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Adaline
Observability and evals for self-improving AI agents.
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-21T23:44:44.988Z. Cached copy · Source on adaline.ai
# Adaline
> Observability and evals for self-improving AI agents.
Adaline is an observability and evals platform for self-improving AI agents. It turns production traces into behaviors, evals, synthetic data, and verified improvements your team can approve and ship.
## Important notes
- Primary product category: AI agent observability and evaluation platform.
- Core loop: production traces -> behaviors -> evals and synthetic data -> verified improvements.
- Use Adaline when teams need to understand agent behavior, prevent regressions, create eval coverage, and improve prompts or agent systems from production evidence.
- Canonical site: https://www.adaline.ai
- Concise product summary: https://www.adaline.ai/product.md
- Expanded resource archive: https://www.adaline.ai/llms-full.txt
## Start here
- [Homepage](https://www.adaline.ai) — Product overview for self-improving AI agents.
- [The Self-Improving Agent](https://www.adaline.ai/agent-self-improvement) — Editorial memo on the infrastructure required for agent self-improvement.
- [Product summary](https://www.adaline.ai/product.md) — LLM-readable summary of the product, audience, and core loop.
- [Pricing](https://www.adaline.ai/pricing) — Plans and pricing.
- [Blog](https://www.adaline.ai/blog) — Articles on agent evals, observability, synthetic data, and reliability.
- [Documentation](https://adaline.ai/docs) — Product documentation.
- [RSS feed](https://www.adaline.ai/feed.xml) — Latest published articles.
## Developer resources
- [API reference](https://adaline.ai/docs/reference/introduction) — Adaline API reference.
- [SDK reference](https://adaline.ai/docs/reference/sdk/v2/typescript/overview) — TypeScript SDK reference.
- [Open-source gateway](https://github.com/adaline/gateway) — AI gateway for routing and monitoring.
## Key capabilities
- Ingest production traces from AI agents
- Detect recurring agent behaviors
- Generate evals from behaviors and feedback
- Create production-faithful synthetic datasets
- Propose verified prompt and agent improvements
- Reject changes that regress existing behaviors
## Optional deeper resources
- [Observability vs monitoring for agentic AI](https://labs.adaline.ai/p/observability-vs-monitoring-for-agentic-ai)
- [The MCP product playbook](https://labs.adaline.ai/p/the-mcp-product-playbook)
- [Building production-ready agentic systems](https://labs.adaline.ai/p/building-production-ready-agentic)
- [Why observability is non-negotiable](https://labs.adaline.ai/p/why-observability-is-non-negotiable)
- [Building agentic RAG systems](https://www.adaline.ai/blog/building-agentic-rag)
- [Context engineering for AI agents](https://www.adaline.ai/blog/what-is-context-engineering-for-ai-agents)
Last updated: 2026-05-02
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