ai-agent-sentry 0.1.1

Crash reporting for AI agents. Catch failures before your users do.

Your AI agent works great in demos. In production, it fails silently. Tool calls timeout. Context windows overflow. Hallucinations slip through. And you have no idea until a user complains.

agent-sentry catches every failure, classifies why it happened, and alerts you in real time.

Quick Start

pip install ai-agent-sentry

Note: The PyPI package is ai-agent-sentry, but you import it as agent_sentry.

from agent_sentry import watch

@watch
def my_agent(query):
    result = call_llm(query)
    return result

# Every call is now tracked. Every failure is caught.
my_agent("summarize this document")

Open the dashboard:

agent-sentry dashboard

Three lines of code. Full visibility into every failure.

Why?

You deployed an agent. It runs 10,000 tool calls a day. Some fail. You don't know which ones, you don't know why, and you don't know how often.

Traditional monitoring sees HTTP 200s and thinks everything is fine. But your agent just hallucinated a fake API endpoint, retried it 5 times, burned $2 in tokens, and returned garbage to the user. Nobody noticed.

agent-sentry is Sentry for AI agents.

Features

Integrations

# OpenAI
from agent_sentry.integrations.openai import SentryOpenAIWrapper
client = SentryOpenAIWrapper(openai_client)
response = client.chat_completions_create(model="gpt-4", messages=[...])

# Anthropic
from agent_sentry.integrations.anthropic import SentryAnthropicWrapper
client = SentryAnthropicWrapper(anthropic_client)
response = client.messages_create(model="claude-sonnet-4-20250514", messages=[...])

# LangChain
from agent_sentry.integrations.langchain import AgentSentryCallbackHandler
llm = ChatOpenAI(callbacks=[AgentSentryCallbackHandler()])

# Any function
@watch(event_type="tool_call", tags=["search"])
def search_web(query):
    return requests.get(f"https://api.search.com?q={query}").json()

Root Cause Categories

Performance

Alerts

from agent_sentry import configure

# Slack
configure(slack_webhook="https://hooks.slack.com/services/YOUR/WEBHOOK/URL")

# Webhooks
configure(webhook_url="https://your-server.com/agent-alerts")

# Custom
from agent_sentry import CallbackAlert
configure(alert_channels=[CallbackAlert(lambda e: print(f"ALERT: {e['error_message']}"))])

CLI

agent-sentry dashboard          # Launch Streamlit dashboard
agent-sentry report             # Terminal report (last 24h)
agent-sentry report --hours 168 # Last 7 days
agent-sentry clear              # Clear all events

Architecture

@watch decorator
    |
    v
EventCapture (intercepts calls, measures latency, catches errors)
    |
    +--> RootCauseClassifier (timeout? hallucination? rate limit?)
    +--> EventStore (SQLite, WAL mode, thread-safe)
    +--> AlertManager (Slack, webhooks, email, callbacks)
    +--> Dashboard (Streamlit, reads from EventStore)

All data stays local. SQLite with WAL mode. Thread-safe. No external services required.

Installation

pip install ai-agent-sentry                                # Core
pip install ai-agent-sentry[dashboard]                     # + Streamlit dashboard
pip install ai-agent-sentry[openai,anthropic,langchain]    # + Framework integrations
pip install ai-agent-sentry[all]                           # Everything

Contributing

See CONTRIBUTING.md. Issues and PRs welcome.

Roadmap

See ROADMAP.md.

License

MIT. See LICENSE.