diff --git a/server/db/pg/schema/schema.ts b/server/db/pg/schema/schema.ts
index 7895b0baf..cbb1e60cb 100644
--- a/server/db/pg/schema/schema.ts
+++ b/server/db/pg/schema/schema.ts
@@ -69,7 +69,7 @@ export const orgs = pgTable("orgs", {
         "settingsLogRetentionDaysAISessions"
     ) // where 0 = dont keep logs and -1 = keep forever and 9001 = end of the following year
         .notNull()
-        .default(0),
+        .default(7),
     sshCaPrivateKey: text("sshCaPrivateKey"), // Encrypted SSH CA private key (PEM format)
     sshCaPublicKey: text("sshCaPublicKey"), // SSH CA public key (OpenSSH format)
     isBillingOrg: boolean("isBillingOrg"),
@@ -1936,8 +1936,15 @@ export const aiSessionLog = pgTable(
         isStream: boolean("isStream").notNull().default(false),
         requestBody: text("requestBody"),
         responseBody: text("responseBody"),
-        // True if requestBody/responseBody were cut short at
-        // AI_SESSION_LOG_MAX_BODY_CHARS before storage.
+        // Capability-agnostic message transcript (JSON-encoded
+        // NormalizedAiMessage[] from server/lib/aiMessageNormalization.ts),
+        // computed at write time so search/display never need per-capability
+        // parsing logic. Null when normalization couldn't recognize the
+        // shape - callers fall back to requestBody/responseBody.
+        normalizedRequest: text("normalizedRequest"),
+        normalizedResponse: text("normalizedResponse"),
+        // True if any of the request/response (raw or normalized) fields
+        // were cut short at AI_SESSION_LOG_MAX_BODY_CHARS before storage.
         truncated: boolean("truncated").notNull().default(false),
         statusCode: integer("statusCode"),
         createdAt: bigint("createdAt", { mode: "number" }).notNull() // epoch ms
diff --git a/server/db/sqlite/schema/schema.ts b/server/db/sqlite/schema/schema.ts
index 3fb15c0d6..8934638d7 100644
--- a/server/db/sqlite/schema/schema.ts
+++ b/server/db/sqlite/schema/schema.ts
@@ -68,7 +68,7 @@ export const orgs = sqliteTable("orgs", {
         "settingsLogRetentionDaysAISessions"
     ) // where 0 = dont keep logs and -1 = keep forever and 9001 = end of the following year
         .notNull()
-        .default(0),
+        .default(7),
     sshCaPrivateKey: text("sshCaPrivateKey"), // Encrypted SSH CA private key (PEM format)
     sshCaPublicKey: text("sshCaPublicKey"), // SSH CA public key (OpenSSH format)
     isBillingOrg: integer("isBillingOrg", { mode: "boolean" }),
@@ -1928,8 +1928,15 @@ export const aiSessionLog = sqliteTable(
             .default(false),
         requestBody: text("requestBody"),
         responseBody: text("responseBody"),
-        // True if requestBody/responseBody were cut short at
-        // AI_SESSION_LOG_MAX_BODY_CHARS before storage.
+        // Capability-agnostic message transcript (JSON-encoded
+        // NormalizedAiMessage[] from server/lib/aiMessageNormalization.ts),
+        // computed at write time so search/display never need per-capability
+        // parsing logic. Null when normalization couldn't recognize the
+        // shape - callers fall back to requestBody/responseBody.
+        normalizedRequest: text("normalizedRequest"),
+        normalizedResponse: text("normalizedResponse"),
+        // True if any of the request/response (raw or normalized) fields
+        // were cut short at AI_SESSION_LOG_MAX_BODY_CHARS before storage.
         truncated: integer("truncated", { mode: "boolean" })
             .notNull()
             .default(false),
diff --git a/server/lib/aiMessageNormalization.ts b/server/lib/aiMessageNormalization.ts
new file mode 100644
index 000000000..1153142e1
--- /dev/null
+++ b/server/lib/aiMessageNormalization.ts
@@ -0,0 +1,538 @@
+import type { AiCapability } from "@server/lib/aiCapabilities";
+import { sseDataFrames, tryParseJson } from "@server/lib/aiUsageExtraction";
+import logger from "@server/logger";
+
+// Uniform, capability-agnostic representation of a chat message, used so
+// the AI session log can be searched/displayed the same way regardless of
+// which provider/capability produced it. Content is flattened to plain text
+// - non-text parts (images, tool calls/results) are rendered as readable
+// placeholders rather than preserved as structured data, which is enough for
+// a transcript-style replay view without a per-capability renderer.
+export type NormalizedRole = "system" | "user" | "assistant" | "tool";
+
+export type NormalizedAiMessage = {
+    role: NormalizedRole;
+    content: string;
+};
+
+function normalizeRole(role: unknown): NormalizedRole {
+    if (
+        role === "system" ||
+        role === "user" ||
+        role === "assistant" ||
+        role === "tool"
+    ) {
+        return role;
+    }
+    if (role === "model") return "assistant"; // Gemini
+    if (role === "function") return "tool"; // OpenAI legacy function role
+    return "user";
+}
+
+function safeJsonStringify(value: unknown): string {
+    try {
+        return JSON.stringify(value ?? {});
+    } catch {
+        return "";
+    }
+}
+
+/**
+ * Flattens one message "part"/"block" (OpenAI content parts, Anthropic
+ * content blocks, Gemini parts, Bedrock converse content blocks - they all
+ * follow the same rough shape) into readable text.
+ */
+function flattenContentPart(part: unknown): string {
+    if (typeof part === "string") return part;
+    if (part == null || typeof part !== "object") return "";
+    const p = part as Record<string, unknown>;
+
+    if (typeof p.text === "string") return p.text;
+
+    if (
+        p.type === "image_url" ||
+        p.type === "image" ||
+        p.type === "input_image" ||
+        p.type === "output_image" ||
+        "inlineData" in p
+    ) {
+        return "[image]";
+    }
+
+    // Anthropic-style tool_use / tool_result blocks
+    if (p.type === "tool_use") {
+        const name = typeof p.name === "string" ? p.name : "tool";
+        return `[tool_call: ${name}(${safeJsonStringify(p.input)})]`;
+    }
+    if (p.type === "tool_result") {
+        const content = p.content;
+        const text =
+            typeof content === "string"
+                ? content
+                : Array.isArray(content)
+                  ? flattenContentParts(content)
+                  : "";
+        return `[tool_result: ${text}]`;
+    }
+
+    // Gemini-style functionCall / functionResponse parts
+    if (p.functionCall && typeof p.functionCall === "object") {
+        const fc = p.functionCall as Record<string, unknown>;
+        return `[tool_call: ${fc.name}(${safeJsonStringify(fc.args)})]`;
+    }
+    if (p.functionResponse && typeof p.functionResponse === "object") {
+        const fr = p.functionResponse as Record<string, unknown>;
+        return `[tool_result: ${fr.name}(${safeJsonStringify(fr.response)})]`;
+    }
+
+    // Bedrock converse-style toolUse / toolResult content blocks
+    if (p.toolUse && typeof p.toolUse === "object") {
+        const tu = p.toolUse as Record<string, unknown>;
+        return `[tool_call: ${tu.name}(${safeJsonStringify(tu.input)})]`;
+    }
+    if (p.toolResult && typeof p.toolResult === "object") {
+        const tr = p.toolResult as Record<string, unknown>;
+        const content = tr.content;
+        const text = Array.isArray(content) ? flattenContentParts(content) : "";
+        return `[tool_result: ${text}]`;
+    }
+
+    return "";
+}
+
+function flattenContentParts(parts: unknown[]): string {
+    return parts.map(flattenContentPart).join("");
+}
+
+function flattenContent(content: unknown): string {
+    if (typeof content === "string") return content;
+    if (Array.isArray(content)) return flattenContentParts(content);
+    return "";
+}
+
+/**
+ * Best-effort scan for every `"text":"..."` JSON string value in raw text,
+ * concatenated in order. Fallback for streaming formats we can't fully parse
+ * as JSON/SSE (Gemini's array-JSON stream, Bedrock's binary event-stream
+ * framing) - same spirit as aiUsageExtraction's scanNumericFields.
+ */
+function scanTextFragments(text: string): string {
+    const out: string[] = [];
+    const re = /"text"\s*:\s*"((?:[^"\\]|\\.)*)"/g;
+    let match: RegExpExecArray | null;
+    while ((match = re.exec(text)) !== null) {
+        try {
+            out.push(JSON.parse(`"${match[1]}"`));
+        } catch {
+            out.push(match[1]);
+        }
+    }
+    return out.join("");
+}
+
+// ---------------------------------------------------------------------------
+// Request (input) normalizers - operate on the already-parsed outbound body.
+// ---------------------------------------------------------------------------
+
+function normalizeOpenAiChatRequest(body: any): NormalizedAiMessage[] {
+    const messages = Array.isArray(body?.messages) ? body.messages : [];
+    return messages.map((m: any) => ({
+        role: normalizeRole(m?.role),
+        content: flattenContent(m?.content)
+    }));
+}
+
+function normalizeOpenAiResponsesRequest(body: any): NormalizedAiMessage[] {
+    const out: NormalizedAiMessage[] = [];
+    if (typeof body?.instructions === "string" && body.instructions) {
+        out.push({ role: "system", content: body.instructions });
+    }
+    const input = body?.input;
+    if (typeof input === "string") {
+        out.push({ role: "user", content: input });
+    } else if (Array.isArray(input)) {
+        for (const item of input) {
+            if (item?.role) {
+                out.push({
+                    role: normalizeRole(item.role),
+                    content: flattenContent(item.content)
+                });
+            } else if (typeof item?.type === "string") {
+                out.push({ role: "tool", content: `[${item.type}]` });
+            }
+        }
+    }
+    return out;
+}
+
+function normalizeAnthropicRequest(body: any): NormalizedAiMessage[] {
+    const out: NormalizedAiMessage[] = [];
+    if (body?.system) {
+        const sys = flattenContent(body.system);
+        if (sys) out.push({ role: "system", content: sys });
+    }
+    const messages = Array.isArray(body?.messages) ? body.messages : [];
+    for (const m of messages) {
+        out.push({
+            role: normalizeRole(m?.role),
+            content: flattenContent(m?.content)
+        });
+    }
+    return out;
+}
+
+function normalizeGeminiRequest(body: any): NormalizedAiMessage[] {
+    const out: NormalizedAiMessage[] = [];
+    const sysParts = body?.systemInstruction?.parts;
+    if (Array.isArray(sysParts)) {
+        const text = flattenContentParts(sysParts);
+        if (text) out.push({ role: "system", content: text });
+    }
+    const contents = Array.isArray(body?.contents) ? body.contents : [];
+    for (const c of contents) {
+        out.push({
+            role: normalizeRole(c?.role),
+            content: Array.isArray(c?.parts) ? flattenContentParts(c.parts) : ""
+        });
+    }
+    return out;
+}
+
+function normalizeBedrockConverseRequest(body: any): NormalizedAiMessage[] {
+    const out: NormalizedAiMessage[] = [];
+    if (Array.isArray(body?.system)) {
+        const text = flattenContentParts(body.system);
+        if (text) out.push({ role: "system", content: text });
+    }
+    const messages = Array.isArray(body?.messages) ? body.messages : [];
+    for (const m of messages) {
+        out.push({
+            role: normalizeRole(m?.role),
+            content: Array.isArray(m?.content)
+                ? flattenContentParts(m.content)
+                : ""
+        });
+    }
+    return out;
+}
+
+/**
+ * bedrock_model_invoke and google_raw_predict are passthroughs - the body
+ * shape depends entirely on the underlying model, not the capability. Try
+ * the two shapes we're most likely to see (Anthropic Claude, then plain
+ * OpenAI-style) and give up otherwise, same fallback spirit
+ * aiUsageExtraction.ts uses for these two capabilities' usage extraction.
+ */
+function normalizeBestEffortRequest(body: any): NormalizedAiMessage[] | null {
+    if (!Array.isArray(body?.messages)) return null;
+    const looksAnthropicShaped = body.messages.some((m: any) =>
+        Array.isArray(m?.content)
+    );
+    return looksAnthropicShaped
+        ? normalizeAnthropicRequest(body)
+        : normalizeOpenAiChatRequest(body);
+}
+
+// ---------------------------------------------------------------------------
+// Response (output) normalizers - operate on the raw response text, which
+// may be a single JSON document (non-streaming) or provider-framed streaming
+// text (SSE `data:` frames, a JSON-array stream, or binary event-stream
+// framing with JSON payloads embedded in it).
+// ---------------------------------------------------------------------------
+
+function normalizeOpenAiChatResponse(
+    text: string,
+    isStream: boolean
+): NormalizedAiMessage[] | null {
+    if (isStream) {
+        let role: unknown = "assistant";
+        let content = "";
+        let found = false;
+        for (const frame of sseDataFrames(text)) {
+            const delta = tryParseJson(frame)?.choices?.[0]?.delta;
+            if (!delta) continue;
+            found = true;
+            if (typeof delta.role === "string") role = delta.role;
+            if (typeof delta.content === "string") content += delta.content;
+        }
+        return found ? [{ role: normalizeRole(role), content }] : null;
+    }
+
+    const message = tryParseJson(text)?.choices?.[0]?.message;
+    if (!message) return null;
+    return [
+        {
+            role: normalizeRole(message.role),
+            content: flattenContent(message.content)
+        }
+    ];
+}
+
+function extractOpenAiResponsesOutputText(response: any): string | null {
+    if (typeof response?.output_text === "string") return response.output_text;
+    const output = Array.isArray(response?.output) ? response.output : [];
+    const pieces: string[] = [];
+    for (const item of output) {
+        if (item?.type === "message" && Array.isArray(item.content)) {
+            pieces.push(flattenContentParts(item.content));
+        }
+    }
+    return pieces.length > 0 ? pieces.join("") : null;
+}
+
+function normalizeOpenAiResponsesResponse(
+    text: string,
+    isStream: boolean
+): NormalizedAiMessage[] | null {
+    if (isStream) {
+        let content = "";
+        let found = false;
+        for (const frame of sseDataFrames(text)) {
+            const parsed = tryParseJson(frame);
+            if (!parsed) continue;
+            if (
+                parsed.type === "response.output_text.delta" &&
+                typeof parsed.delta === "string"
+            ) {
+                content += parsed.delta;
+                found = true;
+            } else if (
+                parsed.type === "response.completed" &&
+                parsed.response
+            ) {
+                const outputText = extractOpenAiResponsesOutputText(
+                    parsed.response
+                );
+                if (outputText != null) {
+                    content = outputText;
+                    found = true;
+                }
+            }
+        }
+        return found ? [{ role: "assistant", content }] : null;
+    }
+
+    const parsed = tryParseJson(text);
+    const outputText = extractOpenAiResponsesOutputText(
+        parsed?.response ?? parsed
+    );
+    return outputText != null
+        ? [{ role: "assistant", content: outputText }]
+        : null;
+}
+
+function normalizeAnthropicResponse(
+    text: string,
+    isStream: boolean
+): NormalizedAiMessage[] | null {
+    if (isStream) {
+        let role: unknown = "assistant";
+        let content = "";
+        let found = false;
+        for (const frame of sseDataFrames(text)) {
+            const parsed = tryParseJson(frame);
+            if (!parsed) continue;
+            if (parsed.type === "message_start" && parsed.message?.role) {
+                role = parsed.message.role;
+            }
+            if (
+                parsed.type === "content_block_start" &&
+                parsed.content_block?.type === "tool_use"
+            ) {
+                const name = parsed.content_block.name ?? "tool";
+                content += `[tool_call: ${name}]`;
+                found = true;
+            }
+            if (
+                parsed.type === "content_block_delta" &&
+                typeof parsed.delta?.text === "string"
+            ) {
+                content += parsed.delta.text;
+                found = true;
+            }
+        }
+        return found ? [{ role: normalizeRole(role), content }] : null;
+    }
+
+    const parsed = tryParseJson(text);
+    if (!parsed || !Array.isArray(parsed.content)) return null;
+    return [
+        {
+            role: normalizeRole(parsed.role ?? "assistant"),
+            content: flattenContentParts(parsed.content)
+        }
+    ];
+}
+
+function geminiCandidateParts(node: any): string {
+    const parts = node?.candidates?.[0]?.content?.parts;
+    return Array.isArray(parts) ? flattenContentParts(parts) : "";
+}
+
+function normalizeGeminiResponse(
+    text: string,
+    _isStream: boolean
+): NormalizedAiMessage[] | null {
+    const frames = sseDataFrames(text);
+    let content = "";
+    let role: unknown = "model";
+    let found = false;
+
+    if (frames.length > 0) {
+        for (const frame of frames) {
+            const parsed = tryParseJson(frame);
+            const piece = geminiCandidateParts(parsed);
+            if (piece) {
+                content += piece;
+                found = true;
+            }
+            const r = parsed?.candidates?.[0]?.content?.role;
+            if (r) role = r;
+        }
+    } else {
+        const parsed = tryParseJson(text);
+        if (Array.isArray(parsed)) {
+            for (const chunk of parsed) {
+                const piece = geminiCandidateParts(chunk);
+                if (piece) {
+                    content += piece;
+                    found = true;
+                }
+                const r = chunk?.candidates?.[0]?.content?.role;
+                if (r) role = r;
+            }
+        } else if (parsed) {
+            const piece = geminiCandidateParts(parsed);
+            if (piece) {
+                content = piece;
+                found = true;
+            }
+            const r = parsed?.candidates?.[0]?.content?.role;
+            if (r) role = r;
+        }
+    }
+
+    if (!found) {
+        const scanned = scanTextFragments(text);
+        return scanned
+            ? [{ role: normalizeRole(role), content: scanned }]
+            : null;
+    }
+    return [{ role: normalizeRole(role), content }];
+}
+
+function normalizeBedrockConverseResponse(
+    text: string,
+    isStream: boolean
+): NormalizedAiMessage[] | null {
+    if (!isStream) {
+        const message = tryParseJson(text)?.output?.message;
+        if (!message) return null;
+        return [
+            {
+                role: normalizeRole(message.role ?? "assistant"),
+                content: Array.isArray(message.content)
+                    ? flattenContentParts(message.content)
+                    : ""
+            }
+        ];
+    }
+    // converse-stream uses AWS's binary event-stream framing, but the JSON
+    // payload of each event survives intact inside it (same assumption
+    // aiUsageExtraction.ts makes for usage) - scan for the text pieces.
+    const scanned = scanTextFragments(text);
+    return scanned ? [{ role: "assistant", content: scanned }] : null;
+}
+
+function normalizeBedrockModelInvokeResponse(
+    text: string,
+    isStream: boolean
+): NormalizedAiMessage[] | null {
+    const anthropicStyle = normalizeAnthropicResponse(text, isStream);
+    if (anthropicStyle) return anthropicStyle;
+    const scanned = scanTextFragments(text);
+    return scanned ? [{ role: "assistant", content: scanned }] : null;
+}
+
+function normalizeGoogleRawPredictResponse(
+    text: string,
+    isStream: boolean
+): NormalizedAiMessage[] | null {
+    const anthropicStyle = normalizeAnthropicResponse(text, isStream);
+    if (anthropicStyle) return anthropicStyle;
+    const scanned = scanTextFragments(text);
+    return scanned ? [{ role: "assistant", content: scanned }] : null;
+}
+
+const REQUEST_NORMALIZERS: Record<
+    AiCapability,
+    (body: any) => NormalizedAiMessage[] | null
+> = {
+    openai_chat: normalizeOpenAiChatRequest,
+    openai_responses: normalizeOpenAiResponsesRequest,
+    anthropic_messages: normalizeAnthropicRequest,
+    gemini_generate_content: normalizeGeminiRequest,
+    google_generate_content: normalizeGeminiRequest,
+    google_raw_predict: normalizeBestEffortRequest,
+    bedrock_model_invoke: normalizeBestEffortRequest,
+    bedrock_converse: normalizeBedrockConverseRequest
+};
+
+const RESPONSE_NORMALIZERS: Record<
+    AiCapability,
+    (text: string, isStream: boolean) => NormalizedAiMessage[] | null
+> = {
+    openai_chat: normalizeOpenAiChatResponse,
+    openai_responses: normalizeOpenAiResponsesResponse,
+    anthropic_messages: normalizeAnthropicResponse,
+    gemini_generate_content: normalizeGeminiResponse,
+    google_generate_content: normalizeGeminiResponse,
+    google_raw_predict: normalizeGoogleRawPredictResponse,
+    bedrock_model_invoke: normalizeBedrockModelInvokeResponse,
+    bedrock_converse: normalizeBedrockConverseResponse
+};
+
+/**
+ * Normalizes an outbound AI gateway request body into a uniform message
+ * transcript, regardless of capability/provider. Returns null if the body
+ * doesn't contain any recognizable messages (or parsing failed) - callers
+ * should fall back to showing the raw request body.
+ */
+export function normalizeAiRequest(
+    capability: AiCapability,
+    body: unknown
+): NormalizedAiMessage[] | null {
+    try {
+        const result = REQUEST_NORMALIZERS[capability](body);
+        return result && result.length > 0 ? result : null;
+    } catch (error) {
+        logger.debug("Failed to normalize AI request messages", {
+            capability,
+            error
+        });
+        return null;
+    }
+}
+
+/**
+ * Normalizes a completed (non-streaming or fully-accumulated streaming) AI
+ * gateway response into a uniform message transcript. Returns null if
+ * nothing recognizable could be extracted - callers should fall back to
+ * showing the raw response body.
+ */
+export function normalizeAiResponse(
+    capability: AiCapability,
+    responseText: string,
+    isStream: boolean
+): NormalizedAiMessage[] | null {
+    try {
+        const result = RESPONSE_NORMALIZERS[capability](responseText, isStream);
+        return result && result.length > 0 ? result : null;
+    } catch (error) {
+        logger.debug("Failed to normalize AI response messages", {
+            capability,
+            error
+        });
+        return null;
+    }
+}
diff --git a/server/lib/aiUsageExtraction.ts b/server/lib/aiUsageExtraction.ts
index f3fc92580..48f19f308 100644
--- a/server/lib/aiUsageExtraction.ts
+++ b/server/lib/aiUsageExtraction.ts
@@ -49,7 +49,10 @@ function scanNumericFields(
     return out;
 }
 
-function sseDataFrames(text: string): string[] {
+// Exported for reuse by server/lib/aiMessageNormalization.ts, which needs
+// the same SSE-frame/JSON-parsing groundwork to extract message content
+// instead of usage numbers.
+export function sseDataFrames(text: string): string[] {
     const frames: string[] = [];
     for (const rawFrame of text.split(/\r?\n\r?\n/)) {
         for (const line of rawFrame.split(/\r?\n/)) {
@@ -63,7 +66,7 @@ function sseDataFrames(text: string): string[] {
     return frames;
 }
 
-function tryParseJson(text: string): any | null {
+export function tryParseJson(text: string): any | null {
     try {
         return JSON.parse(text);
     } catch {
@@ -115,7 +118,10 @@ function extractOpenAiResponses(
     if (isStream) {
         for (const frame of sseDataFrames(text)) {
             const parsed = tryParseJson(frame);
-            if (parsed?.type === "response.completed" && parsed?.response?.usage) {
+            if (
+                parsed?.type === "response.completed" &&
+                parsed?.response?.usage
+            ) {
                 usage = parsed.response.usage;
             } else if (parsed?.usage) {
                 usage = parsed.usage;
@@ -244,7 +250,10 @@ function extractBedrockConverse(
     if (usage) {
         const cacheReadTokens = usage.cacheReadInputTokens ?? 0;
         return {
-            promptTokens: Math.max(0, (usage.inputTokens ?? 0) - cacheReadTokens),
+            promptTokens: Math.max(
+                0,
+                (usage.inputTokens ?? 0) - cacheReadTokens
+            ),
             cacheReadTokens,
             cacheWriteTokens: usage.cacheWriteInputTokens ?? 0,
             completionTokens: usage.outputTokens ?? 0,
@@ -437,7 +446,12 @@ export function stripInjectedUsageFrame(sseText: string): string {
         if (dataLine) {
             const data = dataLine.slice("data:".length).trim();
             const parsed = data !== "[DONE]" ? tryParseJson(data) : null;
-            if (parsed && Array.isArray(parsed.choices) && parsed.choices.length === 0 && parsed.usage) {
+            if (
+                parsed &&
+                Array.isArray(parsed.choices) &&
+                parsed.choices.length === 0 &&
+                parsed.usage
+            ) {
                 continue;
             }
         }
diff --git a/server/routers/aiGateway/logAiSession.ts b/server/routers/aiGateway/logAiSession.ts
index 4c7384497..92723bed4 100644
--- a/server/routers/aiGateway/logAiSession.ts
+++ b/server/routers/aiGateway/logAiSession.ts
@@ -7,6 +7,10 @@ import cache from "#dynamic/lib/cache";
 import { calculateCutoffTimestamp } from "@server/lib/cleanupLogs";
 import { sanitizeString } from "@server/lib/sanitize";
 import type { AiCapability } from "@server/lib/aiCapabilities";
+import {
+    normalizeAiRequest,
+    normalizeAiResponse
+} from "@server/lib/aiMessageNormalization";
 
 // Caps how much of the request/response body we keep per row, so a single
 // huge multimodal payload can't blow up buffer memory or storage.
@@ -197,6 +201,26 @@ export function logAiSession(data: {
             );
             const responseBodyText = truncateBody(data.responseText ?? "");
 
+            // Uniform, capability-agnostic transcript for search/display -
+            // computed from the untruncated originals so normalization sees
+            // the full content; the normalized result gets its own
+            // (typically much smaller) truncation pass below.
+            const normalizedRequestMessages = normalizeAiRequest(
+                data.capability,
+                data.requestBody
+            );
+            const normalizedResponseMessages = normalizeAiResponse(
+                data.capability,
+                data.responseText ?? "",
+                data.isStream
+            );
+            const normalizedRequestText = normalizedRequestMessages
+                ? truncateBody(JSON.stringify(normalizedRequestMessages))
+                : null;
+            const normalizedResponseText = normalizedResponseMessages
+                ? truncateBody(JSON.stringify(normalizedResponseMessages))
+                : null;
+
             // Prevent unbounded buffer growth - drop oldest entries if buffer is too large
             if (sessionLogBuffer.length >= MAX_BUFFER_SIZE) {
                 const dropped = sessionLogBuffer.splice(0, BATCH_SIZE);
@@ -217,8 +241,17 @@ export function logAiSession(data: {
                 isStream: data.isStream,
                 requestBody: sanitizeString(requestBodyText.value),
                 responseBody: sanitizeString(responseBodyText.value),
+                normalizedRequest: normalizedRequestText
+                    ? sanitizeString(normalizedRequestText.value)
+                    : undefined,
+                normalizedResponse: normalizedResponseText
+                    ? sanitizeString(normalizedResponseText.value)
+                    : undefined,
                 truncated:
-                    requestBodyText.truncated || responseBodyText.truncated,
+                    requestBodyText.truncated ||
+                    responseBodyText.truncated ||
+                    (normalizedRequestText?.truncated ?? false) ||
+                    (normalizedResponseText?.truncated ?? false),
                 statusCode: data.statusCode,
                 createdAt: Date.now()
             });