kelet 1.0.0

Kelet SDK - OpenTelemetry integration for AI observability

Automated Root Cause Analysis for AI Agents

Agent failures take weeks to diagnose manually. Kelet runs 24/7 deep diagnosis and suggests targeted fixes.

Kelet analyzes production failures 24/7. Each trace takes 15-25 minutes to debug manually—finding patterns requires analyzing hundreds of traces. That's weeks of engineering time per root cause. Kelet does this automatically, surfacing issues like data imbalance, concept drift, prompt poisoning, and model laziness hidden in production noise.

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What Kelet Does

Kelet runs 24/7 analyzing every production trace:

1. Captures every interaction, user signal, and failure context automatically
2. Analyzes hundreds of failures in parallel to detect repeatable patterns
3. Identifies root causes (data issues, prompt problems, model behavior)
4. Delivers targeted fixes, not just dashboards

Unlike observability tools that show you data, Kelet analyzes it and tells you what to fix.

Not magic: Kelet is in alpha. Won't catch everything yet, needs your guidance sometimes. But it's already doing analysis that would take weeks manually.

Three lines of code to start.

Installation

Using uv (recommended):

uv add git+https://${GITHUB_KELET_PAT}@github.com/Kelet-ai/python-sdk.git

Or using pip:

pip install git+https://${GITHUB_KELET_PAT}@github.com/Kelet-ai/python-sdk.git

Set your API key:

export KELET_API_KEY=your_api_key
export KELET_PROJECT=production  # Optional: organize traces by environment

Or configure in code:

kelet.configure(
    api_key="your_api_key",
    project="production"  # Groups traces by project/environment
)

Quick Start

import kelet

kelet.configure()  # Auto-instruments pydantic-ai and captures sessions

# Your agent code works as-is - instrumentation is automatic
result = await agent.run("Book a flight to NYC")

# Optionally capture user feedback
await kelet.signal(
    kind=kelet.SignalKind.FEEDBACK,
    source=kelet.SignalSource.HUMAN,
    score=0.0,  # User unhappy? Kelet analyzes why.
)

That's it. Kelet now runs 24/7 analyzing every trace, clustering failure patterns, and identifying root causes—work that would take weeks manually.

Manual Session Grouping (Optional)

If your framework doesn't support session tracking, or you want custom session IDs:

with kelet.agentic_session(session_id="user-123-request-456"):
    result = await agent.run("Book a flight to NYC")

But most users don't need this—instrumentation captures sessions automatically from pydantic-ai and other supported frameworks.

Easy Feedback UI for React

Building a React frontend? Use the Kelet Feedback UI component for instant implicit and explicit feedback collection. See the live demo and documentation for full integration guide.

Works with Your Observability Stack

Already using Logfire or another OTEL provider? Kelet integrates seamlessly:

import logfire
import kelet

logfire.configure()
logfire.instrument_pydantic_ai()

kelet.configure()  # Adds Kelet's processor to your existing setup

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What Gets Captured

Kelet is built on OpenTelemetry and supports multiple semantic conventions for AI/LLM observability:

Semantic Convention	Supported Frameworks
GenAI Semantic Conventions	Pydantic AI, LiteLLM, Langfuse SDK
Vercel AI SDK	Next.js, Vercel AI
OpenInference	Arize Phoenix
OpenLLMetry / Traceloop	LangChain, LangGraph, LlamaIndex, OpenAI SDK, Anthropic SDK

Any framework that exports OpenTelemetry traces using the GenAI semantic conventions will work automatically.

Captured data includes:

• LLM calls: Model, provider, tokens, latency, errors
• Agent sessions: Multi-step interactions grouped by user session
• Custom context: User IDs, session metadata, business-specific attributes

All captured automatically when you instrument with kelet.configure().

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Configuration

Set via environment variables:

export KELET_API_KEY=your_api_key    # Required
export KELET_PROJECT=production      # Optional, defaults to "default"
export KELET_API_URL=https://...     # Optional, defaults to api.kelet.ai

Or pass directly to configure():

kelet.configure(
    api_key="your_api_key",
    project="production",
    auto_instrument=True  # Instruments pydantic-ai automatically
)

API Reference

Core Functions:

# Initialize SDK
kelet.configure(api_key=None, project=None, auto_instrument=True)

# Group operations by session for failure correlation
with kelet.agentic_session(session_id="session-id"):
    result = await agent.run(...)

# Capture user feedback
await kelet.signal(
    kind=kelet.SignalKind.FEEDBACK,       # feedback | edit | event | metric | arbitrary
    source=kelet.SignalSource.HUMAN,      # human | label | synthetic
    score=0.0,                            # 0.0 to 1.0
)

# Access current context
session_id = kelet.get_session_id()
trace_id = kelet.get_trace_id()
user_id = kelet.get_user_id()

# Manual shutdown (automatic on exit)
kelet.shutdown()

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Production-Ready

The SDK never disrupts your application:

• Async: Telemetry exports in background, zero blocking
• Fail-safe: Network errors handled silently, no exceptions raised
• Graceful: If Kelet is down, your agent keeps running
• Auto-flush: Spans exported automatically on process exit

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Alpha Status

Kelet is in alpha. What this means:

• It works: Already analyzing thousands of production traces for early users
• Not perfect: Won't catch every failure pattern yet, sometimes needs guidance
• Improving fast: The AI learns from more production data every day
• We need feedback: Help us make it better—tell us what it catches and what it misses

Even in alpha, Kelet does analysis that would take your team weeks to do manually.

The alternative? Manually analyzing 15-25 minutes per trace, across hundreds of failures, trying to spot patterns by hand. Most teams just don't do it—and ship broken agents.

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Learn More

• Website: kelet.ai
• Early Access: We're onboarding teams with production AI agents
• Support: GitHub Issues

Built for teams shipping mission-critical AI agents.

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License

MIT License — see LICENSE.md for details.