Build Self-Improving
AI Agents
TraceLM automatically detects loops, tool failures, and context loss. Your agents learn from their mistakes.
Works with every AI stack
The problem
AI agents fail silently.
Your users find out first.
Infinite loops
Agent calls the same tool 50 times. You find out when the bill arrives.
Silent failures
Tool returns an error, agent says "Done!" Users get garbage output.
Context amnesia
User asked for JSON. Agent sends plain text. Preferences forgotten.
How it works
Four detection engines.
Zero configuration.
TraceLM runs detection automatically on every agent task.
Loop Detection
Detects repeated tool calls, circular patterns, and stuck agents before they drain your budget.
Tool Failure Analysis
Catches explicit errors, silent failures, and mismatches where agent claims success but tools failed.
Context Monitoring
Detects forgotten preferences, repeated questions, and contradictions mid-conversation.
Fact Verification
Multi-source verification against your knowledge base, web search, and cross-model consensus.
Integration
Three lines of code.
No SDK lock-in. No framework dependency. Just change your OpenAI base URL to TraceLM.
# Just change the base_url
from openai import OpenAI
client = OpenAI(
base_url="https://api.tracelm.ai/v1",
default_headers={
"X-Task-ID": "task-123"
}
)More than just tracing
Other tools show you logs. TraceLM finds the problems.
| Feature | TraceLM | Others |
|---|---|---|
| Basic LLM tracing | ||
| Loop detection | — | |
| Tool failure analysis | — | |
| Context monitoring | — | |
| Fact verification | — | |
| Self-improvement insights | — |
Early access
What early users say
“Got early access to TraceLM and the demo by Mayank and Etisha was eye-opening. This is exactly what the future of AI agent observability looks like - can't wait to integrate it into our workflows.”
“After seeing the TraceLM demo, I'm convinced this solves a real pain point. The loop detection and context failure analysis are game-changers. Excited to use this in production soon!”
“As someone working on memory systems for AI agents, TraceLM's approach to observability resonates deeply. The demo by the team showed incredible potential - this is the tooling agents need to self-improve.”
“Checked out TraceLM's early access demo and I'm impressed. The ability to detect silent tool failures and semantic mismatches is something we've been looking for. Looking forward to trying it at scale.”
“The TraceLM demo blew me away. Mayank and Etisha have built something that feels like the missing piece in the AI agent stack. Super excited to see where this goes!”
“Been following TraceLM since early access - the vision Mayank and Etisha have for agent observability is spot on. This is the infrastructure layer AI agents have been missing. Can't wait to deploy it!”
FAQ
Does TraceLM add latency?
Less than 5ms. Detection runs asynchronously after the response.
Do I need to change my framework?
No. Just change your OpenAI base URL. Works with LangChain, CrewAI, or direct SDK usage.
What if TraceLM goes down?
Your agent keeps working. We're a passthrough proxy - LLM calls still reach the provider.
Is my data secure?
Encryption in transit and at rest. SOC 2 compliant. Data isolated per project.
Ship agents
your users can trust
Set up TraceLM in minutes. Start finding problems automatically.