Agent Doesn’t Checkpoint Long-Running Tasks — Loses All Progress on Failure
Symptom
- Agent processes 450 of 500 items, crashes, restarts from item 1
- Long-running extraction pipeline fails at 90% completion — full re-run required
- API rate limit hit mid-batch — entire batch discarded, must retry everything
- Agent OOM-killed after 2 hours; no state saved; operator re-runs from scratch
- Partial results exist in memory but are lost when the process exits
- Multi-step workflow (fetch → analyze → transform → store) loses intermediate step results on any failure
Root Cause
Long-running agent tasks treat all state as ephemeral in-memory structures. When the process terminates — for any reason (exception, OOM, timeout, rate limit, operator Ctrl-C) — all progress is lost. The fix is to write a checkpoint after each unit of committed work, so that any restart can load the checkpoint and skip already-completed items.
Fix
Option 1: File-based checkpoint — simplest durable progress tracking
import anthropic
import json
import os
import time
from pathlib import Path
client = anthropic.Anthropic()
class FileCheckpoint:
"""
Saves progress to a JSON file after each completed item.
On restart, loads the checkpoint and skips completed items.
"""
def __init__(self, checkpoint_path: str):
self.path = Path(checkpoint_path)
self._state = self._load()
def _load(self) -> dict:
if self.path.exists():
with open(self.path) as f:
data = json.load(f)
print(f"[checkpoint] Resumed: {data['completed']} items already done")
return data
return {"completed": [], "results": {}, "started_at": time.time()}
def is_done(self, item_id: str) -> bool:
return item_id in self._state["completed"]
def save(self, item_id: str, result: dict) -> None:
self._state["completed"].append(item_id)
self._state["results"][item_id] = result
self._state["updated_at"] = time.time()
# Atomic write: write to .tmp, then rename
tmp = self.path.with_suffix(".tmp")
with open(tmp, "w") as f:
json.dump(self._state, f, indent=2)
tmp.rename(self.path)
@property
def results(self) -> dict:
return self._state["results"]
def clear(self) -> None:
"""Call when the full job completes successfully."""
self.path.unlink(missing_ok=True)
def analyze_document(doc_id: str, content: str) -> dict:
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=256,
messages=[{"role": "user", "content": f"Summarize in one sentence: {content[:500]}"}]
)
return {"doc_id": doc_id, "summary": response.content[0].text}
def process_documents_with_checkpoint(documents: list[dict], job_id: str) -> dict:
"""
Process documents with file-based checkpointing.
Safe to kill and restart at any time.
"""
cp = FileCheckpoint(f"/tmp/job_{job_id}.checkpoint.json")
for doc in documents:
doc_id = doc["id"]
if cp.is_done(doc_id):
print(f" skip {doc_id} (already done)")
continue
print(f" processing {doc_id}...")
result = analyze_document(doc_id, doc["content"])
cp.save(doc_id, result) # ← checkpoint after each item
print(f" ✓ {doc_id} saved")
all_results = cp.results
cp.clear() # ← clean up on success
return all_results
# Usage:
documents = [{"id": f"doc_{i}", "content": f"Document {i} text..."} for i in range(100)]
results = process_documents_with_checkpoint(documents, job_id="extraction_20240115")
print(f"Completed: {len(results)} documents")
Option 2: SQLite checkpoint — handles large jobs with query support
import anthropic
import sqlite3
import json
import time
from contextlib import contextmanager
from dataclasses import dataclass
from enum import Enum
client = anthropic.Anthropic()
class ItemStatus(str, Enum):
PENDING = "pending"
PROCESSING = "processing"
DONE = "done"
FAILED = "failed"
@dataclass
class CheckpointRecord:
item_id: str
status: ItemStatus
result: dict | None
error: str | None
attempts: int
updated_at: float
class SQLiteCheckpoint:
"""
SQLite-backed checkpoint store.
Supports large jobs, querying by status, retry counting.
"""
def __init__(self, db_path: str, job_id: str):
self.db_path = db_path
self.job_id = job_id
self._init_db()
def _init_db(self):
with self._conn() as conn:
conn.execute("""
CREATE TABLE IF NOT EXISTS checkpoints (
job_id TEXT NOT NULL,
item_id TEXT NOT NULL,
status TEXT NOT NULL DEFAULT 'pending',
result TEXT,
error TEXT,
attempts INTEGER DEFAULT 0,
updated_at REAL,
PRIMARY KEY (job_id, item_id)
)
""")
@contextmanager
def _conn(self):
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
try:
yield conn
conn.commit()
finally:
conn.close()
def register_items(self, item_ids: list[str]) -> None:
"""Register all items upfront (idempotent)."""
with self._conn() as conn:
conn.executemany(
"INSERT OR IGNORE INTO checkpoints (job_id, item_id, status, updated_at) VALUES (?, ?, 'pending', ?)",
[(self.job_id, item_id, time.time()) for item_id in item_ids]
)
def next_pending(self) -> str | None:
"""Claim the next pending item (atomic)."""
with self._conn() as conn:
row = conn.execute(
"SELECT item_id FROM checkpoints WHERE job_id=? AND status='pending' ORDER BY rowid LIMIT 1",
(self.job_id,)
).fetchone()
if not row:
return None
item_id = row["item_id"]
conn.execute(
"UPDATE checkpoints SET status='processing', updated_at=? WHERE job_id=? AND item_id=?",
(time.time(), self.job_id, item_id)
)
return item_id
def mark_done(self, item_id: str, result: dict) -> None:
with self._conn() as conn:
conn.execute(
"UPDATE checkpoints SET status='done', result=?, updated_at=? WHERE job_id=? AND item_id=?",
(json.dumps(result), time.time(), self.job_id, item_id)
)
def mark_failed(self, item_id: str, error: str) -> None:
with self._conn() as conn:
conn.execute(
"UPDATE checkpoints SET status='failed', error=?, attempts=attempts+1, updated_at=? WHERE job_id=? AND item_id=?",
(error, time.time(), self.job_id, item_id)
)
def summary(self) -> dict:
with self._conn() as conn:
rows = conn.execute(
"SELECT status, COUNT(*) as cnt FROM checkpoints WHERE job_id=? GROUP BY status",
(self.job_id,)
).fetchall()
return {row["status"]: row["cnt"] for row in rows}
def all_results(self) -> dict:
with self._conn() as conn:
rows = conn.execute(
"SELECT item_id, result FROM checkpoints WHERE job_id=? AND status='done'",
(self.job_id,)
).fetchall()
return {row["item_id"]: json.loads(row["result"]) for row in rows}
def run_job_with_sqlite_checkpoint(items: list[dict], job_id: str) -> dict:
cp = SQLiteCheckpoint("/tmp/agent_jobs.db", job_id)
cp.register_items([item["id"] for item in items])
# Build lookup for content
content_map = {item["id"]: item["content"] for item in items}
while True:
item_id = cp.next_pending()
if item_id is None:
break
try:
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=256,
messages=[{"role": "user", "content": f"Classify: {content_map[item_id][:300]}"}]
)
cp.mark_done(item_id, {"classification": response.content[0].text})
except Exception as e:
cp.mark_failed(item_id, str(e))
summary = cp.summary()
print(f" Progress: {summary}")
return cp.all_results()
Option 3: Multi-step workflow checkpoint — save state between pipeline stages
import anthropic
import json
import time
from dataclasses import dataclass, asdict
from pathlib import Path
from typing import Any
client = anthropic.Anthropic()
@dataclass
class PipelineState:
"""Tracks progress through a multi-step pipeline."""
job_id: str
stage: str # "fetch" | "analyze" | "transform" | "store"
stage_index: int # which item within the current stage
fetch_results: dict[str, Any]
analyze_results: dict[str, Any]
transform_results: dict[str, Any]
completed_at: str | None = None
class PipelineCheckpoint:
def __init__(self, job_id: str):
self.path = Path(f"/tmp/pipeline_{job_id}.json")
self.state = self._load(job_id)
def _load(self, job_id: str) -> PipelineState:
if self.path.exists():
data = json.loads(self.path.read_text())
print(f"[pipeline] Resuming from stage={data['stage']} idx={data['stage_index']}")
return PipelineState(**data)
return PipelineState(
job_id=job_id,
stage="fetch",
stage_index=0,
fetch_results={},
analyze_results={},
transform_results={}
)
def _save(self):
tmp = self.path.with_suffix(".tmp")
tmp.write_text(json.dumps(asdict(self.state), indent=2))
tmp.rename(self.path)
def advance_stage(self, next_stage: str):
self.state.stage = next_stage
self.state.stage_index = 0
self._save()
def record_fetch(self, item_id: str, raw: str):
self.state.fetch_results[item_id] = raw
self.state.stage_index += 1
self._save()
def record_analysis(self, item_id: str, analysis: dict):
self.state.analyze_results[item_id] = analysis
self.state.stage_index += 1
self._save()
def record_transform(self, item_id: str, transformed: dict):
self.state.transform_results[item_id] = transformed
self.state.stage_index += 1
self._save()
def mark_complete(self):
self.state.completed_at = time.strftime("%Y-%m-%dT%H:%M:%SZ")
self._save()
def run_pipeline(item_ids: list[str], job_id: str) -> list[dict]:
"""
Multi-stage pipeline that checkpoints after each item in each stage.
Can be interrupted at any point and resumed exactly.
"""
cp = PipelineCheckpoint(job_id)
s = cp.state
# Stage 1: Fetch
if s.stage == "fetch":
pending = item_ids[s.stage_index:]
for item_id in pending:
# Simulate fetch
raw_content = f"raw content for {item_id}"
cp.record_fetch(item_id, raw_content)
cp.advance_stage("analyze")
# Stage 2: Analyze (LLM call per item)
if s.stage == "analyze":
done_ids = set(s.analyze_results.keys())
for item_id in item_ids:
if item_id in done_ids:
continue
raw = s.fetch_results[item_id]
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=128,
messages=[{"role": "user", "content": f"Extract key topics from: {raw}"}]
)
cp.record_analysis(item_id, {"topics": response.content[0].text})
cp.advance_stage("transform")
# Stage 3: Transform
if s.stage == "transform":
done_ids = set(s.transform_results.keys())
for item_id in item_ids:
if item_id in done_ids:
continue
analysis = s.analyze_results[item_id]
transformed = {
"id": item_id,
"topics": analysis["topics"].split(", "),
"processed_at": time.time()
}
cp.record_transform(item_id, transformed)
cp.advance_stage("done")
cp.mark_complete()
return list(s.transform_results.values())
Option 4: Redis checkpoint — distributed jobs, multiple workers
import anthropic
import json
import time
import hashlib
from typing import Iterator
client = anthropic.Anthropic()
# Redis checkpoint for distributed multi-worker jobs.
# Requires: pip install redis
try:
import redis
REDIS_AVAILABLE = True
except ImportError:
REDIS_AVAILABLE = False
class RedisCheckpoint:
"""
Redis-backed checkpoint for distributed agent jobs.
Multiple workers can safely process the same job concurrently.
"""
def __init__(self, redis_url: str, job_id: str, ttl_seconds: int = 86400):
if not REDIS_AVAILABLE:
raise ImportError("pip install redis")
self.r = redis.from_url(redis_url)
self.job_id = job_id
self.ttl = ttl_seconds
self.pending_key = f"job:{job_id}:pending"
self.done_key = f"job:{job_id}:done"
self.result_key = f"job:{job_id}:results"
self.failed_key = f"job:{job_id}:failed"
def enqueue(self, items: list[dict]) -> int:
"""Populate the work queue (idempotent via done set)."""
pipe = self.r.pipeline()
enqueued = 0
done_ids = self.r.smembers(self.done_key)
done_ids_str = {d.decode() for d in done_ids}
for item in items:
item_id = item["id"]
if item_id not in done_ids_str:
pipe.lpush(self.pending_key, json.dumps(item))
enqueued += 1
pipe.expire(self.pending_key, self.ttl)
pipe.execute()
print(f"[redis-cp] Enqueued {enqueued} items ({len(done_ids_str)} already done)")
return enqueued
def claim_item(self, timeout: int = 5) -> dict | None:
"""Atomically claim an item (blocking pop)."""
result = self.r.brpop(self.pending_key, timeout=timeout)
if result is None:
return None
_, raw = result
return json.loads(raw)
def complete_item(self, item_id: str, result: dict) -> None:
pipe = self.r.pipeline()
pipe.sadd(self.done_key, item_id)
pipe.hset(self.result_key, item_id, json.dumps(result))
pipe.expire(self.done_key, self.ttl)
pipe.expire(self.result_key, self.ttl)
pipe.execute()
def fail_item(self, item_id: str, error: str, requeue: bool = True) -> None:
self.r.hset(self.failed_key, item_id, error)
if requeue:
self.r.lpush(self.pending_key, json.dumps({"id": item_id, "_retry": True}))
def all_results(self) -> dict:
raw = self.r.hgetall(self.result_key)
return {k.decode(): json.loads(v) for k, v in raw.items()}
def stats(self) -> dict:
return {
"pending": self.r.llen(self.pending_key),
"done": self.r.scard(self.done_key),
"failed": self.r.hlen(self.failed_key),
}
def worker_loop(redis_url: str, job_id: str) -> None:
"""
Worker that claims and processes items from a Redis-backed queue.
Safe to run multiple instances concurrently.
"""
cp = RedisCheckpoint(redis_url, job_id)
while True:
item = cp.claim_item(timeout=5)
if item is None:
print("[worker] Queue empty, exiting")
break
item_id = item["id"]
try:
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=128,
messages=[{"role": "user", "content": f"Process: {item.get('content', '')}"}]
)
cp.complete_item(item_id, {"output": response.content[0].text})
print(f"[worker] ✓ {item_id} | stats: {cp.stats()}")
except Exception as e:
print(f"[worker] ✗ {item_id}: {e}")
cp.fail_item(item_id, str(e), requeue=True)
Option 5: Idempotent operation IDs — prevent duplicate work on retry
import anthropic
import hashlib
import json
import time
from pathlib import Path
client = anthropic.Anthropic()
class IdempotentProcessor:
"""
Assigns a deterministic operation ID to each unit of work.
Re-running with the same inputs returns the cached result without calling the LLM again.
Useful when you can't control retry logic in the caller.
"""
def __init__(self, cache_dir: str = "/tmp/idempotent_cache"):
self.cache_dir = Path(cache_dir)
self.cache_dir.mkdir(parents=True, exist_ok=True)
def _operation_id(self, operation_type: str, **kwargs) -> str:
"""Deterministic ID from operation type + inputs."""
payload = json.dumps({"type": operation_type, **kwargs}, sort_keys=True)
return hashlib.sha256(payload.encode()).hexdigest()[:24]
def _cache_path(self, op_id: str) -> Path:
return self.cache_dir / f"{op_id}.json"
def get_cached(self, op_id: str) -> dict | None:
path = self._cache_path(op_id)
if path.exists():
return json.loads(path.read_text())
return None
def cache_result(self, op_id: str, result: dict) -> None:
path = self._cache_path(op_id)
tmp = path.with_suffix(".tmp")
tmp.write_text(json.dumps(result))
tmp.rename(path)
def analyze(self, text: str, task: str) -> dict:
"""
Idempotent analysis — same text+task always returns the same result.
LLM is only called once per unique (text, task) pair.
"""
op_id = self._operation_id("analyze", text=text[:200], task=task)
cached = self.get_cached(op_id)
if cached:
print(f" [cache hit] op={op_id}")
return cached
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=256,
messages=[{"role": "user", "content": f"Task: {task}\n\nText: {text}"}]
)
result = {
"op_id": op_id,
"output": response.content[0].text,
"input_tokens": response.usage.input_tokens,
"output_tokens": response.usage.output_tokens,
"computed_at": time.time()
}
self.cache_result(op_id, result)
print(f" [computed] op={op_id}")
return result
processor = IdempotentProcessor()
# First run: calls LLM
r1 = processor.analyze("The quick brown fox...", task="sentiment analysis")
# Second run (crash recovery): returns cache instantly
r2 = processor.analyze("The quick brown fox...", task="sentiment analysis")
assert r1["output"] == r2["output"] # same result, no LLM call
Option 6: Checkpoint with progress reporting and ETA
import anthropic
import json
import time
from pathlib import Path
from dataclasses import dataclass, field
client = anthropic.Anthropic()
@dataclass
class JobProgress:
job_id: str
total: int
completed_ids: list[str] = field(default_factory=list)
results: dict = field(default_factory=dict)
timing: list[float] = field(default_factory=list) # seconds per item
started_at: float = field(default_factory=time.time)
error_count: int = 0
class CheckpointedJob:
"""
Checkpointed job runner with progress reporting, ETA estimation,
and per-item timing for performance analysis.
"""
def __init__(self, job_id: str, items: list[dict]):
self.items = items
self.checkpoint_path = Path(f"/tmp/{job_id}.progress.json")
self.progress = self._load_or_init(job_id, len(items))
def _load_or_init(self, job_id: str, total: int) -> JobProgress:
if self.checkpoint_path.exists():
data = json.loads(self.checkpoint_path.read_text())
p = JobProgress(**data)
pct = len(p.completed_ids) / p.total * 100
print(f"[resume] {len(p.completed_ids)}/{p.total} ({pct:.1f}%) already done")
return p
return JobProgress(job_id=job_id, total=total)
def _save(self):
import dataclasses
tmp = self.checkpoint_path.with_suffix(".tmp")
tmp.write_text(json.dumps(dataclasses.asdict(self.progress), indent=2))
tmp.rename(self.checkpoint_path)
def _eta_string(self) -> str:
if len(self.progress.timing) < 2:
return "calculating..."
avg_seconds = sum(self.progress.timing[-10:]) / len(self.progress.timing[-10:])
remaining = self.progress.total - len(self.progress.completed_ids)
eta_seconds = avg_seconds * remaining
if eta_seconds < 60:
return f"{eta_seconds:.0f}s"
elif eta_seconds < 3600:
return f"{eta_seconds/60:.1f}min"
else:
return f"{eta_seconds/3600:.1f}h"
def run(self, process_fn) -> dict:
done_ids = set(self.progress.completed_ids)
for item in self.items:
item_id = item["id"]
if item_id in done_ids:
continue
t0 = time.time()
try:
result = process_fn(item)
elapsed = time.time() - t0
self.progress.completed_ids.append(item_id)
self.progress.results[item_id] = result
self.progress.timing.append(elapsed)
self._save()
done = len(self.progress.completed_ids)
pct = done / self.progress.total * 100
print(f" [{done}/{self.progress.total} {pct:.1f}%] {item_id} ({elapsed:.2f}s) ETA: {self._eta_string()}")
except Exception as e:
self.progress.error_count += 1
self._save()
print(f" [error] {item_id}: {e} (errors: {self.progress.error_count})")
return self.progress.results
def llm_process(item: dict) -> dict:
response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=128,
messages=[{"role": "user", "content": f"Summarize: {item['content'][:300]}"}]
)
return {"summary": response.content[0].text}
job = CheckpointedJob(
job_id="doc_analysis_20240115",
items=[{"id": f"doc_{i}", "content": f"Document content {i}"} for i in range(50)]
)
results = job.run(llm_process)
print(f"Done: {len(results)} results")
Checkpoint Strategy Comparison
| Strategy | Best For | Durability | Distributed | Overhead |
|---|---|---|---|---|
| File-based (Option 1) | Simple sequential jobs | High (atomic rename) | No | Minimal |
| SQLite (Option 2) | Large jobs, retry logic | High (WAL mode) | No | Low |
| Multi-step pipeline (Option 3) | Stage-based workflows | High | No | Medium |
| Redis (Option 4) | Distributed workers | High (with persistence) | Yes | Medium |
| Idempotent IDs (Option 5) | Uncontrolled callers | High | Partial | Minimal |
| Progress + ETA (Option 6) | Long jobs needing monitoring | High | No | Low |
Expected Token Savings
Resume from checkpoint saves all tokens already spent on completed items. A 1000-item job that fails at item 900 wastes 90% of its spend without checkpointing. With checkpointing, only the 100 remaining items need processing — 90% token recovery on restart.
Environment
- Any agent processing > ~20 items in a loop; any multi-stage pipeline; any job running longer than 5 minutes; jobs with external dependencies (databases, APIs, file systems) that can fail mid-run; environments where process interruption is possible (serverless with timeouts, spot instances, operator restarts); the file-based approach (Option 1) is sufficient for single-machine sequential jobs; use Redis (Option 4) when multiple workers or horizontal scaling is needed
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