Opportunities › Affordable memory management for AI agents › Proposed app
Recall Ledger
Model-agnostic memory for AI agents. Store, recall and audit what they learn.
- Score
- 55
- confidence 60
- Evidence
- 7
- items on the page
- Rivals
- 2
- named in sources
- First version
- 30 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: Developers monitor and audit AI agent memory through dashboards, inspect memory entries, and configure models—dense technical work best done at a desk with keyboard input. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Agents
4 agents live12,480
About this appAI proposal
Recall Ledger gives production AI agents a persistent memory that works with any language model. Agents write facts, events and preferences to layered memory and read back only what is relevant to the task. You choose the model that summarises and embeds, and you can change it later.
It is built for developer teams running agents in operations-heavy work such as property management, customer service and back-office automation. Memory is split into session notes, long-term facts and learned procedures, so an agent can recall a tenant's repeat leak without carrying every old chat.
Users of existing memory platforms describe them as too expensive and too tied to specific model providers. Recall Ledger prices by stored memories and recalls, shows a usage meter, and lets you bring your own model keys.
- Tagline
- Give your agents a memory you own, on any model, at a price you can plan for.
- Voice
- PlainPrecisePractical
- Why this name
- A ledger is a trusted record you can read and correct. Recall is what agents do with it. Two plain words that say what the product does.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Bring your own model
Connect any LLM or embedding provider with your own keys. Swap providers per agent without re-importing memory.
No member yet
Three memory layers
Session notes, long-term facts and learned procedures are stored apart, each with its own retention rules and recall limits.
No member yet
Simple recall API
Two REST calls: write and recall. Recall returns ranked memories with source and age so your agent can judge trust.
No member yet
Memory inspector
Browse, edit and delete what an agent remembers about any entity. Trace why an agent gave a wrong answer.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#2F5D50Pine · primary#F6F1E7Paper · surface#1F2A24Ink · text#D9822BAmber · accent#3B6EA5Slate blue · data
Typography
Aa
Recall Ledger
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Give your agents a memory you own, on any model, at a price you can plan for.
PricingAI proposal
Zep's verified pricing page lists paid plans from $125 to $375 a month plus a free plan; Mem0 lists prices on request. These tiers sit below Zep's and are a starting point to test.
Free
Free
- 1 agent
- 5,000 memories
- Bring your own model keys
- Memory inspector
Team
$49 a month
- 10 agents
- 250,000 memories
- Cost caps and alerts
- Forgetting rules
- Email support
Scale
$149 a month
- Unlimited agents
- 2 million memories
- Audit log and export
- Priority support
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$190,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Partners and integrations. Publish adapters and examples for popular agent frameworks and workflow tools, so developers find Recall Ledger when they search for how to add memory to an agent.
- Do they become customers?
- Developers who install an adapter are the buyers, so they can convert. Test whether adapter installs turn into paid Team plans within 30 days.
- First customers
- Small developer teams of 2 to 10 building AI agents for property management companies and their operations staff.
- Where to reach them
- Hacker News and dev forums: Post a build log on agent memory costs and answer threads where people say existing tools are too expensive.
- Agent framework communities: Share adapters and a sample leasing agent with memory in framework Discord and GitHub discussions.
- First milestone
- Five paying teams in property-management automation within 60 days, each running an agent in production.
Why people already pay for this
- Public claimExisting memory solutions for AI agents carry cost burdens that motivate building alternatives
- Public claimMemory management for AI agents is a real production need in deployed systems
- VerifiedMem0 sells paid plans with prices on request, on its own pricing page, plus a free plan and prices on request for larger customers
- VerifiedZep's pricing page lists 2 paid plans, from $125 to $375 a month, plus a free plan and prices on request for larger customers
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Vector searchpgvector on Supabase PostgresEmbedding search for recall without a separate vector database to run.
- This app · Background jobsQueue and scheduled workersSummarising, merging, expiring and re-embedding memories happen off the request path.
RisksAI inference
- CommercialOpen-source memory libraries are free, so teams may self-host. Paid value must come from hosting, inspection and cost control, and this needs testing.
- TechnicalRecall quality decides whether agents improve. Weak ranking or stale facts could make agents worse and drive churn.
- DataMemories can hold personal data about tenants or customers. Deletion, isolation between customers and retention rules must be sound from day one.
- AISwapping embedding models forces re-embedding of stored memory, which costs time and money and may change recall results.
- CommercialThe evidence is public claims from one forum. Real willingness to pay beyond the verified Zep price range is unproven.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.