llm-budget 0.1.0

Pre-flight LLM cost estimation and budget enforcement

llm-budget

Stop overpaying for LLM API calls. One line of code, automatic model selection, same results.

One function. Auto-selects the cheapest model that fits your task. Tracks every dollar.

---

Before / After

# BEFORE: You always use the expensive model. $9/day for 1,000 extraction calls.
response = client.messages.create(
    model="claude-sonnet-4-20250514",  # $0.009/call
    messages=[{"role": "user", "content": "Extract dates from this text..."}],
    max_tokens=1024,
)

# AFTER: llm-budget picks the cheapest model that can handle the task.
response = llm_budget.smart_route(
    client, "Extract dates from this text...",
    task_type="extraction",
)
# -> Used claude-3-5-haiku ($0.003/call). 67% cheaper. Same result.
# -> $3/day instead of $9/day. That's $180/month saved.

smart_route() returns the exact same response object as the native SDK. Your response.content[0].text, response.choices[0].message.content, response.usage -- all unchanged. Your existing parsing code doesn't change. It's literally a 1-line swap.

---

Install

pip install llm-budget

With provider SDKs:

pip install llm-budget[openai]      # OpenAI integration
pip install llm-budget[anthropic]   # Anthropic integration
pip install llm-budget[langchain]   # LangChain integration
pip install llm-budget[crewai]      # CrewAI integration
pip install llm-budget[all]         # Everything

---

How It Works

llm-budget knows the pricing, capability tier, and quality score of 20 models across 7 providers. When you tell it what kind of task you're doing, it picks the cheapest model that won't compromise quality.

Task Type	What It Means	Model Tier Selected
extraction	Parsing, dates, JSON, structured output	Efficient (cheapest)
simple	Formatting, translation, classification	Efficient
moderate	Summarization, Q&A, general tasks	Efficient / Mid
creative	Writing, brainstorming, marketing copy	Frontier
coding	Code generation, debugging, review	Frontier / Reasoning
complex	Multi-step reasoning, deep analysis	Frontier
math	Calculations, proofs, science	Reasoning

---

Quick Start: smart_route() (Recommended)

One function. Auto-selects model, makes the API call, tracks cost.

Anthropic

from anthropic import Anthropic
import llm_budget

client = Anthropic()

# Extraction task -> llm-budget picks haiku (cheap, good enough)
response = llm_budget.smart_route(
    client, "Extract all dates from: 'Started March 15. Deadline June 30.'",
    task_type="extraction",
)
print(response.content[0].text)  # Works exactly like client.messages.create()

# Complex analysis -> llm-budget picks sonnet (frontier, worth the cost)
response = llm_budget.smart_route(
    client, "Analyze the trade-offs between microservices and monoliths",
    task_type="complex",
)
print(response.content[0].text)

# Check what you spent
print(f"Total: ${llm_budget.get_tracker().get_spend('today'):.4f}")

OpenAI

from openai import OpenAI
import llm_budget

client = OpenAI()

# Simple task -> picks gpt-4o-mini ($0.0004/call instead of $0.006 with gpt-4o)
response = llm_budget.smart_route(
    client, "Translate this to French: 'Hello, how are you?'",
    task_type="simple",
)
print(response.choices[0].message.content)  # Same as client.chat.completions.create()

# Coding task -> picks gpt-4o or o3-mini (needs reasoning capability)
response = llm_budget.smart_route(
    client, "Write a Python function to merge two sorted linked lists",
    task_type="coding",
)

Full smart_route() API

response = llm_budget.smart_route(
    client,                          # OpenAI() or Anthropic() client
    "Your prompt here",              # String or messages list
    task_type="moderate",            # simple/moderate/complex/coding/math/creative/extraction
    budget=5.00,                     # Daily budget in USD (influences model selection when low)
    model="gpt-4o",                  # Explicit override (skips routing, still tracks cost)
    max_tokens=1024,                 # Max output tokens
    temperature=0.7,                 # Forwarded to the API
    system="Be helpful",             # Forwarded to the API (Anthropic)
)

Key behaviors:

• task_type controls which models are eligible. "extraction" picks cheap models. "complex" picks powerful ones.
• budget makes routing budget-aware. When remaining budget is low, it favors cheaper models.
• model overrides routing entirely (but still tracks cost).
• All extra kwargs (temperature, system, top_p, etc.) are forwarded to the underlying API call.
• Returns the exact same response as the native SDK. No wrappers, no transformations.
• Cost is automatically recorded to the tracker after every call.

---

More Control: Decorators

@cost_aware -- Auto-swap models per call

Wrap your existing function. The decorator intercepts the model parameter and swaps it to a cheaper suitable model based on task_type.

import llm_budget
from anthropic import Anthropic

client = Anthropic()

@llm_budget.cost_aware(budget=5.00)
def my_step(task, model="claude-sonnet-4-20250514", task_type="moderate"):
    return client.messages.create(
        model=model,
        messages=[{"role": "user", "content": task}],
        max_tokens=1024,
    )

# Decorator auto-swaps to haiku for extraction (saves 67%):
result = my_step("Extract dates from this invoice", task_type="extraction")

# Keeps sonnet for complex reasoning (worth the cost):
result = my_step("Analyze quarterly revenue trends", task_type="complex")

@budget -- Hard budget enforcement

Pre-flight cost estimation + hard limits. Blocks calls that would exceed budget.

from llm_budget import budget, BudgetExceeded

@budget(
    max_cost=5.00,           # $5 daily limit
    period="daily",          # Resets daily
    on_exceed="raise",       # "raise" | "warn" | "skip" | "downgrade:gpt-4o-mini"
    alert_at=0.8,            # Warn at 80% usage
    track_model="gpt-4o",
)
def my_llm_call(model, messages):
    return openai.chat.completions.create(model=model, messages=messages)

try:
    result = my_llm_call(model="gpt-4o", messages=[{"role": "user", "content": "Hello"}])
except BudgetExceeded as e:
    print(f"Budget exceeded: {e}")
    if e.suggested_model:
        print(f"Try cheaper model: {e.suggested_model}")

on_exceed modes:

Mode	Behavior
"raise"	Raises BudgetExceeded exception
"warn"	Logs warning, proceeds anyway
"skip"	Returns None, skips the API call
"downgrade:gpt-4o-mini"	Auto-swaps to cheaper model, proceeds

---

Pre-Flight Cost Estimation

Know what you'll spend before you spend it. No API key required.

from llm_budget import estimate, compare

# Estimate a single call
est = estimate(
    messages=[{"role": "user", "content": "Summarize this 10-page document..."}],
    model="gpt-4o",
    expected_output_tokens=500,
)
print(est)
# gpt-4o: ~42 input + ~500 output tokens = $0.005105

print(est.estimated_cost)   # 0.005105
print(est.input_cost)       # 0.000105
print(est.output_cost)      # 0.005000
print(est.input_tokens)     # 42
print(est.output_tokens)    # 500

# Compare across models (prints a sorted table)
results = compare(
    messages="Explain quantum computing in simple terms",
    models=["gpt-4o", "gpt-4o-mini", "claude-sonnet-4-20250514", "claude-3-5-haiku-20241022", "deepseek-chat"],
)
# Output:
# Model                         Input Tok   Est. Output    Est. Cost
# ---------------------------------------------------------------
# deepseek-chat                         8          ~8    $0.000006
# gpt-4o-mini                           8          ~8    $0.000006
# claude-3-5-haiku-20241022             8          ~8    $0.000048
# ...

---

Spend Tracking

Middleware (auto-tracking, zero code changes)

Wrap your client once. Every API call is automatically tracked.

from openai import OpenAI
from llm_budget import track_openai, get_tracker

client = track_openai(OpenAI())  # Wrap once

# Use normally -- cost is recorded automatically
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
)

# Check your spend
tracker = get_tracker()
print(f"Today: ${tracker.get_spend('today'):.4f}")
print(f"This week: ${tracker.get_spend('this_week'):.4f}")
print(f"This month: ${tracker.get_spend('this_month'):.4f}")
print(tracker.get_spend_breakdown("today"))  # {"gpt-4o": 0.0042, "gpt-4o-mini": 0.0001}

Works with Anthropic too:

from anthropic import Anthropic
from llm_budget import track_anthropic

client = track_anthropic(Anthropic())

Manual tracking

from llm_budget import Tracker

tracker = Tracker()                      # SQLite-backed, persists across sessions
tracker = Tracker(db_path="my_app.db")   # Custom database path

tracker.record(
    model="gpt-4o",
    input_tokens=100,
    output_tokens=50,
    cost_usd=0.00075,
)

tracker.get_spend("today")              # Total spend today
tracker.get_spend("monthly", model="gpt-4o")  # Spend on gpt-4o this month
tracker.get_spend_breakdown("this_week")       # {"gpt-4o": 3.21, "gpt-4o-mini": 0.14}
tracker.get_history(last_n=20)                 # Recent call records

---

Agent Tools (for AI agents that self-optimize)

Give your agent three tools. It calls them to decide which model to use, check budget, and compare options. This is the most powerful mode -- the agent itself makes cost-quality decisions.

OpenAI Agent

from openai import OpenAI
from llm_budget import cost_tools_openai, cost_context, cost_prompt

client = OpenAI()
tools = cost_tools_openai()       # 3 tools: estimate_cost, compare_models, check_budget
system = cost_prompt(daily_budget=5.00, strategy="balanced")

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

# When the agent calls a tool:
for tool_call in response.choices[0].message.tool_calls or []:
    result = cost_context(tool_call)  # Auto-detects format, returns string

Anthropic Agent

from anthropic import Anthropic
from llm_budget import cost_tools_anthropic, cost_context, cost_prompt

client = Anthropic()
tools = cost_tools_anthropic()
system = cost_prompt(monthly_budget=50.00, strategy="quality_first")

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    system=system,
    tools=tools,
    messages=[...],
)

for block in response.content:
    if block.type == "tool_use":
        result = cost_context(block)  # Auto-detects Anthropic format

The Three Agent Tools

Tool	What It Does	When to Call
estimate_cost	Pre-flight cost + capability check + alternatives	Before every LLM call
compare_models	Rank models by cost-quality for a specific task type	When choosing which model to use
check_budget	Live spend, remaining budget, projections, strategy advice	Periodically, or when budget is tight

Strategy Presets

# Balanced (default) -- smart tradeoffs
prompt = cost_prompt(strategy="balanced", daily_budget=5.00)

# Cost-first -- minimize spending, cheapest viable models
prompt = cost_prompt(strategy="cost_first", daily_budget=2.00)

# Quality-first -- best models, only save on trivial tasks
prompt = cost_prompt(strategy="quality_first", monthly_budget=100.00)

MCP Server

Expose the tools via Model Context Protocol for Claude Desktop, Cursor, etc.

pip install llm-budget[mcp]
llm-budget serve-mcp

Or in your MCP config:

{
  "mcpServers": {
    "llm-budget": {
      "command": "python",
      "args": ["-m", "llm_budget.mcp_server"]
    }
  }
}

---

Framework Integrations

LangChain

Budget-aware callback handler that plugs into any LangChain LLM.

pip install llm-budget[langchain] langchain-anthropic  # or langchain-openai

from langchain_anthropic import ChatAnthropic
from llm_budget.integrations.langchain import BudgetCallbackHandler

# Tracking only (no enforcement)
handler = BudgetCallbackHandler()
llm = ChatAnthropic(model="claude-sonnet-4-20250514", callbacks=[handler])
result = llm.invoke("Explain quantum computing")

print(f"Cost: ${handler.total_cost:.6f}")
print(f"Tokens: {handler.total_tokens}")

With budget enforcement:

from llm_budget.integrations.langchain import budget_callback
from llm_budget import BudgetExceeded

with budget_callback(max_cost=1.00, period="daily", on_exceed="raise") as cb:
    llm = ChatAnthropic(model="claude-sonnet-4-20250514", callbacks=[cb])
    try:
        for question in questions:
            result = llm.invoke(question)
    except BudgetExceeded:
        print(f"Budget hit after {cb.call_count} calls (${cb.total_cost:.4f})")

Works with any LangChain LLM: ChatOpenAI, ChatAnthropic, ChatGoogleGenerativeAI, etc.

CrewAI

Budget hooks that register globally with CrewAI's hook system.

pip install llm-budget[crewai] crewai

from llm_budget.integrations.crewai import track_crewai
from llm_budget import BudgetExceeded

with track_crewai(max_cost=10.00, period="daily") as hooks:
    crew = Crew(agents=[researcher, writer], tasks=[...])
    try:
        result = crew.kickoff()
    except BudgetExceeded:
        print(f"Budget hit: ${hooks.total_cost:.4f}")
    print(f"Crew run cost: ${hooks.total_cost:.4f} ({hooks.call_count} LLM calls)")

Or register hooks manually:

from llm_budget.integrations.crewai import BudgetHooks

hooks = BudgetHooks(max_cost=10.00, period="daily")
hooks.register()
# ... run agents ...
hooks.unregister()

---

CLI

# Check your spend
$ llm-budget status
LLM Budget Status
==================================================
  Daily      $0.45
  Weekly     $3.21
  Monthly    $12.54
  Total      $47.89

Breakdown by model (monthly):
  gpt-4o                              $9.80
  gpt-4o-mini                         $2.74

# Compare costs across models
$ llm-budget compare "Explain quantum computing" --models gpt-4o,gpt-4o-mini,claude-sonnet-4-20250514

# Estimate a single call
$ llm-budget estimate "Write a Python tutorial" --model gpt-4o --output-tokens 500

# List all 20 supported models with pricing
$ llm-budget models
$ llm-budget models --provider anthropic

# Update pricing from LiteLLM (stays current)
$ llm-budget update-prices

# Show recent API call history
$ llm-budget history --last 20

---

Supported Models

20 models across 7 providers. Pricing auto-updates via llm-budget update-prices.

Provider	Models	Tier
OpenAI	gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-3.5-turbo, o1, o1-mini, o3-mini	Frontier / Efficient / Reasoning
Anthropic	claude-sonnet-4, claude-opus-4, claude-3.5-haiku, claude-3.5-sonnet	Frontier / Efficient
DeepSeek	deepseek-chat, deepseek-reasoner	Efficient / Reasoning
Google	gemini-2.0-flash, gemini-1.5-pro, gemini-1.5-flash	Frontier / Efficient
Meta	llama-3.1-70b, llama-3.1-8b	Mid / Efficient
Mistral	mistral-large, mistral-small	Mid / Efficient

Capability Tiers

Tier	Quality Score	Cost Range (per call)	Best For
Reasoning	88-98	$0.001 - $0.01	Math, proofs, science, multi-step logic
Frontier	88-96	$0.003 - $0.02	Complex analysis, coding, creative writing
Mid	75-78	$0.001 - $0.003	General tasks, moderate reasoning
Efficient	55-73	$0.0001 - $0.003	Extraction, formatting, translation, Q&A

---

API Reference

Smart Routing

• smart_route(client, task, *, task_type, budget, model, max_tokens, **api_kwargs) -- One-call routing + tracking
• cost_aware(budget, task_type, provider, tracker) -- Decorator for auto model selection

Cost Estimation

• estimate(messages, model, expected_output_tokens) -- Pre-flight cost estimate
• compare(messages, models, expected_output_tokens) -- Compare costs across models
• count_tokens(text_or_messages, model) -- Count tokens

Budget Enforcement

• @budget(max_cost, period, on_exceed, alert_at, track_model) -- Decorator
• BudgetEnforcer(tracker) -- Programmatic enforcement
• BudgetPolicy(max_cost, period, on_exceed, alert_at) -- Policy config
• BudgetExceeded -- Exception (has .suggested_model, .remaining_budget)

Spend Tracking

• Tracker(db_path=None) -- SQLite spend tracker
• get_tracker() -- Default tracker singleton
• tracker.record(model, input_tokens, output_tokens, cost_usd) -- Record a call
• tracker.get_spend(period, model) -- Query spend
• tracker.get_spend_breakdown(period) -- Spend by model
• tracker.get_history(last_n, model) -- Recent records
• track_openai(client, tracker) -- Auto-tracking middleware for OpenAI
• track_anthropic(client, tracker) -- Auto-tracking middleware for Anthropic

Model Intelligence

• recommend_models(prompt, task_type, budget_remaining, available_models) -- Get model recommendations
• is_model_suitable(model, task_type) -- Check if model fits task (bool, reason)
• get_capability_summary(model) -- One-line model summary
• get_pricing(model) -- Get ModelPricing object
• get_registry() -- Access the full pricing registry

Agent Tools

• cost_tools_openai() -- Tool schemas in OpenAI function calling format
• cost_tools_anthropic() -- Tool schemas in Anthropic tool_use format
• cost_tools() -- Framework-agnostic tool definitions
• cost_context(tool_call, tracker, budget_config) -- Universal tool handler (auto-detects format)
• cost_prompt(daily_budget, weekly_budget, monthly_budget, strategy) -- System prompt generator

Framework Integrations

LangChain (from llm_budget.integrations.langchain import ...):

• BudgetCallbackHandler(max_cost, period, on_exceed, alert_at, tracker, model) -- LangChain callback handler
• budget_callback(...) -- Context manager that creates + yields a handler

CrewAI (from llm_budget.integrations.crewai import ...):

• BudgetHooks(max_cost, period, on_exceed, alert_at, tracker, model, default_model) -- CrewAI hook class
• track_crewai(...) -- Context manager that creates + registers hooks

---

Feature Comparison

Feature	llm-budget	TokenCost	LiteLLM
Automatic model routing	Yes (smart_route)	No	No
Pre-flight cost estimation	Yes	No	No
Budget enforcement	Yes (decorator)	No	No
Auto-downgrade on budget	Yes	No	No
Agent cost tools	Yes (3 tools)	No	No
LangChain integration	Yes (callback handler)	No	No
CrewAI integration	Yes (hooks)	No	No
MCP server	Yes	No	No
Standalone (no SDK required)	Yes	Yes	No
Multi-model comparison	Yes	No	Partial
Local SQLite tracking	Yes	No	Yes (proxy)
Zero cloud dependency	Yes	Yes	No
CLI	Yes	No	Yes

---

Contributing

llm-budget is open source under the MIT license. Contributions are welcome!

git clone https://github.com/aman-source/llm-budget.git
cd llm-budget
pip install -e ".[dev]"
pytest

Areas where we'd love help:

• Framework integrations -- Add support for LlamaIndex, AutoGen, or other frameworks
• Async support -- LangChain ainvoke() and streaming response tracking
• Provider coverage -- Add pricing for new models and providers
• Bug fixes -- See open issues

Running tests:

pytest                          # All tests (260+)
pytest --cov=llm_budget -q      # With coverage (target: 90%+)
pytest tests/test_langchain_integration.py -v  # Specific module

All framework integrations (LangChain, CrewAI, MCP) use optional dependencies with mocked tests -- you don't need to install the frameworks to run the test suite.

---

License

MIT