About TraceLM

Making AI agents
trustworthy by default

We believe every AI agent should be observable, verifiable, and continuously improving. TraceLM is the infrastructure that makes that possible.

"As AI agents take on more critical tasks — from customer support to code generation to medical triage — the cost of silent failure grows exponentially. We built TraceLM because the industry needs an observability layer purpose-built for agentic workflows."
The TraceLM Team
4
Detection engines built-in
< 5ms
Added latency per call
99.9%
Proxy uptime target
7
Quality signals tracked

The problem

AI agents fail in ways logs can't explain

Traditional observability tools were built for deterministic software — request in, response out. AI agents are different. They loop, forget context, hallucinate, and fail silently. By the time you notice, your users already have.

TraceLM was purpose-built for this new reality. We don't just log — we detect loops, catch tool failures, monitor context drift, and verify facts. All automatically, with less than 5ms of added latency.

Agent loops 47 times
Tool returns error silently
Context preference forgotten
Agent says "Done!" — output is wrong
Without TraceLM, these go undetected

Our principles

How we build TraceLM

Every decision we make is guided by three core principles.

01

Zero-friction integration

Change one URL and you're live. No SDK lock-in, no agent framework dependency, no code refactoring. If it speaks OpenAI, it works with TraceLM.

02

Detection, not just logging

Logs tell you what happened. Detection tells you what went wrong. We run four specialized engines on every agent task — automatically, in real time.

03

Designed for improvement

Observability is only valuable if it leads to better agents. Every insight TraceLM surfaces is actionable — with clear paths to fixing the underlying issue.

Our approach

Three layers of intelligence

Transparent Proxy

Acts as a passthrough gateway for OpenAI-compatible APIs. Your agents don't even know we're there — but we capture every decision, tool call, and response.

Multi-Source Verification

Verifies agent outputs against your knowledge base, web search results, cross-model consensus, and citation validation. Every claim gets a trust score.

Real-Time Detection

Four detection engines run asynchronously after every response: loop detection, tool failure analysis, context monitoring, and fact verification.

Community

Built for developers,
by developers

TraceLM is built by engineers who've dealt with the pain of debugging AI agents in production. We publish open SDKs for Python and TypeScript, detailed API documentation, and integration guides for every major agent framework.

Pythonpip install tracelm
TypeScriptnpm install tracelm
REST APIOpenAI-compatible — no SDK required

Get in touch

Questions about TraceLM? Want to discuss enterprise needs or partnership opportunities? We'd love to hear from you.

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your users can trust?

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