5 Safety Guards for Auto-Archiving Another AI Agent's Conversations into My Vault: Designing a Manus API Bridge

My Claude Code conversations were already saved to my knowledge vault automatically, but anything I gave to Manus stayed inside its browser UI. I couldn't search those tasks or find them later. Now a small Python bridge checks the Manus API every 5 minutes and saves each task to the same shelf as my Claude Code logs. It uses five safety guards to keep it quiet, self-healing and free of secrets.

Last time, I wrote about how a weekly batch job in my skills CLI had been failing silently. This post continues with the same vault setup. It covers how I automatically collect conversations from an AI agent other than Claude Code (Manus) through its API and store them as primary sources in a shared vault.

The vault has a location called raw/conversations/, and Claude Code conversation logs pile up there automatically. Tasks I sent to Manus were different. The only way to see their contents was to open the task screen in the browser. When I later tried to remember what I had asked for in a task, it didn't show up in vault search or in the shared hot cache.

Manus has a public API that lets you read your own task list and messages. That was the starting point for this bridge: fetch those tasks on a schedule and put them on the same shelf (the raw layer) as the Claude Code conversation logs. launchd runs ~/Documents/claude-obsidian/bin/manus-task-archive.py every 5 minutes, and the script writes its output to ~/Documents/my-knowledge-base/raw/manus-conversations/.

Note

This script only fetches and saves. A separate, safe reflection worker handles summaries and "proposals to the vault." This script never does that work. A core rule of how I run the vault is to keep raw primary sources separate from the layer that interprets them.

I don't want the API key in the code or in any file, so the script reads it from the macOS Keychain on every run.

KEYCHAIN_SERVICE = "manus-vault-memory-archiver"

def keychain_key() -> str:
    result = subprocess.run(
        ["security", "find-generic-password", "-s", KEYCHAIN_SERVICE, "-w"],
        text=True,
        capture_output=True,
        check=False,
    )
    if result.returncode != 0 or not result.stdout.strip():
        raise RuntimeError("Manus archive API key is unavailable in macOS Keychain")
    return result.stdout.strip()

The docstring also states that this worker never passes the key to print or write. If the key can't be fetched, the function raises an exception. The except in main() logs it and the run stops. A broken key doesn't affect other jobs or conversations.

The script fetches Manus tasks by paging through task.list → task.listMessages. Fetching every message on every run would be wasteful. Instead, the script saves each task's update time as its "revision" in a state file and skips the task if that value hasn't changed.

def task_revision(task: dict[str, Any]) -> str:
    for key in ("updated_at", "updatedAt", "modified_at", "timestamp", "created_at", "createdAt"):
        if task.get(key) not in (None, ""):
            return str(task[key])
    return "unknown"
for task in accessible:
    ident = task_id(task)
    if not ident:
        continue
    revision = task_revision(task)
    if state["tasks"].get(ident) == revision:
        continue
    messages = list_messages(credential, ident)
    archive_task(task, messages)
    state["tasks"][ident] = revision

Here is the actual state file (AI/.runtime/manus-task-archive-state.json):

{
  "tasks": {
    "rRkPkzEkUrImaYRXsI3sLY": "1790174319",
    "7hBaAc4dhs9hycZaAnC8z5": "1789628528",
    "5VXpxPKZkzify3yp9FA5LX": "1790077323"
  },
  "accessible_task_count": 3
}

The launchd log (~/.claude/logs/manus-task-archive.log) shows that even though the job runs every 5 minutes, most runs find nothing new:

[2026-09-26 07:43:32 +0900] ok: accessible=3 updated=0
[2026-09-26 07:48:33 +0900] ok: accessible=3 updated=0
[2026-09-26 07:53:34 +0900] ok: accessible=3 updated=0
[2026-09-26 07:58:51 +0900] ok: accessible=3 updated=0

A run of accessible=3 updated=0 lines means the API returns 3 tasks each time, but none of their revisions changed, so nothing gets written. Of all my measurements, this was the clearest proof that the diff check works correctly.

The launchd StartInterval is fixed at 300 seconds (5 minutes), set in ~/Library/LaunchAgents/com.shun.manus-vault-archive.plist.

<key>StartInterval</key>
<integer>300</integer>
<key>ThrottleInterval</key>
<integer>60</integer>
<key>LowPriorityIO</key>
<true/>
<key>Nice</key>
<integer>12</integer>

Overlapping runs on a 5-minute schedule would cause trouble, so the script uses a lock directory created with mkdir to prevent more than one run at a time. The case I paid attention to was a previous run crashing and leaving its lock behind.

def acquire_lock() -> bool:
    STATE_DIR.mkdir(parents=True, exist_ok=True)
    if LOCK_DIR.exists():
        try:
            age = time.time() - LOCK_DIR.stat().st_mtime
            if age > 1800:
                LOCK_DIR.rmdir()
        except OSError:
            pass
    try:
        LOCK_DIR.mkdir()
        return True
    except FileExistsError:
        return False

If a lock is older than 30 minutes (1800 seconds, or six 5-minute cycles), the next run removes it. The system recovers on a later cycle before a human notices and runs rm. Releasing the lock also sits in a finally block, so the lock is always removed, whether the run crashes with an exception or exits normally.

The log shows this working in practice:

[2026-09-26 06:42:45 +0900] ok: accessible=3 updated=0
[2026-09-26 06:48:15 +0900] error: RuntimeError: API request failed: <urlopen error [Errno 8] nodename nor servname provided, or not known>
[2026-09-26 06:53:16 +0900] ok: accessible=3 updated=0

At 06:48, DNS resolution failed briefly and the run crashed with an exception. The finally block had already released the lock, so the next run 5 minutes later (06:53) finished normally with nothing blocking it. The lock design was meant to do exactly this: fix itself before a human notices.

This raw layer becomes "primary source material for future AI agents to read." To keep secrets out of it, the script scrubs them with regular expressions.

SECRET_PATTERNS = [
    (re.compile(r"\bsk-[A-Za-z0-9_\-]{16,}\b"), "[REDACTED_API_KEY]"),
    (re.compile(r"(?i)\b(?:api[_ -]?key|token|password|secret|bearer)\s*[:=]\s*[^\s`'\"]{8,}"), "[REDACTED_SECRET]"),
    (re.compile(r"https?://(?:meet\.google\.com|zoom\.us/j|teams\.microsoft\.com)/[^\s)\]>]+", re.I), "[REDACTED_MEETING_LINK]"),
]

The comment in the code explains the design intent:

The important part is the order of reasoning. I didn't start from "this raw store is personal, so it can be a little loose." I started from "future agents will read it, so keep it conservative." Zoom, Meet and Teams meeting URLs get the same treatment as API key patterns. Conversation logs often contain lines like "join from this link," and I don't want those saved permanently in the primary sources.

Each archived Markdown file's modification time (mtime) is set to the time the task was last updated in Manus, not the time it was archived.

output = RAW_DIR / f"manus_{ident}.md"
atomic_write(output, "\n".join(parts).rstrip() + "\n")
stamp = updated.timestamp()
os.utime(output, (stamp, stamp))

Without this, a task that finished 9 days ago but was archived for the first time today would appear as "something that just happened" in the shared hot cache and other indexes that sort by mtime. In fact, one of the files from the first archive run had an update date of 9/17:

日時: 2026-09-17 07:02 UTC
更新: 2026-09-17 07:02 UTC
Task ID: `7hBaAc4dhs9hycZaAnC8z5`
Status: `waiting`

The file was written today, but its mtime stays at 9/17, so it doesn't appear in the list of "today's events." Writes also go through atomic_write, which writes a temp file and then calls os.replace. If the process dies partway through, no half-written file is left behind.

After writing, if at least one task was updated, the script also runs the shared hot-cache update script:

if updated_files and UPDATE_HOT.exists():
    subprocess.run([sys.executable, str(UPDATE_HOT)], check=False,
                    stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)

This way, Manus tasks reach the vault's "recent sessions" list through the same path as the other raw conversation logs.

task.get("task_id") or task.get("id"), and for revisions, check keys in order from updated_at through createdAt. RuntimeError once paging goes past MAX_MESSAGE_PAGES (100). I chose "fail with an error I'll notice" over an infinite loop.urlopen error failure (2026-09-26 06:48). Because lock release is in finally, the next 5-minute cycle recovered normally with no special handling.raw/ shelf as Claude Code conversation logs, accessible=3 updated=0 is the normal, most common result.rmdir Next time, I plan to write about how the reflection worker safely summarizes the Manus conversations in this raw layer and promotes them into the wiki.

If you archive conversations from more than one AI agent, how do you keep secrets and outdated timestamps out of the shared record?

*Written by Lily — I ship iOS apps and automate my content stack with Claude Code.

Follow along: Portfolio · X · GitHub*