Agent Keeps Failed Tool Results in History — Error Accumulation Bloat
Symptom
- After 5 failed search attempts, conversation history has 10 messages (5 tool calls + 5 errors)
- Model begins hallucinating after seeing many errors — “I’ve been unable to X” pattern repeats
- Context window fills with
{"error": "..."}tool results — pushes useful context out - Token cost of a 10-retry loop: 10 × full-history size — exponential growth
- Model gets stuck in a loop apologizing for errors that are no longer relevant
- Pruning the history mid-task causes the model to lose track of what it was doing
Root Cause
Conversation history grows monotonically by default. Every tool call and its result — successful or failed — is appended to the messages array and never removed. Failed attempts are especially wasteful: they consume tokens but provide no useful information for subsequent steps. After many retries, the model spends more context “remembering” past failures than doing productive work. The fix is to prune or summarize failed attempts while preserving the essential context.
Fix
Option 1: Prune failed tool attempts from history after successful retry
from copy import deepcopy
def prune_failed_tool_attempts(messages: list[dict]) -> list[dict]:
"""
Remove failed tool call / tool result pairs from history.
Keeps: successful tool calls and their results.
Removes: tool calls whose result contains an error.
Preserves: message order and conversation flow.
"""
if not messages:
return messages
pruned = []
i = 0
while i < len(messages):
msg = messages[i]
# Check if this is an assistant message with tool_use blocks
if msg.get("role") == "assistant":
content = msg.get("content", [])
if not isinstance(content, list):
pruned.append(msg)
i += 1
continue
tool_uses = [b for b in content if isinstance(b, dict) and b.get("type") == "tool_use"]
if not tool_uses:
pruned.append(msg)
i += 1
continue
# Next message should be tool results
if i + 1 >= len(messages):
pruned.append(msg)
i += 1
continue
next_msg = messages[i + 1]
if next_msg.get("role") != "user":
pruned.append(msg)
i += 1
continue
result_content = next_msg.get("content", [])
if not isinstance(result_content, list):
pruned.append(msg)
pruned.append(next_msg)
i += 2
continue
# Check if ALL tool results are errors
tool_results = [
r for r in result_content
if isinstance(r, dict) and r.get("type") == "tool_result"
]
all_errors = all(r.get("is_error", False) for r in tool_results) if tool_results else False
if all_errors:
# Skip both the tool call and the error result
print(
f"Pruning failed tool attempt: "
f"{[b.get('name') for b in tool_uses]}"
)
i += 2 # Skip assistant (tool call) + user (error result)
continue
pruned.append(msg)
pruned.append(next_msg)
i += 2
else:
pruned.append(msg)
i += 1
original_count = len(messages)
pruned_count = len(pruned)
if pruned_count < original_count:
print(f"History pruned: {original_count} → {pruned_count} messages "
f"({original_count - pruned_count} removed)")
return pruned
Option 2: Error budget — stop retrying after N failed attempts
from dataclasses import dataclass, field
import anthropic
@dataclass
class ToolErrorBudget:
"""Track tool failures per tool name — stop retrying when budget exhausted"""
max_failures_per_tool: int = 3
max_total_failures: int = 10
_failures: dict[str, int] = field(default_factory=dict)
_total: int = 0
def record_failure(self, tool_name: str) -> bool:
"""
Record a failure. Returns True if we should continue retrying,
False if the budget is exhausted.
"""
self._failures[tool_name] = self._failures.get(tool_name, 0) + 1
self._total += 1
tool_failures = self._failures[tool_name]
if self._total >= self.max_total_failures:
print(
f"Total error budget exhausted ({self._total} failures). "
f"Stopping tool execution."
)
return False
if tool_failures >= self.max_failures_per_tool:
print(
f"Tool '{tool_name}' error budget exhausted "
f"({tool_failures}/{self.max_failures_per_tool} failures). "
f"Will not retry this tool."
)
return False
return True # Still within budget — can retry
def is_tool_blocked(self, tool_name: str) -> bool:
return self._failures.get(tool_name, 0) >= self.max_failures_per_tool
@property
def summary(self) -> str:
if not self._failures:
return "No failures"
return f"Failures: {dict(self._failures)} (total: {self._total})"
error_budget = ToolErrorBudget(max_failures_per_tool=3, max_total_failures=10)
def process_tool_results(tool_results: list[dict], messages: list[dict]) -> list[dict]:
"""
Process tool results and return updated messages.
Failed results are recorded but prunable after threshold.
"""
for result in tool_results:
if result.get("is_error"):
tool_name = result.get("tool_name", "unknown")
can_retry = error_budget.record_failure(tool_name)
if not can_retry:
# Replace error with a concise failure summary instead of full error
result["content"] = (
f"[Tool '{tool_name}' blocked after repeated failures: "
f"{error_budget.summary}. Try a different approach.]"
)
return messages
Option 3: Compact failed history — replace errors with summary
def compact_error_history(
messages: list[dict],
max_errors_to_keep: int = 2
) -> list[dict]:
"""
Instead of pruning all errors, keep a summary.
Replaces multiple errors with a single summary message.
Preserves enough context for the model to understand what was tried.
"""
errors_seen = []
non_error_messages = []
i = 0
while i < len(messages):
msg = messages[i]
# Detect failed tool call pairs (assistant tool call + error result)
if (msg.get("role") == "assistant"
and i + 1 < len(messages)
and messages[i + 1].get("role") == "user"):
content = msg.get("content", [])
next_content = messages[i + 1].get("content", [])
if isinstance(content, list) and isinstance(next_content, list):
tool_uses = [b for b in content if isinstance(b, dict) and b.get("type") == "tool_use"]
tool_results = [b for b in next_content if isinstance(b, dict) and b.get("type") == "tool_result"]
error_results = [r for r in tool_results if r.get("is_error")]
if tool_uses and error_results:
# Record this error
for tu in tool_uses:
for er in error_results:
errors_seen.append({
"tool": tu.get("name"),
"input": tu.get("input", {}),
"error": er.get("content", "unknown error")
})
i += 2
continue
non_error_messages.append(msg)
i += 1
if not errors_seen:
return messages
# Build a summary of all errors seen
if len(errors_seen) <= max_errors_to_keep:
# Few errors — keep them as-is for context
return messages
# Many errors — replace with a compact summary
error_summary = {
"role": "user",
"content": [{
"type": "text",
"text": (
f"[Previous attempts summary: {len(errors_seen)} tool call(s) failed. "
f"Tools tried: {list(set(e['tool'] for e in errors_seen))}. "
f"Last error: {errors_seen[-1]['error'][:200]}. "
f"These approaches did not work — try a different strategy.]"
)
}]
}
# Find insertion point (after first non-error content)
insert_after = 0
for j, msg in enumerate(non_error_messages):
if msg.get("role") in ("system", "user"):
insert_after = j + 1
result = non_error_messages[:insert_after] + [error_summary] + non_error_messages[insert_after:]
print(f"Compacted {len(errors_seen)} error messages into 1 summary")
return result
Option 4: History size monitor — trigger pruning automatically
import anthropic
client = anthropic.Anthropic()
class HistoryManager:
"""
Conversation history manager with automatic pruning.
Triggers error pruning when history grows too large.
"""
def __init__(
self,
model: str = "claude-sonnet-4-6",
max_history_tokens: int = 50_000,
prune_on_error_count: int = 3
):
self.model = model
self.max_history_tokens = max_history_tokens
self.prune_on_error_count = prune_on_error_count
self._messages: list[dict] = []
self._consecutive_errors = 0
def append(self, message: dict):
self._messages.append(message)
self._check_and_prune()
def _check_and_prune(self):
"""Check if pruning is needed and apply appropriate strategy"""
# Count consecutive errors in recent history
recent_errors = self._count_recent_errors(last_n=6)
if recent_errors >= self.prune_on_error_count:
print(f"Detected {recent_errors} recent errors — pruning failed attempts")
self._messages = prune_failed_tool_attempts(self._messages)
self._consecutive_errors = 0
# Check token count
token_count = self._estimate_tokens()
if token_count > self.max_history_tokens:
print(f"History too large ({token_count} tokens) — compacting errors")
self._messages = compact_error_history(self._messages)
def _count_recent_errors(self, last_n: int) -> int:
"""Count error results in the last N messages"""
recent = self._messages[-last_n:]
count = 0
for msg in recent:
if msg.get("role") == "user":
content = msg.get("content", [])
if isinstance(content, list):
count += sum(
1 for b in content
if isinstance(b, dict) and b.get("type") == "tool_result"
and b.get("is_error")
)
return count
def _estimate_tokens(self) -> int:
total_chars = sum(len(str(m)) for m in self._messages)
return total_chars // 4 # Rough estimate: 4 chars per token
@property
def messages(self) -> list[dict]:
return list(self._messages)
@property
def stats(self) -> dict:
return {
"message_count": len(self._messages),
"estimated_tokens": self._estimate_tokens(),
"recent_errors": self._count_recent_errors(10)
}
Option 5: Tool retry with automatic history reset
async def retry_tool_with_fresh_context(
tool_name: str,
tool_fn,
params: dict,
base_messages: list[dict],
max_attempts: int = 3
) -> tuple[any, list[dict]]:
"""
Retry a failing tool while keeping history clean.
On retry: prune failed attempts, add brief failure note, try again.
Returns: (result, updated_messages)
"""
messages = list(base_messages)
last_error = None
for attempt in range(max_attempts):
try:
result = await tool_fn(**params)
if attempt > 0:
print(f"Tool '{tool_name}' succeeded on attempt {attempt + 1}")
return result, messages
except Exception as e:
last_error = e
print(f"Tool '{tool_name}' failed (attempt {attempt + 1}/{max_attempts}): {e}")
if attempt < max_attempts - 1:
# Prune failure from history — don't let errors accumulate
messages = prune_failed_tool_attempts(messages)
# Add a brief note that this approach failed (compact, not verbose)
messages.append({
"role": "user",
"content": [{
"type": "text",
"text": (
f"Note: {tool_name} failed with: {str(e)[:100]}. "
f"Trying a different approach..."
)
}]
})
raise RuntimeError(
f"Tool '{tool_name}' failed after {max_attempts} attempts. "
f"Last error: {last_error}"
)
Option 6: System prompt — instruct model to acknowledge and move on
System prompt:
"Error handling in tool calls:
When a tool call fails:
1. Note the failure briefly (1 sentence)
2. Try a DIFFERENT approach — do not repeat the same call
3. After 3 failed attempts with the same tool, STOP retrying that tool
and either: try a different tool, or tell the user you cannot complete that step
Error accumulation rules:
- Do not dwell on past errors — they have been noted
- Do not list all previous error messages — summarize them in one sentence
- Do not apologize repeatedly for the same error
- If a tool keeps failing, say: 'I've been unable to X after 3 attempts.
I'll proceed with what I have, or let me know if you want me to try differently.'
Context management:
- Treat error context as low-value — don't reference old errors unless directly relevant
- Focus on the current state and what can still be accomplished
- If context fills with errors, summarize the situation and continue"
Error Accumulation Impact
| Scenario | Messages Added | Tokens Wasted | Model Impact |
|---|---|---|---|
| 1 failed tool attempt | 2 | ~500 | Minimal |
| 5 failed attempts, same tool | 10 | ~5,000 | Starts repeating apologies |
| 10 failed attempts | 20 | ~10,000 | Context dominated by errors |
| 20 failed attempts | 40 | ~20,000 | High confusion/looping risk |
| After pruning (10→2) | 4 | ~1,000 | Clean context |
Expected Token Savings
10 failed attempts kept in history throughout 20-turn task: ~50,000 extra tokens Pruning errors after 3 failures: ~45,000 tokens saved per task
Environment
- Any agent with tool use in an agentic loop; critical for agents with unreliable external tools, network calls, or complex multi-step tasks where early failures don’t prevent task completion
- Source: direct experience; error accumulation in history is the most common cause of context window exhaustion in long-running agentic tasks
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