from empathy_os.workflows import SecurityAudit...

Just shipped v3.3.0 of Empathy Framework with features I wish existed when I was running AI at scale:

1. Formatted reports for every workflow (finally, readable output)
2. Cost guardrails so your doc-gen doesn’t blow $50 overnight
3. File export because 50k character terminal limits are real

Here’s what changed—and why it matters.

**The Problem with AI Workflows

Most AI libraries return raw JSON or unstructured text. Fine for prototypes. Terrible for:

• Reports you need to share with stakeholders
• Outputs you need to audit
• Results that exceed terminal/UI display limits

The Solution: Formatted Reports for All Workflows

Every workflow in v3.3.0 now includes a formatted_report with consistent structure:

from empathy_os.workflows import SecurityAuditWorkflow

workflow = SecurityAuditWorkflow()
result = await workflow.execute(code=your_code)

print(result.final_output["formatted_report"])

Output:

============================================================
SECURITY AUDIT REPORT
============================================================

Status: NEEDS_ATTENTION
Risk Score: 7.2/10
Vulnerabilities Found: 3

------------------------------------------------------------
CRITICAL FINDINGS
------------------------------------------------------------
- SQL injection in user_query() at line 42
- Hardcoded credentials in config.py
- Missing input validation in API handler

------------------------------------------------------------
RECOMMENDATIONS
------------------------------------------------------------
1. Use parameterized queries
2. Move secrets to environment variables
3. Add input sanitization layer

============================================================

This works across all 10 workflows: security-audit, code-review, perf-audit, doc-gen, test-gen, and more.

Enterprise Doc-Gen: Built for Large Projects

The doc-gen workflow got a major upgrade for enterprise use:

from empathy_os.workflows import DocumentGenerationWorkflow

workflow = DocumentGenerationWorkflow(
export_path="docs/generated",     # Auto-save to disk
max_cost=5.0,                     # Stop at $5 (prevent runaway costs)
chunked_generation=True,          # Handle large codebases
graceful_degradation=True,        # Partial results on errors
)

result = await workflow.execute(
source_code=your_large_codebase,
doc_type="api_reference",
audience="developers"
)

# Full docs saved to disk automatically
print(f"Saved to: {result.final_output['export_path']}")

What’s New:

Feature	What It Does
Auto-scaling tokens	2000 tokens/section, scales to 64k for large projects
Chunked generation	Generates in chunks of 3 sections to avoid truncation
Cost guardrails	Stops at configurable limit ($5 default)
File export	Saves .md and report to disk automatically
Output chunking	Splits large reports for terminal display

Cost Savings: 80-96%

Smart tier routing still saves 80-96% on API costs:

from empathy_llm_toolkit import EmpathyLLM

llm = EmpathyLLM(provider="hybrid", enable_model_routing=True)

# Automatically routes to the right model
await llm.interact(user_id="dev", task_type="summarize")     # → Haiku ($0.25/M)
await llm.interact(user_id="dev", task_type="fix_bug")       # → Sonnet ($3/M)
await llm.interact(user_id="dev", task_type="architecture")  # → Opus ($15/M)

Real savings:

• Without routing: $4.05/complex task
• With routing: $0.83/complex task
• 80% saved

Persistent Memory

Your AI remembers across sessions:

llm = EmpathyLLM(provider="anthropic", memory_enabled=True)

# Preference survives across sessions
response = await llm.interact(
user_id="dev_123",
user_input="I prefer Python with type hints"
)

Next session—even days later—it remembers.

Quick Start

# Install
pip install empathy-framework==3.3.0

# Configure provider
python -m empathy_os.models.cli provider --set anthropic

# See all commands
empathy cheatsheet

What’s in v3.3.0

• Formatted Reports — Consistent output across all 10 workflows
• Enterprise Doc-Gen — Auto-scaling, cost guardrails, file export
• Output Chunking — Large reports split for display
• Smart Router — Natural language wizard dispatch
• Memory Graph — Cross-wizard knowledge sharing

Resources

• GitHub
• Documentation
• PyPI

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What would you build with enterprise-ready AI workflows that cost 80% less?