diff --git a/docs.json b/docs.json
index 1aa5e4ea71..3b673ccee2 100644
--- a/docs.json
+++ b/docs.json
@@ -624,6 +624,7 @@
                             "pages": [
                               "weave/guides/integrations/openai_agents",
                               "weave/guides/integrations/openai-realtime-audio",
+                              "weave/guides/integrations/bedrock_agents",
                               "weave/guides/integrations/claude_agent",
                               "weave/guides/integrations/claude_code",
                               "weave/guides/integrations/langchain",
diff --git a/weave/guides/integrations/bedrock_agents.mdx b/weave/guides/integrations/bedrock_agents.mdx
new file mode 100644
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+++ b/weave/guides/integrations/bedrock_agents.mdx
@@ -0,0 +1,117 @@
+---
+title: Bedrock Agents
+description: "Trace Amazon Bedrock Agents invocations with Weave, capturing agent inputs, foundation model usage, and completion output."
+---
+
+[Amazon Bedrock Agents](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html) let you build managed AI agents on AWS that orchestrate foundation models, knowledge bases, and action groups. Weave traces calls to the `bedrock-agent-runtime` client so you can inspect each `invoke_agent` invocation, including the foundation model, token usage, session ID, and the agent's response.
+
+<Note>
+The Weave TypeScript SDK doesn't currently Bedrock Agent integration.
+</Note>
+
+## Prerequisites
+
+- A W&B API key. For more information, see [API keys](/platform/app/settings-page/user-settings#api-keys).
+- AWS credentials configured for an account with access to Bedrock Agents (see [Authentication](https://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html)).
+- An existing Bedrock agent and alias. Note the `agentId` and `agentAliasId`. You can return a list of agents associated with your ID by running the following AWS CLI command, replacing `<your-region>` with the region slug your agent resides in:
+
+    ```bash
+    aws bedrock-agent list-agents --region us-east-1
+    ```
+
+## Installation
+
+Install Weave and the AWS SDK:
+
+```bash
+pip install weave boto3
+```
+
+## Trace `invoke_agent` calls
+
+Create a `bedrock-agent-runtime` client and pass it to `patch_client`. Weave detects the client type and wraps the `invoke_agent` method. After patching, use the client as you normally would.
+
+```python lines highlight="15-17"
+import boto3
+
+import weave
+from weave.integrations.bedrock import patch_client
+
+weave.init("your-team-name/bedrock-agents-demo")
+
+# Create and patch the Bedrock Agents runtime client.
+bedrock_agent_client = boto3.client("bedrock-agent-runtime", region_name="us-east-1")
+patch_client(bedrock_agent_client)
+
+# Invoke the agent. Set `enableTrace=True` so Weave can capture the underlying
+# foundation model and token usage from the orchestration trace events.
+response = bedrock_agent_client.invoke_agent(
+    agentId="[YOUR-AGENT-ID]",
+    agentAliasId="[YOUR-AGENT-ALIAS-ID]",
+    sessionId="[YOUR-SESSION-ID]",
+    inputText="What is the capital of France?",
+    enableTrace=True,
+)
+
+# Consume the streaming completion to assemble the final response text.
+final_text = ""
+for event in response["completion"]:
+    chunk = event.get("chunk")
+    if chunk and "bytes" in chunk:
+        final_text += chunk["bytes"].decode("utf-8")
+
+print(final_text)
+```
+
+Each `invoke_agent` call appears in the Weave UI as a `BedrockAgentRuntime.invoke_agent` trace. The trace records:
+
+- The agent inputs (`agentId`, `agentAliasId`, `sessionId`, `inputText`).
+- The extracted assistant text from the completion event stream.
+- The underlying foundation model used by the agent (extracted from the orchestration trace).
+- Token usage (`prompt_tokens`, `completion_tokens`, `total_tokens`) when reported by the agent.
+
+Foundation model and token usage are extracted from `orchestrationTrace` events, which Bedrock only emits when `invoke_agent` is called with `enableTrace=True`. Without this flag, traces still capture the inputs and the generated response text, but the foundation model falls back to `bedrock-agent:<agentId>` and token counts are unavailable.
+
+## Wrap an agent call with `weave.op`
+
+To group an `invoke_agent` call with related logic, such as preprocessing, postprocessing, or chained API calls, wrap your function with `@weave.op`. Weave nests the patched `invoke_agent` trace inside the parent op.
+
+```python lines
+@weave.op
+def ask_agent(question: str, session_id: str) -> str:
+    response = bedrock_agent_client.invoke_agent(
+        agentId="[YOUR-AGENT-ID]",
+        agentAliasId="[YOUR-AGENT-ALIAS-ID]",
+        sessionId=session_id,
+        inputText=question,
+        enableTrace=True,
+    )
+    text = ""
+    for event in response["completion"]:
+        chunk = event.get("chunk")
+        if chunk and "bytes" in chunk:
+            text += chunk["bytes"].decode("utf-8")
+    return text
+
+
+answer = ask_agent("Summarize today's open support tickets.", session_id="session-1")
+```
+
+## Multi-turn conversations
+
+Bedrock Agents preserve conversation state on the service side when you reuse a `sessionId`. To group multiple turns into a single trace in the Weave UI, wrap the turns in `weave.thread`:
+
+```python lines
+with weave.thread("support-conversation") as t:
+    for prompt in [
+        "I can't log in to my account.",
+        "I already tried resetting my password.",
+    ]:
+        ask_agent(prompt, session_id=t.thread_id)
+```
+
+Weave displays each turn in the UI as individual rows in the Threads view.
+
+## View traces
+
+When you run the example, Weave prints a link to the project dashboard. Open the link to inspect the agent inputs, foundation model, token usage, and the generated response for each `invoke_agent` call.