Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Nasdaq Metrio Reviews & Ratings
    14 Ratings
    Company Website
  • Digital WarRoom Reviews & Ratings
    55 Ratings
    Company Website
  • DataHub Reviews & Ratings
    10 Ratings
    Company Website
  • ContractSafe Reviews & Ratings
    326 Ratings
    Company Website
  • SCIKIQ Reviews & Ratings
    14 Ratings
    Company Website
  • AnalyticsCreator Reviews & Ratings
    46 Ratings
    Company Website
  • LogicalDOC Reviews & Ratings
    150 Ratings
    Company Website
  • 3Q Reviews & Ratings
    14 Ratings
    Company Website
  • Gearset Reviews & Ratings
    305 Ratings
    Company Website
  • TheBestPDF Reviews & Ratings
    39,558 Ratings
    Company Website

What is Referencer?

Referencer is a GNOME application designed to assist users in organizing documents or references while also facilitating the creation of a BibTeX bibliography file. Primarily aimed at scientists and researchers, the term "document" typically pertains to research papers, though Referencer accommodates any document type that BibTeX recognizes. A notable feature of Referencer is its capability to autonomously collect bibliographic information, or metadata, for a range of document types. When a user uploads a PDF to their Referencer library, the application identifies key identifiers like a DOI or an arXiv identifier, and if successful, it attempts to retrieve the associated metadata from online sources. Unfortunately, the ability to obtain metadata for more recent arXiv entries has been hindered due to changes in format. Moreover, users can easily import references from BibTeX, Reference Manager, and EndNote, enhancing the application's versatility. In addition, Referencer efficiently retrieves metadata from arXiv, PubMed, and CrossRef for PDFs that include an arXiv ID or DOI code, streamlining the reference management process for users. This feature not only enhances the efficiency of managing references but also promotes a more cohesive integration of various academic resources into the research workflow, ultimately benefiting the scholarly community.

What is EverOS?

EverOS is a persistent memory platform for AI agents that combines long-term context, multimodal ingestion, self-evolving skills, and cross-platform compatibility. The system is designed to sit inside an existing agent loop and retrieve relevant context before each large language model call. Rather than sending an entire conversation or knowledge history into the model every time, EverOS selects the most relevant stored memories for the current task. Its memory architecture is built to support low-latency retrieval while reducing token usage and maintaining continuity across sessions. EverOS also introduces self-evolving skills, which turn successful task executions into cases that can later be consolidated into reusable procedural knowledge. Repeated successful patterns can be promoted into shared skills that are available to multiple agents without requiring manual curation or brittle workflow logic. The platform accepts multimodal sources including PDFs, images, spreadsheets, documents, slides, URLs, and Markdown and automatically parses, chunks, and indexes them for retrieval. EverOS works with agent environments and interfaces such as Claude Code, Codex, OpenClaw, Hermes, MCP, and OpenAI- and Anthropic-compatible SDKs. Its Markdown-first portability model allows organizations to export memories in a readable, version-controllable format and move between hosted and self-managed deployments. Teams can start with EverOS Cloud or deploy the Apache 2.0 open source version on their own infrastructure using the same API and memory format. EverOS is designed for developers, AI infrastructure teams, and enterprises that want agents to retain context, learn reusable procedures, and operate more efficiently across tools and environments. EverOS achieves 93.05% on the LoCoMo long-term memory benchmark (research: arXiv:2601.02163).

Media

Media

Integrations Supported

Integrations Supported

Tanka

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Free: $0/month. 3 Memory Spaces, 50,000 MCU/month, 100,000 Retrieval API calls/month.
Pro: $25/month (free during Early Access). 8 Memory Spaces, 250,000 MCU/month, 500,000 Retrieval API calls/month, includes Self-Evolving Skills.
Enterprise: Custom pricing. Custom limits, dedicated account manager, private deployment available.
Free Version

Supported Platforms

SaaS
Linux

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Launchpad

Date Founded

2004

Company Location

United Kingdom

Company Website

launchpad.net/referencer/

Company Facts

Organization Name

EverMind

Date Founded

2025

Company Location

United States

Company Website

evermind.ai/everos

Categories and Features

Reference Management

Not specified

Categories and Features

AI Memory Layers

Not specified

Popular Alternatives

Check Your Draft Reviews & Ratings

Check Your Draft

WKSolutions

Popular Alternatives

MemClaw Reviews & Ratings

MemClaw

Caura AI
LangMem Reviews & Ratings

LangMem

LangChain
Bebop Reviews & Ratings

Bebop

ALaRI
Membase Reviews & Ratings

Membase

Unibase