comet-memory 0.2.0

CoMeT — Cognitive Memory Tree: Hierarchical memory system for LLM agents

☄️ CoMeT — Cognitive Memory Tree

Lossless structured memory for AI agents.

CoMeT compresses long conversations into a navigable tree of memory nodes.
Unlike naive summarization that loses details, CoMeT preserves raw data behind structured summaries — agents read summaries first, then drill into raw data only when needed.

Architecture

User Input
    │
    ▼
┌─────────┐    SLM (fast)     ┌───────────┐
│  Sensor  │ ───────────────▶ │ L1 Buffer │
└─────────┘   entity/intent   └─────┬─────┘
                                    │ cognitive load trigger
                                    ▼
                              ┌───────────┐
                              │ Compacter │  LLM (slow)
                              └─────┬─────┘
                                    │ summary + trigger + recall_mode + tags
                                    ▼
                         ┌──────────┴──────────┐
                         │                     │
                   ┌───────────┐        ┌─────────────┐
                   │   Store   │        │ VectorIndex │  ChromaDB
                   │  depth 0-2│        │ (dual-path) │  summary + trigger
                   └───────────┘        └──────┬──────┘
                                               │ semantic search
                                               ▼
                                         ┌───────────┐
                                         │ Retriever │  RRF fusion
                                         └───────────┘

Dual-Speed Layer

• Fast (Sensor): SLM extracts entities/intent per turn, detects topic shifts via cognitive load assessment
• Slow (Compacter): Main LLM structures accumulated L1 buffer into MemoryNode with summary, trigger, recall mode, and topic tags

Dynamic Resolution (depth 0 → 1 → 2)

Depth	Content	Use Case
0	Summary + Trigger	Agent's initial context window
1	+ Topic tags + Links	Navigation / node selection
2	Full raw data + Links	Fact retrieval

Recall Mode

Each memory node is classified by recall_mode at compaction time:

Mode	Behavior	Examples
passive	Always included in context window	User identity, persistent preferences
active	Retrieved on-demand via semantic search	Factual details, decisions, events
both	Always in context + searchable via RAG	Core constraints with retrievable details

Dual-Path RAG Retrieval

CoMeT embeds both summary (what the node contains) and trigger (when to recall it) into separate vector collections. At query time:

1. QueryAnalyzer decomposes the query into semantic_query + search_intent
2. Summary path: matches what the information is about
3. Trigger path: matches when the information would be needed
4. ScoreFusion (Reciprocal Rank Fusion): merges results from both paths

Triggers are written from the LLM's perspective ("내가 ~정보가 필요할 때") rather than user-centric, enabling broader semantic matching even without explicit user requests.

Topic-Aware Auto-Linking

Nodes share a global topic tag set. The compacter reuses existing tags when possible, enabling automatic bidirectional linking between related nodes across different conversation segments.

Benchmark (52 turns, 5 conversations, 10 questions)

Method	Context Cost	Accuracy
Full Context Injection	5,198 chars (100%)	10/10
CoMeT	1,397 chars (27%)	9/10
Naive Summary	1,179 chars (23%)	1/10

• CoMeT uses 27% of the tokens while retaining 90% accuracy
• 6/10 questions required link traversal (agent read 2-3 nodes)
• Cross-topic questions: CoMeT 5/5 vs Naive 0/5

Quick Start

Session Memory (within a conversation)

from comet import CoMeT, scope

@scope
def main(config):
    memo = CoMeT(config)

    # Add conversation turns
    memo.add("B200 4대로 월드모델 학습 가능할까?")
    memo.add("2B면 충분하고 커봐야 8B")
    memo.add("DPO 데이터는 negative를 syntax error로 구성했어")

    # Force compact remaining buffer
    memo.force_compact()

    # Navigation
    for node in memo.list_memories():
        print(memo.read_memory(node['node_id'], depth=0))

    # Agent tools (LangChain compatible)
    tools = memo.get_tools()
    # → get_memory_index, read_memory_node, search_memory

main()

Cross-Session RAG Retrieval

from comet import CoMeT, scope

@scope
def main(config):
    config.retrieval.vector_db_path = './memory_store/vectors'

    memo = CoMeT(config)

    # Ingest turns (auto-indexed to VectorIndex on compaction)
    memo.add("JWT 액세스 토큰 만료는 15분, 리프레시는 7일로 설정")
    memo.force_compact()

    # Semantic retrieval across all sessions
    results = memo.retrieve("토큰 만료 설정이 어떻게 되어있어?")
    for r in results:
        print(f"[{r.node.node_id}] score={r.relevance_score:.4f}")
        print(f"  {r.node.summary}")

    # Agent tools include retrieve_memory when retrieval is configured
    tools = memo.get_tools()
    # → get_memory_index, read_memory_node, search_memory, retrieve_memory

main()

Configuration (ato)

# comet/config.py
@scope.observe(default=True)
def default(config):
    config.slm_model = 'gpt-4o-mini'
    config.main_model = 'gpt-4o'
    config.compacting.load_threshold = 3
    config.compacting.max_l1_buffer = 5

    # RAG retrieval (enabled when retrieval block exists)
    config.retrieval.embedding_model = 'text-embedding-3-small'
    config.retrieval.vector_backend = 'chroma'
    config.retrieval.vector_db_path = './memory_store/vectors'
    config.retrieval.top_k = 5

@scope.observe()
def local_slm(config):
    config.slm_model = 'ollama/gemma3:4b'

@scope.observe()
def aggressive(config):
    config.compacting.load_threshold = 2
    config.compacting.max_l1_buffer = 3

# Use default
python main.py

# Local SLM + aggressive compacting
python main.py local_slm aggressive

Project Structure

comet/
├── orchestrator.py    # CoMeT main class
├── sensor.py          # L1 extraction + cognitive load (SLM)
├── compacter.py       # L1→L2 structuring + auto-linking (LLM)
├── storage.py         # JSON key-value store + navigation
├── schemas.py         # MemoryNode, L1Memory, CognitiveLoad, RetrievalResult
├── config.py          # ato scope configuration
├── vector_index.py    # ChromaDB dual-collection vector store
├── retriever.py       # QueryAnalyzer + ScoreFusion + Retriever
└── templates/
    ├── compacting.txt      # Memory structuring prompt
    └── query_analysis.txt  # Query decomposition prompt