11---
2- title : Give Your AI Persistent Memory (and Cut Costs 80%)
2+ title : Enterprise-Ready AI Workflows: Formatted Reports + 80% Cost Savings
33published : false
4- description : How to make Claude/GPT remember your preferences across sessions using the Empathy Framework v3.2.5
4+ description : How Empathy Framework v3.3.0 gives you professional reports, cost guardrails, and persistent memory for production AI
55tags : python, ai, claude, openai, llm
66cover_image :
77---
88
9- # Give Your AI Persistent Memory (and Cut Costs 80%)
9+ # Enterprise-Ready AI Workflows: Formatted Reports + 80% Cost Savings
1010
11- Every conversation with Claude starts from scratch. Tell it you prefer concise code examples, and next session? Forgotten.
11+ Just shipped v3.3.0 of Empathy Framework with features I wish existed when I was running AI at scale:
1212
13- Here's how to fix that—and save 80% on API costs while you're at it.
13+ 1 . ** Formatted reports** for every workflow (finally, readable output)
14+ 2 . ** Cost guardrails** so your doc-gen doesn't blow $50 overnight
15+ 3 . ** File export** because 50k character terminal limits are real
1416
15- ## The Problem
17+ Here's what changed—and why it matters.
1618
17- LLM APIs are stateless. Each request is independent. For simple Q&A, that's fine. But for:
19+ ## The Problem with AI Workflows
1820
19- - Development assistants that learn your coding style
20- - Support bots that remember customer history
21- - Personal tools that adapt to preferences
21+ Most AI libraries return raw JSON or unstructured text. Fine for prototypes. Terrible for:
2222
23- ...you need memory that persists.
23+ - Reports you need to share with stakeholders
24+ - Outputs you need to audit
25+ - Results that exceed terminal/UI display limits
2426
25- ## The Solution: 10 Lines of Python
27+ ## The Solution: Formatted Reports for All Workflows
28+
29+ Every workflow in v3.3.0 now includes a ` formatted_report ` with consistent structure:
2630
2731``` python
28- from empathy_llm_toolkit import EmpathyLLM
32+ from empathy_os.workflows import SecurityAuditWorkflow
2933
30- llm = EmpathyLLM(
31- provider = " anthropic" # or "openai", "ollama", "hybrid"
32- memory_enabled = True
33- )
34+ workflow = SecurityAuditWorkflow()
35+ result = await workflow.execute(code = your_code)
3436
35- # This preference survives across sessions
36- response = await llm.interact(
37- user_id = " dev_123"
38- user_input = " I prefer Python with type hints, no docstrings"
39- )
37+ print (result.final_output[" formatted_report"
4038```
4139
42- That's it. Next time this user connects—even days later—the AI remembers.
43-
44- ## Why This Actually Matters
45-
46- ### 1. Cost Savings (80%)
40+ Output:
41+ ```
42+ ============================================================
43+ SECURITY AUDIT REPORT
44+ ============================================================
45+
46+ Status: NEEDS_ATTENTION
47+ Risk Score: 7.2/10
48+ Vulnerabilities Found: 3
49+
50+ ------------------------------------------------------------
51+ CRITICAL FINDINGS
52+ ------------------------------------------------------------
53+ - SQL injection in user_query() at line 42
54+ - Hardcoded credentials in config.py
55+ - Missing input validation in API handler
56+
57+ ------------------------------------------------------------
58+ RECOMMENDATIONS
59+ ------------------------------------------------------------
60+ 1. Use parameterized queries
61+ 2. Move secrets to environment variables
62+ 3. Add input sanitization layer
63+
64+ ============================================================
65+ ```
4766
48- Smart routing automatically picks the right model for each task:
67+ This works across all 10 workflows: security-audit, code-review, perf-audit, doc-gen, test-gen, and more.
4968
50- | Task | Model | Cost |
51- | ------| -------| ------|
52- | Summarize text | Haiku/GPT-4o-mini | $0.25/M tokens |
53- | Fix bugs | Sonnet/GPT-4o | $3/M tokens |
54- | Design architecture | Opus/o1 | $15/M tokens |
69+ ## Enterprise Doc-Gen: Built for Large Projects
5570
56- ** Real numbers:**
57- - Without routing (all Opus): $4.05/complex task
58- - With routing (tiered): $0.83/complex task
59- - ** Savings: 80%**
71+ The doc-gen workflow got a major upgrade for enterprise use:
6072
6173``` python
62- llm = EmpathyLLM( provider = " anthropic " , enable_model_routing = True )
74+ from empathy_os.workflows import DocumentGenerationWorkflow
6375
64- # Automatically routes to Haiku
65- await llm.interact(user_id = " dev" user_input = " Summarize this" task_type = " summarize"
76+ workflow = DocumentGenerationWorkflow(
77+ export_path = " docs/generated" # Auto-save to disk
78+ max_cost = 5.0 , # Stop at $5 (prevent runaway costs)
79+ chunked_generation = True , # Handle large codebases
80+ graceful_degradation = True , # Partial results on errors
81+ )
6682
67- # Automatically routes to Opus
68- await llm.interact(user_id = " dev" user_input = " Design the system" task_type = " coordinate"
83+ result = await workflow.execute(
84+ source_code = your_large_codebase,
85+ doc_type = " api_reference"
86+ audience = " developers"
87+ )
88+
89+ # Full docs saved to disk automatically
90+ print (f " Saved to: { result.final_output[' export_path' } " )
6991```
7092
71- ### 2. Bug Memory
93+ ### What's New:
7294
73- My debugging wizard remembers every fix:
95+ | Feature | What It Does |
96+ | ---------| --------------|
97+ | ** Auto-scaling tokens** | 2000 tokens/section, scales to 64k for large projects |
98+ | ** Chunked generation** | Generates in chunks of 3 sections to avoid truncation |
99+ | ** Cost guardrails** | Stops at configurable limit ($5 default) |
100+ | ** File export** | Saves .md and report to disk automatically |
101+ | ** Output chunking** | Splits large reports for terminal display |
74102
75- ``` python
76- result = await wizard.analyze({
77- " error_message" " TypeError: Cannot read property 'map' of undefined"
78- " file_path" " src/components/UserList.tsx"
79- })
80-
81- print (result[" historical_matches"
82- # "This looks like bug #247 from 3 months ago"
83- # "Suggested fix: data?.items ?? []"
84- ```
103+ ## Cost Savings: 80-96%
85104
86- Without memory, every bug starts from zero. With it, your AI assistant ** remembers every fix ** and suggests proven solutions.
105+ Smart tier routing still saves 80-96% on API costs:
87106
88- ### 3. Provider Freedom
107+ ``` python
108+ from empathy_llm_toolkit import EmpathyLLM
89109
90- Not locked into one provider. Switch anytime:
110+ llm = EmpathyLLM( provider = " hybrid " , enable_model_routing = True )
91111
92- ``` bash
93- empathy provider set anthropic # Use Claude
94- empathy provider set openai # Use GPT
95- empathy provider set ollama # Use local models
96- empathy provider set hybrid # Best of each
112+ # Automatically routes to the right model
113+ await llm.interact(user_id = " dev" task_type = " summarize" # → Haiku ($0.25/M)
114+ await llm.interact(user_id = " dev" task_type = " fix_bug" # → Sonnet ($3/M)
115+ await llm.interact(user_id = " dev" task_type = " architecture" # → Opus ($15/M)
97116```
98117
99- Use Ollama for sensitive code, Claude for complex reasoning, GPT for specific tasks.
118+ ** Real savings:**
119+ - Without routing: $4.05/complex task
120+ - With routing: $0.83/complex task
121+ - ** 80% saved**
100122
101- ## Smart Router
123+ ## Persistent Memory
102124
103- Natural language routing—no need to know which tool to use :
125+ Your AI remembers across sessions :
104126
105127``` python
106- from empathy_os.routing import SmartRouter
128+ llm = EmpathyLLM( provider = " anthropic " , memory_enabled = True )
107129
108- router = SmartRouter()
109-
110- # Natural language → right wizard
111- decision = router.route_sync(" Fix the security vulnerability in auth.py"
112- print (f " Primary: { decision.primary_wizard} " ) # → security-audit
113- print (f " Confidence: { decision.confidence} " ) # → 0.92
130+ # Preference survives across sessions
131+ response = await llm.interact(
132+ user_id = " dev_123"
133+ user_input = " I prefer Python with type hints"
134+ )
114135```
115136
116- Examples:
117- - "Fix security in auth.py" → SecurityWizard
118- - "Review this PR" → CodeReviewWizard
119- - "Why is this slow?" → PerformanceWizard
137+ Next session—even days later—it remembers.
120138
121139## Quick Start
122140
123141``` bash
124142# Install
125- pip install empathy-framework
126-
127- # Check available providers (auto-detects API keys)
128- empathy provider status
143+ pip install empathy-framework==3.3.0
129144
130- # Set your provider
131- empathy provider set anthropic
145+ # Configure provider
146+ python -m empathy_os.models.cli provider -- set anthropic
132147
133148# See all commands
134149empathy cheatsheet
135150```
136151
137- ## What's in v3.2.5
152+ ## What's in v3.3.0
138153
139- - ** Unified CLI ** — One ` empathy ` command with Rich output
140- - ** Dev Container ** — Clone → Open in VS Code → Start coding
141- - ** Python 3.10-3.13 ** — Full test matrix across all versions
154+ - ** Formatted Reports ** — Consistent output across all 10 workflows
155+ - ** Enterprise Doc-Gen ** — Auto-scaling, cost guardrails, file export
156+ - ** Output Chunking ** — Large reports split for display
142157- ** Smart Router** — Natural language wizard dispatch
143158- ** Memory Graph** — Cross-wizard knowledge sharing
144159
@@ -150,4 +165,4 @@ empathy cheatsheet
150165
151166---
152167
153- * What would you build with an AI that remembers—and costs 80% less?*
168+ * What would you build with enterprise-ready AI workflows that cost 80% less?*
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