openai-cost-calculator-with-lanchain 1.0.0
A library to estimate OpenAI API costs based on token usage, integrating with Langchain. This is a fork from v1.1.1 of openai_cost_calculator by Orkun Kınay and Murat Barkın Kınay (https://github.com/orkunkinay/openai_cost_calculator).
Requires Python: >=3.8.1

# openai_cost_calculator_with_langchain

[![License](https://img.shields.io/badge/license-MIT-blue)](LICENSE)

_This is a package forked of the original [openai_cost_calculator](https://orkunkinay.github.io/openai_cost_calculator/), which integrates support for Langchain_.

Instant, accurate **USD cost estimates** for OpenAI & Azure OpenAI API calls. Works with **Chat Completions** and the **Responses API**, streaming or not. It also works with Langchain `invoke` responses. Offers a **typed** `Decimal`-based API for finance-safe math and a **legacy** string API for drop-ins.

**Docs:** https://orkunkinay.github.io/openai_cost_calculator/

---

## Installation

```bash
pip install openai-cost-calculator-with-langchain
```

> Import name uses underscores: `import openai_cost_calculator-with-langchain`

---

## Quickstart

**Typed (recommended)**

```python
from openai import OpenAI
from openai_cost_calculator import estimate_cost_typed

client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hi there!"}],
)

cost = estimate_cost_typed(resp)  # -> CostBreakdown (Decimal fields)
print(cost.total_cost)            # Decimal('0.00000750')
print(cost.as_dict(stringify=True))  # 8-dp strings if you prefer
```

**Legacy (string output)**

```python
from openai_cost_calculator import estimate_cost
print(estimate_cost(resp))  # dict of 8-dp strings
```

**Responses API**

```python
resp = client.responses.create(model="gpt-4.1-mini", input=[{"role":"user","content":"Hi"}])
from openai_cost_calculator import estimate_cost_typed
print(estimate_cost_typed(resp))
```

**Streaming**

```python
stream = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[{"role":"user","content":"Hi"}],
  stream=True,
  stream_options={"include_usage": True},
)
from openai_cost_calculator import estimate_cost_typed
print(estimate_cost_typed(stream))
```

**Langchain**

```python
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from openai_cost_calculator import estimate_cost_typed

load_dotenv()

model = ChatOpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
    model="gpt-4o-mini",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
)

messages = [
    (
        "system",
        "You are a helpful translator. Translate the user sentence to French.",
    ),
    ("human", "I love programming."),
]

response = model.invoke(messages)
print(estimate_cost_typed(response, is_langchain_response=True))
```
---

## Highlights

- **Typed API:** `CostBreakdown` dataclass with `Decimal` precision  
- **Drop-in legacy API:** 8-decimal strings (backward compatible)  
- **Handles edge cases:** cached tokens, undated model strings, streaming generators, Azure deployment names  
- **Pricing sources:** Remote CSV (24h cache) + **local overrides** and **offline mode**

---

## Pricing utilities

```python
from openai_cost_calculator import (
  refresh_pricing, set_offline_mode,
  add_pricing_entry, add_pricing_entries, clear_local_pricing
)

# Force refresh (bypasses 24h cache)
refresh_pricing()

# Run fully offline (no network calls)
set_offline_mode(True)

# Teach custom prices (per 1M tokens)
add_pricing_entry(
  "ollama/qwen3:30b", "2025-08-01",
  input_price=0.20, output_price=0.60, cached_input_price=0.04
)
```

Remote CSV (auto-fetched, cached 24h):  
`https://raw.githubusercontent.com/orkunkinay/openai_cost_calculator/refs/heads/main/data/gpt_pricing_data.csv`

---

## Errors

Recoverable issues raise `CostEstimateError` with a clear message (missing pricing row, unexpected input shape, etc.).

---

## Troubleshooting

- **“Pricing not found”** → confirm row exists in the CSV; call `refresh_pricing()`.  
- **`cached_tokens = 0`** → ensure `include_usage_details=True` (classic) or `stream_options={"include_usage": True}` (streaming).  
- **Model string has no date** → the latest row with `date ≤ today` is used.

---

## Links

- **Docs & examples:** https://orkunkinay.github.io/openai_cost_calculator/  
- **Source:** https://github.com/orkunkinay/openai_cost_calculator  
- **Issues:** https://github.com/orkunkinay/openai_cost_calculator/issues

---

## License

MIT © 2025 Orkun Kınay & Murat Barkın Kınay