contextcounter 0.1.6

Universal context limits, token counting and tool callings for OpenAI, Anthropic, and Gemini.

📦 ContextCounter

ContextCounter is a lightweight Python library for:

• 🔍 Fetching context window limits across LLM providers (OpenAI, Anthropic, Gemini)
• 📊 Counting tokens in text or chat messages, with safe fallbacks
• ✅ Checking if you’re about to exceed context
• 🛠 Detailed analytics: count tool calls, reasoning tokens, and token splits by role

It works out-of-the-box with no dependencies, and can optionally integrate with official SDKs for precise context sizes.

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🚀 Installation

pip install contextcounter

Optional extras (for better accuracy):

pip install "contextcounter[openai]"       # precise OpenAI context limits
pip install "contextcounter[anthropic]"    # precise Anthropic context limits
pip install "contextcounter[tokenizers]"   # local tokenizers (tiktoken, transformers)

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⚡ Quickstart

from contextcounter import ContextInspector

# create inspector bound to a provider (openai, anthropic, google/vertex)
ctx = ContextInspector(provider="openai")

# fetch context window for a model
limit = ctx.get_limit("gpt-4o")
print("Context window:", limit)

# count tokens in free text
tokens = ctx.count_text("Hello world", model="gpt-4o")
print("Tokens:", tokens)

# count tokens in a chat-style message list
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Summarize this in 5 points."},
]
msg_tokens = ctx.count_messages(messages, model="gpt-4o")
print("Message tokens:", msg_tokens)

# check if it will exceed
print("Will exceed?", ctx.will_exceed(msg_tokens, "gpt-4o", reserve=2048))

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📖 API Reference

ContextInspector

from contextcounter import ContextInspector

ctx = ContextInspector(provider="openai")

• provider: "openai" | "anthropic" | "google" | "vertex" | None
  • If given, you don’t need to repeat provider in every function.
• gemini_overrides (dict, optional): override documented Gemini limits (default is 1_000_000 tokens).

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ctx.get_limit(model: str) -> int | None

Returns the maximum context window for the given model.

• For OpenAI models: will query live API if openai package is installed & authenticated.
• For Anthropic: will query live API if anthropic package is installed & authenticated.
• For Gemini: uses documented defaults (1,000,000) unless overridden.
• Fallback: internal static defaults.

ctx = ContextInspector(provider="anthropic")
print(ctx.get_limit("claude-3-7-sonnet"))  # 200000

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ctx.count_text(text: str, model: str | None = None) -> int

Returns the number of tokens in a plain text string.

• Tries tiktoken if available (for OpenAI models).
• Falls back to GPT-2 tokenizer if transformers installed.
• Last resort: heuristic (≈1 token per ~3.7 characters).

print(ctx.count_text("Hello world", model="gpt-4o"))  # → 5 (approx.)

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ctx.count_messages(messages: list, model: str | None = None) -> int

Returns the number of tokens in a chat-style message list.

• Adds structural overhead tokens per message (default: 3 tokens, 6 for system role).
• Handles content as strings or lists (for multimodal formats).

messages = [
    {"role": "system", "content": "You are helpful."},
    {"role": "user", "content": "Tell me a joke."}
]
print(ctx.count_messages(messages, model="gpt-4o"))  # → ~20

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ctx.will_exceed(tokens: int, model: str, reserve: int = 1024) -> bool | None

Checks whether a given token count plus a reserve buffer will exceed the model’s context window.

• Returns True if it will exceed, False otherwise, None if limit unknown.

tokens = ctx.count_messages(messages, model="gpt-4o")
print(ctx.will_exceed(tokens, "gpt-4o", reserve=2048))

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Standalone Functions

If you don’t want to use the ContextInspector class, you can import the core functions:

from contextcounter import get_context_limit, count_text, count_messages

print(get_context_limit("gpt-4o", provider="openai"))
print(count_text("Hello", model="gpt-4o", provider="openai"))

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🔎 Detailed Analytics

In addition to simple counts, ContextCounter can provide detailed breakdowns:

• Number of tool calls
• Token usage by role (system/user/assistant/tool)
• Prompt vs. completion split
• Reasoning tokens (when provided by the API usage object)

analyze_messages

from contextcounter import analyze_messages

report = analyze_messages(messages, model="gpt-4o", provider="openai")
print(report)
# {
#   "total_tokens": 123,
#   "by_role": {"system": 10, "user": 45, "assistant": 60, "tool": 8, "other": 0},
#   "details": [
#       {"role":"system","content_tokens":4,"overhead":6,"tool_calls":0},
#       {"role":"user","content_tokens":42,"overhead":3,"tool_calls":0},
#       {"role":"assistant","content_tokens":57,"overhead":3,"tool_calls":2},
#   ],
#   "num_messages": 3,
#   "num_tool_calls": 2
# }

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split_prompt_completion

from contextcounter import split_prompt_completion

split = split_prompt_completion(messages, response_text="assistant reply...", model="gpt-4o", provider="openai")
print(split)
# {"prompt_tokens": 88, "completion_tokens": 35, "total_tokens": 123}

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summarize_provider_usage

Normalize usage dictionaries from API responses (OpenAI, Anthropic, etc.) into a consistent schema.

from contextcounter import summarize_provider_usage

openai_usage = {
    "prompt_tokens": 81,
    "completion_tokens": 40,
    "total_tokens": 121,
    "reasoning_tokens": 12,
}
norm = summarize_provider_usage(openai_usage, provider="openai")
print(norm)
# {"prompt_tokens":81,"completion_tokens":40,"reasoning_tokens":12,"total_tokens":121}

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🔧 Supported Models & Defaults

Provider	Model Example	Context Limit (tokens)
OpenAI	gpt-4o, gpt-4.1	128,000
OpenAI	o3	200,000
Anthropic	claude-3-7-sonnet	200,000
Anthropic	claude-3-5-sonnet/haiku	200,000
Google	gemini-1.5-pro/flash	1,000,000

Live API calls (OpenAI, Anthropic) will override these defaults if you have SDKs installed + credentials configured.

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🧪 Testing

pytest tests/

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📌 Roadmap

• ✅ Token counting across providers
• ✅ Context limit retrieval with fallbacks
• ✅ Tool call counting + split by role
• ✅ Prompt vs. completion split
• ✅ Reasoning tokens via provider usage normalization
• 🔜 Native Gemini token counting (when tokenizer becomes public)
• 🔜 More granular overhead estimation per provider

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📄 License

MIT © Nemlig ADK