Reflection allows the agent to critique and improve its own responses before returning them. This “Self-Correction” pattern improves output quality by checking for errors, clarity, and completeness.
Enabling Reflection
To enable reflection, set reflection=True during agent initialization.
from upsonic import Agent, Task
# Create an agent with reflection enabled
agent = Agent(
model="openai/gpt-4o",
reflection=True
)
task = Task("Write a python function to Fibonacci sequence with detailed docstrings.")
# The agent will:
# 1. Generate an initial response
# 2. Critique the response (finding bugs or missing docs)
# 3. Regenerate an improved response
result = agent.do(task)
print(result)
Configuring Reflection
You can customize the reflection process using ReflectionConfig.
from upsonic import Agent, Task
from upsonic.reflection import ReflectionConfig
# Custom reflection configuration
config = ReflectionConfig(
max_iterations=2, # Maximum improvement attempts
acceptance_threshold=0.9 # Minimum score to accept response
)
agent = Agent(
model="openai/gpt-4o",
reflection=True,
reflection_config=config,
debug=True # useful to see the reflection steps
)
task = Task("Optimize this database query...")
result = agent.do(task)
print(result)
When to Use Reflection?
- Coding Tasks: To catch bugs and improve code quality.
- Complex Reasoning: To verify logic and assumptions.
- Creative Writing: To refine style and tone.
Reflection increases the time and token usage for each task as it involves additional model calls for critique and revision.
