Skip to main content
Glama
riponcm

Projectmem

by riponcm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to Projectmem

  • MCP-Mem0

    yellnuts
    A
    license
    Not graded
    quality
    Not graded
    maintenance
    A robust server for managing long-term agent memory using Mem0, providing efficient storage and retrieval of agent memories with a lightweight Python-based implementation.
    MIT
  • Memstate AI - Agent Memory System by memstate-ai

    Memstate AI - Agent Memoryofficial

    memstate-ai
    A
    license
    A
    quality
    D
    maintenance
    Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.
    7
    7 npm
    8
    Apache 2.0

Related Servers

  • memini

    lumayapartners
    A
    license
    A
    quality
    D
    maintenance
    Local-first project memory for AI coding agents. Records failed attempts, fragile files, and decisions per repo, and warns the agent via hooks before it repeats a recorded mistake.
    6
    0
    MIT

  • cogmem

    dcondrey
    A
    license
    A
    quality
    F
    maintenance
    Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.
    8
    2
    Apache 2.0

  • Sugar Memory

    Renjun22360
    F
    license
    Not graded
    quality
    A
    maintenance
    Provides AI coding agents with a local-first persistent memory layer they can read and write during sessions, storing decisions, preferences, error patterns, and research per project or globally. Enables semantic and keyword search, formatted context recall, and cross-project guideline surfacing without API keys or cloud storage.
    -

  • infernoflow

    ronmiz
    A
    license
    B
    quality
    A
    maintenance
    Git-native memory for coding agents, so every session starts warm. Gotchas, decisions, dead ends and bookmarks are saved as plain JSONL in your repo; Claude Code, Cursor and Copilot read them, and teammates get them through git.
    9
    556 npm
    8
    MIT

  • Sugar

    Benjaminn581
    F
    license
    Not graded
    quality
    A
    maintenance
    Enables AI coding agents to read and write persistent, local-first memory across sessions and projects, with semantic search, global guidelines, and optional autonomous task execution.
    -

  • io.github.cdnsteve/sugar

    Wulff641
    F
    license
    Not graded
    quality
    A
    maintenance
    Provides AI coding agents with a local-first, persistent memory layer so they can store and semantically recall project-specific and cross-project knowledge, conventions, and error patterns during sessions. It also optionally enables autonomous task queue execution and issue resolution driven by that same memory.
    -
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A4.1/5.0

Scored across 17 tools

Disambiguation4/5

The read tools (get_summary, get_context, search_events, get_issue) all retrieve memory but are distinguished by scope and budget in their descriptions. list_projects vs current_project is a minor overlap but clearly differentiated (reachable vs resolved project). Write tools each target a distinct event type (issue/attempt/fix/decision/note).

Naming Consistency4/5

Predominantly consistent verb_noun snake_case: get_*, list_*, search_*, record_*, add_*, precheck_*. The lone deviation is current_project, which is noun-only with no verb. Otherwise the pattern is predictable and readable.

Tool Count4/5

17 tools is slightly above the typical 3-15 sweet spot, but each tool maps to a distinct memory operation (12 reads, 5 writes). The read/write split justifies the count, though it leans heavy.

Completeness4/5

Covers the full memory lifecycle: project discovery, reading summary/instructions/map/plan/context, issue logging, attempt/fix recording, decisions, and notes, plus cross-project gotchas and scoring. No update/delete tools exist, but decisions are deliberately append-only and plan edits are done by editing files directly, so gaps are minor and by design.

Maintenance

ActivityMaintained
ResponsivenessWithin a week

Resources