fix(plugin): hook cleanup + identity + compact-summary flow (#5076)

mem0-plugin/README.md

@@ -157,6 +157,32 @@ After installing, confirm the MCP server is connected:
 - **Mem0 SDK Skill** — Guides the AI on how to integrate the Mem0 SDK (Python & TypeScript) into your applications.
 - **Memory Protocol Skill** — Codex-specific skill that instructs the agent to retrieve relevant memories at task start, store learnings on completion, and capture session state before context loss. Complements the lifecycle hooks on Codex.
 
+## Updating the plugin
+
+When the plugin updates (new version pulled from the marketplace, or a fresh local install), the MCP server connection in your existing Claude Code / Cursor / Codex session is left holding a stale handle and stops responding. **Restart your client to reconnect:**
+
+- **Claude Code:** run `/restart` in the prompt, or close and reopen the CLI.
+- **Cursor:** quit and relaunch.
+- **Codex:** restart the editor session.
+
+Your `MEM0_API_KEY` doesn't need to be re-entered — the auth header is re-read from your environment on the new session. The plugin's MCP config uses `${MEM0_API_KEY}` interpolation at session start, not at install time, so as long as the env var is set persistently (in your shell profile or `~/.claude/settings.json` `env` block), reconnection is automatic on restart.
+
+If reconnection still fails after a restart, check that `MEM0_API_KEY` is reachable in the new shell (`echo $MEM0_API_KEY`) and confirm you're using a key that starts with `m0-` (from https://app.mem0.ai/dashboard/api-keys, not a legacy token).
+
+## Optional: tune categories for coding workflows
+
+mem0 auto-tags every memory with one or more `categories` from a project-level list. The default list is consumer-oriented (`food`, `hobbies`, `music` …) — useful for chat assistants, less so for code. A one-shot script in this plugin replaces it with a coding-focused taxonomy:
+
+```bash
+# Dry-run first -- prints current vs proposed, no changes:
+python mem0-plugin/scripts/setup_coding_categories.py
+
+# Actually write:
+python mem0-plugin/scripts/setup_coding_categories.py --apply
+```
+
+Requires the `mem0ai` Python SDK (`pip install mem0ai`) and `MEM0_API_KEY` set. New memories will then auto-tag against `architecture_decisions`, `anti_patterns`, `task_learnings`, `tooling_setup`, `bug_fixes`, `coding_conventions`, `user_preferences`. Re-run with a different list any time; `project.update(custom_categories=[...])` always replaces.
+
 ## MCP Tools
 
 Once installed, the following tools are available:

mem0-plugin/hooks/cursor-hooks.json

@@ -15,10 +15,6 @@
     "preCompact": [
       {
         "command": "${CURSOR_PLUGIN_ROOT}/scripts/on_pre_compact.sh"
-      },
-      {
-        "command": "python3 ${CURSOR_PLUGIN_ROOT}/scripts/on_pre_compact.py",
-        "timeout": 30
       }
     ],
     "stop": [

mem0-plugin/hooks/hooks.json

@@ -30,12 +30,6 @@
             "type": "command",
             "command": "${CLAUDE_PLUGIN_ROOT}/scripts/on_pre_compact.sh",
             "statusMessage": "Preparing pre-compaction summary..."
-          },
-          {
-            "type": "command",
-            "command": "python3 ${CLAUDE_PLUGIN_ROOT}/scripts/on_pre_compact.py",
-            "statusMessage": "Saving session state to mem0...",
-            "timeout": 30
           }
         ]
       }

mem0-plugin/scripts/_identity.py

@@ -0,0 +1,59 @@
+"""Resolve mem0 user_id with deterministic priority.
+
+Resolution priority:
+  1. MEM0_USER_ID env var (explicit override)
+  2. ~/.mem0/identity.json cache (pinned to current MEM0_API_KEY fingerprint)
+  3. Derived: "mem0-" + sha256(MEM0_API_KEY)[:12]
+  4. Fallback: $USER, else "default"
+
+Same MEM0_API_KEY across machines yields the same user_id, which fixes
+the "47 user buckets per account" symptom from running on multiple
+laptops with different $USER values.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from datetime import datetime, timezone
+
+_CACHE_PATH = os.path.expanduser("~/.mem0/identity.json")
+
+
+def resolve_user_id() -> str:
+    explicit = os.environ.get("MEM0_USER_ID", "").strip()
+    if explicit:
+        return explicit
+
+    api_key = os.environ.get("MEM0_API_KEY", "").strip()
+    if api_key:
+        digest = hashlib.sha256(api_key.encode("utf-8")).hexdigest()
+        fingerprint = digest[:8]
+
+        try:
+            with open(_CACHE_PATH, "r") as f:
+                cached = json.load(f)
+            if cached.get("api_key_fingerprint") == fingerprint and cached.get("user_id"):
+                return cached["user_id"]
+        except (OSError, json.JSONDecodeError):
+            pass
+
+        derived = "mem0-" + digest[:12]
+        try:
+            os.makedirs(os.path.dirname(_CACHE_PATH), exist_ok=True)
+            with open(_CACHE_PATH, "w") as f:
+                json.dump(
+                    {
+                        "user_id": derived,
+                        "source": "api_key",
+                        "api_key_fingerprint": fingerprint,
+                        "resolved_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
+                    },
+                    f,
+                )
+        except OSError:
+            pass
+        return derived
+
+    return os.environ.get("USER") or "default"

mem0-plugin/scripts/_identity.sh

@@ -0,0 +1,57 @@
+# Source this file. Sets MEM0_RESOLVED_USER_ID.
+#
+# Resolution priority:
+#   1. MEM0_USER_ID env var (explicit override)
+#   2. ~/.mem0/identity.json cache (pinned to current MEM0_API_KEY fingerprint)
+#   3. Derived: "mem0-" + sha256(MEM0_API_KEY)[:12]
+#   4. Fallback: $USER, else "default"
+#
+# Same MEM0_API_KEY across machines yields the same user_id, which fixes
+# the "47 user buckets per account" symptom from running on multiple
+# laptops with different $USER values.
+
+_mem0_sha256() {
+  if command -v sha256sum >/dev/null 2>&1; then
+    sha256sum | cut -d' ' -f1
+  else
+    shasum -a 256 | cut -d' ' -f1
+  fi
+}
+
+_mem0_resolve_identity() {
+  if [ -n "${MEM0_USER_ID:-}" ]; then
+    printf '%s' "$MEM0_USER_ID"
+    return
+  fi
+
+  local api_key="${MEM0_API_KEY:-}"
+  local cache="$HOME/.mem0/identity.json"
+
+  if [ -n "$api_key" ]; then
+    local digest
+    digest=$(printf '%s' "$api_key" | _mem0_sha256)
+    local fp="${digest:0:8}"
+
+    if [ -f "$cache" ]; then
+      local cached_fp cached_id
+      cached_fp=$(jq -r '.api_key_fingerprint // ""' "$cache" 2>/dev/null)
+      cached_id=$(jq -r '.user_id // ""' "$cache" 2>/dev/null)
+      if [ "$cached_fp" = "$fp" ] && [ -n "$cached_id" ]; then
+        printf '%s' "$cached_id"
+        return
+      fi
+    fi
+
+    local derived="mem0-${digest:0:12}"
+    mkdir -p "$HOME/.mem0" 2>/dev/null && \
+      printf '{"user_id":"%s","source":"api_key","api_key_fingerprint":"%s","resolved_at":"%s"}\n' \
+        "$derived" "$fp" "$(date -u +%FT%TZ)" > "$cache" 2>/dev/null
+    printf '%s' "$derived"
+    return
+  fi
+
+  printf '%s' "${USER:-default}"
+}
+
+MEM0_RESOLVED_USER_ID="$(_mem0_resolve_identity)"
+export MEM0_RESOLVED_USER_ID

mem0-plugin/scripts/block_memory_write.sh

@@ -13,6 +13,10 @@
 
 set -euo pipefail
 
+if [ -n "${MEM0_DEBUG:-}" ]; then
+  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
+fi
+
 INPUT=$(cat)
 
 FILE_PATH=$(echo "$INPUT" | jq -r '.tool_input.file_path // .tool_input.path // ""' 2>/dev/null || echo "")
@@ -22,7 +26,7 @@ if [ -z "$FILE_PATH" ]; then
 fi
 
 case "$FILE_PATH" in
-  */MEMORY.md|*/memory/*.md|*/.claude/*/memory/*)
+  */MEMORY.md|*/.claude/memory/*)
     echo "BLOCKED: Do not write to $FILE_PATH. Use the mem0 MCP \`add_memory\` tool instead to persist memories. This project uses mem0 for all memory storage." >&2
     exit 2
     ;;

mem0-plugin/scripts/capture_compact_summary.py

@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+"""Capture the post-compaction summary into mem0.
+
+PreCompact hooks fire BEFORE the summary is generated, so they can't
+store the actual compact-summary text. This script runs at
+SessionStart with source=compact, reads the transcript, finds the
+most recent entry flagged isCompactSummary=true, and stores it as a
+memory tagged metadata.type=compact_summary.
+
+Input:  JSON on stdin with transcript_path, session_id, source
+Output: stderr logs only (exit 0 always -- must not block)
+
+Spawned in the background by on_session_start.sh; the user-facing
+bootstrap text continues without waiting on the network.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import sys
+import urllib.error
+import urllib.request
+from datetime import date, timedelta
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from _identity import resolve_user_id
+
+log = logging.getLogger("mem0-compact-summary")
+log.setLevel(logging.DEBUG)
+_handler = logging.StreamHandler(sys.stderr)
+_handler.setFormatter(logging.Formatter("[mem0-compact-summary] %(message)s"))
+log.addHandler(_handler)
+
+if os.environ.get("MEM0_DEBUG"):
+    _log_dir = os.path.expanduser("~/.mem0")
+    try:
+        os.makedirs(_log_dir, exist_ok=True)
+        _file_handler = logging.FileHandler(os.path.join(_log_dir, "hooks.log"))
+        _file_handler.setFormatter(logging.Formatter("[mem0-compact-summary] %(asctime)s %(message)s"))
+        log.addHandler(_file_handler)
+    except OSError:
+        pass
+
+API_URL = "https://api.mem0.ai"
+MAX_TAIL_LINES = 2000
+MAX_SUMMARY_CHARS = 50000
+# Compact summaries describe a single session's state -- stale after a quarter.
+COMPACT_SUMMARY_EXPIRY_DAYS = 90
+
+
+def tail_lines(filepath: str, n: int) -> list[str]:
+    try:
+        with open(filepath, "rb") as f:
+            f.seek(0, 2)
+            file_size = f.tell()
+            if file_size == 0:
+                return []
+            chunk_size = min(file_size, n * 4096)
+            f.seek(max(0, file_size - chunk_size))
+            data = f.read().decode("utf-8", errors="replace")
+            return data.splitlines()[-n:]
+    except OSError:
+        return []
+
+
+def find_compact_summary(lines: list[str]) -> str:
+    """Walk transcript backwards, return text content of the most recent
+    entry flagged isCompactSummary=true. Empty string if none found."""
+    for line in reversed(lines):
+        line = line.strip()
+        if not line:
+            continue
+        try:
+            entry = json.loads(line)
+        except json.JSONDecodeError:
+            continue
+        if not entry.get("isCompactSummary"):
+            continue
+
+        message = entry.get("message", {})
+        content = message.get("content", [])
+        if isinstance(content, str):
+            return content[:MAX_SUMMARY_CHARS]
+        if isinstance(content, list):
+            parts = []
+            for block in content:
+                if isinstance(block, str):
+                    parts.append(block)
+                elif isinstance(block, dict) and block.get("type") == "text":
+                    parts.append(block.get("text", ""))
+            return "\n".join(parts).strip()[:MAX_SUMMARY_CHARS]
+    return ""
+
+
+def store_summary(api_key: str, summary: str, user_id: str, session_id: str) -> bool:
+    expires = (date.today() + timedelta(days=COMPACT_SUMMARY_EXPIRY_DAYS)).isoformat()
+    body = {
+        "messages": [{"role": "user", "content": summary}],
+        "user_id": user_id,
+        "metadata": {
+            "type": "compact_summary",
+            "source": "session-start-compact",
+            "session_id": session_id,
+        },
+        "infer": False,
+        "expiration_date": expires,
+    }
+
+    data = json.dumps(body).encode("utf-8")
+    req = urllib.request.Request(
+        f"{API_URL}/v1/memories/",
+        data=data,
+        headers={
+            "Content-Type": "application/json",
+            "Authorization": f"Token {api_key}",
+        },
+        method="POST",
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=15) as resp:
+            if resp.status in (200, 201):
+                log.info("Compact summary stored")
+                return True
+            log.warning("API returned status %d", resp.status)
+            return False
+    except urllib.error.URLError as e:
+        log.warning("API call failed: %s", e)
+        return False
+
+
+def main():
+    api_key = os.environ.get("MEM0_API_KEY", "")
+    if not api_key:
+        log.debug("MEM0_API_KEY not set, skipping capture")
+        return
+
+    try:
+        hook_input = json.loads(sys.stdin.read())
+    except (json.JSONDecodeError, OSError):
+        log.debug("No valid JSON on stdin")
+        return
+
+    transcript_path = hook_input.get("transcript_path", "")
+    if not transcript_path:
+        log.debug("No transcript_path provided")
+        return
+
+    session_id = hook_input.get("session_id", "")
+    user_id = resolve_user_id()
+
+    lines = tail_lines(transcript_path, MAX_TAIL_LINES)
+    if not lines:
+        log.debug("Transcript empty or unreadable: %s", transcript_path)
+        return
+
+    summary = find_compact_summary(lines)
+    if not summary:
+        log.debug("No isCompactSummary entry found")
+        return
+
+    log.info("Capturing compact summary (%d chars)", len(summary))
+    store_summary(api_key, summary, user_id, session_id)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as e:
+        log.error("Unexpected error: %s", e)
+    sys.exit(0)

mem0-plugin/scripts/on_pre_compact.py

@@ -18,8 +18,12 @@ import json
 import logging
 import os
 import sys
-import urllib.request
 import urllib.error
+import urllib.request
+from datetime import date, timedelta
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from _identity import resolve_user_id
 
 log = logging.getLogger("mem0-capture")
 log.setLevel(logging.DEBUG)
@@ -27,11 +31,25 @@ _handler = logging.StreamHandler(sys.stderr)
 _handler.setFormatter(logging.Formatter("[mem0-capture] %(message)s"))
 log.addHandler(_handler)
 
+if os.environ.get("MEM0_DEBUG"):
+    _log_dir = os.path.expanduser("~/.mem0")
+    try:
+        os.makedirs(_log_dir, exist_ok=True)
+        _file_handler = logging.FileHandler(os.path.join(_log_dir, "hooks.log"))
+        _file_handler.setFormatter(logging.Formatter("[mem0-capture] %(asctime)s %(message)s"))
+        log.addHandler(_file_handler)
+    except OSError:
+        pass
+
 API_URL = "https://api.mem0.ai"
 MAX_TAIL_LINES = 500
 MAX_USER_MESSAGES = 30
 MAX_BASH_COMMANDS = 20
 MAX_ASSISTANT_TEXT = 10000
+# session_state captures churn fast (active codebase, files in flight). Past
+# ~3 months they're stale noise. Durable facts (decisions, conventions) are
+# stored separately by the agent without an expiration_date.
+SESSION_STATE_EXPIRY_DAYS = 90
 
 
 def tail_lines(filepath: str, n: int) -> list[str]:
@@ -149,8 +167,9 @@ def build_content(state: dict, source: str) -> str:
     return "\n".join(parts)
 
 
-def store_memory(api_key: str, content: str, user_id: str, source: str) -> bool:
+def store_memory(api_key: str, content: str, user_id: str, source: str, session_id: str = "") -> bool:
     """Store session state as a memory via the Mem0 REST API."""
+    expires = (date.today() + timedelta(days=SESSION_STATE_EXPIRY_DAYS)).isoformat()
     body = {
         "messages": [
             {"role": "user", "content": content}
@@ -159,7 +178,9 @@ def store_memory(api_key: str, content: str, user_id: str, source: str) -> bool:
         "metadata": {
             "type": "session_state",
             "source": source,
+            "session_id": session_id,
         },
+        "expiration_date": expires,
     }
 
     data = json.dumps(body).encode("utf-8")
@@ -207,7 +228,8 @@ def main():
         log.debug("No transcript_path provided")
         return
 
-    user_id = os.environ.get("MEM0_USER_ID", os.environ.get("USER", "default"))
+    session_id = hook_input.get("session_id", "")
+    user_id = resolve_user_id()
 
     lines = tail_lines(transcript_path, MAX_TAIL_LINES)
     if not lines:
@@ -228,7 +250,7 @@ def main():
         len(state["bash_commands"]),
     )
 
-    store_memory(api_key, content, user_id, source)
+    store_memory(api_key, content, user_id, source, session_id)
 
 
 if __name__ == "__main__":

mem0-plugin/scripts/on_pre_compact.sh

@@ -5,12 +5,16 @@
 # the full context before it gets compressed.
 #
 # Output: Text instructions injected into Claude's context.
-# Claude still has the full conversation and can write an accurate summary.
+# Claude still has the full conversation and can write an accurate summary,
-# A companion Python script (on_pre_compact.py) also runs to capture
+# which it stores via add_memory(infer=False) so the platform preserves
-# transcript state directly via the Mem0 REST API as a safety net.
+# the structure verbatim instead of running a second extraction pass.
 
 set -euo pipefail
 
+if [ -n "${MEM0_DEBUG:-}" ]; then
+  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
+fi
+
 cat <<'EOF'
 ## CRITICAL: Pre-Compaction Session Summary
 
@@ -18,7 +22,9 @@ Context compaction is about to happen. You are about to lose most of your conver
 
 ### Step 1: Store session summary
 
-Call `add_memory` with a thorough summary covering ALL of the following:
+Call `add_memory` with `infer=False` and a thorough summary covering ALL of the following.
+
+`infer=False` is critical here: you've already done the extraction work yourself using full context. Without it, the platform runs a second LLM pass that loses your structure and pulls fragmented facts. With it, your summary is preserved verbatim.
 
 ```
 ## Session Summary (Pre-Compaction)
@@ -44,11 +50,19 @@ Call `add_memory` with a thorough summary covering ALL of the following:
 the post-compaction agent continue without asking redundant questions]
 ```
 
-Include metadata: `{"type": "session_state", "source": "pre-compaction"}`
+Tool call shape:
+```
+add_memory(
+  messages=[{"role":"user","content":"<the summary above>"}],
+  user_id="<the active user_id from the SessionStart bootstrap>",
+  metadata={"type":"session_state","source":"pre-compaction"},
+  infer=False,
+)
+```
 
 ### Step 2: Store any unstored learnings
 
-If there are learnings from this session that you haven't stored yet, store them as separate memories:
+If there are learnings from this session that you haven't stored yet, store them as separate memories with `infer=False` (same reasoning -- you've already extracted the fact, don't re-extract):
 - Failed approaches -> metadata `{"type": "anti_pattern"}`
 - Successful strategies -> metadata `{"type": "task_learning"}`
 - Architecture decisions -> metadata `{"type": "decision"}`

mem0-plugin/scripts/on_session_start.sh

@@ -11,9 +11,34 @@
 # even if jq is missing or stdin is malformed.
 set -uo pipefail
 
+if [ -n "${MEM0_DEBUG:-}" ]; then
+  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
+fi
+
+# Skip the bootstrap entirely if no API key is configured -- the agent
+# would otherwise be told to call mem0 MCP tools that will all fail.
+if [ -z "${MEM0_API_KEY:-}" ]; then
+  exit 0
+fi
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+# shellcheck source=_identity.sh
+. "$SCRIPT_DIR/_identity.sh"
+
 INPUT=$(cat)
 SOURCE=$(echo "$INPUT" | jq -r '.source // "startup"' 2>/dev/null || echo "startup")
 
+# Identity line is emitted before every bootstrap variant so the agent
+# uses the same user_id the hooks resolved. Without this, the agent's
+# search_memories/add_memory MCP calls may bind to a different bucket
+# than what the hooks write to.
+echo "## Mem0 Identity"
+echo ""
+echo "Active user_id: \`$MEM0_RESOLVED_USER_ID\`"
+echo ""
+echo "Always include \`{\"user_id\": \"$MEM0_RESOLVED_USER_ID\"}\` (wrapped in an \`AND\` clause) in every \`search_memories\` filter and as \`user_id\` on every \`add_memory\` call. This keeps memories under one bucket regardless of which machine you're on."
+echo ""
+
 if [ "$SOURCE" = "startup" ]; then
   cat <<'EOF'
 ## Mem0 Session Bootstrap
@@ -40,14 +65,22 @@ Continue where you left off.
 EOF
 
 elif [ "$SOURCE" = "compact" ]; then
+  # Capture the just-generated compact summary in the background.
+  # PreCompact fires too early to see this entry; SessionStart-compact
+  # is the first place isCompactSummary=true is in the transcript.
+  echo "$INPUT" | python3 "$SCRIPT_DIR/capture_compact_summary.py" 2>/dev/null &
+
   cat <<'EOF'
 ## Mem0 Post-Compaction Recovery
 
-Context was just compacted. You may have lost important session context.
+Context was just compacted. The Claude Code-generated compact summary
+is being captured to mem0 in the background as `metadata.type=compact_summary`.
 
-1. Call `search_memories` with queries related to what you were working on to reload relevant knowledge.
+1. Call `search_memories` to reload context, layering up to three angles:
-2. Check for any session state memories that were saved before compaction.
+   - `metadata.type=session_state` -- the rich pre-compaction summary you wrote
-3. Continue working based on the recovered context.
+   - `metadata.type=compact_summary` -- the platform-generated condensed summary just now
+   - `metadata.type=decision` / `anti_pattern` -- specific facts you stored during the session
+2. Continue working from the recovered context.
 EOF
 fi
 

mem0-plugin/scripts/on_stop.sh

@@ -12,6 +12,10 @@
 
 set -euo pipefail
 
+if [ -n "${MEM0_DEBUG:-}" ]; then
+  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
+fi
+
 SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
 
 INPUT=$(cat)

mem0-plugin/scripts/on_stop_codex.sh

@@ -17,6 +17,10 @@
 
 set -uo pipefail
 
+if [ -n "${MEM0_DEBUG:-}" ]; then
+  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
+fi
+
 INPUT=$(cat)
 STOP_HOOK_ACTIVE=$(echo "$INPUT" | jq -r '.stop_hook_active // false' 2>/dev/null || echo "false")
 

mem0-plugin/scripts/on_task_completed.sh

@@ -9,6 +9,10 @@
 
 set -euo pipefail
 
+if [ -n "${MEM0_DEBUG:-}" ]; then
+  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
+fi
+
 INPUT=$(cat)
 TASK_SUBJECT=$(echo "$INPUT" | jq -r '.task_subject // "unknown task"' 2>/dev/null || echo "unknown task")
 

mem0-plugin/scripts/on_user_prompt.sh

@@ -13,6 +13,10 @@
 # must never block the user's prompt.
 set -uo pipefail
 
+if [ -n "${MEM0_DEBUG:-}" ]; then
+  mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
+fi
+
 INPUT=$(cat)
 PROMPT=$(echo "$INPUT" | jq -r '.prompt // ""' 2>/dev/null || echo "")
 
@@ -26,7 +30,10 @@ if [ -z "${MEM0_API_KEY:-}" ]; then
   exit 0
 fi
 
-USER_ID="${MEM0_USER_ID:-${USER:-default}}"
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+# shellcheck source=_identity.sh
+. "$SCRIPT_DIR/_identity.sh"
+USER_ID="$MEM0_RESOLVED_USER_ID"
 
 cat <<EOF
 ## Memory check

mem0-plugin/scripts/setup_coding_categories.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""Replace mem0's default category taxonomy with one tuned for coding workflows.
+
+mem0 auto-tags every memory with one or more `categories`. By default the list
+is consumer-oriented (food, hobbies, music, ...), which is meaningless for code.
+This script replaces the project's category list with a coding-focused one.
+
+The change is project-level (per the platform docs, per-request overrides are
+not supported on the managed API). Run once per project; future memories will
+be tagged using the new list automatically.
+
+Usage:
+  python setup_coding_categories.py            # dry-run: show current vs proposed, no changes
+  python setup_coding_categories.py --apply    # actually call project.update()
+
+Requires the mem0ai Python SDK and MEM0_API_KEY to be set.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+
+CODING_CATEGORIES = [
+    {
+        "architecture_decisions": (
+            "Design choices, system structure, technology selection, trade-offs evaluated, "
+            "and architectural patterns adopted in the project."
+        )
+    },
+    {
+        "anti_patterns": (
+            "Approaches that failed, debugging dead-ends, common mistakes to avoid, "
+            "and lessons learned from things that didn't work."
+        )
+    },
+    {
+        "task_learnings": (
+            "Strategies and approaches that succeeded for specific tasks, including tooling "
+            "tricks, workflow shortcuts, and effective problem-solving patterns."
+        )
+    },
+    {
+        "tooling_setup": (
+            "Development environment, build tools, dependencies, package managers, deploy "
+            "pipelines, and configuration steps for the project."
+        )
+    },
+    {
+        "bug_fixes": (
+            "Specific bug fixes with root cause analysis, the fix applied, and how the bug "
+            "was diagnosed -- useful for recognising similar issues later."
+        )
+    },
+    {
+        "coding_conventions": (
+            "Code style, naming patterns, file organisation, error-handling conventions, "
+            "and team agreements about how code is written in this project."
+        )
+    },
+    {
+        "user_preferences": (
+            "User's stated preferences for tools, libraries, languages, formatting, "
+            "and ways of working."
+        )
+    },
+]
+
+
+def _print_categories(label: str, cats):
+    print(f"=== {label} ===")
+    if cats:
+        print(json.dumps(cats, indent=2))
+    else:
+        print("(none / using mem0 defaults)")
+    print()
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument(
+        "--apply",
+        action="store_true",
+        help="Actually call project.update(). Without this flag, runs in dry-run mode.",
+    )
+    args = ap.parse_args()
+
+    if not os.environ.get("MEM0_API_KEY"):
+        print("ERROR: MEM0_API_KEY is not set. Export it and try again.", file=sys.stderr)
+        return 1
+
+    try:
+        from mem0 import MemoryClient
+    except ImportError:
+        print(
+            "ERROR: the mem0ai Python SDK is not installed.\n"
+            "Install with: pip install mem0ai\n"
+            "Then re-run this script.",
+            file=sys.stderr,
+        )
+        return 1
+
+    try:
+        client = MemoryClient()
+    except Exception as e:
+        print(
+            f"ERROR initialising MemoryClient: {e}\n"
+            "Most commonly this is an invalid MEM0_API_KEY -- check the key at "
+            "https://app.mem0.ai/dashboard/api-keys",
+            file=sys.stderr,
+        )
+        return 1
+
+    try:
+        current = client.project.get(fields=["custom_categories"])
+        current_cats = current.get("custom_categories") if isinstance(current, dict) else None
+    except Exception as e:
+        print(f"ERROR fetching current categories: {e}", file=sys.stderr)
+        return 1
+
+    _print_categories("Current project categories", current_cats)
+    _print_categories("Proposed coding categories", CODING_CATEGORIES)
+
+    if not args.apply:
+        print("Dry-run only -- no changes made. Re-run with --apply to write.")
+        return 0
+
+    print("Applying coding categories...")
+    try:
+        response = client.project.update(custom_categories=CODING_CATEGORIES)
+    except Exception as e:
+        print(f"ERROR applying update: {e}", file=sys.stderr)
+        return 1
+
+    print("Done.", response if response else "")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

mem0-plugin/skills/mem0-mcp/SKILL.md

@@ -97,8 +97,47 @@ Extract key learnings and store them using the `add_memory` tool:
 - **Environment/setup discoveries** -> Include metadata `{"type": "environmental"}`
 - **Conventions established** -> Include metadata `{"type": "convention"}`
 
+> `metadata.type` (which you set explicitly) and `categories` (which the platform auto-tags after the project's custom-category list — see `scripts/setup_coding_categories.py`) are complementary. Always set `metadata.type` for explicit filtering; the platform fills in `categories` on its own. Don't try to set `categories` on `add_memory` calls — per-request overrides aren't supported on the managed API.
+
+### Expiration: high-churn vs durable
+
+Some memory types are state snapshots that go stale fast; others are durable facts that should outlive the session that created them. Mark the difference with `expiration_date` on writes.
+
+| Type | Expiration | Why |
+|---|---|---|
+| `session_state`, `compact_summary` | `expiration_date` ≈ today + 90 days | Describe a single moment of project state. Useless after a quarter; clutter the recall surface. |
+| `decision`, `anti_pattern`, `convention`, `user_preference`, `task_learning`, `environmental` | omit `expiration_date` | Durable facts. A decision made last year is still a decision; same for a convention or a user preference. |
+
+`add_memory` accepts `expiration_date` as a string (`"YYYY-MM-DD"`). The two server-side hooks (`on_pre_compact.py`, `capture_compact_summary.py`) already set this for the types they write. When you write directly via the MCP tool, follow the same rule.
+
+### Recency filter on recall
+
+When the user is asking about *current* state ("where were we", "what's the active task", "the latest decision on X"), filter recall to recent memories so stale snapshots don't surface:
+
+```python
+# Last 90 days only
+{"AND": [{"user_id": "<id>"}, {"metadata": {"type": "session_state"}}, {"created_at": {"gte": "<90 days ago, YYYY-MM-DD>"}}]}
+```
+
+Skip the recency filter when the user is asking about durable facts ("what conventions does this project use", "have we hit this bug before") — those are timeless and recency would hide them.
+
 Memories can be as detailed as needed -- include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
 
+### Use `infer=False` for already-structured content
+
+When you've done the extraction work yourself — pre-compaction summaries, decisions, anti-patterns, conventions you've explicitly identified — pass `infer=False` so the platform stores your text verbatim instead of running a second extraction pass over it.
+
+```python
+add_memory(
+    messages=[{"role": "user", "content": "<your structured fact>"}],
+    user_id="<active user_id>",
+    metadata={"type": "decision"},
+    infer=False,
+)
+```
+
+Stick to one mode per distinct piece of content — don't mix `infer=True` (default) and `infer=False` for the same fact, you'll get duplicates. Default (`infer=True`) is right for raw conversational signal you want extracted; `infer=False` is right for pre-extracted structure.
+
 ## Before losing context
 
 If context is about to be compacted or the session is ending, store a comprehensive session summary:

pyproject.toml

@@ -154,3 +154,4 @@ known-first-party = ["mem0", "mem0_cli"]
 profile = "black"
 known_first_party = ["mem0", "mem0_cli"]
 # isort scope kept aligned with [tool.ruff.lint.isort] above.
+# black-equivalent profile here matches the formatter behaviour ruff applies.