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**Give your AI agents a memory. See it, search it, and maintain it — all in a beautiful WebUI on your own machine.**
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## What Engraphis gives an agent
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An agent should not have to reconstruct a project from scattered chat history on every task.
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Engraphis turns local project knowledge into scoped, time-aware memory; retrieves the evidence
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that supports the current question; and returns a bounded, attributable context packet.
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<palign="center">
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<imgsrc="docs/images/engraphis-benefit-flow.png"alt="Diagram: project history becomes scoped and temporal Engraphis memory, hybrid recall, then bounded cited context for an agent"width="100%">
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<br>
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<sup>Store durable project knowledge · retrieve supporting evidence · give the agent only what it needs</sup>
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</p>
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| Agent need | What Engraphis changes |
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|---|---|
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| Remember a project across sessions | Stores typed memory in a `workspace → repo → session` hierarchy and provides a last-session handoff. |
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| Find support for the current task | Fuses vector, lexical, graph, and code-aware retrieval instead of relying on one search signal. |
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| Know what is true now—and what changed | Preserves bi-temporal history and supersession chains instead of silently overwriting a fact. |
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| Avoid confident guesses | Returns cited evidence or explicitly abstains when support is too weak. |
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| Avoid dragging the whole project into every prompt | Packs context to a configured hard budget and can return a compact MCP response. |
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| Keep knowledge in the operator's control | Runs local-first and offline-capable, with scopes, audit records, and optional privacy-safe receipts. |
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The diagram is the essential path. The sections below cover the dashboard, code graph, local
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installation, governance controls, and hosted services in detail. See [measured quality and token
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efficiency](#measured-quality-and-token-efficiency) for the current reproducible evidence behind
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the context-efficiency claim.
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<br>
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<palign="center">
@@ -209,6 +235,12 @@ Agents spend less of their context window carrying irrelevant history, leaving m
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current task and cited evidence. These are controlled, deterministic fixtures—not model-billing,
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task-time, or external benchmark claims.
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<palign="center">
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<imgsrc="docs/images/context-efficiency.png"alt="Normalized chart: Engraphis retains 27.1 percent of retrieved content, 26.1 percent of the evidence-holding record, and 44.6 percent of the compact MCP response in separate controlled fixtures"width="100%">
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<br>
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<sup>Each row uses a separate 100% baseline. The measurements have different counting boundaries and are not additive.</sup>
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</p>
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#### A controlled before-and-after example
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| Retrieval mode | Mean returned memory content | Recall@5 |
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