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Amazon Bedrock Agents 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.
The Weave TypeScript SDK doesn’t currently Bedrock Agent integration.

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Prerequisites

  • A W&B API key. For more information, see API keys.
  • AWS credentials configured for an account with access to Bedrock Agents (see Identity and access management for Amazon Bedrock).
  • 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: 
    aws bedrock-agent list-agents --region us-east-1

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Installation

Install Weave and the AWS SDK for Python: 
pip install weave boto3

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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. 
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.
The 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. 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. 
@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")

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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: 
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.

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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.