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Embeddable VS Agentmemory

Compare Embeddable VS Agentmemory and see what are their differences

Embeddable

The toolkit for building fast, interactive, fully-custom analytics experiences into your app.

Rating
0 reviews
Pricing
Paid
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Agentmemory

Persistent memory for Claude Code, Codex & coding agents

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Castmint
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Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Embeddable seems to be more popular. It has been mentioned 2 times since March 2021.

social mentions
2 vs 0
Data Dashboard popularity
100% vs 0%
alternatives listed
78 vs 55

Base details

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

Embeddable
Agentmemory
Website embeddable.com agent-memory.dev
Pricing
Paid
—
Company Startup from the United Kingdom · 20 - 49 employees · 2023 —
Listed in
Embedded Analytics Dashboard Software Data Visualization Business Intelligence Data Dashboard Customer-facing Analytics Data Analysis Data Analytics Reporting Platform Reporting & Dashboard +7
AI Developer Tools Productivity Utilities +1

About Embeddable and Agentmemory

In their own words, as submitted to SaaSHub.

Embeddable
Agentmemory

Build Remarkable Analytics Experiences. No more 'Build vs. Buy'. Embeddable is the embedded analytics tool where you own the front-end code and we handle everything else. Now you can build fully-bespoke, fast-loading charts and dashboards in your app without the engineering costs. Delight...

Read more about Embeddable

No description of Agentmemory yet.

Features and specs

What each product offers, as listed by its team.

Embeddable 14 features
Agentmemory 5 features
  • Cloud-Hosted Option
  • Self-Hosted Option
  • Frontend SDK
  • No-code Dashboard Builder
  • Performant Embedding
  • Row-Level Security
  • Configurable Cache
  • Compatible with Major Databases
  • Compatible with Charting Libraries
  • Template Charting Components Provided
    Included
  • Dedicated Account Management
  • Version Control
  • Audit Logs
  • Documentation
  • 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.

Analysis

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

Embeddable
Agentmemory

No analysis of Embeddable yet.

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

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
Embeddable
Agentmemory
100% 100%
Data Dashboard
0% 0%
0% 0%
AI
100% 100%
100% 100%
Business Intelligence
0% 0%
0% 0%
Developer Tools
100% 100%

Questions & Answers

As answered by people managing Embeddable and Agentmemory.

How would you describe the primary audience of your product?

Embeddable's answer

Software companies who care about the UX and loading speed of their customer-facing analytics.

What makes your product unique?

Embeddable's answer

Get the best of 'Build vs. Buy' in one stack-agnostic solution. Embeddable gives you full control over the frontend of your analytics experience, and handles the backend for you. No longer do you have to choose between a limited out-of-the-box solution, or building everything from scratch.

What's the story behind your product?

Embeddable's answer

Embeddable is from the team behind Trevor.io -- a popular internal BI tool which also allows you to embed dashboards into your app. We realised embedding dashboards from a BI tool into your app wasn't the 'dream solution', and building analytics from scratch was super expensive... so we built Embeddable from the ground up to enable teams to deliver fully-bespoke, highly-performant analytics in their apps for their customers in 10% of the time.

Who are some of the biggest customers of your product?

Embeddable's answer

  • Scalapay
  • Adthena
  • Irwin
  • EtonX
  • Resident Advisor
  • Facilities Solutions Group (FSG)
  • Multibrain
  • Raydiant
  • ThinkCERCA
  • Tixly
  • Softools
  • Faheem App
  • Just Move In
  • Any Creek

Why should a person choose your product over its competitors?

Embeddable's answer

If you want full control over the UX of your customer-facing analytics experience, but don't want to invest months of developer time on building and maintaining a fully-custom build -- OR -- if you're using an embedded analytics too already that loads slowly and doesn't look and feel like the rest of your platform.

User comments

Share your experience with using Embeddable and Agentmemory. For example, how are they different and which one is better?

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Reviews and articles

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

Embeddable no reviews yet
Agentmemory no reviews yet
  • 6 Best Looker alternatives
    trevor.io · Jun 2024

    After a successful, oversubscribed Private Beta, Embeddable is now publicly available. More information on how to work with Embeddable can be found on their homepage at embeddable.com. Get in touch with the Embeddable...

  • Power BI Embedded vs Looker Embedded: Everything you need to know
    embeddable.com · Nov 2023

    The main differences between Power BI Embedded and Embeddable are performance, price, and customizability. Embeddable gives you full control over your charting components and data models. It’s also built from the...

  • Embedded analytics in B2B SaaS: A comparison
    medium.com · Nov 2023

    I’m happy to say that we’ve enrolled in the beta program of Embeddable. After learning all the above it seems like this is the option we’d want to invest in. We’ll keep you posted on how this pans out, but we’re...

We have no reviews of Agentmemory yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Embeddable 2 mentions
Agentmemory 0 mentions
  • AI in BI tools: why we're not there yet
    Then comes data modeling. BI tools such as Embeddable need to know how different tables and fields relate to each other. Someone has to define what terms like “top customer” or “Q3 revenue” actually mean. Without this, the AI won't know... - Source: dev.to / over 1 year ago
  • Apache Superset
    It’s still pretty new but build by an experienced team. It’s commercial software though. https://embeddable.com/. - Source: Hacker News / over 2 years ago

Tracking Agentmemory since Jun 2026.

Alternatives to Embeddable and Agentmemory

When comparing Embeddable and Agentmemory, you can also consider the following products.

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