Token Cost Compounds Every Turn — Full Context Resent on Each Message
Symptom
- Turn 1: 500 tokens input
- Turn 5: 8,000 tokens input (all previous turns included)
- Turn 20: 60,000 tokens input
- Total cost for a 20-turn conversation: 300,000+ tokens — vastly more than expected
- Tool results (often large) are re-sent on every subsequent call
Root Cause
LLM APIs are stateless — you must resend the full conversation history every call. Without pruning or caching, costs grow as O(n²) where n is the number of turns. Tool results are the worst offender: a single 10KB tool result re-sent 20 times = 200KB of unnecessary input tokens.
Fix
Option 1: Enable Anthropic prompt caching
Anthropic’s prompt caching reduces re-sent context costs by 90%:
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": long_system_context,
"cache_control": {"type": "ephemeral"} # Cache this block
}
]
}
]
Cached blocks cost 10% of normal input price on re-use. Ideal for large static context (docs, tool schemas, long instructions).
Option 2: Summarize old turns instead of keeping them verbatim
SUMMARIZE_AFTER_TURNS = 10
async def get_condensed_history(history):
if len(history) <= SUMMARIZE_AFTER_TURNS * 2:
return history
old_turns = history[:-SUMMARIZE_AFTER_TURNS * 2]
recent_turns = history[-SUMMARIZE_AFTER_TURNS * 2:]
summary = await agent.complete(
f"Summarize these conversation turns in 5 bullet points, "
f"preserving all decisions made and key facts:\n\n{old_turns}"
)
return [
{"role": "user", "content": f"[Earlier context summary: {summary}]"},
{"role": "assistant", "content": "Understood."},
*recent_turns
]
Option 3: Replace tool results with summaries after use
def compress_tool_result(result, tool_name):
"""Replace large tool outputs with compact summaries once the agent has used them"""
if len(str(result)) > 2000:
return f"[{tool_name} result: {len(str(result))} chars — summarized: {str(result)[:500]}...]"
return result
def compress_old_tool_results(messages, keep_last_n=3):
"""Replace tool results in old turns with summaries"""
tool_result_count = 0
for msg in reversed(messages):
if msg.get('role') == 'tool':
tool_result_count += 1
if tool_result_count > keep_last_n:
msg['content'] = f"[Tool result compressed — {len(str(msg['content']))} chars]"
return messages
Option 4: Set a token budget per conversation
MAX_INPUT_TOKENS_PER_TURN = 20000
def enforce_token_budget(messages):
"""Drop oldest non-system messages if context too large"""
while estimate_tokens(messages) > MAX_INPUT_TOKENS_PER_TURN:
# Remove oldest non-system message pair
for i, msg in enumerate(messages):
if msg['role'] != 'system':
messages.pop(i)
if i < len(messages) and messages[i]['role'] != 'system':
messages.pop(i)
break
return messages
Cost Comparison
| Approach | 20-turn conversation cost |
|---|---|
| No optimization | ~300K tokens |
| Prompt caching | ~60K tokens (80% reduction) |
| Summarize after 10 turns | ~80K tokens |
| Compress tool results | ~100K tokens |
| All combined | ~25K tokens |
Expected Token Savings
Per 20-turn agent session: 200,000–275,000 tokens saved Source: direct measurement in production agent deployments
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