Agent Stores Redundant Duplicate Memories — Memory Grows Without Bound
Symptom
- Memory file contains 50 variations of “user works at Acme Corp” from different sessions
- Agent recalls “user prefers Python” — but returns 8 duplicate entries instead of 1
- Memory store grows by hundreds of entries per session — unusable after a week
- Agent saves every user message as a memory — 90% are redundant or irrelevant
- Semantic search returns near-duplicate results — top 5 hits are all the same fact
- Memory retrieval tokens consume half the context before the agent even starts
- Agent contradicts itself — has both “user prefers dark mode” and “user prefers light mode” stored
Root Cause
Agents write memories without checking if equivalent information already exists. Each session may re-learn the same facts and write them again. Without deduplication, the memory store grows proportionally to usage time rather than to new information. Near-duplicate detection requires semantic similarity comparison — exact string matching misses paraphrases. The fix is to check for existing similar memories before writing, merge updates into existing entries, and periodically compact the store.
Fix
Option 1: Semantic deduplication — check similarity before writing
import hashlib
import json
from pathlib import Path
from typing import Optional
import anthropic
client = anthropic.Anthropic()
class DeduplicatingMemoryStore:
"""
Memory store that checks for semantic duplicates before writing.
Uses a fast LLM call to determine if a fact is already known.
"""
def __init__(
self,
store_path: str = "/data/memory.json",
similarity_threshold: float = 0.85,
check_model: str = "claude-haiku-4-5-20251001"
):
self.store_path = Path(store_path)
self.similarity_threshold = similarity_threshold
self.check_model = check_model
self._memories: list[dict] = self._load()
def _load(self) -> list[dict]:
if self.store_path.exists():
return json.loads(self.store_path.read_text())
return []
def _save(self):
tmp = self.store_path.with_suffix(".tmp")
tmp.write_text(json.dumps(self._memories, indent=2))
tmp.replace(self.store_path)
def _find_duplicate(self, new_fact: str) -> Optional[int]:
"""
Check if a semantically equivalent fact already exists.
Returns the index of the duplicate, or None.
"""
if not self._memories:
return None
# Quick exact-match check first (free)
new_lower = new_fact.lower().strip()
for i, mem in enumerate(self._memories):
if mem["content"].lower().strip() == new_lower:
return i
# Semantic check against candidate memories (top 20 by relevance)
# For simplicity here, check all; in production, pre-filter with keywords
candidates = self._memories[-20:] # Check most recent 20
if not candidates:
return None
candidate_text = "\n".join(
f"{i}. {m['content']}"
for i, m in enumerate(candidates)
)
response = client.messages.create(
model=self.check_model,
max_tokens=100,
messages=[{
"role": "user",
"content": (
f"New fact to store: \"{new_fact}\"\n\n"
f"Existing facts:\n{candidate_text}\n\n"
f"Is the new fact semantically equivalent to any existing fact "
f"(same meaning, possibly different wording)?\n"
f"If yes, reply with the number (0-based index). "
f"If no duplicate, reply: 'none'\n"
f"Reply with only a number or 'none'."
)
}]
)
reply = response.content[0].text.strip().lower()
if reply == "none":
return None
try:
idx = int(reply)
if 0 <= idx < len(candidates):
# Map back to original index
offset = len(self._memories) - len(candidates)
return offset + idx
except ValueError:
pass
return None
def write(
self,
content: str,
category: str = "general",
merge_updates: bool = True
) -> dict:
"""
Write a memory, deduplicating against existing entries.
Returns the memory that was written (new or existing).
"""
duplicate_idx = self._find_duplicate(content)
if duplicate_idx is not None:
existing = self._memories[duplicate_idx]
if merge_updates and content != existing["content"]:
# Update the existing memory with newer information
old_content = existing["content"]
existing["content"] = content
existing["updated_count"] = existing.get("updated_count", 0) + 1
existing["previous_versions"] = existing.get("previous_versions", []) + [old_content]
self._save()
print(f"Memory updated (was duplicate): '{old_content[:50]}' → '{content[:50]}'")
else:
print(f"Memory skipped (duplicate): '{content[:60]}'")
return existing
# New memory — write it
import time
memory = {
"id": hashlib.sha256(content.encode()).hexdigest()[:12],
"content": content,
"category": category,
"created_at": time.time(),
"updated_count": 0
}
self._memories.append(memory)
self._save()
print(f"Memory stored: '{content[:60]}'")
return memory
def read_all(self) -> list[dict]:
return self._memories
@property
def stats(self) -> dict:
return {
"total_memories": len(self._memories),
"categories": list({m.get("category", "general") for m in self._memories}),
"updated_entries": sum(1 for m in self._memories if m.get("updated_count", 0) > 0)
}
store = DeduplicatingMemoryStore()
Option 2: Memory compaction — merge and consolidate periodically
import json
import time
from pathlib import Path
import anthropic
client = anthropic.Anthropic()
class MemoryCompactor:
"""
Periodically compacts a memory store:
- Merges near-duplicate entries
- Reconciles contradictions (keeps newest)
- Removes stale or irrelevant entries
- Produces a clean, deduplicated store
"""
def __init__(
self,
store_path: str = "/data/memory.json",
compaction_model: str = "claude-sonnet-4-6"
):
self.store_path = Path(store_path)
self.compaction_model = compaction_model
def should_compact(self, memories: list[dict], threshold: int = 100) -> bool:
"""Compact when memory count exceeds threshold"""
return len(memories) > threshold
def compact(self, memories: list[dict]) -> list[dict]:
"""
Compact a list of memories into a deduplicated, reconciled set.
Uses Claude to identify duplicates and contradictions.
"""
if len(memories) <= 20:
return memories # Small enough — no compaction needed
print(f"Compacting {len(memories)} memories...")
# Process in batches of 50
batch_size = 50
compacted = []
for i in range(0, len(memories), batch_size):
batch = memories[i:i + batch_size]
compacted_batch = self._compact_batch(batch)
compacted.extend(compacted_batch)
print(f"Batch {i // batch_size + 1}: {len(batch)} → {len(compacted_batch)} memories")
# Final dedup pass across all batches
if len(compacted) > batch_size:
compacted = self._compact_batch(compacted)
print(f"Compaction complete: {len(memories)} → {len(compacted)} memories")
return compacted
def _compact_batch(self, memories: list[dict]) -> list[dict]:
"""Compact a single batch of memories"""
memory_text = "\n".join(
f"{i}. [{m.get('category', 'general')}] {m['content']}"
for i, m in enumerate(memories)
)
response = client.messages.create(
model=self.compaction_model,
max_tokens=2000,
messages=[{
"role": "user",
"content": (
f"Below is a list of agent memories. Many are duplicates or near-duplicates.\n\n"
f"Task: Produce a deduplicated list by:\n"
f"1. Merging near-duplicate facts into one entry (keep the most complete version)\n"
f"2. For contradictions, keep the most specific/recent fact\n"
f"3. Removing obviously redundant entries\n"
f"4. Preserving all unique facts\n\n"
f"Return a JSON array of strings — one string per unique memory.\n"
f"Preserve the original wording where possible.\n\n"
f"Memories:\n{memory_text}"
)
}]
)
try:
import re
text = response.content[0].text
# Extract JSON array from response
match = re.search(r'\[.*?\]', text, re.DOTALL)
if match:
compacted_contents = json.loads(match.group())
return [
{
"id": hashlib.sha256(c.encode()).hexdigest()[:12],
"content": c,
"category": "compacted",
"compacted_at": time.time()
}
for c in compacted_contents
]
except Exception as e:
print(f"Compaction parse error: {e} — returning original batch")
return memories # Fallback: return original if parsing fails
def run(self) -> dict:
"""Run compaction on the store file"""
if not self.store_path.exists():
return {"status": "no_store"}
memories = json.loads(self.store_path.read_text())
original_count = len(memories)
if not self.should_compact(memories):
return {"status": "skipped", "count": original_count}
compacted = self.compact(memories)
# Atomic write
tmp = self.store_path.with_suffix(".compact.tmp")
tmp.write_text(json.dumps(compacted, indent=2))
tmp.replace(self.store_path)
return {
"status": "compacted",
"before": original_count,
"after": len(compacted),
"reduction": f"{(1 - len(compacted)/original_count)*100:.0f}%"
}
import hashlib # needed above
compactor = MemoryCompactor()
# Run compaction periodically (e.g., at agent startup when store is large):
# result = compactor.run()
# print(f"Memory compaction: {result}")
Option 3: Category-based deduplication — one canonical entry per topic
from dataclasses import dataclass, field
from typing import Optional
import time, json, hashlib
from pathlib import Path
MEMORY_CATEGORIES = {
"user_preference": "One entry per preference topic — replace, don't append",
"user_identity": "Stable facts about the user — merge, never duplicate",
"project_context": "Current project state — replace on change",
"technical_fact": "Technical knowledge — deduplicate by topic",
"interaction_pattern": "Behavior patterns — aggregate, not duplicate"
}
@dataclass
class CategorizedMemory:
id: str
category: str
topic: str # Unique key within category
content: str
created_at: float
updated_at: float
update_count: int = 0
class TopicKeyedMemoryStore:
"""
Memory store where each category+topic combination has exactly ONE entry.
Writing to an existing topic replaces (or merges with) the existing entry.
Eliminates duplicates by design.
"""
def __init__(self, store_path: str = "/data/keyed_memory.json"):
self.store_path = Path(store_path)
self._store: dict[str, CategorizedMemory] = self._load()
def _key(self, category: str, topic: str) -> str:
return f"{category}::{topic.lower().strip()}"
def _load(self) -> dict:
if not self.store_path.exists():
return {}
raw = json.loads(self.store_path.read_text())
return {k: CategorizedMemory(**v) for k, v in raw.items()}
def _save(self):
data = {k: v.__dict__ for k, v in self._store.items()}
tmp = self.store_path.with_suffix(".tmp")
tmp.write_text(json.dumps(data, indent=2))
tmp.replace(self.store_path)
def write(
self,
category: str,
topic: str,
content: str,
merge_fn=None
) -> CategorizedMemory:
"""
Write a memory entry. Topic is unique within category.
If topic already exists, merges or replaces based on merge_fn.
"""
key = self._key(category, topic)
now = time.time()
if key in self._store:
existing = self._store[key]
old_content = existing.content
if merge_fn:
# Custom merge logic (e.g., append to a list)
existing.content = merge_fn(existing.content, content)
else:
# Default: replace with newer content
existing.content = content
existing.updated_at = now
existing.update_count += 1
self._save()
print(f"Memory updated [{category}::{topic}]: '{old_content[:40]}' → '{existing.content[:40]}'")
return existing
# New topic
memory = CategorizedMemory(
id=hashlib.sha256(key.encode()).hexdigest()[:12],
category=category,
topic=topic,
content=content,
created_at=now,
updated_at=now
)
self._store[key] = memory
self._save()
print(f"Memory created [{category}::{topic}]: '{content[:60]}'")
return memory
def read(self, category: str, topic: str) -> Optional[CategorizedMemory]:
return self._store.get(self._key(category, topic))
def read_category(self, category: str) -> list[CategorizedMemory]:
prefix = f"{category}::"
return [m for k, m in self._store.items() if k.startswith(prefix)]
def format_for_context(self, categories: list[str] = None) -> str:
"""Format memories for injection into agent context"""
if categories:
memories = []
for cat in categories:
memories.extend(self.read_category(cat))
else:
memories = list(self._store.values())
if not memories:
return ""
lines = ["## Memory\n"]
by_category: dict[str, list] = {}
for m in memories:
by_category.setdefault(m.category, []).append(m)
for category, mems in sorted(by_category.items()):
lines.append(f"### {category.replace('_', ' ').title()}")
for m in mems:
lines.append(f"- {m.content}")
lines.append("")
return "\n".join(lines)
keyed_store = TopicKeyedMemoryStore()
# Usage — no duplicates possible:
keyed_store.write("user_preference", "programming_language", "User prefers Python over JavaScript")
keyed_store.write("user_preference", "programming_language", "User prefers Python, especially for data work")
# Second write updates the entry — no duplicate created
Option 4: Staleness-based pruning — automatically expire old memories
import time
import json
from pathlib import Path
class StalenessAwareMemoryStore:
"""
Automatically removes memories that haven't been accessed recently.
Prevents unbounded growth from rarely-needed facts.
"""
def __init__(
self,
store_path: str = "/data/memory_with_ttl.json",
default_ttl_days: int = 90,
max_memories: int = 500
):
self.store_path = Path(store_path)
self.default_ttl = default_ttl_days * 86400
self.max_memories = max_memories
self._memories: list[dict] = self._load()
def _load(self) -> list[dict]:
if not self.store_path.exists():
return []
return json.loads(self.store_path.read_text())
def _save(self):
tmp = self.store_path.with_suffix(".tmp")
tmp.write_text(json.dumps(self._memories, indent=2))
tmp.replace(self.store_path)
def write(self, content: str, category: str = "general", ttl_days: int = None):
import hashlib
ttl = (ttl_days or 90) * 86400
now = time.time()
self._memories.append({
"id": hashlib.sha256(f"{content}{now}".encode()).hexdigest()[:12],
"content": content,
"category": category,
"created_at": now,
"last_accessed_at": now,
"access_count": 0,
"expires_at": now + ttl
})
self._prune()
self._save()
def access(self, memory_id: str):
"""Record access — resets staleness timer"""
for m in self._memories:
if m["id"] == memory_id:
m["last_accessed_at"] = time.time()
m["access_count"] = m.get("access_count", 0) + 1
# Extend expiry on access
m["expires_at"] = time.time() + self.default_ttl
break
self._save()
def _prune(self):
"""Remove expired and excess memories"""
now = time.time()
# Remove expired
before = len(self._memories)
self._memories = [m for m in self._memories if m.get("expires_at", float("inf")) > now]
expired = before - len(self._memories)
if expired:
print(f"Pruned {expired} expired memories")
# If still over limit, remove least recently accessed
if len(self._memories) > self.max_memories:
self._memories.sort(key=lambda m: m.get("last_accessed_at", 0))
excess = len(self._memories) - self.max_memories
self._memories = self._memories[excess:]
print(f"Pruned {excess} least-recently-accessed memories (limit: {self.max_memories})")
def prune_now(self) -> dict:
"""Run pruning explicitly — call at agent startup"""
before = len(self._memories)
self._prune()
self._save()
return {"before": before, "after": len(self._memories), "removed": before - len(self._memories)}
store = StalenessAwareMemoryStore(max_memories=200, default_ttl_days=60)
Option 5: Write-gate — decide if a fact is worth storing at all
import anthropic
client = anthropic.Anthropic()
class MemoryWriteGate:
"""
Before writing any memory, evaluate whether it's worth storing.
Filters out: ephemeral facts, noise, already-known facts.
"""
GATE_PROMPT = """Evaluate whether this fact should be stored in long-term memory.
Fact: "{fact}"
Existing memories (sample):
{existing_sample}
Store in long-term memory only if:
- The fact is stable and will be useful across future sessions
- The fact is NOT already captured by an existing memory
- The fact is specific and actionable (not vague or obvious)
Do NOT store:
- Ephemeral facts ("user is currently busy")
- Facts already in existing memories (check for semantic equivalence)
- Obvious facts ("user types on a keyboard")
- Session-specific context ("user asked about X earlier today")
Reply with one word: YES or NO
Then on a new line, briefly explain why (one sentence)."""
def __init__(self, model: str = "claude-haiku-4-5-20251001"):
self.model = model
def should_store(
self,
fact: str,
existing_memories: list[dict],
sample_size: int = 15
) -> tuple[bool, str]:
"""
Returns (should_store, reason)
"""
# Sample recent existing memories
sample = existing_memories[-sample_size:]
sample_text = "\n".join(f"- {m['content']}" for m in sample) or "(none)"
response = client.messages.create(
model=self.model,
max_tokens=80,
messages=[{
"role": "user",
"content": self.GATE_PROMPT.format(
fact=fact,
existing_sample=sample_text
)
}]
)
reply = response.content[0].text.strip()
lines = reply.split("\n", 1)
decision = lines[0].strip().upper()
reason = lines[1].strip() if len(lines) > 1 else ""
return decision == "YES", reason
gate = MemoryWriteGate()
def gated_memory_write(fact: str, store: DeduplicatingMemoryStore):
"""Only write memory if it passes the write gate"""
should_store, reason = gate.should_store(fact, store.read_all())
if should_store:
store.write(fact)
print(f"Memory stored: {reason}")
else:
print(f"Memory skipped: {reason}")
Memory Growth Failure Modes
| Pattern | Root Cause | Fix |
|---|---|---|
| Same fact stored N times | No duplicate check before write | Semantic dedup before write |
| Growing list of contradictions | Old entries never updated | Topic-keyed store (one per topic) |
| Memory full of session-specific noise | No relevance filter | Write gate — filter ephemeral facts |
| Near-duplicates from paraphrases | Exact-match dedup only | Semantic similarity check |
| Store grows 100+ entries/day | Every message saved | Write gate + category limits |
| Stale facts never cleared | No TTL or pruning | Staleness-based TTL |
Expected Token Savings
1,000-entry memory injected into every context: ~15,000 tokens overhead per session 200-entry deduplicated store: ~3,000 tokens — 80% savings with higher quality
Environment
- Any agent with persistent long-term memory across sessions; critical for personal assistants, customer-facing agents, and autonomous agents that accumulate facts over days and weeks — memory bloat is a slow failure that becomes visible only after weeks of operation
- Source: direct experience; unbounded memory growth is the most common long-term stability failure in agents with persistent state
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