agent-budget-guard 0.2.0

Budget-limited LLM API client wrapper to prevent runaway AI agent costs

Agent Budget Guard

Hard spending limits for LLM API calls. Prevents runaway agent costs.

Wraps OpenAI, Anthropic, and Google Gemini — drop-in replacement for each SDK client with budget enforcement and no other changes to your code.

Install

pip install agent-budget-guard

Quickstart

OpenAI

from agent_budget_guard import BudgetedSession

client = BudgetedSession.openai(budget_usd=5.00)

# Non-streaming — identical to normal OpenAI usage
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

# Streaming — works the same way, cost tracked from final chunk
for chunk in client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True,
):
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

print(client.session.get_summary())

Anthropic

client = BudgetedSession.anthropic(budget_usd=5.00)

# Non-streaming
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.content[0].text)

# Streaming
for event in client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
    stream=True,
):
    if event.type == "content_block_delta":
        print(event.delta.text, end="")

Google Gemini

client = BudgetedSession.google(budget_usd=5.00)

# Non-streaming
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Hello",
)
print(response.text)

# Streaming — Google uses a separate method (mirrors the underlying SDK)
for chunk in client.models.generate_content_stream(
    model="gemini-2.0-flash",
    contents="Hello",
):
    print(chunk.text, end="")

API Keys

Set the standard environment variable for each provider:

export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=...

Or pass api_key= directly to any factory method.

Manual Wrapping

If you already have a client instance, wrap it directly:

from openai import OpenAI
from agent_budget_guard import BudgetedSession

session = BudgetedSession(budget_usd=5.00)
client = session.wrap_openai(OpenAI())

Same pattern for wrap_anthropic() and wrap_google().

Callbacks

client = BudgetedSession.openai(
    budget_usd=5.00,
    on_budget_exceeded=lambda e: print(f"Budget hit: {e}"),
    on_warning=lambda w: print(f"{w['threshold']}% of budget used"),
    warning_thresholds=[50, 90],  # default: [30, 80, 95]
)

on_budget_exceeded — called when a request would exceed the budget. The call returns None instead of raising. Without this callback, a BudgetExceededError is raised.

on_warning — called when utilization crosses a threshold. Each threshold fires once per session. The callback receives:

{
    "threshold": 50,       # which % threshold was crossed
    "spent": 2.51,         # total spent so far
    "remaining": 2.49,     # budget left
    "budget": 5.00         # total budget
}

Concurrent Agents

All agents share the same budget pool with atomic reservation — no race conditions.

import concurrent.futures
from agent_budget_guard import BudgetedSession, BudgetExceededError

client = BudgetedSession.openai(budget_usd=10.00)

def agent_task(task_id):
    for _ in range(10):
        try:
            client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[{"role": "user", "content": f"Task {task_id}"}]
            )
        except BudgetExceededError:
            return

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    executor.map(agent_task, range(5))

How It Works

1. Estimates cost before the API call (token counting + model pricing)
2. Atomically reserves that amount from the budget
3. Makes the API call only if within budget
4. Calculates actual cost from the response (or final stream chunk)
5. Commits actual cost, releases the reservation
6. Fires warning callbacks if thresholds are crossed

spent + reserved <= budget at all times, even under concurrency.

Session API

client.session.get_total_spent()        # USD spent so far
client.session.get_remaining_budget()   # USD remaining (accounts for in-flight calls)
client.session.get_reserved()           # USD reserved for in-flight calls
client.session.get_budget()             # total budget
client.session.get_summary()            # dict with all of the above
client.session.reset()                  # reset to zero (don't use mid-flight)

Supported Models

OpenAI — GPT-5.2, GPT-5.1, GPT-5-mini, GPT-5-nano, GPT-4.1, GPT-4.1-mini, GPT-4.1-nano, GPT-4o, GPT-4o-mini, o1, o1-pro, o3, o3-pro, o4-mini, gpt-4-turbo, gpt-4, gpt-3.5-turbo

Batch pricing: BudgetedSession.openai(budget_usd=5.00, tier="batch")

Anthropic — claude-opus-4-6, claude-sonnet-4-6, claude-haiku-4-5, claude-3-5-sonnet, claude-3-5-haiku, claude-3-opus, claude-3-sonnet, claude-3-haiku

Google Gemini — gemini-2.0-flash, gemini-2.0-flash-lite, gemini-2.0-pro, gemini-1.5-pro, gemini-1.5-flash, gemini-1.5-flash-8b

Development

git clone https://github.com/Digital-Ibraheem/agent-budget-guard.git
cd agent-budget-guard
pip install -e ".[dev]"
pytest