comb-db 0.1.0

Honeycomb-structured lossless context archival for AI agents

     _____ _____ _____ _____
    / ____/ ___ / _  \/ _  \     COMB
   / /   / / / / / / / /_/ /     Chain-Ordered Memory Base
  / /___/ /_/ / / / / ___ /
  \____/\___/_/ /_/_/   \_\      Lossless context archival
                                  for AI agents.
  

Your AI doesn't need a better summary. It needs a better memory.

Quick Start • The Honeycomb • Architecture • CLI • Custom Search

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COMB is a honeycomb-structured, lossless context archival system for AI agents. Instead of summarizing conversations (lossy), COMB archives the full text as documents in a three-directional graph.

Zero dependencies. Pure Python. Single directory storage. Copy the folder, copy the memory.

Why not just summarize?

Every AI memory system today works the same way: conversations get summarized, compressed, or embedded into vectors. Information is lost at every step. Important details — the user's exact phrasing, the nuance of a disagreement, the specific numbers discussed — vanish.

COMB takes a different approach: keep everything.

	Principle
🔒	Lossless	Full conversation text, always recoverable
⛓️	Hash-chained	Tamper-evident, like a blockchain for conversations
🐝	Three-directional links	Navigate by time, by meaning, or by relationship
📐	Schema-on-read	Your data, your interpretation
📁	Serverless	No database, no server, just files in a directory

Architecture

                    ┌─────────┐
               ╱╲   │ Tier 1  │   Agent's context window
              ╱  ╲  │ Active  │   (not managed by COMB)
             ╱    ╲ └─────────┘
            ╱      ╲
    ┌──────╱────────╲──────┐
    │      Tier 2          │   Today's conversation dumps
    │   Daily Staging      │   Append-only JSONL
    │   (append-only)      │
    └──────────┬───────────┘
               │ rollup()
    ┌──────────▼───────────┐
    │      Tier 3          │   One document per day
    │   Chain Archive      │   Hash-chained
    │                      │   Honeycomb-linked
    └──────────────────────┘

comb/
├── core.py          # CombStore — the main interface
├── staging.py       # DailyStaging — append-only JSONL staging
├── archive.py       # ChainArchive — hash-chained document store
├── document.py      # CombDocument — temporal, semantic, social links
├── honeycomb.py     # HoneycombGraph — three-directional link computation
├── search.py        # BM25Search — zero-dependency full-text search
├── cli.py           # Click CLI — stage, rollup, search, show, verify, stats
└── _utils.py        # Hashing, date helpers

Quick Start

pip install comb-db

from comb import CombStore

# Create a store (just a directory)
store = CombStore("./my-memory")

# Stage today's conversations
store.stage("User asked about encryption. Assistant explained AES-256...")
store.stage("User clarified they need RSA for key exchange...")

# Roll up into the archive
doc = store.rollup()
# → hash-chained, semantic + social links computed automatically

# Search
results = store.search("encryption")
for r in results:
    print(r.date, r.similarity_score)

# Navigate the honeycomb
doc = store.get("2026-02-17")
doc.temporal.prev          # previous day
doc.semantic.neighbors     # similar conversations
doc.social.strengthened    # deepening relationships
doc.social.cooled          # cooling relationships

# Verify integrity
assert store.verify_chain()  # no tampering

The Honeycomb

Every archived document lives in a three-directional graph:

         TEMPORAL ←──→  chronological chain (prev/next hash-linked)
         SEMANTIC ←──→  content similarity (BM25 cosine, top-k neighbors)
         SOCIAL   ←──→  relationship gradient (warming ↔ cooling)

⛓️ Temporal Links

A chronological chain. Each document points to the previous and next day. Hash-linked — if any document is tampered with, the chain breaks. Blockchain-grade integrity for conversation history.

🧠 Semantic Links

Computed via term-frequency cosine similarity (built-in, zero dependencies). The top-k most similar documents are linked automatically during rollup. Plug in your own search backend for better results.

💛 Social Links

The novel part. Conversations have relational temperature. COMB tracks:

• Inward fade (strengthening) — engagement is increasing, sentiment is warming
• Outward fade (cooling) — engagement is decreasing, sentiment is cooling

This lets an agent understand not just what was discussed, but how the relationship evolved.

CLI

# Stage from stdin
echo "Today's conversation..." | comb -s ./my-memory stage

# Stage from file
comb -s ./my-memory stage -f conversation.txt

# Roll up
comb -s ./my-memory rollup

# Search
comb -s ./my-memory search "encryption"

# Show a document
comb -s ./my-memory show 2026-02-17

# Verify chain integrity
comb -s ./my-memory verify

# Stats
comb -s ./my-memory stats

Requires pip install comb-db[cli].

Custom Search Backend

The built-in BM25 is good enough for hundreds of documents. For scale, implement the SearchBackend protocol:

from comb import SearchBackend

class MyVectorBackend:
    def index(self, doc_id: str, text: str) -> None:
        ...
    def search(self, query: str, k: int = 5) -> list[tuple[str, float]]:
        ...

store = CombStore("./memory", search_backend=MyVectorBackend())

Storage Format

Everything is JSON. Human-readable. No binary formats. No proprietary encodings.

my-memory/
├── staging/
│   └── 2026-02-17.jsonl    # today's staged conversations
└── archive/
    ├── 2026-02-15.json     # archived, hash-chained
    ├── 2026-02-16.json     # with honeycomb links
    └── 2026-02-17.json

What COMB Is — and Isn't

Is:

• A file-based archival system for conversation history
• A tamper-evident chain of daily conversation documents
• A three-directional graph for navigating memory
• A zero-dependency library. Portable. Copy the directory, copy the memory.

Isn't:

• Not a vector database
• Not a summarization tool
• Not a real-time retrieval system
• Not a replacement for your agent's context window

Lineage

COMB descends from HYBRIDbee, a serverless document database. It inherits the philosophy: schema-on-read, single-directory storage, zero configuration.

Requirements

• Python 3.10+
• Zero dependencies (stdlib only)
• Optional: click for CLI

License

MIT

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Built by Ava Shakil at Artifact Virtual