Module: `tramai-ollama`

One-liner: Provider for locally-hosted Ollama models via the Ollama chat API. Module type: providerSource files: 1 file — OllamaProvider.kt (297 LOC) Test files: 3 — OllamaProviderTest.kt, OllamaProviderTckTest.kt, OllamaProviderIntegrationTest.ktBuild: dev.tramai:tramai-ollama:0.6.0

L1: Quick Start (30-second read)

What

tramai-ollama is a ModelProvider implementation that connects TramAI to any Ollama instance via the Ollama /api/chat endpoint. It supports both synchronous complete() and streaming stream() operations. A single class: OllamaProvider.

Why

Separating the Ollama provider into its own module means:

No runtime dependency on Ollama libraries when using other providers
Clear module boundary — provider code doesn't pollute core/engine
Easy to test and replace

When to use

Use this module when:
- You run local models via Ollama (Llama, Gemma, Qwen, Mistral, CodeGemma, etc.)
- You're developing/testing offline without cloud API keys
- You need privacy-sensitive AI (data never leaves your machine)
- You want zero-cost inference during development

Don't use this module when:
- You need a cloud-hosted proprietary model (use tramai-openai or tramai-anthropic)
- Ollama is not installed on your machine or network

How to add

// build.gradle.kts
dependencies {
    implementation(platform("dev.tramai:tramai-bom:0.6.0"))
    implementation("dev.tramai:tramai-ollama")
}

<!-- Maven -->
<dependency>
    <groupId>dev.tramai</groupId>
    <artifactId>tramai-ollama</artifactId>
    <version>0.6.0</version>
</dependency>

Where to go next

Modules Catalog — Current module inventory and where Ollama fits
Guide: Providers — Provider configuration and registration
tramai-core — The SPI contracts this module implements
tramai-standalone — How to wire it all together without Spring

L2: Usage Guide (5-minute read)

Quick usage

import dev.tramai.core.annotations.AiService
import dev.tramai.core.annotations.Operation
import dev.tramai.ollama.OllamaProvider
import dev.tramai.standalone.Tramai

@AiService
interface LocalChat {
    @Operation(prompt = "What is the capital of Japan?")
    suspend fun ask(): String
}

suspend fun main() {
    val chat = Tramai
        .builder()
        .provider(OllamaProvider("http://localhost:11434"), default = true)
        .model("gemma4:e2b", "ollama")
        .build()
        .create<LocalChat>()

    println(chat.ask()) // "Tokyo"
}

Prerequisites: Ollama running on localhost:11434 with the gemma4:e2b model pulled.

Advanced usage

Custom base URL (remote Ollama):

val provider = OllamaProvider(
    baseUrl = "http://ollama-server.internal:11434",
)

Streaming responses:

import dev.tramai.core.model.StreamChunk
import dev.tramai.core.provider.StreamCapable

@AiService
interface StreamingChat {
    @Operation(prompt = "Count from 1 to 5")
    suspend fun streaming(): String
}

suspend fun main() {
    val provider = OllamaProvider()
    val streamCapable = provider as StreamCapable
    val request = ModelRequest(model = "gemma4:e2b", messages = listOf(
        Message(role = MessageRole.USER, content = "Count from 1 to 5")
    ))

    streamCapable.stream(request).collect { chunk ->
        when (chunk) {
            is StreamChunk.Token -> print(chunk.text)
            is StreamChunk.Complete -> println("\n[Done: ${chunk.usage}]")
            is StreamChunk.Error -> System.err.println("\n[Error: ${chunk.cause}]")
        }
    }
}

Multiple models with the same provider:

val service = Tramai
    .builder()
    .provider(OllamaProvider("http://localhost:11434"), name = "local", default = true)
    .model("gemma4:e2b", "local")
    .model("qwen2.5:7b", "local")
    .build()
    .create<MultiModelService>()

Expert usage

Custom HTTP client configuration:

val customClient = HttpClient.newBuilder()
    .connectTimeout(Duration.ofSeconds(10))
    .build()

val provider = OllamaProvider(
    baseUrl = "http://localhost:11434",
    httpClient = customClient,
)

Custom ObjectMapper with registered modules:

val mapper = JsonMapper.builder()
    .enable(DeserializationFeature.USE_BIG_DECIMAL_FOR_FLOATS)
    .build()

val provider = OllamaProvider(
    objectMapper = mapper,
)

Configuration reference

Parameter	Type	Default	Description
`baseUrl`	`String`	`http://localhost:11434`	Ollama server base URL
`httpClient`	`HttpClient`	`HttpClient.newHttpClient()`	Java HTTP client instance
`objectMapper`	`ObjectMapper`	`ObjectMapper()`	Jackson ObjectMapper for JSON processing

L3: Architecture & Mechanics (15-minute read)

Design philosophy

OllamaProvider is deliberately minimal — a single file with no abstractions beyond what ModelProvider and StreamCapable require. The philosophy is: one provider, one endpoint, one file. No factory pattern, no plugin system, no configuration framework.

Module boundary

Public:
  dev.tramai.ollama.OllamaProvider — ModelProvider + StreamCapable
    constructor(baseUrl, httpClient, objectMapper)
    complete(request): ModelResponse
    stream(request): Flow<StreamChunk>
    providerId(): String -> always "ollama"

Package: dev.tramai.ollama
  1 source file, no sub-packages.

Dependency graph

tramai-ollama
  Depends on:
    - tramai-core (required) — ModelProvider, ModelRequest, ModelResponse, StreamCapable, StreamChunk
    - jackson-databind (transitive) — JSON serialization/deserialization
    - kotlinx-coroutines-core (transitive) — Flow-based streaming

  Depended on by:
    - tramai-standalone (wires via Tramai.builder().provider())
    - tramai-spring-provider-ollama (auto-configuration discovers OllamaProvider beans)

Inner mechanics

1. User calls service.method()
2. Engine builds ModelRequest (model name, messages list, parameters)
3. Engine resolves provider via ProviderRegistry → finds "ollama"
4. Engine calls OllamaProvider.complete(request) or .stream(request)
5. Provider serializes payload:
   {
     "model": "gemma4:e2b",
     "stream": false,
     "messages": [
       {"role": "user", "content": "..."}
     ]
   }
6. HTTP POST to {baseUrl}/api/chat
7. Response parsed:
   - message.content → String output
   - prompt_eval_count → inputTokens
   - eval_count → outputTokens
   - done_reason → FinishReason
8. ModelResponse returned to engine

Streaming mechanics

For streaming requests (stream: true), the provider uses HttpResponse.BodyHandlers.ofInputStream() and reads the NDJSON response line-by-line (readLine, one JSON object per non-blank line). Token-by-token emission via Flow<StreamChunk>:

Each non-empty message.content → StreamChunk.Token
When done: true → StreamChunk.Complete with final usage
On error → StreamChunk.Error

Error model

Exception	Trigger	Recovery
`ProviderException`	Unexpected response role (not `assistant`)	Check Ollama response format compatibility
`ProviderException` with `ProviderFailureCode.HTTP_REJECTED`	Non-2xx HTTP status	Check Ollama is running and model exists
`ProviderException` with `ProviderFailureCode.TIMEOUT` / `CONNECTION_FAILED` / `TRANSPORT_FAILED`	Network error, timeout, connection refused	Verify Ollama is reachable at `baseUrl`

Capabilities

supportsCapability(...) declares exactly two of the four ProviderCapability values:

VISION — image bytes are passed outbound in Ollama's images array
STREAMING — stream() is a real implementation, not a fallback

TOOL_CALLING and STRUCTURED_OUTPUT are declared absent: tool calling and structured output are not implemented by this adapter, and the provider TCK pins them as absent rather than letting the adapter claim them.

Testing strategy

OllamaProviderTest.kt — offline tests against a local com.sun.net.httpserver.HttpServer
OllamaProviderTckTest.kt — enrolled in the shared provider TCK (pinned identity, capability set, streaming lifecycle)
OllamaProviderIntegrationTest.kt — opt-in integration run against a real Ollama instance (TRAMAI_RUN_OLLAMA_INTEGRATION=true)

Module: tramai-ollama

Module: `tramai-ollama`