Skip to main content
Glama
yonro

XMemo

by yonro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Related Servers

Alternatives to XMemo

  • mem0-mcp by tensakulabs

    mem0-mcpofficial

    tensakulabs
    A
    license
    A
    quality
    C
    maintenance
    Self-hosted Mem0 MCP server integrating Qdrant, Neo4j, and Ollama for semantic memory search, graph entity relationships, and memory management via OpenMemory API.
    6
    4
    MIT

Related Servers

  • agents-memory

    Lolaplex
    A
    license
    Not graded
    quality
    A
    maintenance
    A local markdown memory and cross-agent context engine for AI coding assistants. It provides an MCP server with tools to search, add, retrieve, and distill persistent memory across tools like Claude Code, Cursor, and Zed.
    37 PyPI
    5
    MIT

  • mesh-memory

    dklymentiev
    A
    license
    Not graded
    quality
    D
    maintenance
    Self-hosted semantic memory for AI agents. Save worklogs, decisions, and notes via MCP, then recall them across sessions by meaning rather than keyword. Backed by Postgres + pgvector with local embeddings (multilingual-e5-base).
    1
    MIT

  • Knowledge Base Server

    ZaidPrice549
    A
    license
    Not graded
    quality
    A
    maintenance
    Gives AI agents persistent, shared memory by ingesting notes, code, transcripts, and session captures, then serving token-optimized context and hybrid keyword/semantic search over MCP and REST. Agents can classify, promote, and synthesize stored material so each session builds on prior work instead of starting from zero.
    14 npm
    MIT

  • mnemos

    s60yucca
    A
    license
    B
    quality
    C
    maintenance
    Persistent memory engine for AI coding agents. Single Go binary, zero runtime dependencies, MCP-native. Stores, searches, and deduplicates memories across sessions using embedded SQLite with hybrid FTS + semantic search, memory decay, relation graph, and token-budget context assembly.
    10
    23
    MIT

  • Sugar

    Yurik2700
    F
    license
    Not graded
    quality
    A
    maintenance
    Enables AI coding agents to store, search, and retrieve persistent local-first memory — project-specific decisions, preferences, error patterns, and research plus cross-project guidelines — so context survives across sessions without any external API calls. Optionally also exposes a task queue for autonomous execution and GitHub issue resolution in MCP clients such as Claude Code, OpenCode, and Goose.
    -

  • Sugar

    Sitas17424
    F
    license
    Not graded
    quality
    A
    maintenance
    A local-first MCP memory server that lets AI coding agents store, search, and recall project-specific and cross-project knowledge, so architecture decisions, preferences, error patterns, and coding guidelines persist across sessions. It also exposes optional task-queue tools for handing off autonomous work that draws on the same stored context.
    -
Deploy Server
Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

TDQS

B3.4/5.0

Scored across 37 tools

Disambiguation3/5

The tool set has several clusters of overlapping retrieval/write tools, such as recall vs. recall_context vs. search_knowledge vs. read_memory, and remember vs. update_state vs. record_event. The descriptions do a good job clarifying intended boundaries, but an agent still faces multiple plausible choices for common memory tasks.

Naming Consistency3/5

Most names use snake_case, but conventions are mixed: many are verb_noun, while others are noun-first (memory_stats, memory_relationships, dream_view) or bare verbs (remember, forget, recall). The pattern is readable but not consistently predictable.

Tool Count2/5

With 37 tools, the surface is heavy for a memory assistant and includes many specialized operations across projects, dreams, cloud skills, snapshots, proposals, and audit. While the domain is broad, this is well above the typical well-scoped range and increases selection burden.

Completeness4/5

The surface is very complete for memory lifecycle management: create, recall, read, update, delete, restore, version, relate, conflict, TODO, project, snapshot, and audit operations are all represented. Minor gaps remain around full lifecycle coverage for some subdomains, such as updating/deleting Cloud Skills and explicitly listing or creating projects.

Maintenance

ActivityActive
ResponsivenessWithin a week

Resources