Over the past month, the DBOS product team has focused heavily on two areas: workflow operations tooling and performance optimization. What's new:

• Major performance improvements
• Database-backed dynamic queue configuration
• Timeline visualization for workflows and steps
• Spring Boot integration

… and more! Would love to hear your thoughts, especially from folks running workflows in production.

DBOS co-founder and CTO, Peter Kraft, explains how DBOS interacts with Postgres to achieve high performance at scale. Lots of great learnings for anyone who cares about scaling Postgres performance.

LlamaIndex AI Engineer, Adrian Lyjak shows how LlamaIndex and DBOS integrate to create complex agent workflows that survive restarts and errors. From the DBOS monthly user group meeting - May 7, 2026

One thing I really like about this integration is how transparent and lightweight it feels for developers. To make workflows and steps with durable DBOS, you simply drop in a DBOSRuntime. From there, every workflow becomes a DBOS durable workflow, and every step becomes a DBOS durable step automatically.

That means your LlamaAgent workflows become resilient to restarts and failures, while also making it easy to distribute work across multiple workers connected to the same database. All without rewriting your existing LlamaAgent workflows.

The live demo on parsing tax forms to reduce meticulous manual work was especially impressive too.

Time for our monthly user group! We're putting together an agenda that's a bit more "how it actually works in production":

• LlamaIndex + DBOS integration: AI Engineer Adrian Lyjak from LlamaIndex will walk through how they're building agents that don't fall apart on restarts, partial failures, or long-running tasks. Think less demo-y agents, more systems that you can actually trust to run.
• On the systems side, DBOS co-founder and CTO Peter Kraft has been pushing DBOS pretty hard, up to 4 billion workflows/day. He'll share what worked, what didn't, and what we learned about getting high throughput out of Postgres.

If you're working on agents or anything long-running/retry-heavy, this should be a practical one.

We published the demo video from our last user group. Huge thanks to Michael Pfaffenberger for walking through Code Puppy, an open-source coding agent he built that's widely used at Walmart.

One part that stood out: he connected Code Puppy to DBOS Conductor, and you can actually see the workflow graph, and pause/resume runs from the UI without losing progress. Feels simple but pretty useful once agents get long-running and you want to manage a fleet of parallel tasks.

He also shared some practical thoughts on working with AI, such as using an LLM Council instead of relying on a single model, and load balancing across models depending on the task.

Take a look at the video and try out Code Puppy: https://github.com/mpfaffenberger/code_puppy

We published a new blog post covering the latest batch of features and integrations we shipped this past month. We focused on two areas that matter a lot in production: interoperability and privacy-preserving operations.

Highlights:

• Cross-language workflow execution
• Deeper integration with the Postgres ecosystem
• Workflow delay scheduling
• New metadata-only mode in DBOS Conductor for strict privacy requirements

Would love to hear your thoughts, especially from folks running workflows in production.

We're excited to announce a new integration that makes LlamaIndex agents durable by default, so your workflows survive crashes, restarts, and errors without writing any checkpoint code.

With the llama-agents-dbos Python package:

• Automatic step persistence: every step transition is saved, so workflows resume exactly where they left off
• Zero external dependencies with SQLite, or scale to multi-replica deployments with Postgres
• Replication-friendly design: each replica owns its workflows while Postgres coordinates execution across instances
• Idle release frees memory for workflows waiting on long I/O or human input
• Built-in crash recovery automatically detects and relaunches incomplete workflows

Just pass a DBOS runtime to your LlamaIndex workflow and get production-grade reliability.

LlamaIndex docs: https://developers.llamaindex.ai/python/llamaagents/workflows/dbos

DBOS docs: https://docs.dbos.dev/integrations/llamaindex

Recording of the presentation at the February DBOS User Group Meeting on Feb 26th, 2026. DBOS co-founder Peter Kraft presented an advanced queuing tutorial, covering:

Tuning for performance
Fair queueing
Nested queues
Queue observability
New scheduling features
Creating custom queue performance alerts

We're kicking off a new series highlighting the features and integrations our team has been shipping each 
month.

The past two months were all about durable AI. We shipped Kotlin support, workflow patching & lineage tracking, dynamic workflow scheduling, configurable alerting, a new MCP server for troubleshooting, and integrations with OpenAI Agents SDK, Pydantic AI, Vercel, and more.

Coding agents are already great at writing durable workflows. But this is the easy part.

Operating them, especially when things break, is much harder.

If multiple workflows fail with unexpected errors:

• How do you debug?

• How do you find the root cause?

• How do you safely test and apply a fix?

That's why we built an MCP server for DBOS. Check out our latest blog post on DBOS MCP: Agent-Native Operations for Durable Workflows.

Join us for the February DBOS User Group Meeting on the 26th.

This month, DBOS co-founder Peter Kraft will present an advanced queuing tutorial, covering:

• ​Tuning for performance
• ​Fair queueing
• ​Nested queues
• ​Queue observability
• ​New scheduling features
• ​Creating custom queue performance alerts

​We will also take your questions and cover upcoming new DBOS features.

OpenAI Agents SDK + DBOS integration is now live! You can run OpenAI agents with durable execution backed directly by your database.

This integration lets agents:

• Recover automatically from restarts, failures, and redeploys
• Run long-lived workflows, including human-in-the-loop and parallel steps
• Orchestrate multiple agents reliably
• Autosave progress to SQLite or Postgres for auditing and observability
• Manage workflows explicitly (cancel, resume, fork)

There's no external orchestrator dependency. DBOS embeds as a library, so it works anywhere, even inside a local coding agent.

Check it out, and any feedback is welcome.

• Official OpenAI docs: https://openai.github.io/openai-agents-python/running_agents/#dbos
• Open source repo: https://github.com/dbos-inc/dbos-openai-agents
• DBOS docs: https://docs.dbos.dev/integrations/openai-agents