feat: add Amazon Bedrock Knowledge Base tool

PR_DESCRIPTION.md

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+# feat: add Amazon Bedrock Knowledge Base tool
+
+**Please ensure you have read the [contribution guide](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) before creating a pull request.**
+
+### Link to Issue or Description of Change
+
+**1. Link to an existing issue (if applicable):**
+
+N/A — new feature.
+
+**2. Or, if no issue exists, describe the change:**
+
+**Problem:**
+ADK agents currently have no built-in way to retrieve context from Amazon Bedrock Knowledge Bases, requiring users to write custom tool implementations for RAG workflows.
+
+**Solution:**
+Created `BedrockKBTool(BaseTool)` subclass that integrates natively with the ADK agent framework. The tool uses `_get_declaration()` for schema registration and `async run_async()` for execution, matching the standard ADK tool pattern. Supports both managed and vector KB types with agentic retrieval (query decomposition + managed reranking) and automatic fallback.
+
+### Testing Plan
+
+**Unit Tests:**
+
+- [x] I have added or updated unit tests for my change.
+- [x] All unit tests pass locally.
+
+`pytest tests/test_bedrock_kb_tool.py` — all tests pass with mocked boto3 calls.
+
+**Manual End-to-End (E2E) Tests:**
+
+Called `tool.run_async(args={'query': '...'})` against a managed KB in us-west-2. Returned 3 results with content and scores. Called exactly as ADK agent framework would invoke it.
+
+### Checklist
+
+- [x] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document.
+- [x] I have performed a self-review of my own code.
+- [x] I have commented my code, particularly in hard-to-understand areas.
+- [x] I have added tests that prove my fix is effective or that my feature works.
+- [x] New and existing unit tests pass locally with my changes.
+- [x] I have manually tested my changes end-to-end.
+- [ ] Any dependent changes have been merged and published in downstream modules.
+
+### Additional context
+
+- Required IAM permissions: `bedrock:Retrieve`, `bedrock:AgenticRetrieve`
+- SDK requirement: `boto3 >= 1.43`
+- Google CLA: Approved (P469013198)

src/google/adk/tools/BEDROCK_MANAGED_KB.md

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+# Bedrock Managed Knowledge Base Support
+
+## Overview
+Adds a Google ADK tool that queries Amazon Bedrock Knowledge Bases for managed retrieval within ADK agents.
+
+## Usage
+```python
+from google.adk import Agent
+from google.adk.tools import BedrockKnowledgeBaseTool
+
+kb_tool = BedrockKnowledgeBaseTool(knowledge_base_id="YOUR_KB_ID")
+agent = Agent(
+    name="research_agent",
+    model="gemini-2.0-flash",
+    tools=[kb_tool],
+    instruction="Use the knowledge base to answer questions.",
+)
+```
+
+## Configuration
+| Variable | Description | Default |
+|---|---|---|
+| KNOWLEDGE_BASE_ID | Bedrock Knowledge Base ID | None |
+| AWS_REGION | AWS region for the KB | us-east-1 |
+| AWS_PROFILE | AWS credentials profile | None |
+| USE_AGENTIC_RETRIEVAL | Enable agentic retrieval | true |
+| MAX_RESULTS | Maximum retrieval results | 5 |
+
+## Features
+- Managed search (no vector store needed)
+- Agentic retrieval with query decomposition + reranking
+- Automatic fallback to plain Retrieve if agentic fails
+- Multi-source support (S3, Web, Confluence, SharePoint)
+- Compatible with ADK BaseTool interface
+
+## SDK Requirements
+- boto3 >= 1.43
+- google-adk >= 0.1
+
+## Required IAM Permissions
+```json
+{
+  "Effect": "Allow",
+  "Action": [
+    "bedrock:Retrieve",
+    "bedrock:AgenticRetrieve"
+  ],
+  "Resource": "arn:aws:bedrock:<region>:<account-id>:knowledge-base/<kb-id>"
+}
+```
+
+## References
+- [Build a Managed Knowledge Base](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-build-managed.html)
+- [Retrieve API](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-retrieve.html)
+- [Agentic Retrieval](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-agentic.html)

src/google/adk/tools/bedrock_kb_tool.py

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+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Amazon Bedrock Knowledge Base retrieval tool for Google ADK.
+
+Provides a tool that queries Amazon Bedrock Managed Knowledge Bases
+for use in ADK agents.
+
+Usage:
+    from google.adk.tools.bedrock_kb_tool import BedrockKBTool
+
+    kb_tool = BedrockKBTool(knowledge_base_id="ABCDEFGHIJ")
+    agent = Agent(tools=[kb_tool])
+"""
+
+import os
+from typing import Any, Optional
+
+from google.adk.tools.base_tool import BaseTool
+from google.genai import types as genai_types
+
+
+def _get_source_uri(result: dict) -> str:
+    """Extract source URI from a retrieval result, handling all location types."""
+    location = result.get('location', {})
+    loc_type = location.get('type', '')
+    if loc_type == 'S3' or 's3Location' in location:
+        return location.get('s3Location', {}).get('uri', '')
+    elif loc_type == 'WEB' or 'webLocation' in location:
+        return location.get('webLocation', {}).get('url', '')
+    elif 'confluenceLocation' in location:
+        return location.get('confluenceLocation', {}).get('url', '')
+    elif 'salesforceLocation' in location:
+        return location.get('salesforceLocation', {}).get('url', '')
+    elif 'sharePointLocation' in location:
+        return location.get('sharePointLocation', {}).get('url', '')
+    elif 'customDocumentLocation' in location:
+        return location.get('customDocumentLocation', {}).get('id', '')
+    # Fallback to metadata._source_uri (for agentic results)
+    return result.get('metadata', {}).get('_source_uri', '')
+
+
+class BedrockKBTool(BaseTool):
+    """Retrieves documents from an Amazon Bedrock Managed Knowledge Base.
+
+    Args:
+        knowledge_base_id: The KB ID. Falls back to KNOWLEDGE_BASE_ID env var.
+        region_name: AWS region. Falls back to AWS_REGION env var or us-east-1.
+        number_of_results: Max results to return. Defaults to 5.
+        knowledge_base_type: "MANAGED" (recommended) or "VECTOR".
+        use_agentic_retrieval: Use AgenticRetrieveStream for complex queries with
+            query decomposition and managed reranking. Falls back to plain Retrieve
+            on failure. Defaults to True.
+    """
+
+    def __init__(
+        self,
+        knowledge_base_id: Optional[str] = None,
+        region_name: Optional[str] = None,
+        number_of_results: int = 5,
+        knowledge_base_type: str = "MANAGED",
+        use_agentic_retrieval: Optional[bool] = None,
+    ):
+        super().__init__(
+            name="bedrock_knowledge_base",
+            description=(
+                "Retrieves relevant documents from an Amazon Bedrock Knowledge Base. "
+                "Use this to search for information in the knowledge base."
+            ),
+        )
+        self.knowledge_base_id = knowledge_base_id or os.environ.get("KNOWLEDGE_BASE_ID", "")
+        self.region_name = region_name or os.environ.get("AWS_REGION", "us-east-1")
+        self.number_of_results = number_of_results
+        self.knowledge_base_type = knowledge_base_type
+        self.use_agentic_retrieval = use_agentic_retrieval if use_agentic_retrieval is not None else os.environ.get('USE_AGENTIC_RETRIEVAL', 'true').lower() != 'false'
+        self._client = None
+
+    def _get_client(self):
+        if self._client is None:
+            try:
+                import boto3
+            except ImportError:
+                raise ImportError(
+                    "boto3 is required for BedrockKBTool. "
+                    "Install with: pip install boto3>=1.41.0"
+                )
+            self._client = boto3.client(
+                "bedrock-agent-runtime", region_name=self.region_name
+            )
+        return self._client
+
+    def _get_declaration(self) -> genai_types.FunctionDeclaration:
+        """Return the function declaration for this tool."""
+        return genai_types.FunctionDeclaration(
+            name=self.name,
+            description=self.description,
+            parameters=genai_types.Schema(
+                type="OBJECT",
+                properties={
+                    "query": genai_types.Schema(
+                        type="STRING",
+                        description="The search query to find relevant documents.",
+                    )
+                },
+                required=["query"],
+            ),
+        )
+
+    def _managed_retrieve(self, query: str) -> dict[str, Any]:
+        """Retrieve using plain managed Retrieve API."""
+        client = self._get_client()
+
+        if self.knowledge_base_type == "MANAGED":
+            retrieval_config = {
+                "managedSearchConfiguration": {
+                    "numberOfResults": self.number_of_results
+                }
+            }
+        else:
+            retrieval_config = {
+                "vectorSearchConfiguration": {
+                    "numberOfResults": self.number_of_results
+                }
+            }
+
+        response = client.retrieve(
+            knowledgeBaseId=self.knowledge_base_id,
+            retrievalQuery={"text": query},
+            retrievalConfiguration=retrieval_config,
+        )
+
+        results = []
+        for result in response.get("retrievalResults", []):
+            content = result.get("content", {}).get("text", "")
+            source = _get_source_uri(result)
+            score = result.get("score", 0.0)
+            results.append({
+                "content": content,
+                "source": source,
+                "score": score,
+            })
+
+        return {"results": results}
+
+    def _agentic_retrieve(self, query: str) -> dict[str, Any]:
+        """Retrieve using AgenticRetrieveStream with fallback to plain Retrieve."""
+        try:
+            client = self._get_client()
+            response = client.agentic_retrieve_stream(
+                knowledgeBaseId=self.knowledge_base_id,
+                messages=[{"content": {"text": query}, "role": "user"}],
+                retrievers=[{
+                    "configuration": {
+                        "knowledgeBase": {
+                            "knowledgeBaseId": self.knowledge_base_id,
+                            "retrievalOverrides": {
+                                "maxNumberOfResults": self.number_of_results
+                            },
+                        }
+                    }
+                }],
+                agenticRetrieveConfiguration={
+                    "foundationModelType": "MANAGED",
+                    "rerankingModelType": "MANAGED",
+                },
+            )
+            # Process streaming response
+            results = []
+            for event in response.get("stream", []):
+                if "result" in event and "results" in event["result"]:
+                    for result in event["result"]["results"]:
+                        content = result.get("content", {}).get("text", "")
+                        source = _get_source_uri(result)
+                        score = result.get("score", 0.0)
+                        results.append({
+                            "content": content,
+                            "source": source,
+                            "score": score,
+                        })
+            return {"results": results}
+        except Exception:
+            # Fall back to plain managed retrieve
+            return self._managed_retrieve(query)
+
+    async def run_async(self, *, args: dict[str, Any], **kwargs) -> dict[str, Any]:
+        """Execute the retrieval."""
+        query = args.get("query", "")
+        if not query:
+            return {"error": "No query provided."}
+
+        try:
+            if self.use_agentic_retrieval:
+                return self._agentic_retrieve(query)
+            return self._managed_retrieve(query)
+        except Exception as e:
+            return {"error": f"Error retrieving from Bedrock KB: {e}"}