Stash
Persistent, self-hosted memory for AI agents, backed by Postgres.
Stash is a focused, no-frills memory layer for agent developers. Its Postgres-backed design gives you full data ownership and lets you query memories with SQL—a real advantage over opaque hosted memory services. If you're building autonomous agents and want durable, self-hosted memory, Stash is a solid pick. However, it requires your own Postgres setup and MCP integration, so it's not for non-technical users. For managed memory, look at Mem0 or Zep. For privacy-sensitive or offline projects, Stash is hard to beat.
Verified 4d ago · liveness 59/100 · cite: rightaichoice.com/tools/stash
- Developers building autonomous AI agents
- Researchers needing persistent agent memory
- Teams requiring self-hosted memory solutions
- Privacy-conscious users avoiding cloud AI memory
- Users wanting a hosted/managed solution
- Teams without PostgreSQL infrastructure
- Non-developers seeking no-code memory tools
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Skip Stash if you want a hosted, plug-and-play memory service or you're not comfortable running and maintaining your own PostgreSQL instance.
You'll need to run and maintain your own PostgreSQL instance, which can incur infrastructure and operational costs.
Stash is free and open source, which makes it ideal for startups and developers who want to avoid monthly per-seat or per-token fees. Compared to managed memory services like Mem0 (which charges per use) or Zep (free tier with paid plans), Stash's self-hosted approach is cheaper at scale but shifts the operational burden to you.
In short
Stash — Persistent, self-hosted memory for AI agents, backed by Postgres. Best for Developers building autonomous AI agents, Researchers needing persistent agent memory, Teams requiring self-hosted memory solutions. Free to use.
What people actually say about Stash — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
64 mentions across 3 sources (Hacker News, App Store, Lemmy) · researched Jul 3, 2026.
- +Self-hosted with no cloud dependency for privacy.
- +Uses standard PostgreSQL, avoiding vendor lock-in.
- +Single binary deployment is simple to set up.
- +SQL queryable memory enables flexible data analysis.
- +Episodes, facts, and working context are well-structured.
- −No real community feedback to gauge reliability.
- −Requires PostgreSQL setup and maintenance.
- −Lack of integrations limits plug-and-play usage.
- −Unclear support channels if issues arise.
- −Documentation or examples may be sparse.
Viability Score
How well maintained and how widely used is Stash? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Persistent memory for AI agents
- PostgreSQL storage backend
- MCP server integration
- Self-hosted single binary deployment
- Episode logging (conversations, actions)
- Fact extraction and storage
- Working context management
- SQL queryable memory
- Open source
- No vendor lock-in
- Offline capable
- Durable state across sessions
About Stash
Stash gives AI agents a persistent memory layer. Instead of juggling stateless API calls, your agents can log episodes (conversations, action sequences), extract facts, and maintain working context across sessions. Everything is stored in PostgreSQL, so you get durable, SQL-queryable memory with no cloud lock-in. Stash ships as a self-hosted, single binary with an MCP server, making it easy to integrate with agent frameworks. It's designed for developers building long-running agents—research assistants, automation systems, or support bots—that need to remember context without relying on a hosted service. If you value data control and privacy and you're comfortable with Postgres, Stash gives you a straightforward way to add memory to your agents.
Behind the Verdict
Stash is a niche but genuinely useful tool for a specific audience: developers who are building AI agents and need persistent state across sessions. The core value is that it doesn't try to be everything—it does one thing (memory) and does it with a standard, proven technology (Postgres). That's a breath of fresh air in a space crowded with over-engineered AI products. Strengths: First, the SQL-queryable memory is a killer feature. You can run analytical queries over your agent's history, which is powerful for debugging and insight. Second, the self-hosted single binary means no cloud dependency—you control the data, which is a big deal for privacy-sensitive or offline scenarios. Third, the MCP server integration makes it compatible with the growing MCP ecosystem (Claude, etc.), so it slots into existing agent frameworks without a custom API. Weaknesses: Stash does not include its own AI models—it only provides memory. So you still need an agent framework and a model provider. The lack of a managed cloud option means you're on your own for deployment, scaling, and high availability. If you're not comfortable with Postgres and MCP, the learning curve is real. Also, the project seems early-stage; documentation and community are limited. Where it fits: If you're a developer working on a long-running agent (e.g., a research assistant that runs for days) or a multi-step automation that needs to remember state between invocations, Stash is a perfect pairing. It's also ideal for teams that deploy agents in regulated or offline environments where data residency is mandatory. Where it doesn't: If you want a plug-and-play memory solution with a hosted API and a UI, or if you're a non-developer, Stash will frustrate you. Also, if you need real-time synchronization or clustering, this isn't ready for that out of the box.
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Real-world workflow fit
Concrete scenarios for the personas Stash actually fits — and what changes day-one when you adopt it.
You need the agent to remember previous findings and user preferences across sessions.
Outcome: Stash logs episodes and facts in Postgres, so the assistant can recall past context and 'learn' over time, improving accuracy.
You must keep conversation history on-premises to comply with data residency rules.
Outcome: Self-host Stash with your own Postgres, and the bot's memory stays inside your infrastructure—no third-party cloud memory is involved.
An agent misbehaves during a complex workflow, and you need to trace its actions and decisions.
Outcome: Query the SQL-queryable episodic log to see every action sequence and identify where the logic went wrong.
Use Cases
- Store and retrieve conversation history for a customer support agent
- Maintain working context for a multi-step automation assistant
- Log action sequences for debugging agent behavior
- Extract facts from agent interactions and query them in SQL
- Provide persistent state for a research assistant that runs over days
- Self-host a memory backend for privacy-sensitive agent applications
Limitations
- Stash focuses on memory persistence and does not include its own AI models; it relies on external AI agents via MCP.
- It is currently open source with no managed cloud option, limiting ease of use for non-technical users.
- As a binary, it may not support high-availability or clustering out of the box.
as of 2026-08-21
Verification history
We have re-verified Stash 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Stash tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Developers and small teams who want to self-host persistent agent memory without ongoing costs, especially for personal projects or early-stage experiments.
What this tier adds
This is the free, open-source entry point—all core features are included, but you bring your own Postgres and handle deployment and maintenance yourself.
Where the pricing makes sense
The company stage and team size where Stash's pricing actually pencils out — and where peers do it cheaper.
Stash is free and open source, which makes it ideal for startups and developers who want to avoid monthly per-seat or per-token fees. Compared to managed memory services like Mem0 (which charges per use) or Zep (free tier with paid plans), Stash's self-hosted approach is cheaper at scale but shifts the operational burden to you.
Setup time & first value
How long it actually takes to get something useful out of Stash — broken out by persona, not the marketing-page minute.
Within an hour: set up a Postgres instance, run the Stash binary, and connect via MCP to your agent. For non-developers, expect more time and a steeper learning curve.
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Stash
Common stack mates teams adopt alongside Stash, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Stash vs Spider Cloud
Choose Spider Cloud if you need to feed web data into AI agents—its Rust engine, 99.9% success, and new Browser AI commands make it cost-effective for RAG pipelines. Choose Stash if you need a self-hosted memory layer for agents that persist conversations, facts, and state in Postgres—best for privacy-first or offline autonomous systems. They solve different problems: one fetches data, the other remembers it.
Stash vs Presto Voice
Don't buy on the fence: Presto Voice is a specialized drive-thru AI for QSR chains wanting proven revenue lift (up to 6% monthly) and 95% automation — but it's contact-priced and not for non-drive-thru businesses. Stash is a free, self-hosted memory for AI agent developers, not a restaurant tool. Your choice depends entirely on your domain: either you run a multi-location QSR or you code autonomous agents.
Stash vs Temporal Ai
Temporal is the right choice if you need an industrial-grade orchestration platform with retries, rollbacks, and human-in-the-loop for complex workflows (AI agents, microservices). Stash is perfect for developers who want a lightweight, self-hosted memory layer for AI agents, using Postgres with zero vendor lock-in. Pick Temporal for reliability at scale; pick Stash for simple, privacy-first agent memory.
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