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Agent Responses Get Slower Over Time — Latency Grows with Session Length

Symptom

• Response time at session start: 1–2 seconds
• Response time after 30 minutes: 8–12 seconds
• Response time after 1 hour: 15–20 seconds
• Restarting the session restores fast responses
• Longer conversations → slower responses, proportionally
• No errors — just increasing latency

Root Cause

LLM inference time scales with input token count. Each turn sends the entire conversation history. A 100-turn conversation might have 50,000 tokens of context — the model processes all 50,000 tokens to generate each response, even if the question is simple. Inference is O(n²) in attention layers.

Measurement

import time, anthropic

client = anthropic.Anthropic()
history = []

for turn in range(50):
    start = time.time()
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=100,
        messages=history + [{"role": "user", "content": "Quick question"}]
    )
    latency = time.time() - start
    tokens = response.usage.input_tokens
    print(f"Turn {turn}: {latency:.1f}s | {tokens} input tokens")
    history.append({"role": "user", "content": "Quick question"})
    history.append({"role": "assistant", "content": response.content[0].text})

Fix

Option 1: Periodic context summarization

SUMMARIZE_EVERY_N_TURNS = 20
TOKEN_WARNING_THRESHOLD = 50000

async def managed_session(agent_client):
    history = []
    turn_count = 0

    while True:
        user_message = await get_user_input()
        history.append({"role": "user", "content": user_message})

        # Check if summarization needed
        if turn_count > 0 and turn_count % SUMMARIZE_EVERY_N_TURNS == 0:
            summary = await summarize_history(history[:-5])  # Keep last 5 turns
            history = [
                {"role": "user", "content": f"[Session summary: {summary}]"},
                {"role": "assistant", "content": "Understood, continuing."},
                *history[-5:]
            ]
            print(f"Context summarized at turn {turn_count}")

        response = await agent_client.complete(history)
        history.append({"role": "assistant", "content": response})
        turn_count += 1

Option 2: Sliding window context

def sliding_window_history(history, max_tokens=30000, always_keep_first=2):
    """Keep only the most recent N tokens of context"""
    if len(history) <= always_keep_first:
        return history

    # Always keep first few messages (system context, initial setup)
    preserved = history[:always_keep_first]
    recent = history[always_keep_first:]

    # Trim from the middle until within token budget
    while estimate_tokens(preserved + recent) > max_tokens and len(recent) > 2:
        recent = recent[2:]  # Remove oldest user+assistant pair

    return preserved + recent

Option 3: Compress tool results in history

def compress_old_tool_results(history, keep_recent=3):
    """Replace large tool results in old turns with summaries"""
    tool_result_indices = [
        i for i, m in enumerate(history)
        if m.get('role') == 'tool'
    ]

    # Keep recent tool results, compress old ones
    old_tool_indices = tool_result_indices[:-keep_recent]
    for i in old_tool_indices:
        content = str(history[i].get('content', ''))
        if len(content) > 500:
            history[i]['content'] = f"[Tool result: {len(content)} chars, already processed]"

    return history

Option 4: Enable prompt caching for stable content

# Cache stable content (system prompt, reference documents)
# Reduces effective input tokens from 50K to 5K for cached portions
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": large_stable_context,
                "cache_control": {"type": "ephemeral"}  # 90% cost/latency reduction
            },
            {
                "type": "text",
                "text": new_user_message
            }
        ]
    }
]

Option 5: Monitor and alert on context growth

async def complete_with_latency_monitoring(history, agent):
    input_tokens = estimate_tokens(history)
    start = time.time()

    response = await agent.complete(history)

    latency = time.time() - start
    if latency > 5.0:
        print(f"Warning: High latency {latency:.1f}s with {input_tokens} input tokens")
        print("Consider running context summarization")

    return response

Latency vs Context Size (Approximate)

Input tokens	Estimated latency
5,000	1–2s
20,000	3–5s
50,000	8–12s
100,000	15–25s

Expected Token Savings

Long session without management: 50K+ tokens per turn With sliding window (30K cap): constant 30K tokens per turn

Environment

• Any long-running agent session
• Source: direct measurement, Anthropic inference characteristics

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