> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompt Caching

> Cache the large, stable system prompt so repeat Agent calls are re-billed at a discount.

Repeat Agent calls resend the same large system prompt every time. Set `prompt_caching=True` and Swarms marks the stable prefix so the provider reuses it — you pay full price once, then a discount on every call after.

## When to use it

* A large, reusable system prompt (persona, policies, examples) sent on every call.
* The same agent runs many times or holds a multi-turn conversation.
* Tool-heavy agents whose tool schemas stay constant across calls.
* Long context (docs, transcripts) reused turn after turn.

## Basic usage

Flip on `prompt_caching`. On Anthropic, Swarms adds `cache_control` breakpoints to the stable prefix; on OpenAI, caching is automatic and the flag leaves messages untouched. Caching only kicks in above the provider's token minimum (Opus 4.5+ needs \~4,096 input tokens), so the system prompt must be large — here we repeat a string to cross that bar.

```python theme={null}
from swarms import Agent

# A large, stable system prompt is what gets cached.
system_prompt = "You are a senior financial analyst. " * 400

agent = Agent(
    agent_name="CachedAnalyst",
    model_name="claude-opus-4-8",
    system_prompt=system_prompt,
    temperature=None,          # Opus 4.7/4.8 reject a temperature value
    prompt_caching=True,       # the on-switch
    max_loops=1,
)

# First call writes the cache.
agent.run("Summarize the risks of rising interest rates.")

# Second call reuses the cached prefix at a discount.
agent.run("Now summarize the opportunities.")
```

1. `prompt_caching=True` marks the system prompt (plus the last message) as cacheable.
2. The first `run` pays to write the cache.
3. Every later `run` reads the cached prefix instead of re-billing it.

## Tune it with cache\_config

Pass a `cache_config` dict to control caching. The common knob is `ttl` — Anthropic supports a 1-hour cache:

```python theme={null}
from swarms import Agent

agent = Agent(
    agent_name="CachedAnalyst",
    model_name="claude-opus-4-8",
    system_prompt="You are a senior financial analyst. " * 400,
    temperature=None,
    prompt_caching=True,
    cache_config={"ttl": "1h"},   # keep the cache warm for an hour
    max_loops=1,
)

agent.run("Give me a market outlook for Q3.")
```

`cache_config` also accepts `cache_system_prompt`, `cache_messages`, `cache_tools`, `override`, and OpenAI's `prompt_cache_key` / `prompt_cache_retention`. See the full [Prompt Caching](/agents/prompt-caching) guide under Agent Development for the complete list.

## Verify it worked

`Agent.run()` returns a formatted string, so to see token usage read from the agent's own underlying LLM (`agent.llm`) with `return_all = True` — that returns the raw response including the usage block. Check the second call for `cache_read_input_tokens` greater than zero:

```python theme={null}
from swarms import Agent

agent = Agent(
    agent_name="CachedAnalyst",
    model_name="claude-opus-4-8",
    system_prompt="You are a senior financial analyst. " * 400,
    temperature=None,
    prompt_caching=True,
    max_loops=1,
)

# Read from the agent's own llm to expose the raw usage block.
agent.llm.return_all = True

agent.llm.run("First question to write the cache.")   # writes the cache
resp = agent.llm.run("Second question to read the cache.")  # reads it

usage = resp["usage"] if isinstance(resp, dict) else resp.usage
print(usage)   # look for cache_read_input_tokens > 0
```

A non-zero `cache_read_input_tokens` on the second call confirms the cached prefix was reused.

## See also

* [Prompt Caching](/agents/prompt-caching) — Full reference: all `cache_config` keys, provider behavior, and cost details.
* [Context Compression](/examples/agents/context-compression) — Shrink long transcripts before they hit the context limit.