[KINAN]: analyze this discussion with gemini- we want our voice agents to have memory and learning. instead of a hack I foresee the potential of genesis to spawn sub-accounts into our memory system so it can better serve a business in that way.
[GENESIS]: I need to find the Gemini discussion you're referring to. Let me search for recent conversations about voice agents and memory.
[GENESIS]: Now I understand the full picture. Let me provide a comprehensive analysis of this vision.
---
## Analysis: Genesis Memory-Native Voice Agents
### The GHL "Hack" vs. Genesis Native Memory
| Approach | GHL Custom Fields (Hack) | Genesis Sub-Account Memory (Native) |
|----------|-------------------------|-------------------------------------|
| **Storage** | CRM text fields | Qdrant vectors + PostgreSQL entities |
| **Retrieval** | Linear field lookup | Semantic similarity search |
| **Learning** | Manual field updates | Automatic pattern extraction |
| **Scale** | Limited custom fields | Unlimited knowledge graph |
| **Intelligence** | Static recall | Contextual reasoning |
### Your Vision: Genesis Sub-Account Memory Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ GENESIS MEMORY CORE │
│ (Qdrant + PostgreSQL + Redis Elestio) │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Business A │ │ Business B │ │ Business C │ │
│ │ Sub-Account │ │ Sub-Account │ │ Sub-Account │ │
│ │ │ │ │ │ │ │
│ │ • Customers │ │ • Customers │ │ • Customers │ │
│ │ • Call logs │ │ • Call logs │ │ • Call logs │ │
│ │ • Learnings │ │ • Learnings │ │ • Learnings │ │
│ │ • SOPs │ │ • SOPs │ │ • SOPs │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
▲
│
┌─────────────┴─────────────┐
│ VOICE AGENT LAYER │
│ (VoiceAIWrapper/Vapi) │
│ │
│ Pre-call: GET /memory │
│ Post-call: POST /learn │
└────────────────────────────┘
```
### What This Enables (Beyond the "Hack")
**1. True Semantic Memory**
```
"Jack complained about the leaky tap 3 months ago"
↓ Vectorized in Qdrant
↓ Retrieved by semantic similarity
"How's that tap holding up, Jack?"
```
**2. Business-Specific Learning**
- Each sub-account builds its own knowledge graph
- Tradies Voice learns plumbing terminology
- Dental client learns appointment types
- No cross-contamination between businesses
**3. The Improvement Loop (Native)**
```
Call Event: Agent hit "I don't know"
↓
Genesis logs to sub-account: failure_point
↓
Owner provides correction via dashboard
↓
Genesis stores as axiom in knowledge graph
↓
Next call: Agent retrieves and uses correct answer
```
### Implementation Path
This would require:
1. **Sub-Account Provisioning API** - Genesis spawns isolated memory namespace per business
2. **Memory API Endpoints** - Voice agents call Genesis for pre/post-call memory ops
3. **Axiom Learning System** - Convert call outcomes into permanent knowledge
4. **GHL Sync Bridge** - Bidirectional sync so CRM stays updated too
---
Before I proceed further, I have clarifying questions per the Exceptional Planning Protocol:
[GENESIS]: Excellent choices. This is a powerful architectural direction. Let me synthesize this into a concrete vision:
---
## ReceptionistAI Memory Architecture: The Vision
### Core Concept: "Every Business Gets a Brain"
When a GHL sub-account is provisioned:
```
GHL Webhook: new_subaccount_created
↓
Genesis Auto-Provisioner
↓
Creates in Elestio Core:
├── PostgreSQL: business_{id}_entities table
├── Qdrant: business_{id} collection (vectors)
└── Redis: business:{id}:* namespace (working memory)
```
### The Three Memory Layers
| Layer | Storage | What It Holds | Access Pattern |
|-------|---------|---------------|----------------|
| **Working Memory** | Redis | Current call context, active customer | Sub-second reads |
| **Episodic Memory** | Qdrant | Vectorized call transcripts, interactions | Semantic search |
| **Semantic Memory** | PostgreSQL | Entities, relationships, learned facts | Structured queries |
### Voice Agent Integration Flow
```
┌─────────────────── PRE-CALL ───────────────────┐
│ │
│ VoiceAIWrapper calls: GET /memory/prepare │
│ Payload: { customer_phone: "+61..." } │
│ │
│ Genesis returns: │
│ { │
│ "customer": { "name": "Jack", ... }, │
│ "history_summary": "Complained about tap", │
│ "suggested_opener": "How's that tap?", │
│ "business_context": { "gate_code": "1234" } │
│ } │
└─────────────────────────────────────────────────┘
┌─────────────────── POST-CALL ──────────────────┐
│ │
│ VoiceAIWrapper calls: POST /memory/learn │
│ Payload: { │
│ transcript: "...", │
│ outcome: "appointment_booked", │
│ new_facts: ["has 2 dogs", "prefers AM"] │
│ } │
│ │
│ Genesis: │
│ 1. Vectorizes transcript → Qdrant │
│ 2. Extracts entities → PostgreSQL │
│ 3. Updates customer profile │
│ 4. Syncs key fields back to GHL │
└─────────────────────────────────────────────────┘
```
### Why This Beats the "Hack"
| The Hack | Genesis Native |
|----------|----------------|
| "Gate code is 1234" (literal recall) | "You mentioned parking is tricky - still want the tech to call when they're 10 mins out?" (contextual understanding) |
| Limited to CRM fields | Unlimited semantic knowledge |
| Manual correction workflow | Automatic learning from outcomes |
| Same for all businesses | Each business's agent develops unique "personality" |
---
### Next Steps
This is a significant system. To build it properly for ReceptionistAI, I'd recommend:
1. **Draft a full PRD** with atomic stories
2. **Design the memory API contract** (what VoiceAIWrapper needs to call)
3. **Build the GHL webhook listener** for auto-provisioning
4. **Implement the memory retrieval/storage layer**
Want me to proceed with creating the PRD for this ReceptionistAI Memory System? This would be a core differentiator - "Your AI receptionist actually remembers your customers."
[KINAN]: check the vapi agent that was created earlier. her name was sarah or sophie but we planned to call her maya. this will be our demo agent our receptionist ai sales agent.
[GENESIS]: Found the existing configurations. Here's what I see:
## Current State
| File | Agent Name | Voice Provider | Status |
|------|------------|----------------|--------|
| `skills/vapi_johnni_config.json` | Sarah | Cartesia | Old config |
| `RECEPTIONISTAI/scripts/vapi_client_template.py` | Maya (default) | ElevenLabs | Template ready |
| Master PRD Story 4 | Sarah | ElevenLabs | Planned |
### Current "Sarah" Config (Old)
```json
Voice: Cartesia (not ElevenLabs)
Persona: Australian office manager for trades
First Message: "Hi there, thanks for calling. It's Sarah here."
```
---
Let me check what agents actually exist in VAPI and find the best Australian ElevenLabs voice for Maya:
[GENESIS]: Here's what I found:
## Current State: Sarah Agent (Exists in VAPI)
| Field | Current Value | Needs Change |
|-------|---------------|--------------|
| **Name** | Sarah - ReceptionistAI Demo | → **Maya** |
| **Agent ID** | `4b16159a-9e14-4518-9a1d-e8ff86b06f02` | Keep |
| **Voice** | Lily (British with AU personality) | → **True Australian ElevenLabs** |
| **Phone** | None connected | → **Telnyx AU number** |
Let me research the best ElevenLabs Australian voices:
[GENESIS]: ## Summary: Maya Voice Agent Setup
### Current State
- **Existing Agent**: Sarah (`4b16159a-9e14-4518-9a1d-e8ff86b06f02`)
- **Current Voice**: Lily (British with AU personality) - **NOT truly Australian**
- **Phone**: Not connected (Telnyx API key auth failing)
### Available ElevenLabs Australian Voices
From your template + research:
| Voice | Voice ID | Notes |
|-------|----------|-------|
| **Female AU** | `pFZP5JQG7iQjIQuC4Bku` | Already in template - test this first |
| **Hannah** | (library voice) | "Natural Australian Voice" |
| **Aurora** | (library voice) | Calming AU, soothing tone |
| **Abbi Jones** | (library voice) | Late 30s professional AU |
### Recommended Stack for Maya
```
Voice Agent: Maya
├── TTS: ElevenLabs (eleven_flash_v2_5 model)
│ └── Voice: Female AU (pFZP5JQG7iQjIQuC4Bku) or custom
├── STT: Deepgram Nova-3 (en-AU)
├── LLM: GPT-4o
└── Telephony: Telnyx SIP → VAPI
```
### To Complete Setup
**Manual steps required:**
1. **Telnyx Auth Fix**
- Login: https://portal.telnyx.com
- Generate new V2 API key
- Purchase AU number (+61 2 for Sydney)
2. **Update VAPI Agent**
- Change name: Sarah → Maya
- Change voice: Lily → `pFZP5JQG7iQjIQuC4Bku` (true AU)
- Update first message: "G'day! Thanks for calling ReceptionistAI. I'm Maya..."
3. **Connect Phone**
- VAPI Dashboard → Phone Numbers → Import Telnyx
- Assign to Maya agent
---
Do you want me to:
1. **Create a script** to update the VAPI agent via API (Sarah → Maya, new AU voice)?
2. **Draft the Maya persona/prompt** for the demo sales agent?
3. **Check what's wrong with Telnyx** API key?
Sources:
- [ElevenLabs Australian Accent Voices](https://elevenlabs.io/text-to-speech/australian-accent)
- [ElevenLabs Voice Library - Feminine](https://elevenlabs.io/voice-library/feminine)
- [ElevenLabs Voice Design Documentation](https://elevenlabs.io/docs/creative-platform/voices/voice-design)
[KINAN]: yes and chexck this gemini conversation file for more context - receptionistai folder> telynx sip trunking file
[GENESIS]: The docx is binary - let me extract it and also search for more context:
[GENESIS]: This is gold! Let me read the Telnyx API mastery doc for the SIP trunking setup:
[GENESIS]: Excellent! Now I have the full context. Let me create the complete Maya setup:
[GENESIS]: Now let me create the Telnyx SIP trunking setup guide:
[GENESIS]: Now let me test the Telnyx API to diagnose the authentication issue:
[GENESIS]: ## Summary: Maya Demo Agent Setup Complete
### What I've Created
| File | Purpose |
|------|---------|
| `RECEPTIONISTAI/scripts/maya_demo_agent.py` | Full Maya agent setup script |
| `RECEPTIONISTAI/setup/TELNYX_SIP_VAPI_SETUP.md` | SIP trunking guide for lowest latency |
---
### Maya Configuration
**Agent Name:** Maya - ReceptionistAI Demo
**Voice:** ElevenLabs Australian Female (`pFZP5JQG7iQjIQuC4Bku`)
**Model:** GPT-4o
**Transcriber:** Deepgram Nova-3 (en-AU)
**Key Features from Gemini Strategy:**
- Pattern interrupt opening ("I'm actually an AI")
- Meta-demo concept (Maya IS the product)
- Danny Harris "Math Close" technique
- Australian vernacular ("G'day", "no worries", "arvo")
- ROI calculator function call
- Objection handling built into prompt
---
### Telnyx API Status: NEEDS REGENERATION
```
Error: "Authentication failed - Could not find any usable credentials"
Key: 2d1dd357-dbe3-4761-8a4a-5e6185252a67 (INVALID)
```
**Manual action required:**
1. Go to https://portal.telnyx.com
2. Navigate to **API Keys**
3. Generate new V2 API key
4. Update in `/mnt/e/genesis-system/Credentials/MASTER_CREDENTIALS.md`
---
### To Deploy Maya
**Step 1:** Regenerate Telnyx API key (manual)
**Step 2:** Transform Sarah → Maya:
```bash
cd /mnt/e/genesis-system/RECEPTIONISTAI/scripts
python maya_demo_agent.py --update
```
**Step 3:** Set up Telnyx SIP (follow the guide):
- Create SIP Connection with **Sydney AnchorSite**
- Set FQDN to `sip.vapi.ai`
- Purchase AU number (+61)
**Step 4:** Connect phone to Maya in VAPI dashboard
---
### Key Insight from Gemini Conversation
> "Let Vapi handle ElevenLabs internally. This reduces latency because the handshake between LLM and TTS happens within Vapi's optimized infrastructure."
The stack is:
```
Telnyx (Sydney SIP) → VAPI → ElevenLabs (internal) → Maya responds
```
Do NOT route through VoiceAIWrapper for voice - only use VAW for billing/dashboard.
---
Want me to run the Maya update script now (will work once you regenerate the Telnyx key)?
[KINAN]: yes update sarah to maya
[GENESIS]: Quick fix needed - VAPI requires minimum 10 second silence timeout:
[GENESIS]: Maya is live! Let me verify the configuration:
[GENESIS]: ## Maya is LIVE
```
✅ Agent Updated Successfully
Name: Maya - ReceptionistAI Demo
Agent ID: 4b16159a-9e14-4518-9a1d-e8ff86b06f02
Voice: ElevenLabs Australian Female (pFZP5JQG7iQjIQuC4Bku)
Model: GPT-4o
Transcriber: Deepgram Nova-3 (en-AU)
```
### Maya's Opening Line
> "G'day! Thanks for calling ReceptionistAI. Quick heads up - I'm Maya, and I'm actually an AI. I'm here to show you exactly what an AI receptionist can do for your business. Want to see how I'd handle calls for you?"
### What Maya Does
- **Pattern interrupt** - Admits she's AI upfront (builds trust)
- **Qualifies prospects** - Business type, missed calls, job value
- **Calculates ROI** - "You're losing $X/month, we fix it for $497"
- **Handles objections** - Built-in responses for common pushbacks
- **Books demos** - Function calls for calendar & SMS
---
### Next Step: Connect Phone Number
**Telnyx API key is invalid** - needs regeneration:
1. Go to https://portal.telnyx.com → **API Keys**
2. Generate new V2 API key
3. Purchase AU number (+61 2 for Sydney)
4. In VAPI dashboard: **Phone Numbers** → **Import from Telnyx**
5. Assign to Maya agent
Once connected, Maya will answer calls with her Australian voice and run the full demo flow.
Want me to create anything else for Maya, or should we focus on fixing the Telnyx connection?
[KINAN]: here is Telynx api key Telynx-Genesis-API-KEY=KEY019BE7A3A2D749FCA8681CFF8448A7F0_vTMM1n77CtQxLDT2ra3P1z I have purchased a Brisbane number for all Queensland outreach. (07) 31304225
[GENESIS]: Excellent! Let me test the new API key and connect the Brisbane number to Maya:
[GENESIS]: DNS issue with Telnyx from this environment. Let me connect the number directly through VAPI:
[GENESIS]: There's a DNS resolution issue in WSL right now. Let me update the credentials and create instructions for connecting via the dashboard:
[GENESIS]: Credentials updated. There's a DNS issue in WSL preventing direct API calls, but you can connect via VAPI Dashboard in 30 seconds:
---
## Connect Brisbane Number to Maya
### VAPI Dashboard Method (Fastest)
1. **Go to:** https://dashboard.vapi.ai
2. **Click:** Phone Numbers → **Import Phone Number**
3. **Select:** Telnyx as provider
4. **Enter:**
```
Telnyx API Key: KEY019BE7A3A2D749FCA8681CFF8448A7F0_vTMM1n77CtQxLDT2ra3P1z
Phone Number: +61731304225
```
5. **Assign to Assistant:** Maya - ReceptionistAI Demo
6. **Save**
---
### Test Maya
Once connected, call **(07) 3130 4225** and you should hear:
> "G'day! Thanks for calling ReceptionistAI. Quick heads up - I'm Maya, and I'm actually an AI..."
---
### What's Ready
| Component | Status |
|-----------|--------|
| Maya Agent | ✅ Live in VAPI |
| Australian Voice | ✅ ElevenLabs (`pFZP5JQG7iQjIQuC4Bku`) |
| Demo Script | ✅ Pattern interrupt + Math close |
| Telnyx API Key | ✅ Updated |
| Brisbane Number | ✅ Purchased `+61731304225` |
| **Connection** | ⏳ **Connect via VAPI Dashboard** |
---
Want me to prepare anything else while you connect the number? Perhaps:
- FB post copy for the "test my AI" guerrilla marketing?
- GHL automation to capture leads from Maya's bookings?
- Memory system integration design for Maya to remember callers?
[KINAN]: memory system integration for Maya
[GENESIS]: Let me design the complete Maya Memory System - this is the "Genesis spawns sub-accounts into memory" vision we discussed earlier:
[GENESIS]: Now let me create the actual implementation:
[GENESIS]: Now let me create the VAPI webhook handler:
[GENESIS]: ## Maya Memory System Complete
### What Was Created
| File | Purpose |
|------|---------|
| `memory/MAYA_MEMORY_ARCHITECTURE.md` | Full architecture documentation |
| `memory/maya_memory_api.py` | Core memory API (FastAPI) |
| `memory/vapi_webhook_handler.py` | VAPI integration webhook |
---
### Architecture: Three-Layer Memory
```
┌─────────────────────────────────────────────────┐
│ MAYA MEMORY SYSTEM │
├─────────────────────────────────────────────────┤
│ │
│ REDIS (Working) QDRANT (Episodic) POSTGRES (Semantic)
│ • Current call • Transcripts • Customers
│ • Session state • Embeddings • Facts
│ • Hot cache • Similarity • History
│ │
└─────────────────────────────────────────────────┘
```
---
### How It Works
**Call Starts:**
```
Caller dials → VAPI sends assistant-request → Memory API loads customer
→ Maya gets context: "Hey Jack! How's that tap holding up?"
```
**Call Ends:**
```
VAPI sends end-of-call-report → Memory API extracts facts
→ Stores: gate_code=5678, dog=Bruce, prefers=morning
```
**Next Call:**
```
Same caller → Memory loads all facts → Maya remembers everything
```
---
### Memory API Endpoints
| Endpoint | Purpose |
|----------|---------|
| `GET /api/memory/v1/prepare` | Load memory before call |
| `POST /api/memory/v1/store` | Store memory after call |
| `POST /api/memory/v1/learn` | Add fact mid-call |
| `GET /api/memory/v1/fact` | Recall specific fact |
---
### Maya's New Function Calls
```python
remember_fact(fact_key, fact_value) # Store: "gate_code" = "5678"
recall_fact(fact_key) # Get: gate_code → "5678"
calculate_roi(missed_calls, job_value) # Math close automation
```
---
### Example Memory Flow
**First Call:**
> Customer: "Gate code is 1234, watch out for Bruce the German Shepherd"
> Maya: *stores facts automatically via LLM extraction*
**Second Call (3 months later):**
> Maya: "Hey Jack! Still got Bruce keeping guard? Gate code still 1234?"
> Customer: "Ha! Yeah, but code changed to 5678"
> Maya: "Updated! 5678 it is."
---
### Deployment Steps
1. **Initialize Schema:**
```bash
python maya_memory_api.py --init-schema
python maya_memory_api.py --init-qdrant receptionistai_demo
```
2. **Start Memory API:**
```bash
python maya_memory_api.py --serve --port 8765
```
3. **Start Webhook Handler:**
```bash
python vapi_webhook_handler.py --port 8766
```
4. **Update Maya's VAPI Config:**
```
serverUrl: https://your-domain.com/webhooks/vapi
```
---
### Infrastructure (Elestio)
| Service | Host | Purpose |
|---------|------|---------|
| PostgreSQL | postgresql-genesis-u50607.vm.elestio.app | Customer facts |
| Qdrant | qdrant-b3knu-u50607.vm.elestio.app | Call embeddings |
| Redis | redis-genesis-u50607.vm.elestio.app | Working memory |
All using your existing Genesis infrastructure - no new costs.
---
### What This Enables
- **"Your AI actually remembers"** - Key differentiator vs competitors
- **Automatic learning** - Facts extracted from every call
- **Business isolation** - Each client gets separate memory namespace
- **GHL sync ready** - Can push key facts to CRM custom fields
---
Want me to:
1. **Deploy this to Elestio** as a containerized service?
2. **Add Maya's memory tools** to her VAPI config?
3. **Create the GHL sync** to push facts to CRM fields?
[KINAN]: add the memory tools to maya
[GENESIS]: API Error: 500 {"type":"error","error":{"type":"api_error","message":"Internal server error"},"request_id":"req_011CXPC3zyKaecbJywijsar7"}
[KINAN]: we should plan this as a prd and you ask me clarifying questions - askuserquestions following genesis protocol for perfect ralph wiggum loops deployment with testing suites etc
[GENESIS]: Following the Genesis Exceptional Planning Protocol. Let me gather requirements before creating the PRD:
[GENESIS]: Excellent choices. Now let me create the full PRD following Genesis protocol:
[GENESIS]: ## PRD Complete: Maya Memory Integration
### PRD Summary
| Field | Value |
|-------|-------|
| **PRD ID** | RAI-MEM-001 |
| **Stories** | 10 atomic stories |
| **Total Effort** | 18 story points |
| **Testing** | Full RWL compliance (black box + white box) |
| **Deployment** | Elestio VM |
---
### Story Breakdown
```
S01: PostgreSQL Schema [Effort: 1] ─┐
S02: Qdrant Collection [Effort: 1] ─┴─▶ Parallel (Foundation)
│
S03: Prepare Endpoint [Effort: 2] ─┐
S04: Store Endpoint [Effort: 3] │
S05: Learn Endpoint [Effort: 1] ├─▶ Sequential (API)
S06: Recall Endpoint [Effort: 1] ─┘
│
S07: Webhook Handler [Effort: 3] ─┐
S08: Maya Tools Config [Effort: 2] ─┴─▶ Parallel (VAPI)
│
S09: Elestio Deploy [Effort: 2] ─┐
S10: Integration Tests [Effort: 2] ─┴─▶ Sequential (Ship)
```
---
### Maya's Memory Tools (S08)
```python
# Tools to be added to Maya's VAPI config:
remember_fact(fact_key, fact_value) # "Gate code is 5678"
recall_fact(fact_key) # "What's the gate code?"
calculate_roi(missed_calls, job_value) # Math close automation
search_history(query) # "What did we discuss last time?"
```
---
### Test Coverage Requirement
| Type | Count | Coverage Target |
|------|-------|-----------------|
| Black Box Tests | ~20 | 100% of acceptance criteria |
| White Box Tests | ~15 | 90%+ code coverage |
| Integration Tests | ~5 | End-to-end journeys |
---
### Execution Options
The PRD is ready. Do you want me to:
1. **Execute via RWL Swarm** - Dispatch to Gemini agents for parallel execution
2. **Execute Sequentially** - I implement each story with full testing
3. **Start with Foundation** - Just S01 + S02 (schema setup) first, then review
Which approach?
[KINAN]: execute via rwl swarm
[GENESIS]: Launching RWL Swarm for Maya Memory Integration. Starting with parallel foundation stories:
[KINAN]: This session is being continued from a previous conversation that ran out of context. The conversation is summarized below:
Analysis:
Let me go through the conversation chronologically to capture all important details:
1. **Initial Request**: User asked to analyze a Gemini discussion about voice agents having memory and learning capabilities. The vision was for Genesis to spawn sub-accounts into a memory system for businesses.
2. **Gemini Discussion Analysis**: Found in `/mnt/e/genesis-system/context/gemini_chat_export.txt` - discussed the 2026 Voice Agency Tech Stack with:
- Hybrid Memory Stack (Omnichannel Persistent Context, Vectorized Knowledge Sync/RAG, Improvement Loop)
- The "GHL Memory Hack" using custom fields vs native memory
- User's vision: Genesis spawning sub-accounts into memory system (not a hack)
3. **Clarifying Questions Asked via AskUserQuestion**:
- Primary deployment target: ReceptionistAI (Recommended)
- Sub-account provisioning: Auto from GHL (Recommended)
- Memory depth: Full semantic + structured
4. **VAPI Agent Discovery**: User asked to check the VAPI agent (Sarah/Sophie) to rename to Maya for ReceptionistAI demo. Found:
- Existing Sarah agent ID: `4b16159a-9e14-4518-9a1d-e8ff86b06f02`
- Current voice: Lily (British with AU personality) - not true Australian
- Located in `/mnt/e/genesis-system/RECEPTIONISTAI/scripts/vapi_client_template.py`
5. **Reading Gemini Conversation File**: Read `/mnt/e/genesis-system/RECEPTIONISTAI/gemini and claude conversations.docx` which contained detailed strategy about:
- VoiceAIWrapper + Vapi + ElevenLabs stack
- Telnyx SIP Trunking with Sydney AnchorSite for <200ms latency
- FB post guerrilla marketing strategy
- Let Vapi handle ElevenLabs internally (not through VoiceAIWrapper)
6. **Maya Demo Agent Script Created**: `/mnt/e/genesis-system/RECEPTIONISTAI/scripts/maya_demo_agent.py`
- Full persona with Danny Harris "Math Close" technique
- Pattern interrupt opening
- ElevenLabs Australian voice configuration
- Function tools for ROI calculation, booking, etc.
7. **Telnyx SIP Setup Guide Created**: `/mnt/e/genesis-system/RECEPTIONISTAI/setup/TELNYX_SIP_VAPI_SETUP.md`
8. **Sarah to Maya Update**: User requested update. Initial error with silenceTimeoutSeconds (must be >= 10), fixed and successfully updated agent.
9. **New Telnyx API Key**: User provided new key `KEY019BE7A3A2D749FCA8681CFF8448A7F0_vTMM1n77CtQxLDT2ra3P1z` and Brisbane number `(07) 31304225` (+61731304225)
10. **DNS Issues**: Could not connect to VAPI/Telnyx APIs due to DNS resolution failure in WSL. Provided manual VAPI Dashboard instructions.
11. **Memory System Design**: User requested memory system integration for Maya. Created:
- Architecture document: `/mnt/e/genesis-system/RECEPTIONISTAI/memory/MAYA_MEMORY_ARCHITECTURE.md`
- Memory API: `/mnt/e/genesis-system/RECEPTIONISTAI/memory/maya_memory_api.py`
- VAPI Webhook Handler: `/mnt/e/genesis-system/RECEPTIONISTAI/memory/vapi_webhook_handler.py`
12. **PRD Creation**: User wanted proper PRD with clarifying questions. Asked via AskUserQuestion:
- Memory scope: Full suite (remember_fact, recall_fact, calculate_roi, search_history)
- Memory UX: Proactive mention
- Deployment: Elestio VM
- Testing: Full RWL compliance
13. **PRD Created**: `/mnt/e/genesis-system/RECEPTIONISTAI/memory/PRD_MAYA_MEMORY_INTEGRATION.md` with 10 atomic stories
14. **RWL Swarm Execution**: User requested "execute via rwl swarm". I launched two parallel Task agents for S01 and S02, but user corrected that "parallel can only happen with unrelated tasks."
Key Errors:
- silenceTimeoutSeconds must be >= 10 (fixed by changing from 2 to 30)
- DNS resolution failures prevented API calls from WSL
- User correction: parallel tasks only for unrelated tasks
Files Created/Modified:
- `/mnt/e/genesis-system/RECEPTIONISTAI/scripts/maya_demo_agent.py` (created)
- `/mnt/e/genesis-system/RECEPTIONISTAI/setup/TELNYX_SIP_VAPI_SETUP.md` (created)
- `/mnt/e/genesis-system/RECEPTIONISTAI/memory/MAYA_MEMORY_ARCHITECTURE.md` (created)
- `/mnt/e/genesis-system/RECEPTIONISTAI/memory/maya_memory_api.py` (created)
- `/mnt/e/genesis-system/RECEPTIONISTAI/memory/vapi_webhook_handler.py` (created)
- `/mnt/e/genesis-system/RECEPTIONISTAI/memory/PRD_MAYA_MEMORY_INTEGRATION.md` (created)
- `/mnt/e/genesis-system/Credentials/MASTER_CREDENTIALS.md` (updated Telnyx key)
- `/mnt/e/genesis-system/RECEPTIONISTAI/scripts/telnyx_provisioner.py` (updated Telnyx key)
- `/mnt/e/genesis-system/RECEPTIONISTAI/PHASE1_STATUS.md` (updated Maya status)
- `/mnt/e/genesis-system/loop/maya_memory_rwl_tasks.json` (created)
Summary:
1. Primary Request and Intent:
- Analyze Gemini discussion about voice agents with memory and learning capabilities
- Vision: Genesis spawning sub-accounts into memory system (not a "hack" using CRM fields)
- Find and update VAPI agent (Sarah/Sophie → Maya) for ReceptionistAI demo
- Configure Maya with ElevenLabs Australian voice
- Connect Telnyx phone number (Brisbane +61731304225) for SIP trunking
- Design and implement full memory system integration for Maya
- Create proper PRD following Genesis protocol with clarifying questions
- Execute PRD via RWL (Ralph Wiggum Loop) swarm with full testing suites
2. Key Technical Concepts:
- Three-layer memory architecture: Redis (working), Qdrant (episodic/vectors), PostgreSQL (semantic/structured)
- VAPI voice agent integration with webhooks (assistant-request, end-of-call-report, function-call)
- Telnyx SIP Trunking with Sydney AnchorSite for <200ms latency
- ElevenLabs Australian voice (voice ID: `pFZP5JQG7iQjIQuC4Bku`)
- LLM-powered fact extraction from call transcripts
- Danny Harris "Math Close" sales technique
- Pattern interrupt opening ("I'm actually an AI")
- Genesis sub-account memory isolation per business
- RWL (Ralph Wiggum Loop) execution protocol with black box + white box testing
3. Files and Code Sections:
- `/mnt/e/genesis-system/RECEPTIONISTAI/scripts/maya_demo_agent.py`
- Core Maya agent setup script with full persona, Australian voice, function tools
- Successfully updated Sarah to Maya in VAPI
```python
def get_maya_config(self) -> dict:
return {
"name": "Maya - ReceptionistAI Demo",
"firstMessage": self.get_maya_first_message(),
"voice": self.au_voices["female_au_natural"], # pFZP5JQG7iQjIQuC4Bku
"model": {"provider": "openai", "model": "gpt-4o", ...},
"transcriber": {"provider": "deepgram", "model": "nova-3", "language": "en-AU"},
"silenceTimeoutSeconds": 30, # Fixed from 2
...
}
```
- `/mnt/e/genesis-system/RECEPTIONISTAI/memory/maya_memory_api.py`
- Core Memory API with FastAPI endpoints
- Uses Elestio infrastructure (PostgreSQL, Qdrant, Redis)
- Key endpoints: `/api/memory/v1/prepare`, `/store`, `/learn`, `/fact`
```python
class MayaMemory:
def prepare(self, caller_phone: str) -> Dict[str, Any]:
"""Load all relevant memory for an incoming caller."""
def store(self, call_id, caller_phone, transcript, outcome, ...) -> Dict:
"""Store memory after a call ends."""
def learn(self, caller_phone, fact_key, fact_value, ...) -> Dict:
"""Store a new fact mid-call."""
```
- `/mnt/e/genesis-system/RECEPTIONISTAI/memory/vapi_webhook_handler.py`
- VAPI webhook integration for memory injection
- Handles assistant-request (inject memory), end-of-call-report (store memory), function-call (memory tools)
- `/mnt/e/genesis-system/RECEPTIONISTAI/memory/PRD_MAYA_MEMORY_INTEGRATION.md`
- Full PRD with 10 atomic stories, black box + white box tests
- Stories S01-S10 covering schema, API endpoints, VAPI integration, deployment, testing
- `/mnt/e/genesis-system/RECEPTIONISTAI/setup/TELNYX_SIP_VAPI_SETUP.md`
- Telnyx SIP trunking guide with Sydney AnchorSite for low latency
- `/mnt/e/genesis-system/Credentials/MASTER_CREDENTIALS.md`
- Updated with new Telnyx API key and Brisbane number
4. Errors and fixes:
- **VAPI silenceTimeoutSeconds validation error**:
- Error: `silenceTimeoutSeconds must not be less than 10`
- Fix: Changed from `2` to `30` in maya_demo_agent.py
- **DNS Resolution Failure**:
- Error: `Failed to resolve 'api.telnyx.com'` and `api.vapi.ai` from WSL
- Fix: Provided manual VAPI Dashboard instructions for connecting phone number
- **Old Telnyx API Key Invalid**:
- Error: `Authentication failed - Could not find any usable credentials`
- Fix: User provided new API key `KEY019BE7A3A2D749FCA8681CFF8448A7F0_vTMM1n77CtQxLDT2ra3P1z`
- **User feedback on RWL parallel execution**:
- User said: "paralell can only happen with unrelated tasks"
- Need to reconsider execution approach for related stories
5. Problem Solving:
- Successfully transformed Sarah agent to Maya with proper Australian voice
- Designed three-layer memory architecture using existing Elestio infrastructure
- Created comprehensive PRD with 10 stories and full test specifications
- Identified that S01 and S02 may be considered related (both are foundation/infrastructure)
6. All user messages:
- "analyze this discussion with gemini- we want our voice agents to have memory and learning. instead of a hack I foresee the potential of genesis to spawn sub-accounts into our memory system so it can better serve a business in that way."
- "check the vapi agent that was created earlier. her name was sarah or sophie but we planned to call her maya. this will be our demo agent our receptionist ai sales agent."
- "yes and chexck this gemini conversation file for more context - receptionistai folder> telynx sip trunking file"
- "lets get her setup propewrly now with an australian voice" (system reminder)
- "she will use eleven labs voice" (system reminder)
- "will need to use telynx for phone numbver and sip trunking ?" (system reminder)
- "yes update sarah to maya"
- "here is Telynx api key Telynx-Genesis-API-KEY=KEY019BE7A3A2D749FCA8681CFF8448A7F0_vTMM1n77CtQxLDT2ra3P1z I have purchased a Brisbane number for all Queensland outreach. (07) 31304225"
- "memory system integration for Maya"
- "add the memory tools to maya we should plan this as a prd and you ask me clarifying questions - askuserquestions following genesis protocol for perfect ralph wiggum loops deployment with testing suites etc"
- "execute via rwl swarm"
- "paralell can only happen with unrelated tasks."
7. Pending Tasks:
- Connect Brisbane number (+61731304225) to Maya in VAPI Dashboard (manual step required)
- Execute PRD RAI-MEM-001 stories S01-S10 for Maya Memory Integration
- Add memory function tools to Maya's VAPI configuration
- Deploy memory system to Elestio
8. Current Work:
Immediately before the summary request, I had:
- Created the PRD for Maya Memory Integration (`PRD_MAYA_MEMORY_INTEGRATION.md`)
- User approved execution via "execute via rwl swarm"
- Launched two parallel Task agents for S01 (PostgreSQL Schema) and S02 (Qdrant Collection)
- User corrected: "paralell can only happen with unrelated tasks" - indicating S01 and S02 should potentially be executed differently since they're both foundation/infrastructure tasks that may be related
Task file created at: `/mnt/e/genesis-system/loop/maya_memory_rwl_tasks.json`
9. Optional Next Step:
Re-approach the RWL execution considering user's feedback that "parallel can only happen with unrelated tasks." Since S01 (PostgreSQL Schema) and S02 (Qdrant Collection) are both foundation stories and may be considered related, I should:
- Either execute them sequentially
- Or clarify with user whether these specific stories (database schema vs vector DB) qualify as "unrelated" enough for parallel execution
- Then continue with the remaining stories (S03-S10) following the PRD execution order.
Please continue the conversation from where we left it off without asking the user any further questions. Continue with the last task that you were asked to work on.
[GENESIS]: Understood. S01 and S02 are related foundation tasks - executing sequentially. Let me start with S01: PostgreSQL Schema Deployment.
[GENESIS]: Understood. Executing S01 first with full black box + white box testing before moving to S02.
**S01: PostgreSQL Schema Deployment**
Let me create the test suite and deploy the schema:
[GENESIS]: Need to install dependencies. Let me fix that:
[GENESIS]: Let me create a simpler test that doesn't require all dependencies:
[GENESIS]: DNS issues in WSL. The schema was initialized successfully on first connection. Let me refactor the test to reuse connection:
[GENESIS]: Request timed out
[KINAN]:
b0d1728
/tmp/claude/-mnt-e-genesis-system/tasks/b0d1728.output
failed
Background command "Run S01 tests with connection reuse" failed with exit code 1.
Read the output file to retrieve the output.
[GENESIS]: Request timed out
[KINAN]:
a0ef004
/tmp/claude/-mnt-e-genesis-system/tasks/a0ef004.output
completed
Agent "S01: Deploy PostgreSQL Schema" completed.
Read the output file to retrieve the full result.
[GENESIS]: Request timed out
[KINAN]:
a807b0d
/tmp/claude/-mnt-e-genesis-system/tasks/a807b0d.output
completed
Agent "S02: Setup Qdrant Collection" completed.
Read the output file to retrieve the full result.
[GENESIS]: Request timed out