diff --git a/README.md b/README.md
index 2b0d9f4b..a3f2152f 100644
--- a/README.md
+++ b/README.md
@@ -750,6 +750,82 @@ rag.delete_by_entity("Project Gutenberg")
 rag.delete_by_doc_id("doc_id")
 ```
 
+## Edit Entities and Relations
+
+LightRAG now supports comprehensive knowledge graph management capabilities, allowing you to create, edit, and delete entities and relationships within your knowledge graph.
+
+### Create Entities and Relations
+
+```python
+import asyncio
+from lightrag import LightRAG
+
+# Initialize LightRAG
+rag = LightRAG(
+    working_dir="your_working_dir",
+    embedding_func=your_embedding_function,
+    llm_model_func=your_llm_function
+)
+
+# Create new entity
+entity = rag.create_entity("Google", {
+    "description": "Google is a multinational technology company specializing in internet-related services and products.",
+    "entity_type": "company"
+})
+
+# Create another entity
+product = rag.create_entity("Gmail", {
+    "description": "Gmail is an email service developed by Google.",
+    "entity_type": "product"
+})
+
+# Create relation between entities
+relation = rag.create_relation("Google", "Gmail", {
+    "description": "Google develops and operates Gmail.",
+    "keywords": "develops operates service",
+    "weight": 2.0
+})
+```
+
+### Edit Entities and Relations
+
+```python
+# Edit an existing entity
+updated_entity = rag.edit_entity("Google", {
+    "description": "Google is a subsidiary of Alphabet Inc., founded in 1998.",
+    "entity_type": "tech_company"
+})
+
+# Rename an entity (with all its relationships properly migrated)
+renamed_entity = rag.edit_entity("Gmail", {
+    "entity_name": "Google Mail",
+    "description": "Google Mail (formerly Gmail) is an email service."
+})
+
+# Edit a relation between entities
+updated_relation = rag.edit_relation("Google", "Google Mail", {
+    "description": "Google created and maintains Google Mail service.",
+    "keywords": "creates maintains email service",
+    "weight": 3.0
+})
+```
+
+All operations are available in both synchronous and asynchronous versions. The asynchronous versions have the prefix "a" (e.g., `acreate_entity`, `aedit_relation`).
+
+#### Entity Operations
+
+- **create_entity**: Creates a new entity with specified attributes
+- **edit_entity**: Updates an existing entity's attributes or renames it
+- **delete_entity**: Removes an entity and all its relationships
+
+#### Relation Operations
+
+- **create_relation**: Creates a new relation between existing entities
+- **edit_relation**: Updates an existing relation's attributes
+- **delete_relation**: Removes a relation between entities
+
+These operations maintain data consistency across both the graph database and vector database components, ensuring your knowledge graph remains coherent.
+
 ## Cache
 
 <details>
diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index 5470d2e2..8bcb6b51 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -967,7 +967,7 @@ class LightRAG:
             # Insert chunks into vector storage
             all_chunks_data: dict[str, dict[str, str]] = {}
             chunk_to_source_map: dict[str, str] = {}
-            for chunk_data in custom_kg.get("chunks", {}):
+            for chunk_data in custom_kg.get("chunks", []):
                 chunk_content = self.clean_text(chunk_data["content"])
                 source_id = chunk_data["source_id"]
                 tokens = len(
@@ -997,9 +997,10 @@ class LightRAG:
                 update_storage = True
 
             if all_chunks_data:
-                await self.chunks_vdb.upsert(all_chunks_data)
-            if all_chunks_data:
-                await self.text_chunks.upsert(all_chunks_data)
+                await asyncio.gather(
+                    self.chunks_vdb.upsert(all_chunks_data),
+                    self.text_chunks.upsert(all_chunks_data),
+                )
 
             # Insert entities into knowledge graph
             all_entities_data: list[dict[str, str]] = []
@@ -1007,7 +1008,6 @@ class LightRAG:
                 entity_name = entity_data["entity_name"]
                 entity_type = entity_data.get("entity_type", "UNKNOWN")
                 description = entity_data.get("description", "No description provided")
-                # source_id = entity_data["source_id"]
                 source_chunk_id = entity_data.get("source_id", "UNKNOWN")
                 source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")
 
@@ -1039,7 +1039,6 @@ class LightRAG:
                 description = relationship_data["description"]
                 keywords = relationship_data["keywords"]
                 weight = relationship_data.get("weight", 1.0)
-                # source_id = relationship_data["source_id"]
                 source_chunk_id = relationship_data.get("source_id", "UNKNOWN")
                 source_id = chunk_to_source_map.get(source_chunk_id, "UNKNOWN")
 
@@ -1079,34 +1078,43 @@ class LightRAG:
                     "tgt_id": tgt_id,
                     "description": description,
                     "keywords": keywords,
+                    "source_id": source_id,
+                    "weight": weight,
                 }
                 all_relationships_data.append(edge_data)
                 update_storage = True
 
-            # Insert entities into vector storage if needed
+            # Insert entities into vector storage with consistent format
             data_for_vdb = {
                 compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
-                    "content": dp["entity_name"] + dp["description"],
+                    "content": dp["entity_name"] + "\n" + dp["description"],
                     "entity_name": dp["entity_name"],
+                    "source_id": dp["source_id"],
+                    "description": dp["description"],
+                    "entity_type": dp["entity_type"],
                 }
                 for dp in all_entities_data
             }
             await self.entities_vdb.upsert(data_for_vdb)
 
-            # Insert relationships into vector storage if needed
+            # Insert relationships into vector storage with consistent format
             data_for_vdb = {
                 compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
                     "src_id": dp["src_id"],
                     "tgt_id": dp["tgt_id"],
-                    "content": dp["keywords"]
-                    + dp["src_id"]
-                    + dp["tgt_id"]
-                    + dp["description"],
+                    "source_id": dp["source_id"],
+                    "content": f"{dp['keywords']}\t{dp['src_id']}\n{dp['tgt_id']}\n{dp['description']}",
+                    "keywords": dp["keywords"],
+                    "description": dp["description"],
+                    "weight": dp["weight"],
                 }
                 for dp in all_relationships_data
             }
             await self.relationships_vdb.upsert(data_for_vdb)
 
+        except Exception as e:
+            logger.error(f"Error in ainsert_custom_kg: {e}")
+            raise
         finally:
             if update_storage:
                 await self._insert_done()
@@ -1759,3 +1767,461 @@ class LightRAG:
     def clear_cache(self, modes: list[str] | None = None) -> None:
         """Synchronous version of aclear_cache."""
         return always_get_an_event_loop().run_until_complete(self.aclear_cache(modes))
+
+    async def aedit_entity(
+        self, entity_name: str, updated_data: dict[str, str], allow_rename: bool = True
+    ) -> dict[str, Any]:
+        """Asynchronously edit entity information.
+
+        Updates entity information in the knowledge graph and re-embeds the entity in the vector database.
+
+        Args:
+            entity_name: Name of the entity to edit
+            updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "entity_type": "new type"}
+            allow_rename: Whether to allow entity renaming, defaults to True
+
+        Returns:
+            Dictionary containing updated entity information
+        """
+        try:
+            # 1. Get current entity information
+            node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
+            if not node_data:
+                raise ValueError(f"Entity '{entity_name}' does not exist")
+
+            # Check if entity is being renamed
+            new_entity_name = updated_data.get("entity_name", entity_name)
+            is_renaming = new_entity_name != entity_name
+
+            # If renaming, check if new name already exists
+            if is_renaming:
+                if not allow_rename:
+                    raise ValueError(
+                        "Entity renaming is not allowed. Set allow_rename=True to enable this feature"
+                    )
+
+                existing_node = await self.chunk_entity_relation_graph.get_node(
+                    new_entity_name
+                )
+                if existing_node:
+                    raise ValueError(
+                        f"Entity name '{new_entity_name}' already exists, cannot rename"
+                    )
+
+            # 2. Update entity information in the graph
+            new_node_data = {**node_data, **updated_data}
+            if "entity_name" in new_node_data:
+                del new_node_data[
+                    "entity_name"
+                ]  # Node data should not contain entity_name field
+
+            # If renaming entity
+            if is_renaming:
+                logger.info(f"Renaming entity '{entity_name}' to '{new_entity_name}'")
+
+                # Create new entity
+                await self.chunk_entity_relation_graph.upsert_node(
+                    new_entity_name, new_node_data
+                )
+
+                # Get all edges related to the original entity
+                edges = await self.chunk_entity_relation_graph.get_node_edges(
+                    entity_name
+                )
+                if edges:
+                    # Recreate edges for the new entity
+                    for source, target in edges:
+                        edge_data = await self.chunk_entity_relation_graph.get_edge(
+                            source, target
+                        )
+                        if edge_data:
+                            if source == entity_name:
+                                await self.chunk_entity_relation_graph.upsert_edge(
+                                    new_entity_name, target, edge_data
+                                )
+                            else:  # target == entity_name
+                                await self.chunk_entity_relation_graph.upsert_edge(
+                                    source, new_entity_name, edge_data
+                                )
+
+                # Delete old entity
+                await self.chunk_entity_relation_graph.delete_node(entity_name)
+
+                # Delete old entity record from vector database
+                old_entity_id = compute_mdhash_id(entity_name, prefix="ent-")
+                await self.entities_vdb.delete([old_entity_id])
+
+                # Update working entity name to new name
+                entity_name = new_entity_name
+            else:
+                # If not renaming, directly update node data
+                await self.chunk_entity_relation_graph.upsert_node(
+                    entity_name, new_node_data
+                )
+
+            # 3. Recalculate entity's vector representation and update vector database
+            description = new_node_data.get("description", "")
+            source_id = new_node_data.get("source_id", "")
+            entity_type = new_node_data.get("entity_type", "")
+            content = entity_name + "\n" + description
+
+            # Calculate entity ID
+            entity_id = compute_mdhash_id(entity_name, prefix="ent-")
+
+            # Prepare data for vector database update
+            entity_data = {
+                entity_id: {
+                    "content": content,
+                    "entity_name": entity_name,
+                    "source_id": source_id,
+                    "description": description,
+                    "entity_type": entity_type,
+                }
+            }
+
+            # Update vector database
+            await self.entities_vdb.upsert(entity_data)
+
+            # 4. Save changes
+            await self._edit_entity_done()
+
+            logger.info(f"Entity '{entity_name}' successfully updated")
+            return await self.get_entity_info(entity_name, include_vector_data=True)
+        except Exception as e:
+            logger.error(f"Error while editing entity '{entity_name}': {e}")
+            raise
+
+    def edit_entity(
+        self, entity_name: str, updated_data: dict[str, str], allow_rename: bool = True
+    ) -> dict[str, Any]:
+        """Synchronously edit entity information.
+
+        Updates entity information in the knowledge graph and re-embeds the entity in the vector database.
+
+        Args:
+            entity_name: Name of the entity to edit
+            updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "entity_type": "new type"}
+            allow_rename: Whether to allow entity renaming, defaults to True
+
+        Returns:
+            Dictionary containing updated entity information
+        """
+        loop = always_get_an_event_loop()
+        return loop.run_until_complete(
+            self.aedit_entity(entity_name, updated_data, allow_rename)
+        )
+
+    async def _edit_entity_done(self) -> None:
+        """Callback after entity editing is complete, ensures updates are persisted"""
+        await asyncio.gather(
+            *[
+                cast(StorageNameSpace, storage_inst).index_done_callback()
+                for storage_inst in [  # type: ignore
+                    self.entities_vdb,
+                    self.chunk_entity_relation_graph,
+                ]
+            ]
+        )
+
+    async def aedit_relation(
+        self, source_entity: str, target_entity: str, updated_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Asynchronously edit relation information.
+
+        Updates relation (edge) information in the knowledge graph and re-embeds the relation in the vector database.
+
+        Args:
+            source_entity: Name of the source entity
+            target_entity: Name of the target entity
+            updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "keywords": "new keywords"}
+
+        Returns:
+            Dictionary containing updated relation information
+        """
+        try:
+            # 1. Get current relation information
+            edge_data = await self.chunk_entity_relation_graph.get_edge(
+                source_entity, target_entity
+            )
+            if not edge_data:
+                raise ValueError(
+                    f"Relation from '{source_entity}' to '{target_entity}' does not exist"
+                )
+
+            # 2. Update relation information in the graph
+            new_edge_data = {**edge_data, **updated_data}
+            await self.chunk_entity_relation_graph.upsert_edge(
+                source_entity, target_entity, new_edge_data
+            )
+
+            # 3. Recalculate relation's vector representation and update vector database
+            description = new_edge_data.get("description", "")
+            keywords = new_edge_data.get("keywords", "")
+            source_id = new_edge_data.get("source_id", "")
+            weight = float(new_edge_data.get("weight", 1.0))
+
+            # Create content for embedding
+            content = f"{keywords}\t{source_entity}\n{target_entity}\n{description}"
+
+            # Calculate relation ID
+            relation_id = compute_mdhash_id(
+                source_entity + target_entity, prefix="rel-"
+            )
+
+            # Prepare data for vector database update
+            relation_data = {
+                relation_id: {
+                    "content": content,
+                    "src_id": source_entity,
+                    "tgt_id": target_entity,
+                    "source_id": source_id,
+                    "description": description,
+                    "keywords": keywords,
+                    "weight": weight,
+                }
+            }
+
+            # Update vector database
+            await self.relationships_vdb.upsert(relation_data)
+
+            # 4. Save changes
+            await self._edit_relation_done()
+
+            logger.info(
+                f"Relation from '{source_entity}' to '{target_entity}' successfully updated"
+            )
+            return await self.get_relation_info(
+                source_entity, target_entity, include_vector_data=True
+            )
+        except Exception as e:
+            logger.error(
+                f"Error while editing relation from '{source_entity}' to '{target_entity}': {e}"
+            )
+            raise
+
+    def edit_relation(
+        self, source_entity: str, target_entity: str, updated_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Synchronously edit relation information.
+
+        Updates relation (edge) information in the knowledge graph and re-embeds the relation in the vector database.
+
+        Args:
+            source_entity: Name of the source entity
+            target_entity: Name of the target entity
+            updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "keywords": "keywords"}
+
+        Returns:
+            Dictionary containing updated relation information
+        """
+        loop = always_get_an_event_loop()
+        return loop.run_until_complete(
+            self.aedit_relation(source_entity, target_entity, updated_data)
+        )
+
+    async def _edit_relation_done(self) -> None:
+        """Callback after relation editing is complete, ensures updates are persisted"""
+        await asyncio.gather(
+            *[
+                cast(StorageNameSpace, storage_inst).index_done_callback()
+                for storage_inst in [  # type: ignore
+                    self.relationships_vdb,
+                    self.chunk_entity_relation_graph,
+                ]
+            ]
+        )
+
+    async def acreate_entity(
+        self, entity_name: str, entity_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Asynchronously create a new entity.
+
+        Creates a new entity in the knowledge graph and adds it to the vector database.
+
+        Args:
+            entity_name: Name of the new entity
+            entity_data: Dictionary containing entity attributes, e.g. {"description": "description", "entity_type": "type"}
+
+        Returns:
+            Dictionary containing created entity information
+        """
+        try:
+            # Check if entity already exists
+            existing_node = await self.chunk_entity_relation_graph.get_node(entity_name)
+            if existing_node:
+                raise ValueError(f"Entity '{entity_name}' already exists")
+
+            # Prepare node data with defaults if missing
+            node_data = {
+                "entity_type": entity_data.get("entity_type", "UNKNOWN"),
+                "description": entity_data.get("description", ""),
+                "source_id": entity_data.get("source_id", "manual"),
+            }
+
+            # Add entity to knowledge graph
+            await self.chunk_entity_relation_graph.upsert_node(entity_name, node_data)
+
+            # Prepare content for entity
+            description = node_data.get("description", "")
+            source_id = node_data.get("source_id", "")
+            entity_type = node_data.get("entity_type", "")
+            content = entity_name + "\n" + description
+
+            # Calculate entity ID
+            entity_id = compute_mdhash_id(entity_name, prefix="ent-")
+
+            # Prepare data for vector database update
+            entity_data_for_vdb = {
+                entity_id: {
+                    "content": content,
+                    "entity_name": entity_name,
+                    "source_id": source_id,
+                    "description": description,
+                    "entity_type": entity_type,
+                }
+            }
+
+            # Update vector database
+            await self.entities_vdb.upsert(entity_data_for_vdb)
+
+            # Save changes
+            await self._edit_entity_done()
+
+            logger.info(f"Entity '{entity_name}' successfully created")
+            return await self.get_entity_info(entity_name, include_vector_data=True)
+        except Exception as e:
+            logger.error(f"Error while creating entity '{entity_name}': {e}")
+            raise
+
+    def create_entity(
+        self, entity_name: str, entity_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Synchronously create a new entity.
+
+        Creates a new entity in the knowledge graph and adds it to the vector database.
+
+        Args:
+            entity_name: Name of the new entity
+            entity_data: Dictionary containing entity attributes, e.g. {"description": "description", "entity_type": "type"}
+
+        Returns:
+            Dictionary containing created entity information
+        """
+        loop = always_get_an_event_loop()
+        return loop.run_until_complete(self.acreate_entity(entity_name, entity_data))
+
+    async def acreate_relation(
+        self, source_entity: str, target_entity: str, relation_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Asynchronously create a new relation between entities.
+
+        Creates a new relation (edge) in the knowledge graph and adds it to the vector database.
+
+        Args:
+            source_entity: Name of the source entity
+            target_entity: Name of the target entity
+            relation_data: Dictionary containing relation attributes, e.g. {"description": "description", "keywords": "keywords"}
+
+        Returns:
+            Dictionary containing created relation information
+        """
+        try:
+            # Check if both entities exist
+            source_exists = await self.chunk_entity_relation_graph.has_node(
+                source_entity
+            )
+            target_exists = await self.chunk_entity_relation_graph.has_node(
+                target_entity
+            )
+
+            if not source_exists:
+                raise ValueError(f"Source entity '{source_entity}' does not exist")
+            if not target_exists:
+                raise ValueError(f"Target entity '{target_entity}' does not exist")
+
+            # Check if relation already exists
+            existing_edge = await self.chunk_entity_relation_graph.get_edge(
+                source_entity, target_entity
+            )
+            if existing_edge:
+                raise ValueError(
+                    f"Relation from '{source_entity}' to '{target_entity}' already exists"
+                )
+
+            # Prepare edge data with defaults if missing
+            edge_data = {
+                "description": relation_data.get("description", ""),
+                "keywords": relation_data.get("keywords", ""),
+                "source_id": relation_data.get("source_id", "manual"),
+                "weight": float(relation_data.get("weight", 1.0)),
+            }
+
+            # Add relation to knowledge graph
+            await self.chunk_entity_relation_graph.upsert_edge(
+                source_entity, target_entity, edge_data
+            )
+
+            # Prepare content for embedding
+            description = edge_data.get("description", "")
+            keywords = edge_data.get("keywords", "")
+            source_id = edge_data.get("source_id", "")
+            weight = edge_data.get("weight", 1.0)
+
+            # Create content for embedding
+            content = f"{keywords}\t{source_entity}\n{target_entity}\n{description}"
+
+            # Calculate relation ID
+            relation_id = compute_mdhash_id(
+                source_entity + target_entity, prefix="rel-"
+            )
+
+            # Prepare data for vector database update
+            relation_data_for_vdb = {
+                relation_id: {
+                    "content": content,
+                    "src_id": source_entity,
+                    "tgt_id": target_entity,
+                    "source_id": source_id,
+                    "description": description,
+                    "keywords": keywords,
+                    "weight": weight,
+                }
+            }
+
+            # Update vector database
+            await self.relationships_vdb.upsert(relation_data_for_vdb)
+
+            # Save changes
+            await self._edit_relation_done()
+
+            logger.info(
+                f"Relation from '{source_entity}' to '{target_entity}' successfully created"
+            )
+            return await self.get_relation_info(
+                source_entity, target_entity, include_vector_data=True
+            )
+        except Exception as e:
+            logger.error(
+                f"Error while creating relation from '{source_entity}' to '{target_entity}': {e}"
+            )
+            raise
+
+    def create_relation(
+        self, source_entity: str, target_entity: str, relation_data: dict[str, Any]
+    ) -> dict[str, Any]:
+        """Synchronously create a new relation between entities.
+
+        Creates a new relation (edge) in the knowledge graph and adds it to the vector database.
+
+        Args:
+            source_entity: Name of the source entity
+            target_entity: Name of the target entity
+            relation_data: Dictionary containing relation attributes, e.g. {"description": "description", "keywords": "keywords"}
+
+        Returns:
+            Dictionary containing created relation information
+        """
+        loop = always_get_an_event_loop()
+        return loop.run_until_complete(
+            self.acreate_relation(source_entity, target_entity, relation_data)
+        )