Cadenya
Freemium
Developers building agentic workflows
Genesys
FreemiumOpen SourceAPISelf-Hosted
You've told Claude you're training for a marathon. You've told ChatGPT the same thing. Tomorrow you'll tell the next tool, too — because every AI treats the last conversation like it never happened. Genesys is the memory layer that sits underneath all of them.
$8/month · Free tier: 300 memory stores a month, unlimited recall, full causal engine · Checked Oct 24, 2026
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Get This Tool
License: AGPL-3.0 Commercial ok; derivatives must share license
Local-run terms: pip install genesys-memory; run over MCP stdio with own backends under AGPL-3.0-or-later
Summary
Genesys stores what you share in a causal graph you own, then surfaces that context to any app that speaks MCP — so Claude already knows what you told ChatGPT, without you repeating yourself. The graph explains its own reasoning: ask why it remembers something and you get the actual chain of connections, not a confidence score with nothing behind it. Memories fade by a scoring formula tied to relevance and reactivation, so stale data drops out without silently deleting things that still matter. The free tier caps writes at 300 stores per month — heavy users or teams running MCP agents hit that ceiling, then face a choice.
Bottom line: Genesys earns its place as the memory backbone for a solo developer or power user juggling ChatGPT and Claude daily — but teams building agents with high write volumes will hit the free cap fast and need to self-host or pay before they've shipped anything.
Pricing Plans
SubscriptionLast verified 2 weeks ago- Price
- $8/month
- Free Tier
- 300 memory stores a month, unlimited recall, full causal engine
Free
Free
Your AI's memory, everywhere. Store up to 300 memories a month. Recall is always unlimited.
- 300 memory stores a month
- Unlimited recall
- Full causal engine
Founding Member
$8per month
The lowest price we'll ever offer, locked for life. First 500 members. Billed monthly, cancel anytime.
- 1,250 memory stores a month
- Unlimited recall
- Full causal engine
- Priority enrichment
- Early access to new connectors
- Price locked for life
Pro
$8per month
$96/yr
More room to remember. 1,250 stores a month, priority enrichment, and early access to new connectors.
- 1,250 memory stores a month
- Unlimited recall
- Full causal engine
- Priority enrichment
- Early access to new connectors
View full pricing on genesys.astrixlabs.ai →
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
Before you choose Genesys
More options in Agent Frameworks. Compare pricing, platforms, and features side by side.
Brain Memory
Free · Open source
Long-term continuity in multi-session coding projects
Pros
- Cross-app memory over MCP, which means context you shared in ChatGPT appears in Claude without any manual sync — eliminating the re-introduction loop that breaks multi-tool workflows.
- Causal graph with inspect-and-correct capability, so when the memory layer gets something wrong you can trace why and fix it at the source rather than working around a black box.
- Evidence-based memory decay via a published scoring formula, which means stale context fades out without silently deleting nodes that are still connected and active — a common failure mode in simpler vector-store approaches.
- Open-source AGPL-3.0 engine with pip install and self-host support, so teams with data residency requirements or high write volumes can run their own backend instead of depending on the hosted service.
- Permanent, on-demand deletion with no retention games — the vendor states reading is never gated, so your memory graph does not go dark if you stop paying.
Cons
- The free tier caps memory writes at 300 stores per month. An MCP agent that logs context on every turn hits this ceiling within a single moderately active project, forcing a choice between the paid hosted tier or standing up the self-hosted engine — which adds infrastructure overhead before you've validated anything.
- The graph is architected around a single personal memory, not a shared team workspace. Developers building multi-user products where agents need to carry context per-user at scale have no documented path to multi-tenant graph management — teams with that requirement will look at purpose-built agent memory backends like Mem0 or a custom vector store instead.
- MCP is the only integration protocol documented. Applications that do not speak MCP and cannot add a custom connector get no benefit from the graph — teams whose stack is locked to a non-MCP LLM API get nothing without building their own bridge.
About
- Platforms
- Web, Python (pip)
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- July 22, 2026
Best For
Who it's for
- Users of multiple LLMs tired of re-explaining context
- Developers building MCP-native agents needing explainable memory
- Anyone wanting portable, deletable personal AI memory
What it does well
- Maintain consistent context across multiple AI chat apps
- Inspect and correct what AIs remember about the user
- Self-host agent memory with causal relationships and MCP tools
Integrations
Frequently Asked Questions
- Is Genesys free?
- Genesys offers a free option alongside paid upgrades. Pricing: $8/month. Check the current allowances and terms before choosing a plan.
- Is Genesys open source?
- Yes. Genesys is open source.
- Does Genesys have an API?
- Yes. Genesys exposes a developer API. See the official documentation at https://genesys.astrixlabs.ai for details.
- Can I self-host Genesys?
- Yes. Genesys supports self-hosting on your own infrastructure.
- What platforms does Genesys support?
- Genesys is available on: Web, Python (pip).
Curated lists that include this category
Every AI chat application starts from zero. The context you built up over weeks in one tool sits trapped there while every other tool asks you to introduce yourself again. Genesys stores your personal context in a single graph and exposes it over MCP, so ChatGPT, Claude, and any MCP-native agent read from the same memory without manual re-entry. The core workflow: you share something once, Genesys stores it with a causal chain attached, and every connected app can recall and explain it on the next request.
The differentiating feature is explainability at the graph level. Most memory layers return a retrieved chunk and give you no visibility into why. Genesys lets you inspect every memory node, trace the causal relationships behind it, and issue corrections that propagate downstream — the vendor describes this as: one amendment updates everything connected to it. The scoring formula (relevance × connectivity × reactivation) governs what fades and what persists, and the vendor publishes both benchmark results and a failure ledger, claiming 85.55 on LoCoMo ahead of published alternatives under a documented protocol.
Genesys fits best as the personal memory layer for an individual moving across multiple LLM interfaces, or for a developer building an MCP-native agent who needs memory that is inspectable and deletable by design. It breaks down when write volume is high: the free tier allows 300 memory stores per month, and agents that log context aggressively will exhaust that before reaching any meaningful workload. Teams needing shared or multi-user memory graphs will also find the architecture oriented around a single personal graph — not a team workspace. At that point, self-hosting the open-source AGPL-3.0 engine via pip is the documented path forward, which means taking on infrastructure ownership.
Integration is MCP-native: Claude connects via a custom connector URL, and ChatGPT connects through a separate flow — both documented in the developer docs. The open-source engine is installable via pip as genesys-memory, supports MCP stdio, and allows running custom backends. Reading from the graph is never gated, including on the free tier — only writes are capped.
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