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Drop-in observability and guardrails for AI agents.

Project description

Lightsei

Drop-in observability and guardrails for AI agents.

pip install lightsei
import lightsei
import openai

lightsei.init(api_key="bk_...", agent_name="my-bot")

oai = openai.OpenAI()  # auto-instrumented after init()

@lightsei.track
def reply(prompt: str) -> str:
    return oai.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
    ).choices[0].message.content

That's it. Every call now appears at app.lightsei.com with timestamps, model, latency, and token counts. No instrumentation, no manual wrapping.

What you get

  • Observability — runs, events, costs, errors. Out of the box for OpenAI and Anthropic; one line of code per provider.
  • Guardrails — daily cost caps, output validators (schema + content rules), behavioral checks. Caught before delivery, visible in the dashboard.
  • Polaris — a project orchestrator bot you can deploy via Lightsei's PaaS. Reads your MEMORY.md + TASKS.md and proposes the next moves.
  • Notifications — Slack, Discord, Teams, Mattermost, generic webhook. Polaris's plans land in your team chat, validation failures page you, agent crashes get reported.
  • Graceful degradation, non-negotiable — if Lightsei's backend is unreachable or rejects an event, your bot keeps running. SDK never crashes the user's program.

Configuration

lightsei.init(
    api_key="bk_...",            # your workspace key from app.lightsei.com
    agent_name="my-bot",         # appears in dashboard + cost rollups
    version="0.1.0",             # optional — tags events
    base_url="https://api.lightsei.com",  # default
)

Sign up for a workspace API key at app.lightsei.com/signup.

Deploying bots on Lightsei

lightsei deploy ./my-bot --agent my-bot

Zips the directory, uploads to Lightsei's hosted runtime, builds a venv from requirements.txt, runs bot.py. Logs stream into the dashboard.

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