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Agentmemory VS LLMrefs

Compare Agentmemory VS LLMrefs and see what are their differences

Agentmemory

Persistent memory for Claude Code, Codex & coding agents

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LLMrefs

AI SEO Search Visibility & AI Rank Tracking Platform for LLM Search Engines like ChatGPT - GEO/AEO/LLMO

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0 reviews
Pricing
Freemium Free trial $79 / Monthly
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Sugarbug
Connect your tools into a living knowledge graph. Sugarbug captures every signal to deliver compounding insights and unified context. sponsored

Which is more popular?

AI popularity
15% vs 85%
alternatives listed
55 vs 240+

Base details

Website, pricing, platforms and company facts side by side.

Agentmemory
LLMrefs
Website agent-memory.dev llmrefs.com
Pricing —
Freemium Free trial $79 / Monthly Official pricing
Company — Startup from the United Kingdom · 1 - 9 employees · 2025
Listed in
AI Developer Tools Productivity Utilities +1
SEO Tools SEO AI Reputation Management +1

About Agentmemory and LLMrefs

In their own words, as submitted to SaaSHub.

Agentmemory
LLMrefs

No description of Agentmemory yet.

LLMrefs is an AI search analytics platform that tracks brand visibility across generative AI search engines. It shows whether AI assistants like ChatGPT, Perplexity, and Gemini mention your brand when users ask questions about your industry. Supported AI Search Engines LLMrefs monitors 11...

Read more about LLMrefs

Features and specs

What each product offers, as listed by its team.

Agentmemory 5 features
LLMrefs 3 features
  • Simple API
    Agentmemory provides a straightforward and minimal API for creating, searching, updating, and deleting memories, making it easy for developers to integrate memory capabilities into AI agents without dealing with complex configurations.
  • Built on ChromaDB
    It leverages ChromaDB as its underlying vector database, providing reliable semantic search and embedding capabilities out of the box without requiring developers to set up separate infrastructure.
  • Lightweight and Easy to Install
    Agentmemory is a lightweight Python package that can be installed via pip with minimal dependencies, making it quick to get started with and easy to incorporate into existing projects.
  • Category-Based Memory Organization
    Memories can be organized into categories (topics), allowing agents to store and retrieve information in a structured way, which helps with context management and retrieval accuracy.
  • No Server Required
    Agentmemory can run entirely locally without needing a separate server or cloud service, making it suitable for development, prototyping, and privacy-sensitive applications where data should stay on the local machine.

Possible disadvantages

  • Limited Ecosystem and Community
    Agentmemory is a relatively niche and small project with a limited community compared to more established memory and vector database solutions, which means fewer resources, tutorials, and community support are available.
  • Basic Feature Set
    While simplicity is a strength, the library may lack advanced features such as sophisticated memory consolidation, decay mechanisms, importance scoring, or complex querying capabilities that more mature memory frameworks offer.
  • Tight Coupling to ChromaDB
    Being built specifically on ChromaDB means developers are locked into that particular vector store and cannot easily swap it out for alternatives like Pinecone, Weaviate, or FAISS without significant refactoring.
  • Limited Scalability
    As a locally-run, lightweight solution, Agentmemory may not scale well for production applications that require handling large volumes of memories, high concurrency, or distributed deployments.
  • Sparse Documentation and Examples
    The project's documentation, while covering the basics, may lack comprehensive examples, best practices, and advanced usage patterns that developers need when building complex agent-based systems.
  • AI SEO tracking
    Track Share of Voice and Position metrics to see how you rank against competitors.
  • AEO brand visibility
    Monitor how 11 AI search engines respond to your keywords and whether your brand gets cited.
  • GEO data for marketing teams
    View which URLs AI assistants use as sources when they mention your brand.

Analysis

An editorial look at what each product does well and who it suits.

Agentmemory
LLMrefs

Overall verdict

  • AgentMemory (agent-memory.dev) appears to be a solid, purpose-built solution for developers who need persistent memory management in AI agent applications, offering a focused feature set for storing, retrieving, and managing contextual data across agent sessions.

Why this product is good

  • Provides dedicated memory persistence for AI agents, enabling context retention across sessions and conversations
  • Designed specifically for the agentic AI use case, which can simplify development compared to building custom memory layers
  • Likely offers developer-friendly APIs and SDKs to integrate memory capabilities quickly
  • Can improve agent performance by allowing recall of past interactions, user preferences, and long-term context
  • Reduces boilerplate work for teams building conversational or autonomous AI systems

Recommended for

  • Developers building AI agents or LLM-powered applications that require long-term memory
  • Teams creating conversational assistants that need to remember user context across sessions
  • Startups and companies prototyping autonomous or multi-step agent workflows
  • Engineers seeking a managed memory layer instead of building persistence infrastructure from scratch
  • Projects involving personalized AI experiences that depend on retained user data and history

Overall verdict

  • LLMrefs is a solid, purpose-built tool for tracking how your brand and content appear across AI-powered search and large language models, making it a useful choice for teams focused on the emerging field of generative engine optimization (GEO).

Why this product is good

  • Specializes in monitoring brand visibility and citations across AI platforms like ChatGPT, Perplexity, and Google's AI features
  • Helps businesses adapt their SEO strategy to the shift toward AI-driven search and answer engines
  • Provides insights into which prompts and queries surface your brand, aiding content optimization
  • Addresses a growing need as more users rely on LLMs instead of traditional search engines

Recommended for

  • Marketing teams and SEO professionals wanting to track brand presence in AI search results
  • Businesses investing in generative engine optimization (GEO) strategies
  • Content creators aiming to understand how LLMs cite and reference their material
  • Agencies managing multiple clients' visibility across AI platforms

Videos

Walkthroughs and reviews on video.

Agentmemory 0 videos + Add
LLMrefs 1 video + Add

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LLMrefs - AI SEO Keyword Rank Tracker for LLM Search Engines

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Agentmemory
LLMrefs
15% 15%
AI
85% 85%
0% 0%
SEO Tools
100% 100%
100% 100%
Developer Tools
0% 0%
0% 0%
Generative Engine Optimization (GEO)
100% 100%

Questions & Answers

As answered by people managing Agentmemory and LLMrefs.

Why should a person choose your product over its competitors?

LLMrefs's answer:

  1. Track keywords, not prompts Most AI SEO tools make you manage individual prompts. LLMrefs lets you add keywords and automatically generates prompt variations based on real conversations users have with AI chatbots. This saves time and produces more realistic results.

  2. Statistical significance, not magic numbers Many competitors show vague "visibility scores" that are hard to interpret. LLMrefs uses transparent metrics like Share of Voice and Position. Results are aggregated and weighted across every prompt variation to ensure statistical significance.

  3. Affordable with no hidden fees LLMrefs Pro is $79 per month and includes all 11 AI search engines. Competitors often charge extra per engine or have tiered pricing that gets expensive quickly.

  4. Agency-friendly from day one One subscription covers unlimited projects and unlimited team members. Agencies do not need to buy separate accounts for each client.

  5. Data quality focus LLMrefs emphasizes being the only platform that takes data quality seriously. They continuously check prompts and only report results when they have enough data to be statistically significant.

  6. Comprehensive engine coverage Eleven AI search engines tracked in one dashboard. You do not need separate tools for ChatGPT, Perplexity, Google AI Overviews, and others.

User comments

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

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