Beacon vs Memori

Beacon and Memori are both inference engines & infra 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.

Beacon

Beacon is an open-source endpoint telemetry layer that runs locally alongside AI agents, capturing prompts, tool calls, file modifications, and approval workflows before any of that activity disappears into the void. It normalizes that telemetry and forwards it to SIEM platforms like Wazuh, Elastic, or Splunk, so security teams can apply the same detection logic they already run against the rest of the fleet. The architecture is self-hosted by design — no data leaves the endpoint unless you route it there yourself. The project is early-stage; the plugin ecosystem covers the major local agent harnesses but gaps exist for less common runtimes. Teams with agents not yet on the supported list write custom collector plugins — which means more surface area to maintain.

Memori

The vendor states Memori classifies each chat turn into facts, preferences, rules, and summaries, then pulls targeted snippets at recall time rather than re-injecting full history. On the LoCoMo benchmark, the docs report 81.95% accuracy while cutting token usage by 95% versus full-context retrieval — a meaningful number if your cost problem is upstream of the model choice. The memory graph shows how entities connect across sessions, and every recall result ships with lineage explaining why that snippet was included, which matters when an enterprise audit asks why the agent said what it said. The ceiling appears when your retrieval logic needs fine-grained control the SDK's zero-configuration defaults don't expose — teams at that point are writing wrapper logic to compensate. Self-hosted deployment is available, so organizations with data-residency requirements are not locked into the cloud path.

Attribute	Beacon	Memori
Pricing	Free	Paid
Price	—	$19/month
Free trial	No	No
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Windows	Cloud (Memori Cloud), Self-hosted via open-source SDK
Released	—	2024
Pros	Runs entirely on the local endpoint with no external data forwarding required, so organizations in regulated industries can capture AI agent telemetry without breaching data residency requirements. Normalizes agent activity into structured telemetry compatible with Wazuh, Elastic, and Splunk, so security teams can write detection rules against AI agent behavior using the same tooling they already maintain for the rest of the infrastructure. Captures the full activity chain — prompts, tool calls, file edits, approval workflows — which means audit trails hold up when a compliance team asks exactly what an agent touched and when, rather than reconstructing context after the fact. MIT-licensed and free with no paid tier, so there is no licensing negotiation before a regulated-industry proof of concept, and the full source is auditable by the security team before deployment. Structured for MDM-managed deployments, so enterprise IT teams can push Beacon alongside agent runtimes through existing device management pipelines rather than requiring manual per-machine setup.	Classifies memory into typed categories (facts, preferences, rules, summaries) at write time, so recall is targeted rather than probabilistic — which means your agent isn't paying token costs to re-read irrelevant history on every turn. The vendor reports 95% token reduction versus full-context retrieval on the LoCoMo benchmark, so teams with high-volume agents stop absorbing LLM spend just to maintain conversational continuity. Every recall result includes lineage tracing the entity, time, and source of inclusion, so when an enterprise audit asks why the agent surfaced a specific piece of context, there is a concrete answer rather than an opaque embedding distance. LLM-agnostic architecture means switching the underlying model — from OpenAI to a self-hosted alternative, for example — does not force a memory layer rewrite. Self-hosted deployment is available, so teams with data-residency or compliance requirements are not forced onto the cloud path.
Cons	Plugin coverage is scoped to the major local agent harnesses the project explicitly supports; agents running on runtimes outside that list produce no telemetry until a custom collector plugin is written and maintained — which delays security coverage for any team adopting a newer or less common agent framework. There is no hosted dashboard or managed backend, which means the security team owns the full stack: endpoint deployment, SIEM routing, schema mapping, and alert logic. Teams without an operational SIEM who want a turnkey monitoring UI will abandon Beacon for a hosted observability product before the first sprint ends. The project carries a small contributor base at the time of publication; teams depending on active maintenance for fast-moving agent runtimes accept the risk that plugin support lags runtime updates, requiring internal engineering to bridge the gap or switch to a vendor with a dedicated support contract.	Multi-hop recall accuracy benchmarks at 72.70% and open-domain at 63.54% — agents that chain several inferential steps across memory or handle unconstrained queries will surface wrong context at a measurable rate, and teams building those workflows are adding custom retrieval logic on top, at which point they are maintaining two systems. The zero-configuration SDK default is fast to ship but exposes precious little surface area for teams that need fine-grained control over retrieval scoring, memory expiry policies, or scoping rules beyond what the defaults provide — those teams end up writing wrapper logic that grows in complexity as production edge cases accumulate. Closed-source with no self-service inspection of the classification or recall logic means when the memory layer returns unexpected results, debugging is limited to the lineage output the tool surfaces — teams that need to audit or modify the core retrieval behavior switch to an open-source alternative they can instrument directly.

Bottom line

Beacon is free while Memori is paid; Beacon is open source; only Memori exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Beacon and Memori?

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

Is Beacon better than Memori?

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.

Beacon vs Memori: which should I pick?

Pick Beacon if its pricing model, openness, or platform fit matches your constraints; pick Memori otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.