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Agent Loses Tool Results After Context Prune — Re-runs Already-Completed Steps

Symptom

• Agent searches for something, finds it, continues work
• Later in a long session, asks for the same search again
• Re-runs tool calls that were already completed
• User sees duplicate API calls or tool executions
• Token cost doubles for already-completed work
• May produce different results if tool output changed between calls

Root Cause

When context pruning removes old messages (including old tool call results), the agent has no persistent memory of what it already retrieved. From its perspective, it has never run those tool calls — so it runs them again.

Fix

Option 1: Persist key tool results to a summary file

import json

TOOL_MEMORY_FILE = "/workspace/.agent_tool_memory.json"

def save_tool_result(tool_name, args, result, summary=None):
    """Save tool result to persistent file that survives context pruning"""
    try:
        memory = json.loads(open(TOOL_MEMORY_FILE).read()) if os.path.exists(TOOL_MEMORY_FILE) else {}
    except Exception:
        memory = {}

    key = f"{tool_name}:{hash(str(args))}"
    memory[key] = {
        "tool": tool_name,
        "args": str(args)[:200],
        "summary": summary or str(result)[:500],
        "timestamp": time.time()
    }

    with open(TOOL_MEMORY_FILE, 'w') as f:
        json.dump(memory, f, indent=2)

def get_cached_result(tool_name, args, max_age_seconds=3600):
    """Check if we already ran this tool call"""
    if not os.path.exists(TOOL_MEMORY_FILE):
        return None
    memory = json.loads(open(TOOL_MEMORY_FILE).read())
    key = f"{tool_name}:{hash(str(args))}"
    if key in memory:
        age = time.time() - memory[key]['timestamp']
        if age < max_age_seconds:
            return memory[key]['summary']
    return None

Option 2: Include tool memory summary in system prompt refresh

def build_system_with_tool_memory():
    """Load persisted tool results and include in system prompt"""
    if not os.path.exists(TOOL_MEMORY_FILE):
        return BASE_SYSTEM_PROMPT

    memory = json.loads(open(TOOL_MEMORY_FILE).read())
    if not memory:
        return BASE_SYSTEM_PROMPT

    summary_lines = [
        f"- {v['tool']}({v['args'][:50]}): {v['summary'][:100]}"
        for v in list(memory.values())[-10:]  # Last 10 tool results
    ]

    return BASE_SYSTEM_PROMPT + "\n\nAlready retrieved this session:\n" + "\n".join(summary_lines)

Option 3: Add tool call deduplication at the framework level

class DeduplicatingToolRunner:
    def __init__(self, session_id):
        self.session_id = session_id
        self.call_cache = {}  # In-memory for this session

    async def run_tool(self, tool_name, args):
        cache_key = f"{tool_name}:{json.dumps(args, sort_keys=True)}"

        if cache_key in self.call_cache:
            cached = self.call_cache[cache_key]
            print(f"Tool cache hit: {tool_name} (original result from {cached['when']})")
            return cached['result']

        result = await actual_tool_runner(tool_name, args)
        self.call_cache[cache_key] = {
            'result': result,
            'when': time.strftime('%H:%M:%S')
        }
        return result

Option 4: Session handoff file for context resets

When intentionally resetting context, write a handoff:

HANDOFF_FILE = "/workspace/.session_handoff.md"

def write_session_handoff(completed_steps, key_findings, current_goal):
    """Write handoff before context reset"""
    content = f"""# Session Handoff — {time.strftime('%Y-%m-%d %H:%M')}

## Completed steps (do NOT redo these)
{chr(10).join(f'- {step}' for step in completed_steps)}

## Key findings from tool calls
{chr(10).join(f'- {k}: {v}' for k, v in key_findings.items())}

## Current goal
{current_goal}

## Next step
{get_next_step()}
"""
    with open(HANDOFF_FILE, 'w') as f:
        f.write(content)

Expected Token Savings

Re-running 5 tool calls that were already done: ~10,000 tokens Tool result persistence: prevents all duplicate calls

Environment

• Long agent sessions with context pruning enabled
• Agents with expensive or slow tool calls (web search, API calls)
• Source: direct experience with multi-hour agent sessions

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