langchain-tokonomics 0.1.0

LangChain integration for Tokonomics — AI cost metering, budget alerts, and spending caps for LLM API calls.

langchain-tokonomics

LangChain integration for Tokonomics — automatic cost tracking, budget alerts, and spending caps for LLM API calls.

Features

• Automatic cost tracking for every LangChain LLM call (OpenAI, Anthropic, Google, DeepSeek, Mistral, Cohere, xAI, and more)
• 30+ models with up-to-date pricing (June 2026)
• Budget alerts — get warnings when spending approaches a threshold
• Per-model breakdowns — see which models cost the most
• Thread-safe — works with async and parallel chains
• No API key required — runs fully local by default
• Optional Tokonomics API — for persistent tracking, team dashboards, and hard spending caps

Installation

pip install langchain-tokonomics

Quick Start

Track costs locally (no API key needed)

from langchain_openai import ChatOpenAI
from langchain_tokonomics import TokonomicsCallbackHandler

# Create handler with a $5 budget
handler = TokonomicsCallbackHandler(budget_usd=5.00)

# Use with any LangChain model
llm = ChatOpenAI(model="gpt-4o", callbacks=[handler])
response = llm.invoke("Explain quantum computing in 3 sentences.")

# Check costs
print(f"Cost: ${handler.total_cost:.6f}")
print(f"Tokens: {handler.total_tokens:,}")
print(handler.summary())

With budget warnings

def on_warning(spent, budget):
    print(f"WARNING: ${spent:.2f} of ${budget:.2f} budget used!")

handler = TokonomicsCallbackHandler(
    budget_usd=10.00,
    warning_threshold=0.8,  # Warn at 80%
    on_budget_warning=on_warning,
)

llm = ChatOpenAI(model="gpt-4o", callbacks=[handler])

for question in my_questions:
    if handler.tracker.budget_exceeded:
        print("Budget exceeded, stopping.")
        break
    llm.invoke(question)

With Tokonomics API (persistent tracking)

Sign up at tokonomics.ca for a free API key. This gives you a dashboard with historical cost data, team breakdowns, and hard spending caps.

handler = TokonomicsCallbackHandler(
    api_key="mk_your_api_key_here",
    budget_usd=50.00,
)

llm = ChatOpenAI(model="gpt-4o", callbacks=[handler])

Cost estimation without making API calls

from langchain_tokonomics.pricing import calculate_cost, get_pricing

# Check pricing for a model
pricing = get_pricing("gpt-4o")
print(f"GPT-4o: ${pricing['input']}/1M input, ${pricing['output']}/1M output")

# Estimate cost
cost = calculate_cost("gpt-4o", input_tokens=1000, output_tokens=500)
print(f"Estimated cost: ${cost:.6f}")

Per-model breakdown

handler = TokonomicsCallbackHandler()

# Run multiple models...
llm_4o = ChatOpenAI(model="gpt-4o", callbacks=[handler])
llm_mini = ChatOpenAI(model="gpt-4o-mini", callbacks=[handler])

llm_4o.invoke("Complex analysis task...")
llm_mini.invoke("Simple formatting task...")

# See cost breakdown
for model, cost in handler.cost_by_model().items():
    print(f"  {model}: ${cost:.6f}")

Supported Models

30+ models across 8 providers:

Provider	Models
OpenAI	GPT-4o, GPT-4o Mini, GPT-4.1, o3, o3-mini, o4-mini
Anthropic	Claude Sonnet 4, Claude Opus 4, Claude Haiku 3.5
Google	Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash
DeepSeek	DeepSeek V3, DeepSeek R1
Meta	Llama 4 Maverick, Llama 4 Scout
Mistral	Mistral Large, Mistral Small
xAI	Grok 3, Grok 3 Mini
Cohere	Command A

Unknown models fall back to GPT-4o Mini pricing.

Why Tokonomics?

LangChain makes it easy to build LLM applications, but production costs can spiral without visibility. langchain-tokonomics gives you:

1. Real-time cost awareness during development and production
2. Budget guardrails to prevent unexpected bills
3. Model cost comparison to pick the right model for each task
4. Team accountability when connected to the Tokonomics dashboard

Links

• Tokonomics — AI cost metering API
• Tokonomics Blog — LLM cost analysis and optimization guides
• LLM Cost Calculator — Compare pricing across models
• Documentation

License

MIT