Skip to main content
Token counting helps you estimate context token count for an Agent run. Token counting can be used for features like token-based context compression and memory optimization.

​

Context can include:
  • Messages
    • Content of the message - Includes the system message, user message, and the assistant message content.
    • Tool call arguments and results
    • Optional reasoning content
    • Multimodal content blocks
  • Tools
    • Tool definitions can be a meaningful part of the total token count, especially with large parameter schemas or long descriptions.
  • Output schema
    • If you use an Output Schema, the schema is included in the token count.
  • Multimodal attachments
    • Images, audio, video, and files attached to messages are counted using conservative estimates.
Token counts are estimates. Provider billing and exact tokenization can differ due to model/provider behavior, hidden/system prompts, and how tools/schemas are serialized internally.
For better local token-count estimates, install tokenizers: 
pip install -U tiktoken tokenizers
  • tiktoken: used when available for OpenAI-style tokenization.
  • tokenizers: used for certain open-source tokenizers when available.
  • If neither is available for the given model, we fall back to heuristic estimates.

​
Example: counting tokens

from pydantic import BaseModel

from agno.models.message import Message
from agno.models.openai import OpenAIChat


class Answer(BaseModel):
    answer: str


model = OpenAIChat(id="gpt-4o")

messages = [
    Message(role="system", content="You are a concise assistant."),
    Message(role="user", content="Summarize context compression in 2 sentences."),
]

# Tool definitions can be passed as OpenAI-style tool dicts
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for a query.",
            "parameters": {
                "type": "object",
                "properties": {"query": {"type": "string"}},
                "required": ["query"],
            },
        },
    }
]

tokens = model.count_tokens(messages=messages, tools=tools, output_schema=Answer)
print(f"Estimated tokens: {tokens}")

​
Token counting in token-based context compression

When you set compress_token_limit, Agno checks the estimated token count during the run loop and triggers compression when the threshold is reached. Because token counting can include message history, tool definitions, and the output schema/response format, it more closely matches the “true” request size than counting only message text.

​
Multimodal estimates

Agno uses conservative estimates for multimodal inputs to support context planning:
  • Images: estimated via a tile-based approach (vision-style counting)
  • Audio: estimated using tokens-per-second
  • Video: estimated as frames counted similarly to images (with conservative defaults if fps/dimensions are unknown)
  • Files: estimated based on file type/size

​
Notes:

  • Token counting is still in Beta. We do our best to provide an estimate but we do not claim it be be 100% accurate across all providers and models. Please be wary of using this token count for calculating costs.
  • For some providers like Claude we are able to call the exact endpoint and get an exact token count. This is not supported for all providers yet.