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Agentmemory VS Stoked

Compare Agentmemory VS Stoked and see what are their differences

Agentmemory

Persistent memory for Claude Code, Codex & coding agents

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Stoked

Stoked is peer-to-peer brand advocacy software that lets prospects talk to a brand's real customers by text, email, or chat before buying high-ticket products.

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0 reviews
Pricing
Paid $99 / Monthly (Basic: 100 advocates, 50 conversations/mo)
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HTMLCSS to Image API
Capture website screenshots, render HTML/CSS, or create templated graphics. Render images or PDFs. Use MCP with AI, no-code automation, or the REST API. No browsers needed. sponsored
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?

AI popularity
100% vs 0%
alternatives listed
55 vs 13

Base details

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

Agentmemory
Stoked
Website agent-memory.dev get.stokedhq.com
Pricing —
Paid $99 / Monthly (Basic: 100 advocates, 50 conversations/mo) Official pricing
Platforms —
Web
Company — Startup from the United States · 1 - 9 employees · 2025
Listed in
AI Developer Tools Productivity Utilities +1
Sales And Marketing eCommerce Marketing Reputation Management Referral Marketing Customer Advocacy +2

About Agentmemory and Stoked

In their own words, as submitted to SaaSHub.

Agentmemory
Stoked

No description of Agentmemory yet.

Stoked is peer-to-peer brand advocacy software for direct-to-consumer brands that sell high-ticket, high-consideration products like e-bikes, outdoor gear, saunas, and fitness equipment. Brands invite their happiest customers to become advocates. Shoppers find a nearby owner on an embeddable map...

Read more about Stoked

Features and specs

What each product offers, as listed by its team.

Agentmemory 5 features
Stoked 7 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.
  • Advocate map
    Embeddable map of nearby owners; one script tag
  • Private conversations
    SMS, email, and web chat proxy; no contact details shared
  • Advocate enrollment
    Invite customers or import them by CSV
  • Rewards
    Points, cash, or experiences for advocates
  • Conversion attribution
    Tracks conversations through to sales
  • Admin dashboard
    KPIs for advocates, conversations, and revenue
  • Countries
    45+ countries; SMS in US and Canada, email worldwide

Analysis

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

Agentmemory
Stoked

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

No analysis of Stoked yet.

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
Stoked
100% 100%
AI
0% 0%
0% 0%
Reputation Management
100% 100%
100% 100%
Developer Tools
0% 0%
0% 0%
Referral Marketing
100% 100%

Questions & Answers

As answered by people managing Agentmemory and Stoked.

What makes your product unique?

Stoked's answer:

Stoked is built around one thing: letting a shopper talk to a real owner before they buy. Most advocacy and referral tools focus on discount codes, social posts, or affiliate links. Stoked puts a map of nearby owners on the brand's site, and the shopper starts a private 1:1 conversation by text, email, or web chat. Contact details stay hidden on both sides, and the brand sees every conversation and the sales it leads to.

Why should a person choose your product over its competitors?

Stoked's answer:

  • Real conversations, not codes. Shoppers ask an owner their real questions ("Will this fit in my garage?") instead of reading another review.
  • Privacy built in. Messages go through a proxy, so neither side sees the other's phone number or email.
  • Proven results. Bunch Bikes drives 40% of its sales through advocate conversations and cut program admin from 90 to 20 minutes a day.
  • Fast setup. One script tag adds the owner map to your site. Plans start at $99/month.

How would you describe the primary audience of your product?

Stoked's answer:

Direct-to-consumer brands with roughly $1M–$7M in revenue that sell products priced at $1,000 or more: e-bikes and cargo bikes, outdoor gear, saunas and cold plunges, fitness equipment, and similar considered purchases. The typical buyer is the founder, CMO, head of marketing, or community director.

Who are some of the biggest customers of your product?

Stoked's answer:

Brands using Stoked include Bunch Bikes (electric cargo bikes), Cruzbike (recumbent bikes), Secret Creek (yurts and canvas structures), and FounderWell. Bunch Bikes, a founding customer, drives 40% of its sales through advocate conversations.

User comments

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