Cerver vs Genesys

Cerver and Genesys are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Cerver

Cerver is session infrastructure for AI agent fleets: each session carries its full transcript, cost record, model choice, and compute target as a single object you control. You write routing policies — or let auto-routing handle it — so routine tasks go to cheaper models and complex work earns the frontier. Mid-session you can swap the underlying model or compute without losing the transcript. The local relay option means sessions that need your repo or CLI attach to your machine and run on Claude Max or ChatGPT subscriptions you already pay for, which drops marginal token cost close to zero. Spending caps ship on by default, so a runaway parallel agent fleet stops at your number.

Genesys

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.

Attribute	Cerver	Genesys
Pricing	Paid	Paid
Price	$89/mo + $10/dev, max $300/mo	$0-$8/mo
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	—	Web, Python (pip)
Pros	Routing policies direct routine tasks to cheaper models automatically, so teams that previously ran every session on a frontier model by default can cut token spend without manually triaging each request. Spending caps are on by default for every account, which means a parallelized agent fleet that goes wrong stops at a number you set — not at an invoice that arrives later. Transcript persistence across model and compute swaps means switching from a hosted model to a local machine mid-session does not restart context, so experiments and recovery from compute failures do not lose work. Local relay sessions run on Claude Max or ChatGPT subscriptions already in place, so teams with existing paid subscriptions offload token costs entirely for local compute workloads. The side-by-side agent comparison runs inside one session and surfaces a real output diff, so choosing between two models or runtimes is based on actual task results rather than benchmark averages.	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	Cerver does not provide a workflow builder or pipeline canvas — teams that need to define multi-step agent logic with branching based on prior step output have no native way to express that inside the platform. They build the branching logic externally and use Cerver only for session management, which means maintaining two systems from the start. The supported harness list — Claude Code, Codex CLI, OpenAI SDK, xAI — is fixed by the vendor. Teams running agents on frameworks outside that set, such as LangChain or custom tool chains, will find no documented integration path. At that point the platform's session tracking provides no value, and those teams move to infrastructure that supports their stack. The session-focused model means observability is scoped to what happens inside a Cerver-managed session. Teams that need tracing, evals, or logging that spans systems outside those sessions — for example, database calls, external APIs, or queue workers — get no visibility from Cerver and must instrument those layers separately.	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.

Bottom line

Genesys is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Cerver and Genesys?

Cerver is Paid, while Genesys is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Cerver better than Genesys?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.