useagentlens 0.1.0

See where your LLM money goes. Get told exactly how to cut it.

agentlens

One import. See where your LLM money goes. Get told exactly how to cut it.

AgentLens wraps the Anthropic and OpenAI Python SDKs to capture cost and performance telemetry — without changing how your code works or adding any latency.

Install

pip install useagentlens

Quickstart

Anthropic

# Before
from anthropic import Anthropic

# After — one line change
from agentlens import Anthropic

client = Anthropic(
    agentlens_key="al_live_...",   # from useagentlens.com/settings
)

# Everything else stays the same
msg = client.messages.create(
    model="claude-haiku-4-5-20251001",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)

OpenAI

# Before
from openai import OpenAI

# After — one line change
from agentlens import OpenAI

client = OpenAI(
    agentlens_key="al_live_...",   # from useagentlens.com/settings
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
)

Labelling requests

Tag your requests to group costs by feature, user session, or agent step:

client = Anthropic(
    agentlens_key="al_live_...",   # from useagentlens.com/settings
    endpoint="customer-support",      # groups costs by feature in the dashboard
    session_id=user_session_id,       # tracks cost across a full conversation
    step=step_number,                 # tracks cost growth across agent steps
)

# Or override per-call without creating a new client
step2_client = client.with_options(step=2)

How it works

• Your existing Anthropic/OpenAI API key is passed straight through — AgentLens never sees it
• After each API response, usage data is read and sent to the AgentLens ingest API on a background thread
• Zero latency added to your application
• If the AgentLens API is unreachable, events are silently dropped — your code always continues normally

Dashboard

Sign in at useagentlens.com to see:

• Total spend over time
• Cost breakdown by endpoint, model, and session
• P95 latency alongside average latency
• Actionable insights: system prompt bloat, model mismatch, agent loop explosion

Optional dependencies

Install only what you need:

pip install "useagentlens[anthropic]"   # Anthropic wrapper only
pip install "useagentlens[openai]"      # OpenAI wrapper only
pip install "useagentlens[all]"         # Both

Debug mode

No API key? Print events to stdout instead of sending them:

client = Anthropic(agentlens_debug=True)

License

MIT