dbos-python
DBOS Python SDK for building reliable, fault-tolerant applications with durable workflows. Use this skill when writing Python code with DBOS, creating workflows and steps, using queues, using DBOSClient from external applications, or building applications that need to be resilient to failures.
Best use case
dbos-python is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. DBOS Python SDK for building reliable, fault-tolerant applications with durable workflows. Use this skill when writing Python code with DBOS, creating workflows and steps, using queues, using DBOSClient from external applications, or building applications that need to be resilient to failures.
DBOS Python SDK for building reliable, fault-tolerant applications with durable workflows. Use this skill when writing Python code with DBOS, creating workflows and steps, using queues, using DBOSClient from external applications, or building applications that need to be resilient to failures.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "dbos-python" skill to help with this workflow task. Context: DBOS Python SDK for building reliable, fault-tolerant applications with durable workflows. Use this skill when writing Python code with DBOS, creating workflows and steps, using queues, using DBOSClient from external applications, or building applications that need to be resilient to failures.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/dbos-python/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dbos-python Compares
| Feature / Agent | dbos-python | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
DBOS Python SDK for building reliable, fault-tolerant applications with durable workflows. Use this skill when writing Python code with DBOS, creating workflows and steps, using queues, using DBOSClient from external applications, or building applications that need to be resilient to failures.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# DBOS Python Best Practices
Guide for building reliable, fault-tolerant Python applications with DBOS durable workflows.
## When to Use
Reference these guidelines when:
- Adding DBOS to existing Python code
- Creating workflows and steps
- Using queues for concurrency control
- Implementing workflow communication (events, messages, streams)
- Configuring and launching DBOS applications
- Using DBOSClient from external applications
- Testing DBOS applications
## Rule Categories by Priority
| Priority | Category | Impact | Prefix |
|----------|----------|--------|--------|
| 1 | Lifecycle | CRITICAL | `lifecycle-` |
| 2 | Workflow | CRITICAL | `workflow-` |
| 3 | Step | HIGH | `step-` |
| 4 | Queue | HIGH | `queue-` |
| 5 | Communication | MEDIUM | `comm-` |
| 6 | Pattern | MEDIUM | `pattern-` |
| 7 | Testing | LOW-MEDIUM | `test-` |
| 8 | Client | MEDIUM | `client-` |
| 9 | Advanced | LOW | `advanced-` |
## Critical Rules
### DBOS Configuration and Launch
A DBOS application MUST configure and launch DBOS inside its main function:
```python
import os
from dbos import DBOS, DBOSConfig
@DBOS.workflow()
def my_workflow():
pass
if __name__ == "__main__":
config: DBOSConfig = {
"name": "my-app",
"system_database_url": os.environ.get("DBOS_SYSTEM_DATABASE_URL"),
}
DBOS(config=config)
DBOS.launch()
```
### Workflow and Step Structure
Workflows are comprised of steps. Any function performing complex operations or accessing external services must be a step:
```python
@DBOS.step()
def call_external_api():
return requests.get("https://api.example.com").json()
@DBOS.workflow()
def my_workflow():
result = call_external_api()
return result
```
### Key Constraints
- Do NOT call `DBOS.start_workflow` or `DBOS.recv` from a step
- Do NOT use threads to start workflows - use `DBOS.start_workflow` or queues
- Workflows MUST be deterministic - non-deterministic operations go in steps
- Do NOT create/update global variables from workflows or steps
## How to Use
Read individual rule files for detailed explanations and examples:
```
references/lifecycle-config.md
references/workflow-determinism.md
references/queue-concurrency.md
```
## References
- https://docs.dbos.dev/
- https://github.com/dbos-inc/dbos-transact-pyRelated Skills
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