Overview - Adaline

Instrument is how your AI agent starts learning from production. Send logs from your application to Adaline so every important request, model call, tool call, retrieval step, command, outcome, and user signal can become evidence for Behaviors, Evaluators, Datasets, Improve cycles, and safer deployments. The goal is not just visibility. Good instrumentation gives Adaline enough context to understand repeated patterns, triage issues, generate coverage, and suggest prompt improvements your team can review.

What to instrument

Start by capturing the parts of the agent run that explain what happened.

Evidence	What to send	Why it matters
Traces	One trace for each request, conversation turn, job, or agent task.	Gives Adaline the full unit of work to inspect, search, group, and improve.
Spans	Model calls, tool calls, retrieval, embeddings, guardrails, functions, commands, and custom workflow steps.	Shows where the agent spent time, used tools, failed, retried, or produced the final answer.
Sessions	Stable session, conversation, journey, task, or trajectory identifiers.	Connects multiple traces into a user journey or coding-agent run.
Inputs and outputs	Prompt messages, model responses, tool arguments, tool responses, retrieved context, and safe summaries.	Lets reviewers and evaluators understand the exact evidence behind a result.
Operational signals	Status, latency, tokens, cost, model, provider, environment, release, route, prompt, and agent identity.	Makes filtering, release review, cost analysis, and regression investigation practical.
Quality signals	User feedback, application outcome, evaluator score, escalation state, task completion, or human-review result.	Helps Adaline distinguish healthy traffic from failures that need coverage or improvement.

If you are starting from scratch, send one useful trace before trying to model everything. A useful trace has a readable name, spans for model/tool work, status, timing, model/provider details, environment metadata, and enough input/output context for a teammate to understand the run.

Choose an integration path

Use the path that matches how much control you need.

Path	Best for	What you get
With Adaline Integrations	Apps already using AI frameworks, providers, gateways, agents, or OpenTelemetry.	Framework-native logging with less custom trace code.
With Adaline SDKs	TypeScript or Python applications that need explicit traces, spans, sessions, variables, and metadata.	Full control over how production evidence is structured.
With Adaline API	Any language, backend, queue, worker, edge runtime, CI job, or custom system.	Direct REST calls for traces, spans, updates, feedback, and platform resources.
With Adaline Proxy	Quick provider-compatible setup when changing the AI provider base URL is easiest.	Automatic traces for provider calls with minimal code changes.
Coding-agent integration	Teams using Cursor, Windsurf, Claude Code, Cline, or another coding agent.	Copy-paste integration context so your coding agent can wire Adaline into your app.

Integration methods

Integrations
SDKs
API
Proxy

Use Adaline Integrations when your agent already runs through a supported framework, provider SDK, gateway, or OpenTelemetry pipeline.Common starting points include LangChain, LangGraph, LlamaIndex, LiteLLM, Vercel AI SDK, OpenTelemetry, and provider integrations.Use this when: you want Adaline logs without hand-modeling every span yourself.

Use Adaline SDKs when you want precise control over traces and spans.

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

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

const trace = monitor.logTrace({ name: "user-request" });
const span = trace.logSpan({ name: "model:answer-user" });

Use this when: your workflow has multiple agents, tools, retrieval steps, custom metadata, or sessions that need to be modeled clearly.

Use the Adaline API when you need language-agnostic logging or want to send traces from systems where the SDK is not available.

POST /v2/logs/trace
Authorization: Bearer your-api-key
Content-Type: application/json

Use this when: logs come from a backend service, worker, queue, edge function, CI job, or custom runtime.

Use Adaline Proxy when your application already calls an AI provider with an OpenAI-compatible or provider SDK and you want the fastest path to logs.

from openai import OpenAI

client = OpenAI(
    api_key="your-openai-key",
    base_url="https://gateway.adaline.ai/v1/openai/",
    default_headers={
        "adaline-api-key": "your-adaline-key",
        "adaline-project-id": "your-project-id",
        "adaline-prompt-id": "your-prompt-id",
    },
)

Use this when: changing the provider base URL is easier than adding SDK or API calls throughout your app.

Metadata that makes logs useful

Metadata is what turns raw logs into product, engineering, support, and release workflows.

Metadata	Examples	Used by
Environment	`production`, `staging`, `qa`	Release review, filtering, dashboards, saved views.
Release or build	Commit SHA, deployment ID, version, experiment arm.	Drift investigation and post-release watch windows.
Route or workflow	`checkout-assistant`, `support-chat`, `training-plan-generator`.	Behavior maps, Monitor breakdowns, support triage.
Prompt or agent identity	Prompt name, prompt ID, agent name, agent type, workflow step.	Improve cycles, prompt ownership, evaluator routing.
Session or trajectory ID	Conversation ID, task ID, coding-agent run ID.	Sessions, trajectories, multi-turn investigation.
Safe customer segment	Plan tier, region, beta cohort, hashed tenant ID.	Customer-safe filtering without exposing private identifiers.
Outcome	Success, failed, escalated, partial, user-reported issue, task complete.	Behavior severity, issue detection, evaluation, and triage.

Avoid raw emails, API keys, access tokens, secrets, unnecessary personal data, and private customer text that your team should not store.

Verify your first trace

After instrumenting, run a known request in staging and inspect it in Logs. You are integrated when:

The trace appears in Adaline within a few seconds.
The trace name describes the workflow or request.
Spans show the model calls, tool calls, retrieval, command, or custom steps that matter.
Status, latency, model, provider, tokens, and cost appear where supported.
Tags or attributes identify environment, release, route, prompt, agent, and session context.
A teammate can open the trace and understand what happened without asking the original engineer.

If the trace is hard to read, fix the names and metadata before sending more traffic. Clean evidence now makes Behaviors and Improve much stronger later.

From logs to improvement

Instrumentation feeds every later Platform workflow:

Workflow	How instrumentation helps
Logs	Gives your team exact traces, spans, sessions, filters, search, costs, latency, tokens, and evaluator scores.
Behaviors	Lets Adaline group repeated user, assistant, tool, and coding-agent patterns from production evidence.
Evaluators and Datasets	Turns real traces, feedback, edge cases, and outcomes into durable quality checks.
Improve	Gives improvement cycles enough evidence to propose prompt candidates with reviewable examples and regressions.
Deploy	Lets teams compare a release against the same metadata, behaviors, evals, and logs after shipping.

With Adaline Integrations

Connect frameworks, providers, agents, gateways, and OpenTelemetry pipelines.

With Adaline SDKs

Create traces, spans, sessions, variables, and metadata from TypeScript or Python.

With Adaline API

Send logs directly from any language, runtime, worker, or custom system.

With Adaline Proxy

Route provider calls through Adaline for fast logging with minimal code changes.

Log user feedback

Attach user feedback, outcomes, and review signals to production traces.

Advanced usage

Model complex workflows, streaming, custom spans, reference IDs, and multi-service traces.

​What to instrument

​Choose an integration path

​Integration methods

​Metadata that makes logs useful

​Verify your first trace

​From logs to improvement

With Adaline Integrations

With Adaline SDKs

With Adaline API

With Adaline Proxy

Log user feedback

Advanced usage

What to instrument

Choose an integration path

Integration methods

Metadata that makes logs useful

Verify your first trace

From logs to improvement