AI observability for production agents

Find AI Agent Failures in Seconds

See every step, error, token, and cost in one trace timeline. Install with one prompt and get your first signal fast.

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Example Trace Timeline

plannersuccess · 210ms
retrievalsuccess · 480ms
tool_call:web_searcherror · timeout (12.4s)

Root cause is visible in one screen, not scattered logs.

Install in one prompt

Primary

Use your coding assistant to detect stack, install dependencies, and wire tracing automatically.

Choose your coding assistant

Best for CLI-driven refactors in existing repos.

install-prompt.txt
You are Claude Code working directly inside my repository.

Goal:
Install ZappyBee tracing as fast as possible with zero product regressions.

Critical constraints:
1) Detect framework(s), language(s), runtime(s), and package manager(s) automatically.
2) Keep existing app behavior intact. No breaking changes.
3) Do NOT hardcode secrets. Use env vars only.
4) Do NOT commit or push anything.
5) At the end, output:
   - changed files
   - exact run command
   - exact smoke test command

Configuration:
- SDK package name: zappybee
- API key env var: ZAPPYBEE_API_KEY
- Base URL env var: ZAPPYBEE_BASE_URL
- Base URL value: https://tokencat-api-riuvb.ondigitalocean.app

Implementation tasks:
1) Detect stack
- Identify whether this repo uses TypeScript/JavaScript, Python, or both.
- Detect package manager preference from lockfiles/config:
  pnpm-lock.yaml > package-lock.json > yarn.lock > bun.lockb
- For Python detect toolchain:
  uv > poetry > pipenv > pip

2) Install dependencies
- If TS/JS exists, install zappybee with the detected package manager.
- If Python exists, install zappybee with the detected Python tool.
- If OpenAI or Anthropic SDKs are already present, keep using them.
- Do not remove existing dependencies.

3) Configure environment
- Add/update env examples so they include:
  ZAPPYBEE_API_KEY=
  ZAPPYBEE_BASE_URL=https://tokencat-api-riuvb.ondigitalocean.app
- If there is an app env loader, wire both vars there.
- Never place a real API key in source files.

4) Add SDK initialization
- Add one shared initialization location per runtime (TS and/or Python).
- Initialize with:
  apiKey from ZAPPYBEE_API_KEY
  baseUrl from ZAPPYBEE_BASE_URL (fallback to https://tokencat-api-riuvb.ondigitalocean.app if needed)

5) Add automatic tracing where possible
- If Anthropic/OpenAI clients are found, apply ZappyBee.wrap(client).
- Keep existing client options and behavior unchanged.

6) Add manual tracing for unsupported/custom flows
- Add one traced execution path with:
  startTrace -> startStep -> step.end -> trace.end
- Include model, prompt/completion tokens, status, output/error.
- Use try/catch/finally (or equivalent) so traces close on failures.

7) Add smoke test path
- Add a minimal script or command that triggers one traced run.
- Script should work locally without exposing secrets in code.
- Add clear comment on how to run it.

8) Validate
- Run existing build/typecheck/lint commands where available.
- Fix only issues introduced by your changes.

9) Final report
- Print:
  a) what was detected (framework/language/package manager)
  b) exact files changed
  c) install command(s) used
  d) run command
  e) smoke test command
  f) where to check traces in UI

Now apply the changes directly.
Manual fallback
Optional
import { ZappyBee } from "zappybee";
import Anthropic from "@anthropic-ai/sdk";

ZappyBee.init({
  apiKey: process.env.ZAPPYBEE_API_KEY || "tc_live_...",
  baseUrl: process.env.ZAPPYBEE_BASE_URL, // optional
});

const client = new Anthropic();
ZappyBee.wrap(client); // auto-traces all calls

const response = await client.messages.create({
  model: "claude-sonnet-4-20250514",
  max_tokens: 512,
  messages: [{ role: "user", content: "Hello!" }],
});

Everything you need to ship AI with confidence

From prototype to production. Know exactly what your agents are doing, how much they cost, and when something breaks.

Prompt Install

Copy one install prompt into Claude Code, Codex, Cursor, Lovable, Antigravity, or another coding assistant.

Trace Every Step

Full trace timeline: LLM calls, tool calls, reasoning steps, inputs/outputs, tokens, latency, and cost.

Works With Your Models

Claude, GPT, Gemini, Grok, Mistral, Llama, DeepSeek and more. Auto-wrap Anthropic/OpenAI or use manual tracing for anything.

Analytics That Matter

Model usage, daily spend, error rate, average duration, and top models across your agents and projects.

Prompt Versioning

Version prompts, ship changes safely, and compare performance with real usage and outcomes.

Alerts to Email + Slack

Trigger alerts on error rate, cost, and latency. Get notified via email or Slack webhook.

Custom Webhooks

Send signed events (HMAC) for traces, steps, and alert triggers to your incident pipeline.

Teams, Sharing, Retention

Invite teammates, create shareable dashboards, and enforce retention policies for production readiness.

Works with your stack

One SDK plus integrations for popular frameworks

Anthropic Claude
OpenAI GPT
Google Gemini
xAI Grok
Mistral
Llama
DeepSeek
LangChain
CrewAI
TypeScript
Python

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