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agent-framework/python/samples/getting_started/evaluation/self_reflection
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Eduard van Valkenburg 977c3adfb2 Python: replace pre-commit with prek, add PEP 723 script deps, clean up dev dependencies (#3748)
* python: replace pre-commit with prek, add PEP 723 script deps, clean up dev dependencies

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* python: use markdown-code-lint with fixed globs instead of prek file list

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Core package changes still trigger checks on all packages.

* feat(ci): split code quality into 4 parallel jobs

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Fixes https://github.com/microsoft/agent-framework/issues/3578
977c3adfb2 · 2026-02-09 17:51:01 +00:00
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Self-Reflection Evaluation Sample

This sample demonstrates the self-reflection pattern using Agent Framework and Azure AI Foundry's Groundedness Evaluator. For details, see Reflexion: Language Agents with Verbal Reinforcement Learning (NeurIPS 2023).

Overview

What it demonstrates:

  • Iterative self-reflection loop that automatically improves responses based on groundedness evaluation
  • Batch processing of prompts from JSONL files with progress tracking
  • Using AzureOpenAIChatClient with Azure CLI authentication
  • Comprehensive summary statistics and detailed result tracking

Prerequisites

Azure Resources

  • Azure OpenAI: Deploy models (default: gpt-4.1 for both agent and judge)
  • Azure CLI: Run az login to authenticate

Python Environment

pip install agent-framework-core azure-ai-projects pandas --pre

Environment Variables

# .env file
AZURE_AI_PROJECT_ENDPOINT=https://<your-ai-resource>.services.ai.azure.com/api/projects/<your-ai-project>/

Running the Sample

# Basic usage
python self_reflection.py

# With options
python self_reflection.py --input my_prompts.jsonl \
                          --output results.jsonl \
                          --max-reflections 5 \
                          -n 10

CLI Options:

  • --input, -i: Input JSONL file
  • --output, -o: Output JSONL file
  • --agent-model, -m: Agent model name (default: gpt-4.1)
  • --judge-model, -e: Evaluator model name (default: gpt-4.1)
  • --max-reflections: Max iterations (default: 3)
  • --limit, -n: Process only first N prompts

Understanding Results

The agent iteratively improves responses:

  1. Generate initial response
  2. Evaluate groundedness (1-5 scale)
  3. If score < 5, provide feedback and retry
  4. Stop at max iterations or perfect score (5/5)

Example output:

[1/31] Processing prompt 0...
  Self-reflection iteration 1/3...
  Groundedness score: 3/5
  Self-reflection iteration 2/3...
  Groundedness score: 5/5
  ✓ Perfect groundedness score achieved!
  ✓ Completed with score: 5/5 (best at iteration 2/3)