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116524e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | # Async Learning
By default, learning (Reflect, Tag, Update, Apply) runs synchronously after each sample. With async learning, the Agent returns immediately while learning continues in the background.
## Architecture
```mermaid
graph LR
S1[Sample 1] --> A[Agent]
S2[Sample 2] --> A
S3[Sample 3] --> A
A -->|foreground| E[Environment]
E -->|background| R1[Reflector 1]
E --> R2[Reflector 2]
E --> R3[Reflector 3]
R1 --> Q[Queue]
R2 --> Q
R3 --> Q
Q -->|serialized| SM[SkillManager]
SM --> SK[Skillbook]
```
- **Reflectors** run concurrently (safe — they only read the skillbook)
- **SkillManager** runs sequentially (required — it writes to the skillbook)
- The Agent uses whatever skillbook state is available (eventual consistency)
## Basic Usage
Pass `wait=False` to `run()`:
```python
from ace import ACE
runner = ACE.from_roles(
agent=agent,
reflector=reflector,
skill_manager=skill_manager,
environment=environment,
)
# Agent returns fast — learning continues in background
results = runner.run(samples, epochs=3, wait=False)
# Use results immediately
for r in results:
print(r)
# Wait before saving
runner.wait_for_background()
runner.save("learned.json")
```
## Monitoring Progress
```python
stats = runner.learning_stats
# {'active': 5, 'completed': 25}
```
## With ACELiteLLM
```python
from ace import ACELiteLLM, Sample, SimpleEnvironment
agent = ACELiteLLM.from_model("gpt-4o-mini")
samples = [Sample(question="...", context="", ground_truth="...")]
results = agent.learn(samples, environment=SimpleEnvironment(), wait=False)
# Agent is immediately available
answer = agent.ask("New question")
# Wait when you need to save
agent.wait_for_background()
agent.save("learned.json")
```
## Why This Architecture
| Component | Parallelizable? | Reason |
|-----------|----------------|--------|
| Reflector | Yes | Only reads the skillbook, produces independent analysis |
| SkillManager | No | Writes to the skillbook, handles deduplication |
This gives ~3x faster learning when the Reflector LLM calls run concurrently.
## What to Read Next
- [Full Pipeline Guide](full-pipeline.md) — synchronous pipeline setup
- [Testing](testing.md) — test async learning with MagicMock
|