obsidian-mem 0.4.0

AI memory as a typed knowledge graph in Obsidian. Like Mem0, but you own your data.

🧠 ObsidianMem

AI memory as a typed knowledge graph in Obsidian.

Every conversation with your AI builds a structured second brain — people, projects, technologies, companies — all as .md files with [[wikilinks]] you can browse in Obsidian.

Like Mem0, but you own your data.

---

Why ObsidianMem?

	Mem0	Basic Memory	ObsidianMem
Storage	Cloud vectors	Flat markdown	Typed knowledge graph in .md
Entity types	❌ Flat facts	❌ One note per chat	✅ Person, Project, Technology, Company
Relations	❌	❌	✅ works_at, uses, depends_on
Obsidian graph	❌	Partial	✅ Full [[wikilinks]] + graph view
Semantic search	✅ Cloud	❌	✅ Local embeddings (384D)
Own your data	❌ Cloud lock-in	✅	✅ Plain .md files
LLM agnostic	❌	Partial	✅ Claude / GPT / Ollama
Pricing	$24/mo+	$14/mo	Free & open source

What it actually does

You chat with Claude (or any LLM). ObsidianMem automatically:

1. Extracts entities, facts, and relationships from your conversations
2. Creates typed .md files in your Obsidian vault
3. Links everything with [[wikilinks]] and YAML frontmatter
4. Indexes with local vector embeddings for semantic search
5. Recalls relevant context when you need it — by meaning, not just keywords

You: "I work at Uzum Bank, backend developer on Spring Boot"
                    ↓ LLM extracts knowledge
         ┌─────────────────────────────┐
         │  vault/Ali.md               │
         │  type: person               │
         │  - backend developer        │
         │  - → works_at [[Uzum Bank]] │
         │  - → uses [[Spring Boot]]   │
         └─────────────────────────────┘
         ┌──────────────────────────────┐
         │  vault/Uzum Bank.md          │
         │  type: company               │
         │  - ← works_at [[Ali]]        │
         └──────────────────────────────┘

Open in Obsidian → see your knowledge graph growing from every conversation.

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Quick Start

1. Install

pip install obsidian-mem[all]

2. Setup (one command)

obsidian-mem init

This will:

• Ask for your LLM provider and API key
• Create ~/.obsidian-mem/config.yaml and vault
• Auto-configure Claude Desktop MCP integration
• Tell you to restart Claude Desktop

That's it. Talk to Claude — it remembers and recalls automatically.

Non-interactive:

obsidian-mem init --provider anthropic --api-key sk-ant-...

Other commands:

obsidian-mem status    # Check setup
obsidian-mem stats     # Vault statistics
obsidian-mem server    # Start MCP server manually

---

Python SDK (Mem0-compatible API)

from obsidian_mem import Memory

m = Memory(
    vault_path="./my-brain",
    llm_provider="anthropic",
    api_key="sk-ant-..."
)

# Remember
m.add("I work at Uzum Bank, backend on Spring Boot and PostgreSQL", user_id="ali")

# Semantic search (finds by MEANING, not just keywords)
results = m.search("database issues", user_id="ali")
for r in results:
    print(f"{r.memory.name} (score={r.score:.2f})")
    print(r.memory.facts)

# Get everything
all_memories = m.get_all(user_id="ali")

# Stats
print(m.stats(user_id="ali"))

MCP Server (Claude Desktop)

obsidian-mem init sets this up automatically. Manual setup:

obsidian-mem server --config ~/.obsidian-mem/config.yaml

Auto-Memory Middleware

Drop-in wrapper that automatically remembers and recalls:

from obsidian_mem import Memory
from obsidian_mem_middleware import AutoMemory

m = Memory(vault_path="./vault", llm_provider="anthropic", api_key="sk-ant-...")
auto = AutoMemory(memory=m, user_id="ali")

# Automatically: recall → inject context → LLM → remember
response = auto.chat("Help me fix the PostgreSQL connection pool issue")

---

How It Works

Conversation → Extractor (LLM) → Entities + Facts + Relations
                                          ↓
                                   Vault Manager → .md files (Obsidian)
                                          ↓
                                   Vector Index → embeddings (SQLite)
                                          ↓
                                   Recall: Vector Search + Graph Expansion

Semantic Search (Hybrid)

3-level search strategy:

1. Vector Search — all-MiniLM-L6-v2 (80MB, runs locally). Finds "database" when you search "PostgreSQL" — by meaning, not keywords.
2. Graph Expansion — follows [[wikilinks]] from top results. Found PostgreSQL? Also returns linked Project Alpha.
3. Text Fallback — substring match for edge cases.

Entity Types

Type	Examples
person	Team members, contacts
project	Services, repos, products
technology	PostgreSQL, Spring Boot, Kafka
company	Employers, clients, partners
concept	Patterns, strategies, ideas

File Format

---
type: technology
created: 2024-02-10 15:30
updated: 2024-02-11 09:15
tags: [technology]
---

# PostgreSQL

## Facts

- Main database, version 15
- Connection pool issue in [[Project Alpha]]

## Relations

- ← uses [[Project Alpha]]: Main DB
- ← uses [[Ali]]: Primary expertise

---

Configuration

# config.yaml
vault_path: "./vault"

llm:
  provider: "anthropic"  # anthropic | openai | ollama | mock
  anthropic:
    api_key: "sk-ant-..."
    model: "claude-sonnet-4-20250514"

semantic_search:
  enabled: true

Provider	Install	Cost
Anthropic (Claude)	pip install obsidian-mem[anthropic]	API pricing
OpenAI (GPT)	pip install obsidian-mem[openai]	API pricing
Ollama (local)	Install ollama	Free

---

Roadmap

• Typed entity extraction (person, project, technology, company)
• Obsidian vault with [[wikilinks]] + YAML frontmatter
• MCP Server for Claude Desktop
• Semantic search with local embeddings
• Hybrid retrieval (vector + graph)
• Mem0-compatible Python SDK
• Auto-memory middleware
• Entity deduplication
• Obsidian plugin (TypeScript)
• Web dashboard
• REST API

Contributing

git clone https://github.com/alibaizhanov/obsidian-mem
cd obsidian-mem
pip install -e ".[all,dev]"
pytest

License

MIT