kazenai

Stop your AI agents from burning your budget. Catch loops before they catch you.

Install: PyPI · kazenai · Customer SDK: kazenai-finops · Docs: docs.kazenai.com · Products: kazenai.com

Source: github.com/kazenai-ai/kazenai-core

What is this?

kazenai is the core Python SDK behind KazenAI’s economic control layer for AI agents. One call — monitor() — wraps your OpenAI or Anthropic client and:

1. Blocks overspend before the provider call (BudgetExceeded)
2. Detects runaway loops before another LLM request
3. Optionally ships evidence to Agent FinOps / Agent Lens as canonical KazenEvents

Most observability tools tell you what went wrong after the bill. KazenAI can stop the call that would cause it.

Traditional tools:  LLM call → response → log cost → dashboard shows overspend
KazenAI:            Pre-flight check → BLOCKED → LLM call never made

Install

python -m pip install "kazenai[openai]==1.1.1"
# Most teams install the customer package instead:
python -m pip install "kazenai-finops[openai]==1.1.1"
# Anthropic:
# python -m pip install "kazenai-finops[anthropic]==1.1.1"
# Both providers:
# python -m pip install "kazenai-finops[providers]==1.1.1"

kazenai-finops re-exports this SDK and is the recommended install for product integrations. Provider extras pin the certified SDK ranges so a first-run install cannot silently resolve OpenAI 3.x or Anthropic 1.x.

The Control 1.1.x certification covers openai>=1.40,<2 and anthropic>=0.39,<1. Later provider-SDK major versions are unverified until they are added to the supported matrix. Release 1.1.1 is a compatibility/distribution patch, not a new runtime capability line.

Requires Python 3.10+.

Quick start

import os
from openai import OpenAI
from kazenai import monitor, BudgetExceeded

# Optional FinOps ingest (env only — not monitor kwargs):
#   export KAZENAI_FINOPS_API_KEY=kz_...
#   export KAZENAI_FINOPS_INGEST_URL=https://finops.example.com

client = monitor(
    OpenAI(),
    agent_id="support-agent",
    max_budget_usd=5.00,
    debug=True,
)

try:
    client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "hello"}],
    )
except BudgetExceeded as e:
    print("hard budget cap:", e)

Your call sites stay the same — monitor() wraps the client.

With debug=True you’ll see spend as the run progresses:

[KazenAI] step=1  gpt-4o-mini  cost=$0.0043  total=$0.0043  proj=$0.21/$5.00  OK
[KazenAI] step=8  gpt-4o-mini  cost=$0.0039  total=$0.43    proj=$4.91/$5.00  WARN:budget_87pct
[KazenAI] step=9  gpt-4o-mini  BLOCKED:budget  spent=$5.00  limit=$5.00

Illustrative debug output — not a customer bill or savings claim.

What you get

Supported today

Streaming

from kazenai import StreamCutoffError, monitor
from openai import OpenAI

client = monitor(
    OpenAI(),
    max_budget_usd=1.00,
    stream_cutoff_usd=0.01,  # optional local observable-output guard
)

try:
    with client.chat.completions.create(
        model="gpt-4o-mini",
        stream=True,
        messages=[{"role": "user", "content": "Explain this result"}],
    ) as stream:
        for chunk in stream:
            ...
except StreamCutoffError:
    # The client attempted to close future output. Final provider billing may
    # remain pending when authoritative final usage was not received.
    pass

monitor() requests final OpenAI usage and settles exact cost when authoritative usage arrives. OpenAI chat.completions.stream(...), Anthropic messages.create(..., stream=True), and the messages.stream(...) context manager follow the same finalize-once accounting lifecycle. A shared reservation is marked in-flight at provider dispatch. Early cancellation, provider failure, or missing final usage moves it to pending reconciliation—never an exact zero and never released as an unstarted call.

stream_cutoff_usd estimates only output observable by the client. It cannot see hidden reasoning or guarantee that the provider stopped generating or billing immediately. stream_enforcement=True is deprecated.

The official lazy OpenAI helper is supported as well. Its reservation is marked provider_started when the context manager is entered:

with client.chat.completions.stream(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain this result"}],
) as stream:
    for event in stream:
        ...

Local and shared enforcement

• Hard cap works without FinOps network — deny even when ingest is offline
• Low overhead — budget and loop checks stay on the hot path
• Shared policies can fail closed — production/staging modes deny when a required shared reservation cannot be obtained

An explicitly configured development fail-open path retains only the local client safeguards. It does not preserve a cross-process or account-wide budget guarantee.

Optional FinOps environment variables

Pass max_budget_usd, org_id, project_id, workspace_id and attribution fields directly to monitor(); they are not inferred from similarly named environment variables.

Packages in the stack

Contributing

Issues and PRs that improve budget/loop reliability are welcome. Keep claims aligned with the supported paths above.

Maintainers: follow RELEASING.md for the clean-checkout and public-PyPI verification sequence.

Products and design-partner enquiries: kazenai.com · founder@kazenai.com

License

Apache License 2.0. See LICENSE and NOTICE.