🗄️ Cache

Note

COMING SOON! 🚀 Cache is not yet implemented in Python, but is available today in TypeScript

Overview

Caching is a technique used to temporarily store copies of data or computation results to improve performance by reducing the need to repeatedly fetch or compute the same data from slower or more resource-intensive sources.

In the context of AI applications, caching provides several important benefits:

🚀 Performance improvement - Avoid repeating expensive operations like API calls or complex calculations
💰 Cost reduction - Minimize repeated calls to paid services (like external APIs or LLM providers)
⚡ Latency reduction - Deliver faster responses to users by serving cached results
🔄 Consistency - Ensure consistent responses for identical inputs

BeeAI framework provides a robust caching system with multiple implementations to suit different use cases.

Core concepts

Cache types

BeeAI framework offers several cache implementations out of the box:

Type	Description
UnconstrainedCache	Simple in-memory cache with no limits
SlidingCache	In-memory cache that maintains a maximum number of entries
FileCache	Persistent cache that stores data on disk
NullCache	Special implementation that performs no caching (useful for testing)

Each cache type implements the BaseCache interface, making them interchangeable in your code.

Usage patterns

BeeAI framework supports several caching patterns:

Usage pattern	Description
Direct caching	Manually store and retrieve values
Function decoration	Automatically cache function returns
Tool integration	Cache tool execution results
LLM integration	Cache model responses

Basic usage

Caching function output

The simplest way to use caching is to wrap a function that produces deterministic output:

import asyncio
import sys
import traceback

from beeai_framework.cache import UnconstrainedCache
from beeai_framework.errors import FrameworkError


async def main() -> None:
    cache: UnconstrainedCache[int] = UnconstrainedCache()

    async def fibonacci(n: int) -> int:
        cache_key = str(n)
        cached = await cache.get(cache_key)
        if cached:
            return int(cached)

        if n < 1:
            result = 0
        elif n <= 2:
            result = 1
        else:
            result = await fibonacci(n - 1) + await fibonacci(n - 2)

        await cache.set(cache_key, result)
        return result

    print(await fibonacci(10))  # 55
    print(await fibonacci(9))  # 34 (retrieved from cache)
    print(f"Cache size {await cache.size()}")  # 10


if __name__ == "__main__":
    try:
        asyncio.run(main())
    except FrameworkError as e:
        traceback.print_exc()
        sys.exit(e.explain())

Source: examples/cache/unconstrained_cache_function.py

Using with tools

BeeAI framework's caching system seamlessly integrates with tools:

import asyncio
import sys
import traceback

from beeai_framework.cache import SlidingCache
from beeai_framework.errors import FrameworkError
from beeai_framework.tools.search.wikipedia import (
    WikipediaTool,
    WikipediaToolInput,
)


async def main() -> None:
    wikipedia_client = WikipediaTool({"full_text": True, "cache": SlidingCache(size=100, ttl=5 * 60)})

    print(await wikipedia_client.cache.size())  # 0
    tool_input = WikipediaToolInput(query="United States")
    first = await wikipedia_client.run(tool_input)
    print(await wikipedia_client.cache.size())  # 1

    # new request with the EXACTLY same input will be retrieved from the cache
    tool_input = WikipediaToolInput(query="United States")
    second = await wikipedia_client.run(tool_input)
    print(first.get_text_content() == second.get_text_content())  # True
    print(await wikipedia_client.cache.size())  # 1


if __name__ == "__main__":
    try:
        asyncio.run(main())
    except FrameworkError as e:
        traceback.print_exc()
        sys.exit(e.explain())

Source: examples/cache/tool_cache.py

Using with LLMs

You can also cache LLM responses to save on API costs:

import asyncio
import sys
import traceback

from beeai_framework.adapters.ollama import OllamaChatModel
from beeai_framework.backend import ChatModelParameters, UserMessage
from beeai_framework.cache import SlidingCache
from beeai_framework.errors import FrameworkError


async def main() -> None:
    llm = OllamaChatModel("llama3.1")
    llm.config(parameters=ChatModelParameters(max_tokens=25), cache=SlidingCache(size=50))

    print(await llm.cache.size())  # 0
    first = await llm.create(messages=[UserMessage("Who is Amilcar Cabral?")])
    print(await llm.cache.size())  # 1

    # new request with the EXACTLY same input will be retrieved from the cache
    second = await llm.create(messages=[UserMessage("Who is Amilcar Cabral?")])
    print(first.get_text_content() == second.get_text_content())  # True
    print(await llm.cache.size())  # 1


if __name__ == "__main__":
    try:
        asyncio.run(main())
    except FrameworkError as e:
        traceback.print_exc()
        sys.exit(e.explain())

Source: examples/cache/llm_cache.py

Cache types

UnconstrainedCache

The simplest cache type with no constraints on size or entry lifetime. Good for development and smaller applications.

import asyncio
import sys
import traceback

from beeai_framework.cache import UnconstrainedCache
from beeai_framework.errors import FrameworkError


async def main() -> None:
    cache: UnconstrainedCache[int] = UnconstrainedCache()

    # Save
    await cache.set("a", 1)
    await cache.set("b", 2)

    # Read
    result = await cache.has("a")
    print(result)  # True

    # Meta
    print(cache.enabled)  # True
    print(await cache.has("a"))  # True
    print(await cache.has("b"))  # True
    print(await cache.has("c"))  # False
    print(await cache.size())  # 2

    # Delete
    await cache.delete("a")
    print(await cache.has("a"))  # False

    # Clear
    await cache.clear()
    print(await cache.size())  # 0


if __name__ == "__main__":
    try:
        asyncio.run(main())
    except FrameworkError as e:
        traceback.print_exc()
        sys.exit(e.explain())

Source: examples/cache/unconstrained_cache.py

SlidingCache

Maintains a maximum number of entries, removing the oldest entries when the limit is reached.

import asyncio
import sys
import traceback

from beeai_framework.cache import SlidingCache
from beeai_framework.errors import FrameworkError


async def main() -> None:
    cache: SlidingCache[int] = SlidingCache(
        size=3,  # (required) number of items that can be live in the cache at a single moment
        ttl=1,  # // (optional, default is Infinity) Time in seconds after the element is removed from a cache
    )

    await cache.set("a", 1)
    await cache.set("b", 2)
    await cache.set("c", 3)

    await cache.set("d", 4)  # overflow - cache internally removes the oldest entry (key "a")

    print(await cache.has("a"))  # False
    print(await cache.size())  # 3


if __name__ == "__main__":
    try:
        asyncio.run(main())
    except FrameworkError as e:
        traceback.print_exc()
        sys.exit(e.explain())

Source: examples/cache/sliding_cache.py

FileCache

Persists cache data to disk, allowing data to survive if application restarts.

Coming soon

Source: examples/cache/fileCache.py

With custom provider

You can customize how the FileCache stores data:

Coming soon

Source: examples/cache/fileCacheCustomProvider.py

NullCache

A special cache that implements the BaseCache interface but performs no caching. Useful for testing or temporarily disabling caching.

The reason for implementing is to enable Null object pattern.

Advanced usage

Cache decorator

The framework provides a convenient decorator for automatically caching function results:

Coming soon

Source: examples/cache/decoratorCache.py

For more complex caching logic, you can customize the key generation:

Coming soon

Source: /examples/cache/decoratorCacheComplex.py

CacheFn helper

For more dynamic caching needs, the CacheFn helper provides a functional approach:

Coming soon

Source: /examples/cache/cacheFn.py

Creating a custom cache provider

You can create your own cache implementation by extending the BaseCache class:

from typing import TypeVar

from beeai_framework.cache import BaseCache

T = TypeVar("T")


class CustomCache(BaseCache[T]):
    async def size(self) -> int:
        raise NotImplementedError("CustomCache 'size' not yet implemented")

    async def set(self, _key: str, _value: T) -> None:
        raise NotImplementedError("CustomCache 'set' not yet implemented")

    async def get(self, key: str) -> T | None:
        raise NotImplementedError("CustomCache 'get' not yet implemented")

    async def has(self, key: str) -> bool:
        raise NotImplementedError("CustomCache 'has' not yet implemented")

    async def delete(self, key: str) -> bool:
        raise NotImplementedError("CustomCache 'delete' not yet implemented")

    async def clear(self) -> None:
        raise NotImplementedError("CustomCache 'clear' not yet implemented")

Source: examples/cache/custom.py

Examples

All cache examples are coming soon in python.

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🗄️ Cache

Table of Contents

Overview

Core concepts

Cache types

Usage patterns

Basic usage

Caching function output

Using with tools

Using with LLMs

Cache types

UnconstrainedCache

SlidingCache

FileCache

With custom provider

NullCache

Advanced usage

Cache decorator

CacheFn helper

Creating a custom cache provider

Examples

Files

cache.md

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History

cache.md

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🗄️ Cache

Table of Contents

Overview

Core concepts

Cache types

Usage patterns

Basic usage

Caching function output

Using with tools

Using with LLMs

Cache types

UnconstrainedCache

SlidingCache

FileCache

With custom provider

NullCache

Advanced usage

Cache decorator

CacheFn helper

Creating a custom cache provider

Examples