Skip to main content

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.

Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lightsei-0.1.6.tar.gz (40.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

lightsei-0.1.6-py3-none-any.whl (37.7 kB view details)

Uploaded Python 3

File details

Details for the file lightsei-0.1.6.tar.gz.

File metadata

  • Download URL: lightsei-0.1.6.tar.gz
  • Upload date:
  • Size: 40.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.2

File hashes

Hashes for lightsei-0.1.6.tar.gz
Algorithm Hash digest
SHA256 81e3743da3c1c70cac705635647095c2310c99da70caba6b7af208a2e0a1dd0f
MD5 15ca02586a32b7fd78bbd9114c4a1d3f
BLAKE2b-256 c161dfc072cbfc8fa3c93288934bd6164dd1200ff1645c77d9baa768948d8944

See more details on using hashes here.

File details

Details for the file lightsei-0.1.6-py3-none-any.whl.

File metadata

  • Download URL: lightsei-0.1.6-py3-none-any.whl
  • Upload date:
  • Size: 37.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.2

File hashes

Hashes for lightsei-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 7db24e7fff39b48c8912dcfa01c338249a46e68e5b4b4f5c4df1988260cfcd78
MD5 fcdd259256ea4e02f131376b4ac183cb
BLAKE2b-256 34603cac1c23f56ebd9cda145880cee463ef68a0809c80b6b33d381ea6990818

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page