mcp-local-memory

★ 60

kaeru

★ 40

mcp-local-memory vs kaeru

Q: Which is better, mcp-local-memory or kaeru?

By GitHub stars, mcp-local-memory has more community adoption, but the best choice depends on your specific use case.

mcp-local-memory: mcp-local-memory is a local-first MCP server designed to provide AI agents with shared, durable memory without relying on Docker or cloud services. It achieves this through a powerful combination of hybrid semantic search, SQLite FTS5, and a structured knowledge graph, supporting features like temporal recall and adaptive ranking.; kaeru: Kaeru is a local-first, cross-agent cognitive engine for LLM agents, providing a typed property graph and a recollection layer for long-term memory. It enables multi-session and multi-agent continuity, allowing agents to maintain full context, follow provenance, and build on shared knowledge over time.

TL;DR

mcp-local-memoryMemory & Context · ★ 60

Provide persistent and shared memory for AI agents in local environments.

Read mcp-local-memory full review →

kaeruMemory & Context · ★ 40

An agent needs to recall its thought process and project context from a previous session.

Read kaeru full review →

Side-by-Side Comparison

Field

mcp-local-memory

kaeru

Features

mcp-local-memory

01Shared memory across multiple AI agents

02Hybrid semantic and keyword recall

03Structured knowledge graph for context management

04Temporal memory recall with natural language dates

05Adaptive memory ranking based on multiple factors

kaeru

01Two-tier graph (operational/hippocampus and archival/cortex) for active thinking and settled knowledge.

02Bi-temporal nature with native assertion/retraction history for time-travel queries and non-destructive conflict resolution.

03Per-initiative scoping and layered re-entry to restore project context across sessions and access archival knowledge on demand.

04Optional cross-agent sharing through a cloud tier, allowing trusted teams to share settled knowledge with safety gates.

05Structural recall based on exact name lookup, typed graph traversal, and FTS fuzzy fallback, rather than primarily vector embeddings.

Use Cases

mcp-local-memory

↳Provide persistent and shared memory for AI agents in local environments.

↳Build knowledge bases and manage context for complex AI applications.

↳Facilitate local-first AI agent development and testing without external dependencies.

↳Serve as a centralized memory server for distributed AI agent systems via HTTP/SSE.

kaeru

↳An agent needs to recall its thought process and project context from a previous session.

↳A team of agents needs to share and build upon a collective, long-term knowledge base.

↳An agent wants to capture new observations or hypotheses within a specific project scope.

↳Tracing the provenance and history of a piece of knowledge or a decision made by an agent.

FAQ

What is the difference between mcp-local-memory and kaeru?

Both mcp-local-memory and kaeru are in the Memory & Context category. mcp-local-memory has 60 stars, while kaeru has 40 stars.

Which is better, mcp-local-memory or kaeru?

The best choice depends on your use case. Choose mcp-local-memory if Provide persistent and shared memory for AI agents in local environments., and kaeru if An agent needs to recall its thought process and project context from a previous session..

Is mcp-local-memory free or open source?

Yes, mcp-local-memory is open source on GitHub (MIT).

Is kaeru free or open source?

Yes, kaeru is open source on GitHub (MIT).

Cloud Infrastructure

Deploy on DigitalOcean

Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.

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Alternatives to mcp-local-memory →Alternatives to kaeru →mcp-local-memory details →kaeru details →

TL;DR

Side-by-Side Comparison

Features

Use Cases

FAQ

Deploy on DigitalOcean

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