Waggle-mcp
★ 44
vs
engram-memory
★ 38
Waggle-mcp vs engram-memory
Waggle-mcp: Waggle-mcp provides AI agents with a persistent, graph-backed memory that remembers decisions, reasons, and contradictions across sessions, overcoming the limitations of short context windows. It acts as a local-first memory layer for MCP-compatible AI clients, allowing for rich, structured context retrieval instead of simple text snippets.; engram-memory: Engram Memory provides AI agents with persistent, self-hosted memory, eliminating session-end forgetfulness and API costs. It features a three-tiered recall engine for efficient storage, search, and retrieval of semantic embeddings on your own hardware.
01
TL;DR
Waggle-mcpMemory & Context · ★ 44
Maintaining long-term conversational memory for AI coding assistants across development sessions.
engram-memoryMemory & Context · ★ 38
Providing long-term memory for AI agents (e.g., Claude Code, Cursor) to remember codebase details, user preferences, and architectural decisions across sessions.
02
Side-by-Side Comparison
Field
Waggle-mcpengram-memoryCategory
Memory & Context
Memory & Context
Stars
★ 44
★ 38
License
Apache-2.0
NOASSERTION
Install Command
pipx install waggle-mcp && waggle-mcp setup --yes
docker run -d --name engram-memory --restart unless-stopped -p 6333:6333 -p 11435:11435 -p 8585:8585 -v engram_data:/data engrammemory/engram-memory:latest
Updated
1d ago
4mo ago
Open Source
Yes
Yes
Tags
Persistent Memory, Knowledge Graph, AI Agent Memory
AI Agent Memory, Self-hosted, Vector Database
03
Features
Waggle-mcp01Persistent graph memory with SQLite backend by default, with optional Neo4j support.
02Integrates as an MCP server with automatic memory hooks for various AI clients like Claude, Cursor, and Gemini CLI.
03Recursive context assembly to provide compact, high-signal context packs, optimizing token usage.
04Stores relationships between facts, decisions, and contradictions, enabling temporal queries, diffs, and conflict resolution.
05Local embeddings with a deterministic offline fallback, ensuring privacy and local operation.
engram-memory01Three-tiered recall engine (Hot-Tier Cache, Multi-Head Hash Index, Hybrid Vector Search) for fast and efficient memory retrieval.
02Persistent memory management with semantic embedding (store, search, recall, forget, consolidate) for AI agents.
03Local-first operation with self-hosted components (Qdrant, FastEmbed, MCP server) for data privacy and zero API costs.
04Kuzu-backed entity graph for tracking entities, co-retrieval patterns, and spreading activation.
05Advanced memory capabilities including conflict detection, temporal queries, and an "answer from memory" function.
06Hive Memory for multi-device sharing, enabling shared memory pools across different agents and devices.
04
Use Cases
Waggle-mcp↳Maintaining long-term conversational memory for AI coding assistants across development sessions.
↳Retrieving past decisions, constraints, and preferences to inform current AI agent actions.
↳Tracking changes and resolving contradictions in evolving project requirements or design choices.
↳Exporting and sharing AI agent memory snapshots for collaboration or migration between machines/sessions.
↳Debugging AI agent reasoning and understanding context by inspecting the underlying knowledge graph via Graph Studio.
engram-memory↳Providing long-term memory for AI agents (e.g., Claude Code, Cursor) to remember codebase details, user preferences, and architectural decisions across sessions.
↳Enabling multi-agent systems to share a common knowledge base and collaborate effectively through shared memory pools (Hive Memory).
↳Answering complex questions by synthesizing information from an agent's stored memories, both locally and optionally with cloud enhancement.
↳Auditing an agent's knowledge at specific points in time or over date ranges using temporal queries for incident analysis or historical context.
↳Storing and retrieving user preferences, facts, decisions, goals, and insights efficiently and semantically for personalized agent interactions.
FAQ
FAQ
What is the difference between Waggle-mcp and engram-memory?
Both Waggle-mcp and engram-memory are in the Memory & Context category. Waggle-mcp has 44 stars, while engram-memory has 38 stars.
Which is better, Waggle-mcp or engram-memory?
The best choice depends on your use case. Choose Waggle-mcp if Maintaining long-term conversational memory for AI coding assistants across development sessions., and engram-memory if Providing long-term memory for AI agents (e.g., Claude Code, Cursor) to remember codebase details, user preferences, and architectural decisions across sessions..
Is Waggle-mcp free or open source?
Yes, Waggle-mcp is open source on GitHub (Apache-2.0).
Is engram-memory free or open source?
Yes, engram-memory is open source on GitHub (NOASSERTION).
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