Unnecessary Conversation History Included in Every API Call — Wasted Tokens
Symptom
- Simple question costs 30,000 tokens because of conversation history
- Multi-turn conversation gets progressively slower and more expensive
- Agent includes debugging conversation from 30 turns ago when answering a new question
- API cost grows with session length even when questions are independent
- Old tool results and failed attempts bloat every subsequent call
Root Cause
Most agent frameworks append every turn to a running history and include the full history in every API call. This is correct for stateful conversations where context matters, but wasteful when:
- The current question is independent of history
- History contains failed attempts and dead ends
- Tool results from early turns are no longer relevant
Fix
Option 1: Sliding window — keep only recent turns
def sliding_window_history(
history: list,
max_turns: int = 10,
always_keep_system: bool = True
) -> list:
"""Keep only the N most recent turns"""
if not history:
return history
# Separate system messages from conversation
system_messages = [m for m in history if m.get("role") == "system"]
conversation = [m for m in history if m.get("role") != "system"]
# Keep only recent turns
if len(conversation) > max_turns * 2: # *2 because each turn = user + assistant
trimmed = conversation[-(max_turns * 2):]
print(f"History trimmed: {len(conversation)} → {len(trimmed)} messages")
else:
trimmed = conversation
return (system_messages if always_keep_system else []) + trimmed
Option 2: Topic-based history reset
TOPIC_CHANGE_PHRASES = [
"new question", "different topic", "change subject", "forget the previous",
"start over", "let's talk about", "now i want to", "moving on"
]
def detect_topic_change(user_message: str) -> bool:
"""Detect when user is starting a fresh topic"""
msg_lower = user_message.lower()
return any(phrase in msg_lower for phrase in TOPIC_CHANGE_PHRASES)
async def manage_history(history: list, new_user_message: str) -> list:
if detect_topic_change(new_user_message):
print("Topic change detected — resetting conversation history")
return [] # Fresh start for new topic
# Keep only recent relevant history
return sliding_window_history(history, max_turns=8)
Option 3: Relevance-based history filtering
async def filter_relevant_history(
history: list,
current_query: str,
agent,
max_kept: int = 5
) -> list:
"""Keep only history turns relevant to current query"""
if len(history) <= max_kept * 2:
return history # Small enough — keep all
# Ask model to identify relevant turns
history_summary = "\n".join([
f"Turn {i//2 + 1}: {m['content'][:100]}"
for i, m in enumerate(history)
if m['role'] == 'user'
])
relevance_check = await agent.complete([{
"role": "user",
"content": f"""Current question: {current_query}
Previous conversation turns:
{history_summary}
Which turn numbers (if any) contain information relevant to the current question?
List only the relevant turn numbers, comma-separated. If none, say 'none'."""
}])
# Parse relevant turn numbers
relevant_turns = set()
for token in relevance_check.replace("none", "").split(","):
try:
relevant_turns.add(int(token.strip()) - 1) # 0-indexed
except ValueError:
pass
# Extract relevant turns + last 2 turns always
user_messages = [m for m in history if m['role'] == 'user']
filtered = []
for i, (user_msg, asst_msg) in enumerate(zip(user_messages, history[1::2])):
if i in relevant_turns or i >= len(user_messages) - 2:
filtered.extend([user_msg, asst_msg])
print(f"History filtered: {len(history)} → {len(filtered)} messages")
return filtered
Option 4: Separate short-term and long-term memory
class TieredMemory:
"""Short-term: recent turns. Long-term: key facts extracted from old turns."""
def __init__(self, short_term_turns: int = 5):
self.short_term_turns = short_term_turns
self.recent_history = []
self.long_term_facts = []
def add_turn(self, user: str, assistant: str):
self.recent_history.extend([
{"role": "user", "content": user},
{"role": "assistant", "content": assistant}
])
# When history gets too long, compress oldest turns
if len(self.recent_history) > self.short_term_turns * 2 + 2:
oldest_pair = self.recent_history[:2]
self.recent_history = self.recent_history[2:]
# Extract key fact from old turn (async in real impl)
fact = self._extract_fact(oldest_pair)
if fact:
self.long_term_facts.append(fact)
def _extract_fact(self, turn_pair: list) -> str | None:
"""Extract key information worth preserving from old turn"""
content = turn_pair[-1].get("content", "")
# Simple heuristic: keep if contains key decisions or data
keywords = ["decided", "confirmed", "the answer is", "result:", "found:"]
if any(k in content.lower() for k in keywords):
return content[:200]
return None
def build_messages(self, new_message: str) -> list:
messages = []
if self.long_term_facts:
facts_context = "Key facts from earlier in our session:\n" + \
"\n".join(f"- {f}" for f in self.long_term_facts)
messages.append({"role": "user", "content": facts_context})
messages.append({"role": "assistant", "content": "Understood."})
messages.extend(self.recent_history)
messages.append({"role": "user", "content": new_message})
return messages
Option 5: Token budget for history
def trim_history_to_budget(
history: list,
token_budget: int = 20000,
always_keep_last_n: int = 4 # Always keep last 2 user+assistant pairs
) -> list:
"""Trim history to fit within token budget"""
def estimate_tokens(messages: list) -> int:
return sum(len(str(m.get("content", ""))) // 4 for m in messages)
if estimate_tokens(history) <= token_budget:
return history
# Always keep last N messages
preserved_tail = history[-always_keep_last_n:]
trimable = history[:-always_keep_last_n]
# Remove oldest messages until within budget
while trimable and estimate_tokens(trimable + preserved_tail) > token_budget:
trimable = trimable[2:] # Remove oldest user+assistant pair
print(f"Trimmed: {len(trimable)} messages remaining in trimable")
result = trimable + preserved_tail
print(f"History: {len(history)} → {len(result)} messages, ~{estimate_tokens(result):,} tokens")
return result
When History Is Needed vs. Wasteful
| Scenario | Keep history? | How much |
|---|---|---|
| Multi-step task in progress | Yes | Full recent history |
| New independent question | No | Reset or topic-switch |
| Debugging iterative code | Yes | All relevant turns |
| General Q&A chatbot | Partial | Last 5-10 turns |
| Document analysis | Partial | Recent + pinned context |
| Batch processing | No | Fresh context per item |
Token Cost of History Strategies
| Strategy | Tokens per turn (10-turn session) | Cost ratio |
|---|---|---|
| Full history always | Growing: 5K → 50K | 10× at end |
| Sliding window (5 turns) | Constant ~5K | 1× always |
| Topic-reset | Constant ~1K | 0.2× per topic |
| Relevance filter | ~2-3K | 0.5× |
Expected Token Savings
10-turn session, full history: ~50,000 total tokens 10-turn session, sliding window (5 turns): ~25,000 total tokens (50% savings) Independent questions with reset: ~5,000 total tokens (90% savings)
Environment
- Multi-turn conversational agents; most impactful for long sessions and Q&A bots
- Source: direct measurement, context management best practices
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