With Adaline SDKs

The Adaline SDK is the recommended way to integrate your AI application with Adaline. Beyond sending traces and spans, the SDK gives you deployment management with automatic caching, smart buffering with batched flushes, built-in retries with exponential backoff, and health monitoring — all production-ready out of the box.

What the SDK gives you

Capability	Description
Deployment management	Fetch deployed prompts by ID or environment, with automatic background refresh and local caching.
Structured observability	Log traces and spans with 8 content types covering LLM calls, tool executions, retrieval, embeddings, guardrails, and custom logic.
Smart buffering	Traces and spans are buffered in memory and flushed in configurable batches — no request-per-span overhead.
Automatic retries	Failed API calls are retried with exponential backoff (5xx and network errors). 4xx errors fail immediately.
Health monitoring	Inspect flush status and background refresh health at runtime to detect issues before they become incidents.
Nested spans	Model complex workflows with parent-child span hierarchies — agents calling tools calling sub-agents.
Session tracking	Group related traces by session ID to follow multi-turn conversations or multi-request workflows.
Evaluation variables	Attach variable values to spans so they flow into continuous evaluations and datasets automatically.

Install

TypeScript
Python

npm install @adaline/client

pip install adaline-client

Initialize the client

TypeScript
Python

import { Adaline } from "@adaline/client";

const adaline = new Adaline({ apiKey: "your-api-key" });

from adaline.main import Adaline

adaline = Adaline(api_key="your-api-key")

Set the ADALINE_API_KEY environment variable and omit the apiKey parameter to avoid hardcoding secrets. The SDK reads from this variable automatically.

Manage deployments

The SDK can fetch deployed prompt configurations — including the model, provider settings, messages, tools, and variables — so your application always uses the latest version without redeploying code.

Fetch a specific deployment

TypeScript
Python

const deployment = await adaline.getDeployment({
  promptId: "your-prompt-id",
  deploymentId: "your-deployment-id",
});

const config = deployment.prompt.config;
console.log(config.providerName);  // "openai"
console.log(config.model);         // "gpt-4"
console.log(config.settings);      // { temperature: 0.7, maxTokens: 1000 }

const messages = deployment.prompt.messages;
const tools = deployment.prompt.tools;
const variables = deployment.prompt.variables;

deployment = await adaline.get_deployment(
    prompt_id="your-prompt-id",
    deployment_id="your-deployment-id",
)

config = deployment.prompt.config
print(config.provider_name)  # "openai"
print(config.model)          # "gpt-4"
print(config.settings)       # {"temperature": 0.7, "maxTokens": 1000}

messages = deployment.prompt.messages
tools = deployment.prompt.tools
variables = deployment.prompt.variables

Fetch the latest deployment for an environment

TypeScript
Python

const deployment = await adaline.getLatestDeployment({
  promptId: "your-prompt-id",
  deploymentEnvironmentId: "your-environment-id",
});

deployment = await adaline.get_latest_deployment(
    prompt_id="your-prompt-id",
    deployment_environment_id="your-environment-id",
)

Auto-refresh deployments in production

For long-running services, use initLatestDeployment to set up a cached deployment that refreshes automatically (default every 60 seconds) in the background. When you deploy a new prompt version in Adaline, your application picks it up without a restart.

TypeScript
Python

const controller = await adaline.initLatestDeployment({
  promptId: "your-prompt-id",
  deploymentEnvironmentId: "your-environment-id",
});

// Use the cached deployment in request handlers
const deployment = await controller.get();

// Force a fresh fetch (bypasses cache)
const fresh = await controller.get(true);

// Check background refresh health
const status = controller.backgroundStatus();
// { stopped: false, consecutiveFailures: 0, lastError: null, lastRefreshed: Date }

// Stop the background refresh when shutting down
controller.stop();

controller = await adaline.init_latest_deployment(
    prompt_id="your-prompt-id",
    deployment_environment_id="your-environment-id",
)

deployment = await controller.get()
fresh = await controller.get(force_refresh=True)

status = controller.get_background_status()
# {"stopped": False, "consecutive_failures": 0, "last_error": None, "last_refreshed": datetime}

await controller.stop()

Initialize the monitor

The monitor manages the lifecycle of traces and spans — buffering them in memory, batching them together, and flushing them to the Adaline API on a timer or when the buffer fills up.

TypeScript
Python

const monitor = adaline.initMonitor({ projectId: "your-project-id" });

monitor = adaline.init_monitor(project_id="your-project-id")

Create a trace

A trace represents a single end-to-end request flow — for example, one user message that triggers an LLM call, a tool execution, and a final response.

TypeScript
Python

const trace = monitor.logTrace({
  name: "user-request",
  status: "unknown",
  sessionId: "session-abc123",
  referenceId: "custom-uuid",
  tags: ["production", "v2.1"],
  attributes: {
    userId: "user-456",
    region: "us-east-1",
    featureFlag: "new-prompt-v2",
  },
});

trace = monitor.log_trace(
    name="user-request",
    status="unknown",
    session_id="session-abc123",
    reference_id="custom-uuid",
    tags=["production", "v2.1"],
    attributes={
        "userId": "user-456",
        "region": "us-east-1",
        "featureFlag": "new-prompt-v2",
    },
)

Add spans

Each operation inside a trace is a span. Spans carry a content type that tells Adaline what kind of operation it represents — an LLM call, a tool execution, a vector retrieval, and more.

Create a span

TypeScript
Python

const span = trace.logSpan({
  name: "chat-completion",
  promptId: "your-prompt-id",
  deploymentId: "your-deployment-id",
  runEvaluation: true,
  tags: ["v2.1", "production"],
  attributes: { region: "us-east-1", customerId: "dd2-cew3d2-saf32" },
});

span = trace.log_span(
    name="chat-completion",
    prompt_id="your-prompt-id",
    deployment_id="your-deployment-id",
    run_evaluation=True,
    tags=["v2.1", "production"],
    attributes={"region": "us-east-1", "customerId": "dd2-cew3d2-saf32" },
)

Update and end a span

After performing the operation, update the span with the result and end it:

TypeScript
Python

const response = await openai.chat.completions.create({ ... });

span.update({
  status: "success",
  content: {
    type: "Model",
    provider: "openai",
    model: "gpt-4",
    input: JSON.stringify({ messages }),
    output: JSON.stringify(response),
  },
});

span.end();

from adaline_api.models.log_span_content import LogSpanContent
from adaline_api.models.log_span_model_content import LogSpanModelContent

response = openai.chat.completions.create(...)

span.update({
    "status": "success",
    "content": LogSpanContent(
        actual_instance=LogSpanModelContent(
            type="Model",
            provider="openai",
            model="gpt-4",
            input=json.dumps({"messages": messages}),
            output=json.dumps(response.model_dump()),
        )
    ),
})

span.end()

Nested spans

Spans can contain child spans to model hierarchical workflows — an agent span containing tool call spans, or a RAG span containing embedding and retrieval sub-spans:

const agentSpan = trace.logSpan({ name: "agent-orchestrator" });

const toolSpan = agentSpan.logSpan({ name: "search-tool" });
// ... execute tool ...
toolSpan.update({ status: "success", content: { type: "Tool", ... } });
toolSpan.end();

const llmSpan = agentSpan.logSpan({ name: "final-response" });
// ... generate response ...
llmSpan.update({ status: "success", content: { type: "Model", ... } });
llmSpan.end();

agentSpan.end();

Span content types

The content.type field tells Adaline what kind of operation a span represents. Each type carries input and output as JSON strings, plus type-specific fields.

Model

LLM chat completions and text generation. Captures the provider, model, cost, and optionally expected output for evaluation. For the best experience, stringify the exact request payload you send to your AI provider as input and the full response as output. When you use a supported provider, Adaline automatically extracts token usage, calculates cost, and surfaces model metadata. See Span content: input and output for full details and examples. You can also use Adaline’s own content schema for input and output, although this is more advanced and requires custom transformations.

span.update({
  status: "success",
  content: {
    type: "Model",
    provider: "openai",
    model: "gpt-4",
    input: JSON.stringify(params),
    output: JSON.stringify(response),
    cost: 0.0032, // optional, override the cost calculated by Adaline
  },
});

ModelStream

Streaming LLM responses. Captures both the raw stream chunks and an aggregated output.

span.update({
  status: "success",
  content: {
    type: "ModelStream",
    provider: "openai",
    model: "gpt-4",
    input: JSON.stringify({ messages }),
    output: rawStreamChunks,
    aggregateOutput: JSON.stringify(aggregatedResponse),
    cost: 0.0028, // optional, override the cost calculated by Adaline
  },
});

Tool

Function or tool call execution.

span.update({
  status: "success",
  content: {
    type: "Tool",
    input: JSON.stringify({ index: 0, id: "tool-123", name: "get-weather", arguments: JSON.stringify({ city: "San Francisco" }) }),
    output: JSON.stringify({ index: 0, id: "tool-123", name: "get-weather", data: JSON.stringify({ temperature: 62, condition: "foggy" }) }),
  },
});

Retrieval

Vector search, document retrieval, or any RAG retrieval step.

span.update({
  status: "success",
  content: {
    type: "Retrieval",
    input: JSON.stringify({ query: "password reset steps" }),
    output: JSON.stringify({ documents: [
      { id: "doc1", score: 0.95, text: "..." },
      { id: "doc2", score: 0.89, text: "..." }
    ] }),
  },
});

Embeddings

Embedding generation.

span.update({
  status: "success",
  content: {
    type: "Embeddings",
    input: JSON.stringify({ texts: ["How do I reset my password?"] }),
    output: JSON.stringify({ embeddings: [[0.012, -0.034, ...]] }),
  },
});

Function

Custom business logic, data transforms, or any application-specific operation.

span.update({
  status: "success",
  content: {
    type: "Function",
    input: JSON.stringify({ operation: "get-user-plan", userId: "user-456" }),
    output: JSON.stringify({ operation: "get-user-plan", userId: "user-456", plan: "enterprise", features: [...] }),
  },
});

Guardrail

Safety checks, content filters, PII detection, or compliance rules.

span.update({
  status: "success",
  content: {
    type: "Guardrail",
    input: JSON.stringify({ text: userMessage, checks: ["toxicity", "pii"] }),
    output: JSON.stringify({ text: userMessage, checks: ["toxicity", "pii"], passed: true, flags: [] }),
  },
});

Other

Any operation that doesn’t fit the types above.

span.update({
  status: "success",
  content: {
    type: "Other",
    input: JSON.stringify({ custom: "input" }),
    output: JSON.stringify({ custom: "output" }),
  },
});

All content types share type, input (JSON string), and output (JSON string).

End the trace and flush

Always end the trace and flush remaining data. Calling end() on a trace recursively ends all child spans that haven’t been ended yet.

TypeScript
Python

trace.update({ status: "success" });
trace.end();

await monitor.flush();

trace.update({"status": "success"})
trace.end()

await monitor.flush()

Traces and spans that are never ended will never be flushed. Always call end() — use try/finally blocks to guarantee it runs even when errors occur.

Attach variables for evaluation

Attach variable values to spans so they flow into continuous evaluations and can be captured into datasets. Variables are set on the span’s content object (specifically on Model or ModelStream content types), not on logSpan() directly:

TypeScript
Python

const span = trace.logSpan({ name: "chat-completion" });

span.update({
  status: "success",
  content: {
    type: "Model",
    provider: "openai",
    model: "gpt-4",
    variables: {
      user_question: { name: "user_question", value: { modality: "text", value: userInput } },
      context: { name: "context", value: { modality: "text", value: retrievedContext } },
    },
    input: JSON.stringify(params),
    output: JSON.stringify(response),
  },
});

from adaline_api.models.log_span_content import LogSpanContent
from adaline_api.models.log_span_model_content import LogSpanModelContent

span = trace.log_span(name="chat-completion")

span.update({
    "status": "success",
    "content": LogSpanContent(
        actual_instance=LogSpanModelContent(
            type="Model",
            provider="openai",
            model="gpt-4",
            variables={
                "user_question": {"modality": "text", "value": user_input},
                "context": {"modality": "text", "value": retrieved_context},
            },
            input=json.dumps(params),
            output=json.dumps(response.model_dump()),
        )
    ),
})

Variables support text, image, and pdf modalities. See Log attachments for full details on attaching variables, attributes, and tags.

Buffering, batching, and retries

The SDK handles reliability so you don’t have to.

How the buffer works

When you call logTrace() or logSpan(), entries are added to an in-memory buffer.
A background timer flushes the buffer every flushInterval seconds.
If the buffer reaches maxBufferSize, it flushes immediately.
Each flush sends a batch of entries in a single API call.

Retry behavior

Scenario	Behavior
5xx server errors	Retried with exponential backoff (1s initial, 10s max, 20s total budget).
Network errors	Retried with exponential backoff (same as 5xx).
4xx client errors	Fail immediately — no retry. Check your API key and request payload.
Consecutive failures	After `maxContinuousFlushFailures` consecutive failures, the background timer stops.

Health monitoring

Inspect the monitor’s flush status at runtime to detect issues:

TypeScript
Python

console.log(`Sent: ${monitor.sentCount}, Dropped: ${monitor.droppedCount}, Buffered: ${monitor.buffer.length}`);

print(f"Sent: {monitor.sent_count}, Dropped: {monitor.dropped_count}, Buffered: {len(monitor.buffer)}")

Graceful shutdown

In production, handle process signals to flush remaining data before the process exits:

Serverless environments (AWS Lambda, Vercel Functions, Cloudflare Workers, etc.): The SDK flushes buffered traces and spans on a background interval, but serverless functions can exit before the next flush fires. Always call await monitor.flush() (TypeScript) or await monitor.flush() (Python) explicitly before your handler returns to ensure nothing is lost.

TypeScript
Python

process.on("SIGTERM", async () => {
  await monitor.flush();
  monitor.stop();
  controller.stop();
  process.exit(0);
});

import signal

async def shutdown(sig, loop):
    await monitor.flush()
    monitor.stop()
    await controller.stop()

loop = asyncio.get_event_loop()
loop.add_signal_handler(signal.SIGTERM, lambda: asyncio.create_task(shutdown(signal.SIGTERM, loop)))

Next steps

Advanced Tracing Patterns

Multi-step workflows, tool-calling agents, session tracking, and error handling patterns.

Log User Feedback

Attach thumbs up/down, ratings, and comments to traces.

Log Attachments

Attach attributes, tags, variables, and metadata to traces and spans.

SDK Reference

Complete class and type reference for the TypeScript and Python SDKs.

Get started

Instrument

Monitor

Iterate

Evaluate

Deploy

Admin

Others

​What the SDK gives you

​Install

​Initialize the client

​Manage deployments

​Fetch a specific deployment

​Fetch the latest deployment for an environment

​Auto-refresh deployments in production

​Initialize the monitor

​Create a trace

​Add spans

​Create a span

​Update and end a span

​Nested spans

​Span content types

​Model

​ModelStream

​Tool

​Retrieval

​Embeddings

​Function

​Guardrail

​Other

​End the trace and flush

​Attach variables for evaluation

​Buffering, batching, and retries

​How the buffer works

​Retry behavior

​Health monitoring

​Graceful shutdown

​Next steps

Advanced Tracing Patterns

Log User Feedback

Log Attachments

SDK Reference

What the SDK gives you

Install

Initialize the client

Manage deployments

Fetch a specific deployment

Fetch the latest deployment for an environment

Auto-refresh deployments in production

Initialize the monitor

Create a trace

Add spans

Create a span

Update and end a span

Nested spans

Span content types

Model

ModelStream

Tool

Retrieval

Embeddings

Function

Guardrail

Other

End the trace and flush

Attach variables for evaluation

Buffering, batching, and retries

How the buffer works

Retry behavior

Health monitoring

Graceful shutdown

Next steps