Initial Arka plugin registry: Official plugins mirror + Arka-signed index

11 plugins from github.com/librefang/librefang-registry plugins/.
index.json / index.json.sig are signed with Arka's Ed25519 key
(not upstream stats.librefang.ai). Private key is not in this repo.

plugins/topic-memory/README.md

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+# topic-memory
+
+Topic-aware memory recall using keyword clustering. Tracks conversation topics per agent and recalls related context when similar topics arise in future conversations.
+
+## How it works
+
+**After each turn**, the plugin extracts keywords from user and assistant messages, then either merges them into an existing topic cluster (Jaccard similarity > 0.3) or creates a new one. Each cluster stores a keyword set, a summary, a hit count, and a last-seen timestamp.
+
+**On ingest**, the plugin scores all stored topic clusters against the incoming message keywords using Jaccard similarity and returns the top 3 matches (threshold > 0.15) as contextual memories.
+
+This gives agents cross-conversation topic awareness -- if a user discussed Python async patterns last week, bringing up `asyncio` today will recall that context.
+
+## Hooks
+
+| Hook | Script | Description |
+|------|--------|-------------|
+| ingest | `hooks/ingest.py` | Scores stored topics against message keywords, returns top matches |
+| after_turn | `hooks/after_turn.py` | Extracts keywords, merges or creates topic clusters |
+
+## Storage
+
+Topic clusters are stored at `~/.librefang/plugins/topic-memory/{agent_id}.json`. Max 50 clusters per agent (lowest hit-count evicted when full).
+
+## Usage
+
+Installed automatically when enabled in agent configuration.

plugins/topic-memory/hooks/after_turn.py

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+#!/usr/bin/env python3
+"""Topic-memory after_turn hook.
+
+After each conversation turn, extracts keywords from the latest exchange,
+then either merges into an existing topic cluster or creates a new one.
+
+This hook is WRITE-ONLY -- it never returns memories.
+
+Receives via stdin:
+    {"type": "after_turn", "agent_id": "...", "messages": [
+        {"role": "user"|"assistant", "content": "..."}
+    ]}
+
+Prints to stdout:
+    {"type": "ok"}
+"""
+import json
+import os
+import re
+import sys
+from datetime import datetime, timezone
+
+# ---------------------------------------------------------------------------
+# Stopwords & constants
+# ---------------------------------------------------------------------------
+
+STOPWORDS = frozenset({
+    "a", "an", "the", "and", "or", "but", "in", "on", "at", "to", "for",
+    "of", "with", "by", "from", "is", "are", "was", "were", "be", "been",
+    "being", "have", "has", "had", "do", "does", "did", "will", "would",
+    "could", "should", "may", "might", "shall", "can", "need", "must",
+    "it", "its", "i", "me", "my", "you", "your", "he", "she", "we",
+    "they", "them", "their", "this", "that", "these", "those", "what",
+    "which", "who", "how", "when", "where", "why", "if", "then", "so",
+    "not", "no", "just", "also", "very", "too", "about", "up", "out",
+    "all", "some", "any", "each", "every", "into", "over", "after",
+})
+
+MIN_WORD_LEN = 3
+MAX_TOPICS = 50
+MERGE_THRESHOLD = 0.3
+SUMMARY_MAX_LEN = 200
+
+STORE_DIR = os.path.join(os.path.expanduser("~"), ".librefang", "plugins", "topic-memory")
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def extract_keywords(text):
+    """Extract lowercase keywords from text, filtering stopwords and short tokens."""
+    words = re.findall(r"[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9]|[a-zA-Z]", text)
+    seen = set()
+    keywords = set()
+    for w in words:
+        lower = w.lower()
+        if lower not in STOPWORDS and len(lower) >= MIN_WORD_LEN and lower not in seen:
+            seen.add(lower)
+            keywords.add(lower)
+    return keywords
+
+
+def jaccard_similarity(set_a, set_b):
+    """Compute Jaccard similarity between two sets."""
+    if not set_a or not set_b:
+        return 0.0
+    intersection = len(set_a & set_b)
+    union = len(set_a | set_b)
+    if union == 0:
+        return 0.0
+    return intersection / union
+
+
+def load_store(agent_id):
+    """Load the topic store JSON for an agent. Returns empty structure on any error."""
+    path = os.path.join(STORE_DIR, f"{agent_id}.json")
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        if isinstance(data, dict) and isinstance(data.get("topics"), list):
+            return data
+    except (OSError, json.JSONDecodeError, ValueError):
+        pass
+    return {"topics": []}
+
+
+def save_store(agent_id, store):
+    """Persist the topic store to disk."""
+    os.makedirs(STORE_DIR, exist_ok=True)
+    path = os.path.join(STORE_DIR, f"{agent_id}.json")
+    tmp_path = path + ".tmp"
+    with open(tmp_path, "w", encoding="utf-8") as f:
+        json.dump(store, f, ensure_ascii=False, indent=2)
+    os.replace(tmp_path, path)
+
+
+def next_topic_id(topics):
+    """Generate the next t_XXX topic ID."""
+    max_num = 0
+    for t in topics:
+        tid = t.get("id", "")
+        if tid.startswith("t_"):
+            try:
+                num = int(tid[2:])
+                if num > max_num:
+                    max_num = num
+            except ValueError:
+                pass
+    return f"t_{max_num + 1:03d}"
+
+
+def build_summary(user_content, assistant_content):
+    """Build a truncated summary from the latest user + assistant exchange."""
+    parts = []
+    if user_content:
+        parts.append(f"User: {user_content.strip()}")
+    if assistant_content:
+        parts.append(f"Assistant: {assistant_content.strip()}")
+    raw = " | ".join(parts)
+    if len(raw) > SUMMARY_MAX_LEN:
+        return raw[: SUMMARY_MAX_LEN - 3] + "..."
+    return raw
+
+
+def now_iso():
+    """Return current UTC time as ISO 8601 string."""
+    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+
+def ok_result():
+    """Return the standard ok response."""
+    return json.dumps({"type": "ok"})
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+def main():
+    try:
+        request = json.loads(sys.stdin.read())
+    except (json.JSONDecodeError, ValueError):
+        print(ok_result())
+        return
+
+    agent_id = request.get("agent_id", "")
+    messages = request.get("messages", [])
+
+    if not agent_id or not isinstance(messages, list) or not messages:
+        print(ok_result())
+        return
+
+    # Extract the latest user and assistant messages
+    latest_user = ""
+    latest_assistant = ""
+    for msg in reversed(messages):
+        role = msg.get("role", "")
+        content = msg.get("content", "")
+        if role == "assistant" and not latest_assistant:
+            latest_assistant = content
+        elif role == "user" and not latest_user:
+            latest_user = content
+        if latest_user and latest_assistant:
+            break
+
+    if not latest_user and not latest_assistant:
+        print(ok_result())
+        return
+
+    # Extract keywords from the combined exchange
+    combined_text = f"{latest_user} {latest_assistant}"
+    current_keywords = extract_keywords(combined_text)
+
+    if not current_keywords:
+        print(ok_result())
+        return
+
+    store = load_store(agent_id)
+    topics = store.get("topics", [])
+    timestamp = now_iso()
+
+    # Find the best matching existing topic cluster
+    best_sim = 0.0
+    best_idx = -1
+    for idx, topic in enumerate(topics):
+        topic_kw = set(topic.get("keywords", []))
+        sim = jaccard_similarity(current_keywords, topic_kw)
+        if sim > best_sim:
+            best_sim = sim
+            best_idx = idx
+
+    if best_sim >= MERGE_THRESHOLD and best_idx >= 0:
+        # Merge into existing topic cluster
+        topic = topics[best_idx]
+        existing_kw = set(topic.get("keywords", []))
+        merged_kw = existing_kw | current_keywords
+        topic["keywords"] = sorted(merged_kw)
+        topic["summary"] = build_summary(latest_user, latest_assistant)
+        topic["last_seen"] = timestamp
+        topic["hit_count"] = topic.get("hit_count", 0) + 1
+    else:
+        # Create a new topic cluster
+        new_topic = {
+            "id": next_topic_id(topics),
+            "keywords": sorted(current_keywords),
+            "summary": build_summary(latest_user, latest_assistant),
+            "last_seen": timestamp,
+            "hit_count": 1,
+        }
+        topics.append(new_topic)
+
+    # Evict lowest hit_count topics if over capacity
+    if len(topics) > MAX_TOPICS:
+        topics.sort(key=lambda t: (t.get("hit_count", 0), t.get("last_seen", "")))
+        topics = topics[len(topics) - MAX_TOPICS:]
+
+    store["topics"] = topics
+    save_store(agent_id, store)
+
+    print(ok_result())
+
+
+if __name__ == "__main__":
+    main()

plugins/topic-memory/hooks/ingest.py

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+#!/usr/bin/env python3
+"""Topic-memory ingest hook.
+
+Reads the topic store for the given agent and returns the top matching
+topic summaries based on Jaccard similarity between the incoming message
+keywords and each stored topic cluster.
+
+This hook is READ-ONLY -- it never modifies the topic store.
+
+Receives via stdin:
+    {"type": "ingest", "agent_id": "...", "message": "user message text"}
+
+Prints to stdout:
+    {"type": "ingest_result", "memories": [{"content": "..."}]}
+"""
+import json
+import os
+import re
+import sys
+
+# ---------------------------------------------------------------------------
+# Stopwords & constants
+# ---------------------------------------------------------------------------
+
+STOPWORDS = frozenset({
+    "a", "an", "the", "and", "or", "but", "in", "on", "at", "to", "for",
+    "of", "with", "by", "from", "is", "are", "was", "were", "be", "been",
+    "being", "have", "has", "had", "do", "does", "did", "will", "would",
+    "could", "should", "may", "might", "shall", "can", "need", "must",
+    "it", "its", "i", "me", "my", "you", "your", "he", "she", "we",
+    "they", "them", "their", "this", "that", "these", "those", "what",
+    "which", "who", "how", "when", "where", "why", "if", "then", "so",
+    "not", "no", "just", "also", "very", "too", "about", "up", "out",
+    "all", "some", "any", "each", "every", "into", "over", "after",
+})
+
+MIN_WORD_LEN = 3
+MAX_RESULTS = 3
+MIN_SIMILARITY = 0.15
+
+STORE_DIR = os.path.join(os.path.expanduser("~"), ".librefang", "plugins", "topic-memory")
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def extract_keywords(text):
+    """Extract lowercase keywords from text, filtering stopwords and short tokens."""
+    words = re.findall(r"[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9]|[a-zA-Z]", text)
+    seen = set()
+    keywords = set()
+    for w in words:
+        lower = w.lower()
+        if lower not in STOPWORDS and len(lower) >= MIN_WORD_LEN and lower not in seen:
+            seen.add(lower)
+            keywords.add(lower)
+    return keywords
+
+
+def jaccard_similarity(set_a, set_b):
+    """Compute Jaccard similarity between two sets."""
+    if not set_a or not set_b:
+        return 0.0
+    intersection = len(set_a & set_b)
+    union = len(set_a | set_b)
+    if union == 0:
+        return 0.0
+    return intersection / union
+
+
+def load_store(agent_id):
+    """Load the topic store JSON for an agent. Returns empty structure on any error."""
+    path = os.path.join(STORE_DIR, f"{agent_id}.json")
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        if isinstance(data, dict) and isinstance(data.get("topics"), list):
+            return data
+    except (OSError, json.JSONDecodeError, ValueError):
+        pass
+    return {"topics": []}
+
+
+def empty_result():
+    """Return an empty ingest result."""
+    return json.dumps({"type": "ingest_result", "memories": []})
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+def main():
+    try:
+        request = json.loads(sys.stdin.read())
+    except (json.JSONDecodeError, ValueError):
+        print(empty_result())
+        return
+
+    message = request.get("message", "")
+    agent_id = request.get("agent_id", "")
+
+    if not message.strip() or not agent_id:
+        print(empty_result())
+        return
+
+    msg_keywords = extract_keywords(message)
+    if not msg_keywords:
+        print(empty_result())
+        return
+
+    store = load_store(agent_id)
+    topics = store.get("topics", [])
+
+    # Score each topic cluster against the current message keywords
+    scored = []
+    for topic in topics:
+        topic_kw = set(topic.get("keywords", []))
+        sim = jaccard_similarity(msg_keywords, topic_kw)
+        if sim >= MIN_SIMILARITY:
+            scored.append((sim, topic))
+
+    # Sort descending by similarity, take top N
+    scored.sort(key=lambda x: x[0], reverse=True)
+    top = scored[:MAX_RESULTS]
+
+    memories = []
+    for _sim, topic in top:
+        summary = topic.get("summary", "")
+        if summary:
+            memories.append({"content": f"[topic-memory] Related context: {summary}"})
+
+    print(json.dumps({"type": "ingest_result", "memories": memories}))
+
+
+if __name__ == "__main__":
+    main()

plugins/topic-memory/plugin.toml

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+name = "topic-memory"
+version = "0.1.0"
+description = "Topic-aware memory recall with keyword clustering for cross-conversation context"
+author = "librefang"
+
+[hooks]
+ingest = "hooks/ingest.py"
+after_turn = "hooks/after_turn.py"
+
+
+[i18n.zh]
+name = "话题记忆"
+description = "按话题召回记忆，结合关键词聚类实现跨会话的上下文关联。"
+
+[i18n.zh-TW]
+name = "話題記憶"
+description = "依話題召回記憶，結合關鍵字分群實現跨會話的上下文關聯。"
+
+[i18n.ja]
+name = "トピックメモリ"
+description = "トピック単位のメモリ想起にキーワードクラスタリングを組み合わせ、セッションを跨ぐ文脈を維持。"
+
+[i18n.ko]
+name = "주제 메모리"
+description = "주제별 기억 회상과 키워드 클러스터링으로 세션 간 컨텍스트를 연결."
+
+[i18n.de]
+name = "Topic-Gedächtnis"
+description = "Themenbewusster Gedächtnisabruf mit Keyword-Clustering für sitzungsübergreifenden Kontext."
+
+[i18n.es]
+name = "Memoria por temas"
+description = "Recuerdo de memoria consciente del tema con agrupamiento de palabras clave para contexto entre conversaciones."
+
+[i18n.fr]
+name = "Mémoire par sujets"
+description = "Rappel de mémoire orienté sujet avec clustering de mots-clés pour un contexte inter-conversations."
+
+[integrity]
+"hooks/after_turn.py" = "d2a52d8070250c6308de1af8739dfee0691b4dc73ee45448f32b7ed446645d28"
+"hooks/ingest.py" = "9ee56481f5b7039a12d4a6f373b7f54af7ead8f790995280551739658997cc0f"