refactor: replace LightRag class-name checks with ManagedKnowledgeBackend protocol

cookbook/07_knowledge/04_advanced/03_graph_rag.py

@@ -1,7 +1,7 @@
 """
 Graph RAG: LightRAG Integration
 =================================
-LightRAG is a managed knowledge backend that builds a knowledge graph
+LightRAG is an external knowledge provider that builds a knowledge graph
 from your documents. It handles its own ingestion and retrieval,
 providing graph-based RAG capabilities.
 
@@ -24,10 +24,10 @@
 # ---------------------------------------------------------------------------
 
 try:
-    from agno.vectordb.lightrag import LightRag
+    from agno.knowledge.external_provider import LightRagProvider
 
     knowledge = Knowledge(
-        vector_db=LightRag(
+        external_provider=LightRagProvider(
             server_url="http://localhost:9621",
         ),
     )

cookbook/07_knowledge/05_integrations/README.md

@@ -1,13 +1,14 @@
 # Integrations
 
-Specific reader, cloud storage, and vector database integrations.
+Specific reader, cloud storage, vector database, and external provider integrations.
 
 ## Prerequisites
 
 1. Run Qdrant: `./cookbook/scripts/run_qdrant.sh`
 2. Set `OPENAI_API_KEY` environment variable
 3. For cloud: set provider-specific credentials (see each file)
 4. For managed DBs: install provider packages (see each file)
+5. For external providers: run the provider server (see each file)
 
 ## Readers
 
@@ -34,6 +35,15 @@ Specific reader, cloud storage, and vector database integrations.
 | [vector_dbs/02_local.py](./vector_dbs/02_local.py) | ChromaDB + LanceDB (local development) |
 | [vector_dbs/03_managed.py](./vector_dbs/03_managed.py) | Pinecone + PgVector (managed/production) |
 
+## External Providers
+
+External providers manage their own indexing pipeline (chunking, embedding, storage).
+You send documents to them and they handle everything internally.
+
+| File | Provider |
+|------|----------|
+| [external_providers/01_lightrag.py](./external_providers/01_lightrag.py) | LightRAG (graph-based RAG) |
+
 ## Running
 
 ```bash

cookbook/07_knowledge/05_integrations/external_providers/01_lightrag.py

@@ -0,0 +1,97 @@
+"""
+LightRAG: Graph-Based External Provider
+=========================================
+LightRAG is an external knowledge provider that manages its own
+graph-based indexing pipeline. Unlike vector databases where Agno
+handles chunking, embedding, and storage, LightRAG runs its own
+server and handles everything internally.
+
+How it works:
+- Documents are sent to LightRAG's HTTP API
+- LightRAG extracts entities and relationships
+- Queries traverse the knowledge graph for multi-hop reasoning
+
+This is useful when you want graph-based RAG without managing
+the graph construction yourself.
+
+Requirements:
+- LightRAG server running (default: http://localhost:9621)
+- pip install lightrag-agno
+"""
+
+import asyncio
+from os import getenv
+
+from agno.agent import Agent
+from agno.knowledge.knowledge import Knowledge
+from agno.models.openai import OpenAIResponses
+
+# ---------------------------------------------------------------------------
+# Setup
+# ---------------------------------------------------------------------------
+
+try:
+    from agno.knowledge.external_provider import LightRagProvider
+
+    # LightRagProvider implements the ExternalKnowledgeProvider protocol.
+    # It handles ingestion, querying, and deletion via LightRAG's HTTP API.
+    provider = LightRagProvider(
+        server_url=getenv("LIGHTRAG_SERVER_URL", "http://localhost:9621"),
+        api_key=getenv("LIGHTRAG_API_KEY"),
+    )
+
+    # Pass the provider directly — no vector_db needed.
+    knowledge = Knowledge(
+        name="lightrag-demo",
+        external_provider=provider,
+    )
+
+    agent = Agent(
+        model=OpenAIResponses(id="gpt-4o"),
+        knowledge=knowledge,
+        search_knowledge=True,
+        markdown=True,
+    )
+
+except ImportError:
+    provider = None
+    knowledge = None
+    agent = None
+    print("LightRAG not installed. Run: pip install lightrag-agno")
+
+
+# ---------------------------------------------------------------------------
+# Run Demo
+# ---------------------------------------------------------------------------
+
+if __name__ == "__main__":
+
+    async def main():
+        if not knowledge or not agent:
+            print("Skipping demo: LightRAG not installed.")
+            return
+
+        # 1. Ingest a document — LightRAG builds a knowledge graph from it
+        print("\n--- Ingesting document into LightRAG ---\n")
+        await knowledge.ainsert(
+            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
+        )
+
+        # 2. Query via the agent — retrieval goes through LightRAG's graph
+        print("\n" + "=" * 60)
+        print("LightRAG: graph-based knowledge retrieval")
+        print("=" * 60 + "\n")
+
+        agent.print_response(
+            "What ingredients are commonly shared across Thai recipes?",
+            stream=True,
+        )
+
+        # 3. You can also query the provider directly for raw results
+        print("\n--- Direct provider query ---\n")
+        results = await provider.aquery("What Thai dishes use coconut milk?")
+        if results:
+            print(f"Got {len(results)} result(s)")
+            print(f"References: {results[0].meta_data.get('references', [])}")
+
+    asyncio.run(main())

cookbook/07_knowledge/09_archive/vector_dbs/lightrag.py

@@ -10,14 +10,14 @@
 from os import getenv
 
 from agno.agent import Agent
+from agno.knowledge.external_provider import LightRagProvider
 from agno.knowledge.knowledge import Knowledge
 from agno.knowledge.reader.wikipedia_reader import WikipediaReader
-from agno.vectordb.lightrag import LightRag
 
 # ---------------------------------------------------------------------------
 # Setup
 # ---------------------------------------------------------------------------
-vector_db = LightRag(
+provider = LightRagProvider(
     server_url=getenv("LIGHTRAG_SERVER_URL", "http://localhost:9621"),
     api_key=getenv("LIGHTRAG_API_KEY"),
 )
@@ -29,7 +29,7 @@
 knowledge = Knowledge(
     name="LightRAG Knowledge Base",
     description="Knowledge base using LightRAG for graph-based retrieval",
-    vector_db=vector_db,
+    external_provider=provider,
 )
 
 
@@ -70,7 +70,7 @@ async def main() -> None:
         markdown=True,
     )
 
-    results = await vector_db.async_search("What skills does Jordan Mitchell have?")
+    results = await provider.aquery("What skills does Jordan Mitchell have?")
     if results:
         doc = results[0]
         print(f"References: {doc.meta_data.get('references', [])}")

cookbook/92_integrations/rag/agentic_rag_with_lightrag.py

@@ -9,19 +9,19 @@
 from os import getenv
 
 from agno.agent import Agent
+from agno.knowledge.external_provider import LightRagProvider
 from agno.knowledge.knowledge import Knowledge
 from agno.knowledge.reader.wikipedia_reader import WikipediaReader
-from agno.vectordb.lightrag import LightRag
 
 # ---------------------------------------------------------------------------
 # Setup
 # ---------------------------------------------------------------------------
-vector_db = LightRag(api_key=getenv("LIGHTRAG_API_KEY"))
+provider = LightRagProvider(api_key=getenv("LIGHTRAG_API_KEY"))
 
 knowledge = Knowledge(
     name="My LightRag Knowledge Base",
-    description="Knowledge base using a LightRag vector database",
-    vector_db=vector_db,
+    description="Knowledge base using LightRAG as an external provider",
+    external_provider=provider,
 )
 
 # ---------------------------------------------------------------------------

libs/agno/agno/knowledge/__init__.py

@@ -1,3 +1,4 @@
+from agno.knowledge.external_provider import ExternalKnowledgeProvider
 from agno.knowledge.filesystem import FileSystemKnowledge
 from agno.knowledge.knowledge import Knowledge
 from agno.knowledge.protocol import KnowledgeProtocol
@@ -6,4 +7,5 @@
     "FileSystemKnowledge",
     "Knowledge",
     "KnowledgeProtocol",
+    "ExternalKnowledgeProvider",
 ]

libs/agno/agno/knowledge/external_provider/__init__.py

@@ -0,0 +1,5 @@
+from agno.knowledge.external_provider.lightrag import LightRagProvider
+from agno.knowledge.external_provider.protocol import ExternalKnowledgeProvider
+from agno.knowledge.external_provider.schemas import ProcessingResult
+
+__all__ = ["LightRagProvider", "ExternalKnowledgeProvider", "ProcessingResult"]