AI budget guardrails for LLM applications
Project description
TetherAI
Financial circuit breaker for AI agents. Stop runaway LLM costs before they happen.
AI agents don't crash when they fail — they loop, hallucinate, and retry. A stuck CrewAI crew can silently burn hundreds of dollars in LLM API calls over a weekend. Traditional monitoring tools like Datadog will show you an API cost spike on Monday morning. TetherAI kills the agent before it spends your money.
Install
pip install tetherai-python
With CrewAI support:
pip install tetherai-python[crewai]
Quick Start
The @enforce_budget decorator wraps any function with budget guardrails:
from tetherai import tether, BudgetExceededError
import litellm
@tether.enforce_budget(max_usd=0.05)
def my_workflow():
for i in range(100):
response = litellm.completion(
model="gpt-4o-mini",
messages=[{"role": "user", "content": f"Count to {i}"}]
)
print(f"Call {i+1}: {response.choices[0].message.content[:50]}...")
try:
my_workflow()
except BudgetExceededError as e:
print(f"\n🛑 Stopped! Spent ${e.spent_usd:.4f} of ${e.budget_usd:.2f} budget")
Expected output:
Call 1: 0...
Call 2: 0, 1...
Call 3: 0, 1, 2...
🛑 Stopped! Spent $0.0501 of $0.05 budget
CrewAI Integration
For CrewAI crews, use protect_crew() to wrap budget enforcement around the entire crew:
from tetherai import protect_crew, BudgetExceededError
from crewai import Agent, Task, Crew, Process
researcher = Agent(
role="Research Analyst",
goal="Find comprehensive information on AI observability tools",
backstory="You are a thorough research analyst known for detailed analysis.",
verbose=True,
)
task = Task(
description="Research the competitive landscape of AI observability tools",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task], process=Process.sequential)
protected_crew = protect_crew(crew, max_usd=0.10)
try:
result = protected_crew.kickoff()
except BudgetExceededError as e:
print(f"Budget exceeded: ${e.spent_usd:.2f} of ${e.budget_usd:.2f}")
What Happens When Budget Is Exceeded
When the budget is exceeded, TetherAI raises BudgetExceededError with details about the run:
{
"run_id": "run_abc123",
"budget_usd": 0.10,
"spent_usd": 0.13,
"turns": 7,
"spans": [
{
"span_type": "llm_call",
"model": "gpt-4o-mini",
"input_tokens": 1250,
"output_tokens": 340,
"cost_usd": 0.0019,
"status": "ok"
}
]
}
How It Works
TetherAI patches litellm.completion at runtime to intercept every LLM call your agent makes. Before each call, it counts input tokens locally using tiktoken and checks the projected cost against your budget. If the budget would be exceeded, the call is blocked and a BudgetExceededError is raised. After each successful call, actual token usage from the LLM response is recorded for accurate cost tracking.
Supported Frameworks
| Framework | Status | Integration |
|---|---|---|
| CrewAI | ✅ Supported | protect_crew() |
| LiteLLM (direct) | ✅ Supported | @enforce_budget decorator |
| LangChain | 🔜 Coming soon | — |
| smolagents | 🔜 Coming soon | — |
Contributing
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
License
Apache 2.0
Project details
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