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Beacon vs Engram

Beacon and Engram 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

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.

Engram

Engram

Engram sits between your IDE and its file reads, maintaining a local SQLite summary of your codebase so agents pull compressed context instead of raw files. The vendor states an 89% measured token reduction. It installs via npm, runs locally with zero cloud dependency, and connects to Claude Code, Cursor, Cline, Continue, Aider, Codex, Windsurf, and Zed through a combination of OpenVSX extensions, an Anthropic plugin, and adapter scripts. The bug-prevention layer surfaces past mistakes from revert history before the agent touches that code path again. This is a passive interceptor, not an agent — it does not plan tasks or run autonomously.

AttributeBeaconEngram
PricingFreeFree
Free trialNoNo
Open sourceYesYes
Has APINoYes
Self-hosted optionYesYes
PlatformsLinux, macOS, WindowsNode.js (npm); works in Claude Code, Cursor, Cline, Continue, Aider, Codex CLI, Windsurf, Zed
Released2026-04
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.
  • Local SQLite storage with no cloud dependency, which means your codebase summary never leaves your machine — relevant for teams under data-residency constraints that rule out cloud-hosted context tools.
  • The vendor states an 89% measured token reduction on repeated file reads, so usage-based billing in tools like Cursor or rate-limited Claude Code sessions consume significantly fewer tokens per session.
  • Bug-prevention indexing pulls from your repo's revert history, so an agent approaching a previously broken file sees the failure pattern before it writes — instead of repeating it.
  • A single context store shared across Claude Code, Cursor, Cline, Continue, Aider, Codex, Windsurf, and Zed, which means switching tools mid-project or running two tools in parallel does not require rebuilding context from scratch.
  • Apache 2.0 license with self-hosted operation, so teams can audit the full codebase, fork it, or adapt the adapter layer without negotiating a commercial agreement.
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.
  • When the codebase changes rapidly — active feature branches, frequent refactors, multiple contributors merging daily — the SQLite summaries drift from the actual file state. The agent works from a compressed snapshot that no longer matches reality. Teams in this situation either rebuild the index on every session (reducing the cost savings) or accept that the context is partially stale.
  • The bug-prevention layer depends on revert history existing and being parseable. Greenfield projects or repos with shallow or non-standard Git history get no benefit from that feature — it simply does not fire.
  • Engram has no UI, no observability dashboard, and no way to inspect what the agent is actually receiving as context. When an agent produces unexpected output, diagnosing whether the cause is a stale summary requires digging into the SQLite database directly. Teams that need audit trails or explainability for agent decisions will hit this ceiling and move to a tool that exposes its context pipeline.
Bottom line

Only Engram exposes a public API. Choose based on which difference matters most for your workflow.

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