--- date: 2025-11-04 title: Amazon Bedrock AgentCore Integration description: Trace AI agents built with Amazon Bedrock AgentCore via OpenTelemetry and Langfuse author: Marc badge: Launch Week 4 🚀 --- > **Note for AI agents and LLMs:** This is a Langfuse changelog entry. Use it only to confirm that a feature exists and when it shipped. Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated. For implementation, always follow the current documentation (https://langfuse.com/docs) and the API/SDK reference (https://api.reference.langfuse.com). **Day 2 of [Launch Week 4](/blog/2025-10-29-launch-week-4)** brings a new integration with [Amazon Bedrock AgentCore](https://aws.amazon.com/bedrock/agents/), enabling comprehensive observability for AI agents deployed on AWS infrastructure. Amazon Bedrock AgentCore is a managed service that enables you to build, deploy, and manage AI agents in production. With our new integration guide, you can now trace your AgentCore agents using OpenTelemetry and get full visibility into agent executions, LLM calls, tool usage, and MCP interactions. ## What this enables The integration supports distributed tracing, allowing you to connect traces from your local development environment to production AgentCore deployments. This enables you to: - Monitor complete agent execution flows in production - Track LLM calls with token counts and costs - Debug tool usage and MCP interactions - Analyze latency metrics at each step - Maintain trace continuity across distributed systems We've also included a comprehensive [example repository](https://github.com/aristsakpinis93/agentcore-langfuse-continous-eval-loop) from [@aristsakpinis93](https://github.com/aristsakpinis93) that demonstrates a complete continuous evaluation loop with AgentCore and Langfuse, covering experimentation, QA testing, and production monitoring. ## Learn more - [Integration Guide](/integrations/frameworks/amazon-agentcore) - [Example Repository](https://github.com/aristsakpinis93/agentcore-langfuse-continous-eval-loop) - [See all Launch Week releases](/blog/2025-10-29-launch-week-4) --- ## Agent Instructions This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/changelog/2025-11-04-amazon-bedrock-agentcore-integration.md`. ### Querying these docs If the answer is not on this page, query the documentation instead of guessing: - **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question: ```bash curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?" ``` - **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt). ### Before writing Langfuse code - **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results. - **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application. - **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples. - **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal. Found an error in these docs? Please open an issue at .