kmf 1.0.0

KMF — Knowledge Memory Format: portable, local, versioned AI memory

KMF — Knowledge Memory Format

Portable, local, versioned AI memory. v1.0

KMF is an open format and Python library for structured AI memory. It solves the problem that current AI systems either have no persistent memory, or store everything as unstructured text blobs with no provenance, policy, or control.

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Merging Repositories

# Merge two repos (e.g. work laptop + home laptop)
kmf merge work.kmf home.kmf --output merged.kmf

# Dry run — see what would change
kmf merge work.kmf home.kmf --dry-run

# Merge only specific scopes
kmf merge personal.kmf colleague.kmf --scopes work

base = KMF.load("work.kmf")
home = KMF.load("home.kmf")
result = base.merge(home)
print(result.summary())
base.save("merged.kmf")

Merge strategy: same ID → newer wins. Conflicting facts (same subject+predicate, different value) → Conflict object created. Preferences → newer wins.

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LangChain Integration

pip install "kmf[langchain]"

from langchain.chains import ConversationChain
from langchain_openai import ChatOpenAI
from kmf.integrations.langchain import KMFMemory, KMFRetriever

# Drop-in memory replacement
memory = KMFMemory.from_file("personal.kmf", api_key="sk-ant-...")
chain = ConversationChain(llm=ChatOpenAI(), memory=memory)
chain.predict(input="What did we decide about the database?")
# → loads KMF context, answers, stores new memory after each turn

# Retriever for RAG chains
retriever = KMFRetriever.from_file("personal.kmf")
docs = retriever.get_relevant_documents("Python architecture")

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Semantic Search

KMF uses local embeddings for recall — no cloud, no API key needed.

# Install (one-time, ~80MB model download)
pip install "kmf[semantic]"

# Build index
kmf index personal.kmf

After indexing, recall() automatically uses semantic similarity instead of keyword matching. If sentence-transformers is not installed, KMF silently falls back to keyword search — nothing breaks.

Default model: all-MiniLM-L6-v2 — fast on CPU, works well in German and English.

# Custom model
kmf = KMF.load("personal.kmf")
bundle = kmf.recall("what did we decide about the database?")
# → uses semantic search if indexed, keywords otherwise

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MCP Server (Claude Desktop / claude.ai)

KMF ships a built-in MCP server. Once configured, Claude automatically has access to your memory — no manual context injection needed.

Setup

1. Add to claude_desktop_config.json:

{
  "mcpServers": {
    "kmf": {
      "command": "kmf",
      "args": ["mcp", "/path/to/personal.kmf"]
    }
  }
}

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Restart Claude Desktop. KMF tools appear automatically.

Available Tools

Tool	What it does
kmf_recall	Load relevant memory context for a query
kmf_remember	Store a fact, preference, or decision directly
kmf_ingest_text	Extract and store memory from a conversation
kmf_forget	Archive (soft-delete) an outdated memory
kmf_stats	Repository statistics
kmf_conflicts	List unresolved contradictions

Workflow

Start of conversation:
  → Claude calls kmf_recall("user preferences and recent decisions")
  → Gets compact TOON context block (~200-500 tokens vs 2000+ raw)

During conversation:
  → Nothing — chat as normal

End of conversation:
  → Claude calls kmf_ingest_text(transcript) using Haiku
  → New facts/decisions stored for next time

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Install

pip install kmf

Or from source:

git clone https://github.com/you/kmf
cd kmf
pip install -e .

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Quick Start

from kmf import KMF, SessionInput

# Create a repository
kmf = KMF.create("personal")

# Consolidate a session into memory
session = SessionInput(
    title="Architecture discussion",
    summary_short="Decided on local-first approach",
    proposed_facts=[{
        "subject": "project",
        "predicate": "architecture",
        "object": "local-first",
        "confidence": 0.95,
        "writer": "explicit_user_statement",
    }],
    proposed_preferences=[{
        "domain": "language",
        "key": "response_language",
        "value": "de",
        "strength": "strong",
        "writer": "explicit_user_statement",
    }],
    proposed_decisions=[{
        "title": "Local-first Architecture",
        "decision": "Repository lives locally with the user",
        "rationale": ["Control", "Portability", "Privacy"],
        "tradeoffs": ["Sync complexity"],
        "confidence": 0.95,
        "writer": "explicit_user_statement",
    }],
)
result = kmf.consolidate(session)

# Recall context for a query (token-budgeted)
bundle = kmf.recall("what did we decide about the architecture?")
print(bundle.rendered)

# Save to file
kmf.save("personal.kmf")

# Load later
kmf = KMF.load("personal.kmf")

Output:

[MEMORY CONTEXT — bundle:ctx_... — scopes: — budget:2000tok]

[PROFILE]
prefs[1]{domain,key,value,strength}:
language,response_language,de,strong

[FACTS]
facts[1]{subject,predicate,object,conf}:
project,architecture,local-first,0.95

[DECISIONS]
decisions[1]{title,decision,conf}:
Local-first Architecture,Repository lives locally with the user,0.95

[RECENT EPISODES]
• Architecture discussion: Decided on local-first approach

[/MEMORY CONTEXT]

---

CLI

# Create a new repository
kmf init personal --description "My personal memory"

# Consolidate a session JSON file
kmf consolidate personal.kmf session.json

# Recall context for a query
kmf recall personal.kmf "what did we decide about the database?"

# List objects
kmf list personal.kmf --type fact

# Show history
kmf history personal.kmf

# Stats
kmf stats personal.kmf

---

Core Concepts

Objects

Every memory item is a structured object with metadata:

Type	Purpose
episode	A completed session or interaction
fact	A declarative statement with subject/predicate/object
preference	A user preference
decision	A decision with rationale and alternatives
entity	A named entity (person, project, concept)
summary	A condensed view of other objects
conflict	An explicit contradiction between two objects
rule	A user-defined policy

Every object carries: confidence, importance, sensitivity, provenance, permissions, valid_time.

Key Principles

Storing ≠ Prompting — An object can be stored but never sent to a prompt. Three separate permission layers.

Consolidation is separate from chat — The chat produces raw signal. A post-session consolidation process decides what persists. This prevents contamination from smalltalk, irony, or model errors.

Conflicts are explicit — When two facts contradict each other, a conflict object is created and surfaced in recall. No silent overwrites.

Local-first — The repository lives with the user. Cloud sync is optional, not the default.

TOON Format

Prompt bundles use TOON (Token-Optimized Output Notation) instead of JSON — saving 30–85% tokens for tabular data:

# JSON: ~580 tokens
# TOON: ~85 tokens for the same 3 decisions

decisions[3]{title,decision,conf}:
Local-first Architecture,Repository lies locally with the user,0.95
Dual-Format JSON+TOON,JSON in store / TOON in prompt,0.92
Consolidation separate from chat,No direct writes from chat,0.90

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Session JSON Format

{
  "title": "Session title",
  "summary_short": "One-line summary",
  "summary_long": "Longer summary",
  "topics": ["topic1", "topic2"],
  "proposed_facts": [
    {
      "subject": "project",
      "predicate": "uses",
      "object": "PostgreSQL",
      "confidence": 0.9,
      "writer": "explicit_user_statement"
    }
  ],
  "proposed_preferences": [
    {
      "domain": "style",
      "key": "format",
      "value": "direct, no filler",
      "strength": "strong",
      "writer": "explicit_user_statement"
    }
  ],
  "proposed_decisions": [
    {
      "title": "Decision title",
      "decision": "What was decided",
      "rationale": ["reason 1", "reason 2"],
      "tradeoffs": ["tradeoff 1"],
      "confidence": 0.95,
      "writer": "explicit_user_statement"
    }
  ],
  "proposed_entities": [
    {
      "entity_type": "project",
      "canonical_name": "Project Alpha",
      "aliases": ["alpha"],
      "confidence": 0.95
    }
  ]
}

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Architecture

KMF
├── models/        Pydantic object models (KMFObject, Chunk, Commit, Manifest)
├── storage/       SQLite-backed repository with .kmf import/export
├── recall/        7-stage recall pipeline with scoring formula
├── consolidation/ Post-session engine: raw input → persistent objects
├── render/        TOON renderer for prompt bundles
├── policy/        Sensitivity and access control
└── cli/           Command-line interface

Recall Scoring

score = semantic_relevance * 0.35
      + importance         * 0.20
      + confidence         * 0.15
      + recency            * 0.15
      + scope_match        * 0.10
      - sensitivity_penalty * 0.05

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Roadmap

• v0.2: Embedding-based semantic search (model-agnostic)
• v0.2: Multi-device sync protocol
• v0.3: Multimodal support (image captions, audio transcripts)
• v0.3: LangChain / LlamaIndex integration
• v1.0: Formal interoperability standard

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License

MIT

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KMF is an open specification. No company, no lock-in. The goal: a common standard for portable AI memory.