tei-loop 0.1.2

Target, Evaluate, Improve: A self-improving loop for agentic systems

TEI Loop

Target, Evaluate, Improve — a self-improving loop for agentic systems.

TEI wraps any Python agent as a black box, evaluates its output across 4 dimensions using assertion-based LLM judges, and automatically applies targeted fixes when failures are detected.

Install

pip install tei-loop

# With your preferred LLM provider:
pip install 'tei-loop[openai]'    # OpenAI
pip install 'tei-loop[anthropic]' # Anthropic
pip install 'tei-loop[google]'    # Google Gemini
pip install 'tei-loop[all]'       # All providers

Quick Start

import asyncio
from tei_loop import TEILoop

# Your existing agent — any function that takes input and returns output
def my_agent(query: str) -> str:
    # ... your agent logic ...
    return result

async def main():
    loop = TEILoop(agent=my_agent)

    # Evaluate only (baseline measurement)
    result = await loop.evaluate_only("your test query")
    print(result.summary())

    # Full TEI loop (evaluate + improve + retry)
    result = await loop.run("your test query")
    print(result.summary())

    # Before/after comparison
    comparison = await loop.compare("your test query")
    print(f"Baseline: {comparison['baseline'].baseline_score:.2f}")
    print(f"With TEI: {comparison['improved'].final_score:.2f}")

asyncio.run(main())

CLI

# Evaluate your agent (baseline)
tei evaluate my_agent.py --query "test input" --verbose

# Run full improvement loop
tei improve my_agent.py --query "test input" --max-retries 3

# Before/after comparison
tei compare my_agent.py --query "test input"

# Generate config file
tei init

TEI CLI looks for a function named agent, run, or main in your Python file.

How It Works

1. Target

Define what success looks like. TEI evaluates across 4 dimensions:

Dimension	What it checks
Target Alignment	Did the agent pursue the correct objective?
Reasoning Soundness	Was the reasoning logical and non-contradictory?
Execution Accuracy	Were the right tools called with correct parameters?
Output Integrity	Is the output complete, accurate, and consistent?

2. Evaluate

TEI runs 4 LLM judges in parallel (~3-5 seconds). Each judge produces verifiable assertions, not subjective scores. Every claim is backed by evidence from the agent's output.

3. Improve

When a dimension fails, TEI applies the targeted fix strategy:

Failure	Fix Strategy
Target drift	Re-anchor to original objective
Flawed reasoning	Regenerate plan with failure context
Execution errors	Correct tool calls and parameters
Output issues	Repair factual errors and fill gaps

The loop retries automatically. Each cycle is sharper because the last was diagnosed.

Two Modes

Runtime mode (default): Per-query, 1-3 retries, fixes individual failures in seconds.

Development mode: Across many queries, proposes permanent prompt improvements.

# Development mode
dev_results = await loop.develop(
    queries=["query1", "query2", "query3", ...],
    max_iterations=50,
)
print(f"Avg improvement: {dev_results['avg_improvement']:+.2f}")

Configuration

TEI auto-detects your LLM provider from environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY). No new accounts or API keys needed.

loop = TEILoop(
    agent=my_agent,
    eval_llm="gpt-5.2",          # Smartest for evaluation
    improve_llm="gpt-5-mini",  # Cost-effective for fixes
    max_retries=3,
    verbose=True,
)

Or via .tei.yaml:

tei init  # generates config file

Cost

Scenario	Cost
Agent passes all dimensions	~$0.005
One improvement cycle	~$0.025
Full 3-retry loop	~$0.07

TEI shows the cost estimate before running.

Works With

TEI wraps any Python callable. No framework lock-in:

• LangGraph agents
• CrewAI crews
• Custom Python functions
• FastAPI endpoints
• Any callable that takes input and returns output

License

MIT