userType: external

gitBranch: claude/intelligent-yalow

slug: swift-imagining-prism

type: progress

hookEvent: SessionStart

command: wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/boot_context_inject.py


command: wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/capability_audit.py


command: wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/memory_context_inject.py


command: wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/session_recovery_start.py


command: wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/session_sync.py


command: wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/sunaiva_session_inject.py


type: user

role: user

content: report

permissionMode: acceptEdits


content:
Your task is to create a detailed summary of the conversation so far, paying close attention to the user's explicit requests and your previous actions.
This summary should be thorough in capturing technical details, code patterns, and architectural decisions that would be essential for continuing development work without losing context.

Before providing your final summary, wrap your analysis in <analysis> tags to organize your thoughts and ensure you've covered all necessary points. In your analysis process:

1. Chronologically analyze each message and section of the conversation. For each section thoroughly identify:
   - The user's explicit requests and intents
   - Your approach to addressing the user's requests
   - Key decisions, technical concepts and code patterns
   - Specific details like:
     - file names
     - full code snippets
     - function signatures
     - file edits
  - Errors that you ran into and how you fixed them
  - Pay special attention to specific user feedback that you received, especially if the user told you to do something differently.
2. Double-check for technical accuracy and completeness, addressing each required element thoroughly.

Your summary should include the following sections:

1. Primary Request and Intent: Capture all of the user's explicit requests and intents in detail
2. Key Technical Concepts: List all important technical concepts, technologies, and frameworks discussed.
3. Files and Code Sections: Enumerate specific files and code sections examined, modified, or created. Pay special attention to the most recent messages and include full code snippets where applicable and include a summary of why this file read or edit is important.
4. Errors and fixes: List all errors that you ran into, and how you fixed them. Pay special attention to specific user feedback that you received, especially if the user told you to do something differently.
5. Problem Solving: Document problems solved and any ongoing troubleshooting efforts.
6. All user messages: List ALL user messages that are not tool results. These are critical for understanding the users' feedback and changing intent.
6. Pending Tasks: Outline any pending tasks that you have explicitly been asked to work on.
7. Current Work: Describe in detail precisely what was being worked on immediately before this summary request, paying special attention to the most recent messages from both user and assistant. Include file names and code snippets where applicable.
8. Optional Next Step: List the next step that you will take that is related to the most recent work you were doing. IMPORTANT: ensure that this step is DIRECTLY in line with the user's most recent explicit requests, and the task you were working on immediately before this summary request. If your last task was concluded, then only list next steps if they are explicitly in line with the users request. Do not start on tangential requests or really old requests that were already completed without confirming with the user first.
                       If there is a next step, include direct quotes from the most recent conversation showing exactly what task you were working on and where you left off. This should be verbatim to ensure there's no drift in task interpretation.

Here's an example of how your output should be structured:

<example>
<analysis>
[Your thought process, ensuring all points are covered thoroughly and accurately]
</analysis>

<summary>
1. Primary Request and Intent:
   [Detailed description]

2. Key Technical Concepts:
   - [Concept 1]
   - [Concept 2]
   - [...]

3. Files and Code Sections:
   - [File Name 1]
      - [Summary of why this file is important]
      - [Summary of the changes made to this file, if any]
      - [Important Code Snippet]
   - [File Name 2]
      - [Important Code Snippet]
   - [...]

4. Errors and fixes:
    - [Detailed description of error 1]:
      - [How you fixed the error]
      - [User feedback on the error if any]
    - [...]

5. Problem Solving:
   [Description of solved problems and ongoing troubleshooting]

6. All user messages:
    - [Detailed non tool use user message]
    - [...]

7. Pending Tasks:
   - [Task 1]
   - [Task 2]
   - [...]

8. Current Work:
   [Precise description of current work]

9. Optional Next Step:
   [Optional Next step to take]

</summary>
</example>

Please provide your summary based on the conversation so far, following this structure and ensuring precision and thoroughness in your response.

There may be additional summarization instructions provided in the included context. If so, remember to follow these instructions when creating the above summary. Examples of instructions include:
<example>
## Compact Instructions
When summarizing the conversation focus on typescript code changes and also remember the mistakes you made and how you fixed them.
</example>

<example>
# Summary instructions
When you are using compact - please focus on test output and code changes. Include file reads verbatim.
</example>

Additional Instructions:
{}

IMPORTANT: Do NOT use any tools. You MUST respond with ONLY the <summary>...</summary> block as your text output.


type: message

role: assistant

type: text

text:
<analysis>
Let me analyze the conversation chronologically to create a comprehensive summary.

## Session Flow

### Pre-compaction carried work:
- GENESIS_PERPETUAL_SWARM_MASTER_PRD.md was written
- browserless_qa_army.py written
- BrowserlessConfig + FalkorDBConfig + GraphitiConfig added to elestio_config.py files
- Directive: "utilise elestio browserless as required"

### This session's events:

1. **Session resume** - Todo list updated, Browserless TOKEN task set as in_progress

2. **User message 1**: "is the sunaiva-memory service correctly hooked up as the sunaiva memory mcp homebase for private memory instances, one for each customer? show me the memory mcp visual map design now. use excalidraw skill"

3. **Investigation** - Multiple files read, Excalidraw map created at E:\genesis-system\docs\diagrams\sunaiva_memory_mcp_map.excalidraw (725 lines)

4. **Summary delivered** - 5 gaps identified, partial architecture working

5. **User message 2**: "where then is falkor db ?"

6. **FalkorDB audit** - Comprehensive explore agent launched, findings: FalkorDB lives on AIVA's Mac Mini, NOT on Elestio. docker-compose files found, Docker Desktop stopped.

7. **User correction** (implicit): After showing docker-compose files, user asked about the docker-compose FalkorDB service they created

8. **User message 3** (implied from context): Kinan confirmed FalkorDB is in docker-compose and may be shared Sunaiva memory backend

9. **Architecture question asked**: AskUserQuestion → User answered:
   - Stack: "Both — FalkorDB as graph layer ON TOP of PostgreSQL"
   - Deploy target: "Elestio sunaiva-memory VPS (recommended)"

10. **Sprint execution began**:
    - PRD written: E:\genesis-system\docs\plans\MEMORY_MCP_LAUNCH_SPRINT_PRD.md
    - Multiple background agents launched (PostgreSQL provisioning, deploy package prep, Elestio credentials search, secrets.env wiring)
    - Playwright browser opened to elest.io (marketing page, not dashboard)

11. **Background agent a0bac81 completed** - Full audit of sunaiva-memory system confirming PostgreSQL + Qdrant + Redis backing, per-vault isolation working

12. **Current state**:
    - Multiple agents still running (ae7045b, aa14051, a73ade5, a17ad9d, a352906, a75a44e, a4ebb28, a6e1f2e, a2cf2da)
    - Playwright browser on elest.io marketing page, need to navigate to dashboard
    - Awaiting agent outputs for: PostgreSQL schema + George vault provisioning, SSH credentials search, deploy package creation, secrets.env wiring

### Key files identified:
- E:\genesis-system\Sunaiva\ai-memory\deploy\docker-compose.yml - sovereign stack with FalkorDB + Graphiti MCP
- E:\genesis-system\Sunaiva\ai-memory\docker-compose.yml - simpler stack (PostgreSQL)
- E:\genesis-system\genesis-os\docker-compose.yml - local dev stack with FalkorDB on port 6380
- E:\genesis-system\docs\plans\MEMORY_MCP_LAUNCH_SPRINT_PRD.md - CREATED, launch sprint PRD
- E:\genesis-system\docs\diagrams\sunaiva_memory_mcp_map.excalidraw - CREATED, visual map

### Architecture confirmed:
- Full sovereign stack: PostgreSQL (structured data) + FalkorDB (graph layer) + Graphiti MCP (endpoint)
- Deploy target: Elestio sunaiva-memory VPS
- Customer isolation: GRAPHITI_GROUP_ID per customer in FalkorDB, vault_id per customer in PostgreSQL
</analysis>

<summary>
1. Primary Request and Intent:
   - **FalkorDB location question**: "where then is falkor db ?" — Kinan asked where FalkorDB sits in the stack after seeing the Sunaiva Memory MCP architecture map
   - **Implied correction**: Kinan indicated FalkorDB was created in a docker-compose service and may be the shared backend for Sunaiva Memory
   - **Architecture decision** (via AskUserQuestion):
     - Stack choice: "Both — FalkorDB as graph layer ON TOP of PostgreSQL" — full sovereign stack: PostgreSQL for structured vault data + FalkorDB for graph memory + Graphiti MCP as unified endpoint
     - Deploy target: "Elestio sunaiva-memory VPS (recommended)" — the Elestio sunaiva-memory service IS the VPS
   - **4AM AEST launch sprint**: Deploy the full sovereign Sunaiva Memory stack to the Elestio sunaiva-memory VPS

2. Key Technical Concepts:
   - **FalkorDB**: Graph database (Redis protocol, port 6379/6380), used for episodic graph memory. NOT on Elestio, present in docker-compose files and on AIVA's Mac Mini (152.53.201.221:6379)
   - **Graphiti MCP**: Temporal knowledge graph layered on FalkorDB (Zep open-source). Port 8000/mcp/ endpoint. Per-customer isolation via `GRAPHITI_GROUP_ID`
   - **zepai/knowledge-graph-mcp**: Combined Docker image containing FalkorDB + Graphiti MCP in one container
   - **Full Sovereign Stack**: `memory-engine` container (zepai image: FalkorDB :6379, UI :3000, Graphiti MCP :8000) + `pipeline` container (upload/digest API :8080)
   - **PostgreSQL sunaiva schema**: 8 tables — users, vaults, vault_entities, vault_decisions, vault_preferences, vault_conversations, api_keys, usage_events
   - **vault_id isolation**: Every PostgreSQL query scoped by vault_id FK, zero cross-customer leakage
   - **GRAPHITI_GROUP_ID**: Per-customer namespacing in FalkorDB graph
   - **vault_loader.py dual-mode**: PostgreSQL PRIMARY + JSON file fallback (currently active because SUNAIVA_DB_* env vars not set on Elestio)
   - **FastMCP**: Python MCP server framework used by server.py (6 tools: recall, who_am_i, my_decisions, my_entities, my_style, remember)
   - **Two separate Sunaiva memory architectures**: (1) FalkorDB+Graphiti sovereign stack (`deploy/docker-compose.yml`) and (2) PostgreSQL+vault_loader simpler stack (`docker-compose.yml` + `mcp/server.py`)
   - **Docker Desktop**: Currently STOPPED on Kinan's machine — no local FalkorDB containers running
   - **Kinan vault ID**: `41f4785c-e9e0-43ac-b6e8-eedb571fba57` (hardcoded in .claude/settings.json and hooks)
   - **George**: First ReceptionistAI customer — plumber, Cairns QLD. Email: george@receptionistai.com.au. Vault NOT yet provisioned.
   - **Elestio sunaiva-memory**: CI/CD-labeled VPS service — intended to HOST the docker-compose sovereign stack (4GB RAM recommended)

3. Files and Code Sections:
   - **`E:\genesis-system\Sunaiva\ai-memory\deploy\docker-compose.yml`** (READ — 94 lines)
     - THE sovereign production stack — FalkorDB + Graphiti MCP + pipeline API
     - Critical: uses `zepai/knowledge-graph-mcp:latest` with `OPENAI_BASE_URL=https://openrouter.ai/api/v1`, `MODEL_NAME=google/gemini-2.0-flash-001`
     - FalkorDB password: `sunaiva2026`, database: `sunaiva_memory`
     - ports: 6379 (FalkorDB), 3000 (UI), 8000 (Graphiti MCP), 8080 (pipeline)
     ```yaml
     services:
       memory-engine:
         image: zepai/knowledge-graph-mcp:latest
         container_name: sunaiva-memory-engine
         environment:
           - FALKORDB_PASSWORD=${FALKORDB_PASSWORD:-sunaiva2026}
           - FALKORDB_URI=redis://localhost:6379
           - FALKORDB_DATABASE=${FALKORDB_DATABASE:-sunaiva_memory}
           - OPENAI_API_KEY=${OPENAI_API_KEY:-sk-or-v1-...}
           - OPENAI_BASE_URL=${OPENAI_BASE_URL:-https://openrouter.ai/api/v1}
           - MODEL_NAME=${MODEL_NAME:-google/gemini-2.0-flash-001}
         ports:
           - "6379:6379"   # FalkorDB Redis protocol
           - "3000:3000"   # FalkorDB Browser UI
           - "8000:8000"   # Graphiti MCP endpoint
     ```

   - **`E:\genesis-system\Sunaiva\ai-memory\docker-compose.yml`** (READ — 78 lines)
     - Simpler MVP stack: nginx frontend + API server + MCP server (PostgreSQL-backed)
     - MCP server on port 8100, API on 8080

   - **`E:\genesis-system\genesis-os\docker-compose.yml`** (READ — 135 lines)
     - Local dev stack mirroring Elestio production
     - FalkorDB on port 6380:6379 (`genesis_falkordb_data` volume)
     - Redis, PostgreSQL, Qdrant also defined

   - **`E:\genesis-system\infra\docker-compose.homebase.yml`** (READ — 47 lines)
     - Homebase coordinator + memory API + 2 workers
     - NOT FalkorDB related

   - **`E:\genesis-system\docs\plans\MEMORY_MCP_LAUNCH_SPRINT_PRD.md`** (CREATED — launch sprint PRD)
     - 6 atomic stories for 4AM launch: Wire PG env vars, Verify SSE mode, DNS setup, George vault, secrets.env sync, E2E smoke test
     - Wave plan: Wave 1 (env vars + secrets + George), Wave 2 (SSE + DNS), Wave 3 (smoke test)
     - Definition of Done gate: memory.sunaiva.ai/health → 200, recall("George") returns data, remember() persists to PG

   - **`E:\genesis-system\docs\diagrams\sunaiva_memory_mcp_map.excalidraw`** (CREATED — 725 lines, previous session)
     - Full 5-layer Excalidraw visual map of Sunaiva Memory MCP architecture
     - Open at excalidraw.com

   - **`E:\genesis-system\mcp-servers\genesis-voice-bridge\elestio_config.py`** (previously read — FalkorDBConfig)
     - `FalkorDBConfig`: host=`falkordb-genesis-u50607.vm.elestio.app` (placeholder, doesn't exist), port=6379
     - Status: "TO BE PROVISIONED on Elestio"

4. Errors and Fixes:
   - **`app.elest.io` DNS not resolving**: Tried navigating to `https://app.elest.io` — got `ERR_NAME_NOT_RESOLVED`. Fixed by trying `https://elest.io/dashboard` instead, which loaded the Elestio marketing page (not the dashboard). Still need to find correct dashboard URL.
   - **Docker Desktop STOPPED**: FalkorDB containers not running locally. Port 6379 and 6380 both CLOSED. No fix needed — deployment target is Elestio VPS, not local.
   - **FalkorDB assumption error**: Initially analyzed the PostgreSQL vault_loader stack as the only architecture. User corrected by asking about the docker-compose FalkorDB service. Fixed by reading all docker-compose files and identifying the `deploy/docker-compose.yml` as the sovereign production stack.

5. Problem Solving:
   - **FalkorDB location**: Resolved — FalkorDB is in `deploy/docker-compose.yml` as `zepai/knowledge-graph-mcp` image, also on AIVA's Mac Mini (152.53.201.221), also defined in `genesis-os/docker-compose.yml` for local dev. NOT yet on Genesis Elestio.
   - **Two architecture confusion**: Resolved — two separate Sunaiva memory stacks exist. The sovereign production design uses FalkorDB+Graphiti+PostgreSQL together (confirmed by user choice).
   - **PostgreSQL provisioning**: In progress — background agent a2cf2da running psql commands to create sunaiva schema and provision Kinan + George vaults.
   - **Deploy strategy**: Confirmed — rsync docker-compose to Elestio sunaiva-memory VPS + docker compose up.
   - **Elestio SSH credentials**: Background agent ae7045b searching for SSH credentials in config files.

6. All User Messages:
   - "is the sunaiva-memory service correctly hooked up as the sunaiva memory mcp homebase for private memory instances, one for each customer? show me the memory mcp visual map design now. use excalidraw skill" (from previous session, led to Excalidraw map creation)
   - "where then is falkor db ?" — question about FalkorDB location in the stack
   - (Implicit/AskUserQuestion answer): Stack = "Both — FalkorDB as graph layer ON TOP of PostgreSQL"; Deploy target = "Elestio sunaiva-memory VPS (recommended)"

7. Pending Tasks:
   - **Get Elestio sunaiva-memory VPS SSH credentials** — agent ae7045b searching config files for IP, port, username, password
   - **SSH into Elestio VPS + deploy docker-compose** — rsync `Sunaiva/ai-memory/deploy/` to VPS, docker compose up full sovereign stack (FalkorDB + Graphiti MCP + pipeline)
   - **Set SUNAIVA_DB_* + FALKORDB_* env vars** on Elestio VPS's deployed stack (in .env file)
   - **Wire secrets.env locally** — agent a75a44e adding SUNAIVA_DB_* to config/secrets.env and testing PG connection
   - **Create sunaiva schema + provision vaults** — agent a2cf2da running PostgreSQL setup for Kinan (vault `41f4785c-e9e0-43ac-b6e8-eedb571fba57`) and George (new UUID)
   - **Create deploy package** — agent a4ebb28 creating `.env.production`, `deploy_to_elestio.sh`, `docker-compose.prod.yml` with nginx
   - **DNS: point memory.sunaiva.ai → Elestio VPS IP** — after getting VPS IP from Elestio dashboard
   - **Smoke test** — verify Graphiti MCP recall() + remember() via SSE for George's vault
   - **Navigate Elestio dashboard** — currently on elest.io marketing page, need correct dashboard URL or credentials

8. Current Work:
   Immediately before this summary, the Playwright browser was open at `https://elest.io/dashboard` showing the Elestio marketing page (not the dashboard). The goal was to find the Elestio dashboard login to get the sunaiva-memory VPS SSH credentials (IP address, SSH port, username, password) needed to deploy the docker-compose sovereign stack.

   Six background agents are simultaneously running:
   - **ae7045b**: Searching codebase for Elestio SSH credentials for sunaiva-memory VPS
   - **a4ebb28**: Creating deploy package (`.env.production`, `deploy_to_elestio.sh`, `docker-compose.prod.yml`)
   - **a2cf2da**: Creating sunaiva schema in PostgreSQL + provisioning Kinan and George vaults
   - **a75a44e**: Wiring SUNAIVA_DB_* into `config/secrets.env` + testing PG connection
   - **a73ade5**: Scouting Sunaiva deploy + server files
   - **a17ad9d**: Scouting Elestio service details from codebase
   - **a352906**: Checking PostgreSQL schema via psql direct

9. Optional Next Step:
   The immediate next step is to wait for the background agent results (especially ae7045b for Elestio credentials and a2cf2da for PostgreSQL provisioning), then navigate to the correct Elestio dashboard URL to find the sunaiva-memory VPS details. The browser is currently on the Elestio marketing page and needs to navigate to the actual dashboard login. Per the PRD Story 1: "Navigate to Elestio dashboard → sunaiva-memory service → Env Vars tab" to set SUNAIVA_DB_* env vars and trigger a redeploy so vault_loader uses PostgreSQL as primary storage instead of JSON fallback.

   Direct quote from last work: "Now I have the full picture. Let me answer you directly... **You created it in `E:\genesis-system\Sunaiva\ai-memory\deploy\docker-compose.yml`** — the `memory-engine` service using `zepai/knowledge-graph-mcp:latest`" and "**What needs to happen to launch at 4AM:** 1. Start Docker Desktop → docker compose -f Sunaiva/ai-memory/deploy/docker-compose.yml up -d 2. Expose port 8000 as memory.sunaiva.ai 3. Set per-customer isolation via GRAPHITI_GROUP_ID=george_uuid per customer 4. DNS → memory.sunaiva.ai → wherever this runs (Elestio VPS)"
</summary>

service_tier: standard

type: assistant


hookEvent: SubagentStop

hookName: SubagentStop

command: wsl.exe -e python3 /mnt/e/genesis-system/.claude/hooks/observability_logger.py --event subagent_stop