oghma 0.4.0

Unified AI memory layer for coding assistants

Oghma

Unified AI memory layer for coding assistants. Watches transcripts from Claude Code, Codex, OpenClaw, and OpenCode. Extracts memories via LLM. Searches with FTS5, vector similarity, and hybrid (RRF) retrieval.

Features

• Multi-tool support — Parses transcripts from Claude Code, Codex, OpenClaw, and OpenCode
• LLM extraction — Uses GPT-4o-mini, Gemini Flash, or any OpenRouter model to extract structured memories
• Hybrid search — Keyword (FTS5), vector (sqlite-vec), and hybrid (RRF fusion) with recency boost
• Inline embedding — Memories are embedded immediately on extraction, no separate migration step
• Cross-source dedup — Same insight from different tools is stored once
• Smart filtering — Skips trivial sessions (cron heartbeats, tool-only runs) to save API costs
• MCP server — Native integration with Claude Code and other MCP-compatible tools
• Write API — Add memories directly via MCP or CLI, not just from transcripts

Install

pip install oghma

For local embeddings (optional):

pip install oghma[local]

Quick Start

export OPENAI_API_KEY=sk-...          # for embeddings
export OPENROUTER_API_KEY=sk-or-...   # for extraction (if using OpenRouter models)

oghma init
oghma start
oghma search "python typing"
oghma export -o ./memories

Configuration

Config at ~/.oghma/config.yaml. Key settings:

Setting	Default	Description
daemon.poll_interval	300	Seconds between checks for new transcripts
extraction.model	gpt-4o-mini	LLM for memory extraction
embedding.model	text-embedding-3-small	Embedding model for vector search
embedding.provider	openai	Embedding provider (openai or local)

Model Selection

Oghma supports both OpenAI and OpenRouter models for extraction:

Model	Provider	Quality	Cost	Notes
gpt-4o-mini	OpenAI	Good	~$0.30/M	Default, factual
google/gemini-3-flash-preview	OpenRouter	Excellent	~$1.50/M	Best quality/cost
google/gemini-2.0-flash-001	OpenRouter	Good	~$0.25/M	Budget option
deepseek/deepseek-chat-v3-0324	OpenRouter	Good	~$0.14/M	Cheapest

Set via config:

extraction:
  model: google/gemini-3-flash-preview

Or environment variable: OGHMA_EXTRACTION_MODEL=google/gemini-3-flash-preview

Commands

Command	Description
oghma init	Create default config
oghma status	Show daemon and database status
oghma start	Start background daemon
oghma stop	Stop daemon
oghma search "query"	Search memories (--mode keyword/vector/hybrid)
oghma export	Export memories to files
oghma migrate-embeddings	Backfill embeddings for existing memories

Search Modes

Mode	How it works	Best for
keyword (default)	FTS5 full-text search, ordered by recency	Exact term matching
vector	Cosine similarity via sqlite-vec embeddings	Semantic/conceptual search
hybrid	RRF fusion of keyword + vector with recency boost	Best overall relevance

oghma search "async patterns" --mode hybrid --limit 20

MCP Server

Native integration with Claude Code, Codex, and other MCP-compatible tools.

Add to ~/.claude.json:

{
  "mcpServers": {
    "oghma": {
      "command": "uvx",
      "args": ["--from", "oghma", "oghma-mcp"]
    }
  }
}

MCP Tools

Tool	Description
oghma_search	Search memories (keyword, vector, or hybrid mode)
oghma_get	Get a memory by ID
oghma_stats	Database statistics (counts by category and source)
oghma_add	Write a memory directly with auto-embedding
oghma_categories	List categories with counts

Environment Variables

Variable	Required	Description
OPENAI_API_KEY	Yes (embeddings)	OpenAI API key for text-embedding-3-small
OPENROUTER_API_KEY	If using OpenRouter	API key for Gemini, DeepSeek, etc.
OGHMA_DB_PATH	No	Override database path
OGHMA_POLL_INTERVAL	No	Override poll interval
OGHMA_LOG_LEVEL	No	Set log level (DEBUG/INFO/WARNING/ERROR)
OGHMA_EXTRACTION_MODEL	No	Override extraction model

Adding Custom Parsers

Implement BaseParser with can_parse() and parse() methods, then register in src/oghma/parsers/__init__.py.

License

MIT