mcp-name: io.github.turbyho/mem-context

mem-context — Temporal Memory MCP Server

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Why

AI assistants lose context between sessions. mem-context persists what matters — decisions, patterns, bugs, architecture choices — and surfaces them when relevant via vector search. Memories decay over time unless reinforced by repeated access, mimicking human memory.

Features

• Vector search with dual backend — LanceDB ANN index for fast approximate nearest-neighbor queries. Primary embedding via Ollama mxbai-embed-large (1024d, ~670 MB). Local all-MiniLM-L6-v2 (384d) fallback when Ollama is unavailable — no GPU or network required. Embeddings are auto-padded to match schema dimension; switching backends is transparent.

• Multi-factor relevance scoring — six independent factors combine into a single 0–1 relevance score. Each factor models a different aspect: vector_score (semantic similarity), weight_score (stored importance × time decay), recency_score (age in days), scope_score (project match), access_boost (usage reinforcement), type_boost (permanent > semantic > episodic). The model balances "what's relevant" with "what's still valid."

• Weight decay with natural memory model — each memory type has a configurable decay_rate: 0.15/day for episodic (session captures fade fast), 0.03/day for semantic (extracted knowledge persists), 0 for permanent (never decays). Decay is exponential: weight × e^(−rate × days). Frequently accessed memories get a counteracting boost — the system reinforces what you use, archives what you don't.

• Deduplication by cosine similarity — new memories are compared against existing ones before insertion. At similarity > 0.82, the new memory is merged into the existing one (weight boost + content update) instead of creating a duplicate. Prevents memory fragmentation from repeated captures of the same conclusion across sessions.

• LLM-driven consolidation pipeline — 3-phase: extract (3 days), merge (7 days), archive (30 days). The server prepares candidates and prompts; the host model (Claude, DeepSeek, GPT, or local Ollama) does the reasoning. Episodic session captures → extracted conclusions (semantic) → merged permanent knowledge → archived if unused. Runs in the background when remember() or recall() is called — no cron needed.

• Multi-modal storage — LanceDB columns for text content, code diffs, file lists, error traces, tags, and metadata. Each modality is indexed separately; vector search operates on the combined embedding. Stores not just "what happened" but the diff and stack trace that caused it.

• Automatic conversation capture — hooks for Claude Code (Stop event) and manual capture for OpenCode. The wrapper binary finds the current session's transcript, parses it into structured messages, and imports them as episodic memories. No manual action needed — every session is archived automatically.

• Portable export/import — JSON export strips embeddings (re-generated on import), keeps all metadata. Use for backup, cross-device sync, or migrating between machines. Import deduplicates by ID — safe to run multiple times.

• **One-co

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