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 DBOSC...
Best use case
dbos-python is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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 DBOSC...
Teams using dbos-python should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
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 DBOSC...
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.
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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