# Adaline

Observability and evaluation tools for teams improving AI agents from production data.

- Findings24: <https://findings24.com/zh-CN/products/adaline>
- 网站: <https://adaline.ai>
- 价格: Freemium
- 分类: AI 可观测性, 大语言模型评测

## 什么是 Adaline？

Adaline is an observability and evaluation platform for product and engineering teams building AI agents. It analyzes production logs and traces to identify recurring behaviors, then uses those patterns to generate evaluations, datasets, and suggested prompt improvements.

Its core workflow connects production evidence to changes a team can inspect and deploy. Automated log parsing and triage help teams investigate agent behavior without manually reviewing every interaction, while evaluations provide checks before changes reach users.

## Adaline 能做什么？

### Production behavior maps

Groups recurring agent patterns into inspectable behavior maps. Logs, sessions, traces, and spans can also be searched, exported, and viewed through monitoring dashboards.

### Automatically generated evaluations

Creates evaluators from detected behaviors, user feedback, and quality rules to check for regressions before agent changes ship.

### Synthetic datasets

Generates examples based on production scenarios and stores them alongside real cases, golden paths, and regression rows for testing.

### Prompt development and deployment

Provides a playground to run prompts and inspect results, plus tools to version, evaluate, optimize, and deploy prompts. Teams can review suggested improvements and deploy them with one click.

### Application integration

Python and TypeScript SDKs support observability and management of prompts, datasets, evaluations, and logs. A REST API exposes endpoints for deployments, projects, and these resources.

### Unified model gateway

Provides a single interface to more than 300 LLMs, supporting chat completions, streaming, embeddings, and tool calls.

## 使用场景

### Investigate recurring production failures

An engineering team can use behavior maps and trace inspection to identify repeated agent issues and decide which patterns need attention.

### Validate a prompt revision

Before deploying a prompt change, a team can evaluate it against stored production cases and regression rows to assess whether expected behavior holds.

### Expand coverage for edge cases

Teams with gaps in their test datasets can generate examples grounded in production scenarios, including cases discovered through recurring behavior analysis.

## 常见问题

### What do I send to Adaline to get started?

Create an account and integrate your agent to send logs, traces, spans, sessions, feedback, and metadata. The documentation also provides quickstarts for running a prompt and an evaluation.

### Can teams review improvements before deploying them?

Yes. Adaline suggests prompt improvements for teams to inspect and supports one-click deployment after review.

### How does API authentication work?

The API uses bearer-token authentication with workspace API keys. The production base URL is https://api.adaline.ai/v2.

### What enterprise controls does Adaline list?

Adaline lists SOC 2 Type II auditing and certification, GDPR and HIPAA compliance, SSO/SAML, and data processing agreements.

## 快速信息

- **Category**: AI agent observability and evaluation
- **Primary users**: Product and engineering teams
- **Available SDKs**: Python and TypeScript
- **Workflow**: Production traces → behaviors → evaluations and data → prompt improvements
- **Website**: https://adaline.ai