by a Java class.
Typed conversion belongs naturally at the ChatClient boundary because the fluent request can combine the system instruction, user data, options, advisors, and the final target type in one chain. Infrastructure code that must preserve provider-specific fields should retain the ChatResponse instead of immediately reducing it to a domain object.
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One model, many clients
Spring Boot’s auto-configured ChatClient builder is prototype-scoped. You can build multiple ChatClient instances over the same ChatModel, each with different defaults, such as a concise support client and a verbose analysis client.
@Configuration
class AiClients {
@Bean
ChatClient supportClient(ChatClient.Builder builder) {
return builder
.defaultSystem("Answer support questions in short steps.")
.build();
}
@Bean
ChatClient analysisClient(ChatClient.Builder builder) {
return builder
.defaultSystem("Explain assumptions and alternatives in detail.")
.build();
}
}Different model types
For a client backed by a different model type, you can call ChatClient.create(chatModel) or ChatClient.builder(chatModel). Those direct factory calls bypass auto-configured observability and builder customizers. If those integrations matter, use ChatClientBuilderConfigurer so the custom client retains the application-wide configuration.
Several ChatModel beans
When multiple ChatModel beans are present, injection can become ambiguous. Mark one bean @Primary when there is a clear default, or define an explicitly qualified builder/client for each model. Qualifiers make the provider choice visible at the injection point and prevent an accidental switch when another starter is added.
Portability and provider-specific behavior
ChatModel is the portability seam: application code can target the common interface while configuration selects OpenAI, Ollama, Amazon Bedrock, Mistral, Anthropic, or another supported implementation. ChatClient does not remove provider differences. Model names, option ranges, tool behavior, token accounting, structured-output support, and streaming characteristics can still vary by provider.
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Keep portable options in shared code and isolate provider-specific options behind a provider-aware configuration or adapter. If a feature depends on a provider extension, document that dependency rather than presenting it as a guarantee of the ChatModel interface.
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Common mistakes and how to correct them
Injecting ChatClient into infrastructure code by default
If a gateway needs exact prompts, model options, response metadata, or provider fallback logic, ChatModel is usually the cleaner dependency. ChatClient can do the job, but its orchestration layer may hide decisions that the infrastructure component must own.
Using ChatModel for every controller prompt
Direct calls force each endpoint to repeat message assembly, defaults, conversion, and advisor wiring. Build a ChatClient for application-facing flows and reserve ChatModel for components that truly need the lower-level contract.
Calling call() and expecting execution immediately
With ChatClient, execution occurs when a terminal result such as content(), chatResponse(), or responseEntity() is requested. Ensure your chain reaches the intended terminal operation before measuring latency or troubleshooting a missing request.
Forgetting the conversation ID
A memory advisor without ChatMemory.CONVERSATION_ID fails with IllegalArgumentException. Add the identifier to every memory-enabled request, including follow-up turns.
Assuming reactive means non-blocking
Streaming uses the reactive stack, but non-streaming model calls and tool execution can block. Inspect the selected implementation and move blocking work off event-loop threads.
Overriding the wrong option layer
A value in a request Prompt overrides the ChatModel’s startup option. In ChatClient, request customizers modify builder defaults. Check both layers when a temperature, model name, or token limit appears incorrect.
Losing observability on a second model
Directly creating a ChatClient around another ChatModel can bypass auto-configured observability and builder customizers. Use the builder configurer when those features are required across all clients.
A practical selection procedure
- Identify the boundary. If the class integrates a provider, fallback policy, or shared model infrastructure, start with
ChatModel. If it implements a user-facing conversation, start withChatClient. - List response requirements. Choose direct
ChatResponsehandling for generations and metadata; choose ChatClient conversion for text or typed domain output. - Decide whether context is assembled dynamically. Advisors, RAG retrieval, memory, and tools favor ChatClient.
- Choose the execution model. Use the streaming method for incremental output, and verify that your servlet or reactive stack can handle the selected path without blocking the wrong threads.
- Define configuration ownership. Put stable defaults in the builder or model startup configuration; put request-specific overrides at the request layer.
- Resolve model multiplicity explicitly. Add
@Primary, qualifiers, or named ChatClient beans before multiple providers enter the application. - Pin documentation to your Spring AI line. The project currently lists Spring AI 2.0.1 as stable, with 1.1.8 and 1.0.9 also stable; 2.1.0-M1 is preview. API signatures and implementation details can change between lines, so use the reference documentation matching the version in your build.
Bottom line
ChatModel is the portable, explicit engine contract: construct a Prompt, call or stream it, and inspect the resulting ChatResponse. ChatClient is the fluent application layer on top: define defaults, assemble prompts, apply advisors, invoke tools, and convert the result to text or a typed object. Inject ChatClient for most feature-level conversation code; inject ChatModel when the component’s job is model infrastructure or when complete control over requests and responses is the requirement.
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