steer-sdk 0.3.0

The Active Reliability Layer for AI Agents

Steer

steer-labs.com

The Active Reliability Layer for AI Agents.

Stop debugging. Start teaching.
Steer turns runtime hallucinations into permanent fixes instantly.

Mission Control: Catching hallucinations locally and fixing them with one click.

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The Problem

When an agent fails in production (e.g., outputs bad JSON), logging the error is insufficient. You typically have to:

1. Dig through logs to find the specific prompt.
2. Edit your prompt template manually.
3. Redeploy the application.

The Solution

Steer wraps your agent function with deterministic Reality Locks. When a failure is detected, Steer blocks the output and logs it to a local dashboard. You click Teach to provide a correction (e.g., "Use Strict JSON"), and Steer injects that rule into the agent context for future runs without a code change.

Installation

pip install steer-sdk

Quickstart

Generate the example scripts to see the workflow in action:

steer init
# Generates 01_structure_guard.py, 02_safety_guard.py, 03_logic_guard.py, 04_slop_guard.py

steer ui
# Starts the local dashboard at http://localhost:8000

Reality Locks in Action

The Steer workflow follows a simple loop: Catch → Teach → Fix.

1. Structure Guard (JSON)

Problem: Agent wraps JSON in Markdown backticks, breaking your parser. Fix: Block the output and enforce raw JSON formatting.

2. Safety Guard (PII)

Problem: Agent accidentally leaks customer emails or internal keys. Fix: Block the response and enforce redaction protocols.

3. Logic Guard (Ambiguity)

Problem: Agent guesses a city (e.g., Springfield, IL) instead of asking for clarification. Fix: Force the agent to ask the user clarifying questions.

4. Slop Filter (Brand Voice)

Problem: Agent uses "AI-voice" (emojis, em dashes, apologies) that signals uncurated slop. Fix: Block LLM fingerprints and enforce a blunt, professional signal.

Cookbook

Explore the cookbook/ directory for enterprise-grade implementations.

RAG Reliability

Demonstrates how to enforce strict data schemas and grounding citations in a Retrieval-Augmented Generation (RAG) pipeline.

• Pydantic Schema Enforcement: Ensuring the agent always returns a valid, typed data structure.
• Citation Verification: Hard-locking the agent to cite its sources, preventing ungrounded claims.

View RAG Cookbook

Integration

To add Steer to your own agent, add steer_rules to your function arguments. Steer populates this automatically based on your dashboard teaching.

from steer import capture
from steer.verifiers import JsonVerifier

# 1. Define Verifiers
json_check = JsonVerifier(name="Strict JSON")

# 2. Decorate your Agent Function
@capture(verifiers=[json_check])
def my_agent(user_input, steer_rules=""):
    
    # 3. Pass 'steer_rules' to your system prompt.
    system_prompt = f"You are a helpful assistant.\n{steer_rules}"
    
    # ... Your LLM call ...
    return llm.call(system_prompt, user_input)

Data Engine: From Guardrails to Fine-Tuning

Every time a rule is applied or an agent succeeds, Steer logs the interaction. You can export these logs into a standard fine-tuning format (JSONL) compatible with OpenAI and Anthropic.

Export Training Data

Run this command to convert local logs into a dataset:

steer export

Output: steer_fine_tune.jsonl

The Fine-Tuning Workflow

1. Capture: Run your agent with Steer. Fix issues in the Dashboard.
2. Export: Run steer export to generate the dataset.
3. Train: Upload the file to your provider to fine-tune a model.
4. Remove: Once the model learns the boundaries, you can often remove the guardrails.

What is the "Confident Idiot" Problem?

The Confident Idiot is a failure mode where an LLM generates a factually incorrect or structurally broken response with high probability (confidence). LLMs fail silently and plausibly.

• Example: User asks "Weather in Springfield". The agent confidently guesses "Springfield, IL" (ignoring the fact that there are 33 other Springfields in the USA).
• The Fix: Steer prevents this by enforcing Reality Locks (deterministic checks) that run after generation but before the user sees the response.

Read the viral discussion on Hacker News.

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