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PMB

Local-first memory for AI coding agents with hybrid recall (BM25 + vectors + entity graph), MCP-native with ~35ms recall

★★★★☆ 4 Unknown
📖 Editorial Review Snapshot Reviewed Jul 14, 2026
🕒 Temporal Notice: This article reflects our editorial evaluation snapshot on Jul 14, 2026. Live specifications and pricing are maintained separately in the Registry and reflect current facts.

PMB Review 2026: Local-First Memory Engine for AI Coding Agents

In-depth review of PMB — a local-first, MCP-native memory engine that combines BM25, vector embeddings, and entity graphs for ~35ms hybrid recall. Built for AI coding agents that need persistent, private cross-session memory.

💡 9bests Editorial Buying Advice

Why choose PMB: Local-first memory for AI coding agents with hybrid recall (BM25 + vectors + entity graph), MCP-native with ~35ms recall

Optimal workflow match: Workflow fit varies by team and should be verified.

Pros / Key Advantages

  • Hybrid recall engine — combines BM25 (keyword), vector embeddings (semantic), and entity graph (relational) for multi-strategy retrieval
  • Local-first architecture — all memory and indexes live on-device; no cloud dependency, no API calls for core operations
  • MCP-native integration — exposes memory operations as standard MCP tools, plug-and-play with any MCP-compatible agent harness
  • ~35ms recall latency — engineered for low-latency retrieval, suitable for real-time agent decision loops
  • Entity graph layer — tracks relationships between code symbols, files, decisions, and context across sessions

Cons / Limitations

💰 Pricing Plans & Structure

Unknown

Pricing details are gathered from public sources and are subject to change. Please visit the official website for real-time rates and trial terms.

Pricing verified from official public sources · Reviewed by Bill (Lead Editor)

🎯 Who should use PMB

Best suited for users focused on digital productivity and AI automation who value hybrid recall engine — combines bm25 (keyword), vector embeddings (semantic), and entity graph (relational) for multi-strategy retrieval.

⚠️ Who should look elsewhere

Users who require features outside its core scope or cannot accommodate current platform constraints may benefit from exploring alternative tools in this category.

🚀 Common use cases

Hybrid recall engine — combines BM25 (keyword), vector embeddings (semantic), and entity graph (relational) for multi-strategy retrieval

Local-first architecture — all memory and indexes live on-device; no cloud dependency, no API calls for core operations

MCP-native integration — exposes memory operations as standard MCP tools, plug-and-play with any MCP-compatible agent harness

⚖️ Direct Head-to-Head Comparisons

Curated Matchups

❓ Frequently asked questions

Is PMB free?

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PMB is paid; pricing: Unknown.

What is PMB used for and what are its strengths?

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Key strengths of PMB: Hybrid recall engine — combines BM25 (keyword), vector embeddings (semantic), and entity graph (relational) for multi-strategy retrieval, Local-first architecture — all memory and indexes live on-device; no cloud dependency, no API calls for core operations. Local-first memory for AI coding agents with hybrid recall (BM25 + vectors + entity graph), MCP-native with ~35ms recall

What is the best alternative to PMB?

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If you're looking for an alternative to PMB, consider Cursor: it stands out for State-of-the-art multi-file Composer & Cloud Agents, Deep codebase-wide semantic indexing.

How do I choose the right alternative to PMB?

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Selection advice: compare ratings, pricing, and core features within the AI Coding category, then match to your own workflow. See the comparison matrix and Top alternatives list on this page.

🔄 Top Alternatives to PMB

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