LLM Integration Guide

This guide helps you choose the right Camel AI component and apply common LLM integration patterns in production routes. It addresses practical gaps reported by users building document-processing and chat pipelines — structured extraction, streaming to browsers, dynamic prompts, and prompt management at scale.

For a catalog of all AI components, start at AI Components. For coding-agent context about Camel itself, see llms.txt.

Choosing an AI component

Camel ships several AI components. Each documents itself well in isolation, but picking the wrong one early leads to provider lock-in or missing features you need later.

Decision matrix

Use case Recommended component Why

OpenAI or OpenAI-compatible chat (Ollama, vLLM, LM Studio, OpenRouter, Azure OpenAI)

OpenAI

Native OpenAI SDK integration, MCP tool calling, agentic loops, embeddings, audio, structured JSON output via jsonSchema / outputClass

Multi-provider chat without tying routes to one vendor

LangChain4j Chat

Provider-agnostic ChatModel beans (OpenAI, Anthropic, Ollama, Vertex, and more via LangChain4j). Strong fit for RAG pipelines with Embedding Store and content enricher

Autonomous agents with Camel route tools and/or MCP servers

LangChain4j Agent

Multi-turn tool calling, guardrails, memory, concurrent tool execution. Complements MCP Tool Calling for OpenAI-native agentic loops

Spring Boot + Spring AI stack

Spring AI Chat

When the application already standardizes on Spring AI beans

Vector search / semantic retrieval only

OpenAI embeddings or LangChain4j Embeddings

Generate embeddings, then store/query via PGVector, Milvus, etc.

OpenAI vs LangChain4j Chat — quick comparison

Choose OpenAI when:

  • You target the OpenAI API (or a compatible proxy) and want first-class MCP, Responses API, embeddings, and audio in one component

  • You need built-in conversation memory on the exchange, agentic tool loops, or OpenAI-specific response headers

  • Your team prefers configuring everything through Camel URI options and headers

Choose LangChain4j Chat when:

  • You may switch LLM providers (OpenAI today, Anthropic or local Ollama tomorrow) without rewriting routes

  • You orchestrate RAG with Camel EIPs (content enricher, split, aggregate) and LangChain4j ChatModel / EmbeddingModel beans

  • You use prompt templates with `{{variable}}` substitution (see Prompt templates and management at scale)

Both components can call the same underlying models. The difference is abstraction level and which advanced features (MCP, agent guardrails, Spring Boot starters) you need around the chat call.

Structured output (recommended for extraction)

For tasks like resume parsing, invoice extraction, or classification, do not hand-roll JSON parsing in a follow-up processor unless you have a specific reason. The OpenAI component can constrain the model to return JSON matching a schema.

JSON Schema (works in Java, XML, and YAML routes)

- route:
    from:
      uri: direct:extract-resume
      steps:
        - setBody:
            simple: "${body}"
        - to:
            uri: openai:chat-completion
            parameters:
              model: gpt-4o-mini
              temperature: 0.1
              jsonSchema: resource:classpath:schemas/resume.schema.json
        - unmarshal:
            json:
              library: Jackson
              unmarshalType: com.example.Resume

The jsonSchema option (or CamelOpenAIJsonSchema header) tells the model to emit JSON conforming to your schema. The response body is a JSON string — use Camel’s Jackson data format or json-validator if you need typed objects or strict validation.

See Structured Output with JSON Schema for header-based and inline schema examples.

Java classes (Java DSL only)

When running Java routes, outputClass derives the schema from a POJO:

from("direct:extract-person")
    .setBody(constant("Generate a software engineer profile"))
    .to("openai:chat-completion?outputClass=com.example.Person")
    .unmarshal().json(JsonLibrary.Jackson, Person.class);

Tips for reliable structured extraction

Generation parameters (temperature and more)

Chat temperature on OpenAI

The OpenAI component exposes temperature (0.0–2.0) and topP as endpoint options. You can also set them per exchange via headers:

Option / header Purpose

temperature URI parameter

Default sampling temperature for the endpoint

CamelOpenAITemperature header

Override temperature for a single exchange

topP / CamelOpenAITopP

Nucleus sampling alternative to temperature

 
- to:
    uri: openai:chat-completion
    parameters:
      model: gpt-4o-mini
      temperature: 0.1

For provider-specific parameters not exposed as first-class options, use additionalBodyProperty:

- to:
    uri: openai:chat-completion
    parameters:
      model: gpt-4o-mini
      additionalBodyProperty.seed: "42"
Prefer first-class URI options when available — they are clearer and validated by the component. Use additionalBodyProperty for vendor-specific knobs without a dedicated option (for example seed for reproducible sampling).

LangChain4j Chat

Configure temperature on the ChatModel bean (Spring Boot properties or programmatic builder). See LangChain4j Chat — Using a specific Chat Model.

Dynamic prompts per exchange

Prompts can change on every message using Camel’s expression languages — no static userMessage URI option required.

Body as prompt

The message body is the user prompt when no header overrides it:

- from:
    uri: direct:ask
    steps:
      - to:
          uri: openai:chat-completion
          parameters:
            model: gpt-4o-mini

Send "Summarize this CV: …​" as the exchange body.

Header override with Simple

Use CamelOpenAIUserMessage to build prompts from exchange data:

- route:
    from:
      uri: direct:score-candidate
      steps:
        - setHeader:
            name: CamelOpenAIUserMessage
            simple: "Rate this candidate 1-10 for role ${header.jobTitle}. CV: ${body}"
        - to:
            uri: openai:chat-completion
            parameters:
              model: gpt-4o-mini
              temperature: 0.2

This pattern works well when the body carries a document (file, JSON, text) and the header carries instructions.

Prompt templates and management at scale

Inline prompt strings in every route become hard to maintain. Common patterns:

External prompt files

Load prompts from the classpath or file system and pass them as the body or via a processor:

- from:
    uri: file:prompts/extract-resume.txt?noop=true
    steps:
      - setHeader:
          name: CamelOpenAIUserMessage
          simple: "${body}\n\nDocument to parse:\n${exchangeProperty.documentText}"
      - to: openai:chat-completion?jsonSchema=resource:classpath:schemas/resume.schema.json

See OpenAI — file-based prompts.

Property placeholders

Store reusable prompt fragments in application.properties or Kubernetes ConfigMaps:

resume.system.prompt=You are a recruiter assistant. Extract only facts present in the document.
resume.user.template=Extract structured data from this resume:\n\n{{body}}

Reference with Camel property placeholders in URI options or `{{property.name}}` in YAML routes.

LangChain4j template variables

For LangChain4j Chat, use CHAT_SINGLE_MESSAGE_WITH_PROMPT with `{{variable}}` placeholders — see Send a prompt with variables.

Organization tips

  • One schema file + one prompt file per extraction task

  • Version prompts alongside routes (Git) or in external config for non-developer edits

  • Keep system instructions in CamelOpenAISystemMessage (OpenAI) or AiAgentBody.systemMessage (LangChain4j Agent) separate from user content

Streaming responses

With streaming=true, the OpenAI component returns an Iterator<ChatCompletionChunk> in the message body. Process it with Camel streaming EIPs (split + streaming()).

In-route streaming (log, transform, aggregate)

- from:
    uri: direct:stream-chat
    steps:
      - to:
          uri: openai:chat-completion
          parameters:
            userMessage: Explain Apache Camel in one paragraph
            streaming: true
      - split:
          streaming: true
          simple: ${body}
          steps:
            - log:
                message: "chunk: ${body}"
Conversation memory is not updated for streaming responses. Use non-streaming mode when you need multi-turn history on the same exchange.

Streaming to a browser (SSE)

For web clients, combine OpenAI streaming with Platform HTTP and Server-Sent Events. Add camel-platform-http-vertx (or another platform-http implementation) to the classpath.

Java example — extract text deltas and emit SSE frames
from("platform-http:/chat/stream?httpMethodRestrict=POST")
    .setHeader("CamelOpenAIUserMessage", simple("${body}"))
    .to("openai:chat-completion?streaming=true")
    .setHeader(Exchange.CONTENT_TYPE, constant("text/event-stream"))
    .split(body()).streaming()
        .process(exchange -> {
            ChatCompletionChunk chunk = exchange.getMessage().getBody(ChatCompletionChunk.class);
            String delta = chunk.choices().isEmpty()
                    ? ""
                    : chunk.choices().get(0).delta().content().orElse("");
            exchange.getMessage().setBody("data: " + delta + "\n\n");
        })
    .end();

Enable useStreaming=true on the platform-http endpoint when passing large streamed bodies (supported on Vert.x).

When to stream through Camel vs. bypass it

Stream through Camel Use a dedicated async handler

You already orchestrate auth, enrichment, logging, and routing in Camel

You need minimum latency token delivery and already have a reactive Web layer

You want one integration path for batch and interactive modes

You only proxy an upstream SSE stream verbatim (consider A2A RAW passthrough)

Moderate throughput chat UI backed by integration logic

High-concurrency fan-out where Camel adds little value between HTTP and the LLM SDK

End-to-end example: document extraction pipeline

A typical resume-processing pipeline (the use case from community feedback):

- route:
    id: resume-extraction
    from:
      uri: direct:process-resume
      steps:
        # 1. Optional: extract text upstream (docling, tika, etc.)
        - setProperty:
            name: documentText
            simple: "${body}"
        # 2. Structured LLM extraction — no manual JSON parsing
        - setHeader:
            name: CamelOpenAIUserMessage
            simple: "Extract candidate fields from this resume:\n\n${exchangeProperty.documentText}"
        - to:
            uri: openai:chat-completion
            parameters:
              model: gpt-4o-mini
              temperature: 0.1
              jsonSchema: resource:classpath:schemas/resume.schema.json
        # 3. Optional: validate then route to HR system
        - to: json-validator:validate?schema=resource:classpath:schemas/resume.schema.json
        - to: direct:store-candidate

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