diff --git a/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/__init__.py b/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/__init__.py
new file mode 100644
index 000000000..7d68d2238
--- /dev/null
+++ b/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/__init__.py
@@ -0,0 +1,18 @@
+"""Knowledge-base retrieval: hybrid search rendered as citable evidence.
+
+Public surface is the service (``search_knowledge_base_context``) and its input
+value object (``SearchScope``); the rest are building blocks.
+"""
+
+from __future__ import annotations
+
+from .models import ChunkHit, DocumentHit, SearchScope
+from .service import build_context, search_knowledge_base_context
+
+__all__ = [
+    "ChunkHit",
+    "DocumentHit",
+    "SearchScope",
+    "build_context",
+    "search_knowledge_base_context",
+]
diff --git a/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/reranking.py b/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/reranking.py
new file mode 100644
index 000000000..0e3387018
--- /dev/null
+++ b/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/reranking.py
@@ -0,0 +1,51 @@
+"""Reorder retrieved documents with the configured reranker (no-op if disabled).
+
+Ranking is by concatenated matched-chunk content; ``DocumentHit`` order is
+rewritten to follow the reranker's result.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from .models import DocumentHit
+
+if TYPE_CHECKING:
+    from app.services.reranker_service import RerankerService
+
+
+def rerank_hits(
+    query: str,
+    hits: list[DocumentHit],
+    reranker: RerankerService | None,
+) -> list[DocumentHit]:
+    """Return ``hits`` reordered by the reranker; unchanged when none is set."""
+    if reranker is None or len(hits) < 2:
+        return hits
+
+    hit_by_id = {hit.document_id: hit for hit in hits}
+    ranked = reranker.rerank_documents(query, [_as_document(hit) for hit in hits])
+    reordered = [
+        hit_by_id[doc["document_id"]]
+        for doc in ranked
+        if doc.get("document_id") in hit_by_id
+    ]
+    # Fall back to the original order if the reranker dropped or garbled ids.
+    return reordered if len(reordered) == len(hits) else hits
+
+
+def _as_document(hit: DocumentHit) -> dict[str, Any]:
+    """The minimal dict shape ``RerankerService.rerank_documents`` scores on."""
+    return {
+        "document_id": hit.document_id,
+        "content": "\n\n".join(chunk.content for chunk in hit.chunks),
+        "score": hit.score,
+        "document": {
+            "id": hit.document_id,
+            "title": hit.title,
+            "document_type": hit.document_type,
+        },
+    }
+
+
+__all__ = ["rerank_hits"]
diff --git a/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/service.py b/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/service.py
new file mode 100644
index 000000000..812592ff8
--- /dev/null
+++ b/surfsense_backend/app/agents/chat/multi_agent_chat/shared/retrieval/service.py
@@ -0,0 +1,66 @@
+"""Search the knowledge base and render it as model-facing ``<retrieved_context>``.
+
+The retrieval spine end to end: hybrid search → rerank → adapt → render, with
+each shown passage registered for ``[n]`` citation along the way.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.agents.chat.multi_agent_chat.shared.citations import CitationRegistry
+from app.agents.chat.multi_agent_chat.shared.retrieved_context import (
+    render_retrieved_context,
+)
+
+from .adapter import to_retrieved_document
+from .hybrid_search import search_chunks
+from .models import DocumentHit, SearchScope
+from .reranking import rerank_hits
+
+if TYPE_CHECKING:
+    from app.services.reranker_service import RerankerService
+
+_DEFAULT_TOP_K = 10
+
+
+async def search_knowledge_base_context(
+    db_session: AsyncSession,
+    *,
+    search_space_id: int,
+    query: str,
+    registry: CitationRegistry,
+    scope: SearchScope | None = None,
+    reranker: RerankerService | None = None,
+    top_k: int = _DEFAULT_TOP_K,
+) -> str | None:
+    """Retrieve KB evidence for ``query`` and render it, registering each ``[n]``.
+
+    Returns ``None`` when nothing matched, so the caller can skip the block.
+    """
+    hits = await search_chunks(
+        db_session,
+        search_space_id=search_space_id,
+        query=query,
+        scope=scope or SearchScope(),
+        top_k=top_k,
+    )
+    return build_context(query, hits, registry, reranker=reranker)
+
+
+def build_context(
+    query: str,
+    hits: list[DocumentHit],
+    registry: CitationRegistry,
+    *,
+    reranker: RerankerService | None = None,
+) -> str | None:
+    """Rerank → adapt → render. Pure given ``hits``, so it is unit-testable."""
+    ranked = rerank_hits(query, hits, reranker)
+    documents = [to_retrieved_document(hit) for hit in ranked]
+    return render_retrieved_context(documents, registry)
+
+
+__all__ = ["build_context", "search_knowledge_base_context"]