Memory
## Phase 0 Plan: MVP Validation (July 2026) ### Decisions Confirmed 1. **Template:** assistant-ui Base template (most flexible) 2. **MCP:** Add later (not in Phase 0) 3. **Storage:** DO SQLite (standard CF runtime, not SurrealDB yet) 4. **Model:** Workers AI binding (user has CF Workers paid plan) ### Architecture - CF Agents Worker (localhost:8787) — AIChatAgent subclass - assistant-ui Frontend (localhost:5173) — useAgent + useAgentChat + useAISDKRuntime - WebSocket for real-time streaming - DO SQLite for message persistence - Workers AI for LLM inference ### What We Build 1. Scaffold CF Agents Worker from starter template 2. Modify Worker: Workers AI binding, custom tools (calculate, lookup) 3. Scaffold assistant-ui with Base template 4. Wire useAgent + useAgentChat + useAISDKRuntime 5. Test end-to-end: chat, tools, streaming, persistence ### Key Files - worker/src/server.ts — Worker entry point (routeAgentRequest) - worker/src/chat.ts — Chat agent (AIChatAgent subclass) - worker/src/tools/calculate.ts — Simple calculation tool - worker/src/tools/lookup.ts — Simple lookup tool - worker/wrangler.jsonc — CF config (DO, Workers AI binding) - frontend/app/assistant.tsx — Runtime wiring - frontend/app/page.tsx — Main page ### What We Learn - Is the UX smooth? - Do tools work correctly? - Does persistence work across refreshes? - Is Workers AI sufficient for responses? - What's missing for Phase 1? ### Phase 1 Preview (After Phase 0) - Add SurrealDB for knowledge graph - Add SurrealQL tools (defineTable, defineField, runQuery) - Add MCP servers (GitHub, Slack) - Add knowledge tools (createEntity, createRelation, search) - Add workflow tools (createWorkflow, transitionStep) ### Status: Plan complete, ready for implementation
[Update 7/19/2026]: ## Updated: Phase 0 Implementation Details (July 19, 2026)
### Decisions Finalized
1. **Template:** assistant-ui Base template (most flexible)
2. **MCP:** Add later (not in Phase 0)
3. **Storage:** DO SQLite (standard CF runtime, not SurrealDB yet)
4. **Model:** Workers AI binding (user has CF Workers paid plan)
5. **Project structure:** worker/ + frontend/ (two separate directories)
### CF Agents SDK + assistant-ui Integration Pattern
```
Worker (CF Agents SDK) ←→ WebSocket ←→ Frontend (assistant-ui)
- Worker: AIChatAgent subclass, Workers AI binding, custom tools
- Frontend: useAgent + useAgentChat + useAISDKRuntime
- Persistence: DO SQLite (built-in)
- Streaming: WebSocket (built-in)
```
### MCP Integration (Phase 1)
- CF Agents SDK has built-in MCP client: `addMcpServer()`
- Tools available via `this.mcp.getAITools()`
- Pass to `streamText({ tools: { ...mcpTools, ...myTools } })`
- assistant-ui renders via ToolFallback or custom renderers
### Key Code Patterns
```typescript
// Worker: src/chat.ts
export class Chat extends AIChatAgent<Env> {
async onChatMessage(onFinish) {
return streamText({
model: this.env.AI("@cf/meta/llama-3.1-8b-instruct"),
messages: await convertToModelMessages(this.messages),
tools: { calculate: calculateTool, lookup: lookupTool },
onFinish,
});
}
}
```
```tsx
// Frontend: app/assistant.tsx
const agent = useAgent({ agent: "Chat", name: "default", host: ... });
const chat = useAgentChat({ agent });
const runtime = useAISDKRuntime(chat);
```
### Timeline: 3-4 hours for Phase 0
### Status: Ready to implement
[Update 7/19/2026]: ## Phase 0 COMPLETE (July 19, 2026)
### What Works
- Chat interface (assistant-ui + CF Agents SDK)
- Streaming responses via WebSocket
- Message persistence (DO SQLite)
- Workers AI via AI Gateway (OpenAI-compatible endpoint)
### Final Stack
- **Worker:** agents@0.12.4, @cloudflare/ai-chat@0.7.0, ai@6.0.197, @ai-sdk/openai@3.0.86
- **Frontend:** assistant-ui default template + agents@0.12.4, @cloudflare/ai-chat@0.7.0
- **Model:** workers-ai/@cf/meta/llama-3.3-70b-instruct-fp8-fast via AI Gateway
- **AI Gateway URL:** https://gateway.ai.cloudflare.com/v1/{account_id}/{gateway_id}/compat
### Key Learnings
1. **AI Gateway /compat endpoint** requires provider prefix in model name: `workers-ai/@cf/meta/llama-3.3-70b-instruct-fp8-fast`
2. **@ai-sdk/openai@3.x** uses Responses API by default — need `compatibility: "compatible"` + `openai.chat()` for Chat Completions API
3. **Hydration issues** with Next.js + assistant-ui — fixed with `dynamic({ ssr: false })` for Thread component
4. **ai@^6.0.0** required by agents@0.12.4 — cannot use ai@^7.0.x
5. **@ai-sdk/openai@3.0.86** is compatible with ai@6.0.197
### Files
- worker/src/chat.ts — ChatAgent with AI Gateway
- worker/src/index.ts — Worker entry point with routeAgentRequest
- worker/wrangler.jsonc — DO binding, compatibility_date 2026-07-19
- frontend/app/assistant.tsx — Runtime wiring (useAgent → useAgentChat → useAISDKRuntime)
- frontend/.env.local — NEXT_PUBLIC_AGENT_HOST=http://localhost:8787
### Status: Phase 0 complete, ready for Phase 1
[Update 7/19/2026]: ## Phase 0 FINAL (July 19, 2026) — Clean Architecture
### Final Stack (No Worker needed)
- **Frontend only:** assistant-ui + Next.js + AI SDK
- **Server-side API route:** `/api/chat/route.ts` with `streamText`
- **AI Gateway:** ai-gateway-provider package (proper AI SDK provider)
- **Model:** workers-ai/@cf/meta/llama-3.3-70b-instruct-fp8-fast
### Key Packages
- `@assistant-ui/react` — Thread, AssistantRuntimeProvider
- `@assistant-ui/react-ai-sdk` — useChatRuntime
- `ai-gateway-provider` — createAiGateway, createUnified
- `ai` — streamText, convertToModelMessages
### Architecture
```
Frontend (useChatRuntime) ←→ /api/chat/route.ts (streamText + ai-gateway-provider) ←→ AI Gateway
```
### Critical Learnings
1. **ai-gateway-provider** is the correct way to use AI Gateway with AI SDK — not raw fetch, not @ai-sdk/openai
2. **convertToModelMessages** is required to convert assistant-ui's UIMessage[] to AI SDK's ModelMessage[]
3. **AssistantRuntimeProvider** comes from `@assistant-ui/react`, NOT `@assistant-ui/react-ai-sdk`
4. **useChatRuntime** comes from `@assistant-ui/react-ai-sdk`
5. **No CF Agents Worker needed** for basic chat — assistant-ui + AI SDK handles everything client-side
### Files
- frontend/app/assistant.tsx — useChatRuntime + AssistantRuntimeProvider
- frontend/app/api/chat/route.ts — streamText + ai-gateway-provider
- frontend/.env.local — AI_GATEWAY_KEY
### Status: Phase 0 COMPLETE — ready for Phase 1
[Update 7/19/2026]: ## Phase 0 COMPLETE — Final State (July 19, 2026)
### What's Working
1. **Chat interface** — assistant-ui with Thread, Composer, ThreadListSidebar
2. **Streaming responses** — AI SDK streamText via AI Gateway
3. **Model selector** — Dropdown with Fast/Smart models, thinking effort levels
4. **Sidebar branding** — Shield icon, "DocuClear Clearspace" title, no GitHub link
5. **Breadcrumb** — Home link back to welcome screen
6. **Welcome screen** — "How can I help you today?" with suggestions
### Architecture (No Worker needed)
```
Frontend (Next.js) ←→ /api/chat/route.ts (streamText + ai-gateway-provider) ←→ AI Gateway
```
### Key Files
- `config/models.ts` — Model definitions (id, name, providerModel, efforts)
- `app/api/chat/route.ts` — Server-side API route with streamText + model mapping
- `app/assistant.tsx` — useChatRuntime + AssistantRuntimeProvider + sidebar layout
- `components/thread.tsx` — Thread with ModelSelector in composer
- `components/threadlist-sidebar.tsx` — Sidebar with Shield icon, DocuClear branding
### Packages Installed
- `@assistant-ui/react` — Thread, AssistantRuntimeProvider
- `@assistant-ui/react-ai-sdk` — useChatRuntime, AssistantChatTransport
- `ai-gateway-provider` — createAiGateway, createUnified (AI Gateway SDK)
- `ai@7.0.31` — Vercel AI SDK (latest)
- `cmdk` — Command palette (dependency for model-selector)
- `openai` — OpenAI SDK (installed but not used directly)
### Model Selector Config
```typescript
// config/models.ts
export const MODELS = [
{ id: "fast", name: "Fast", providerModel: "workers-ai/@cf/meta/llama-3.3-70b-instruct-fp8-fast", efforts: true },
{ id: "smart", name: "Smart", providerModel: "workers-ai/@cf/meta/llama-3.3-70b-instruct-fp8-fast", efforts: true },
];
```
### API Route Pattern
```typescript
const { messages, config } = await req.json();
const modelConfig = getModelConfig(config?.modelName);
const model = aigateway(unified(modelConfig.providerModel));
const result = streamText({ model, messages: await convertToModelMessages(messages) });
return result.toUIMessageStreamResponse();
```
### Deployment Plan
- Use `@cloudflare/next-on-pages` to deploy to Cloudflare Pages/Workers
- Set AI_GATEWAY_KEY as Worker secret
- Standard Web APIs (fetch, Request, Response) — no Node.js-specific code
### What's NOT Working Yet
- Thread persistence (in-memory only, lost on refresh)
- MCP tools (deferred to Phase 1)
- Authentication/user profiles (deferred to Phase 1)
- SurrealDB knowledge graph (Phase 1)
- Procedural graphs/workflows (Phase 1)
- Document ingestion (Phase 2)
### Status: Phase 0 COMPLETE — ready for Phase 1
[Update 7/20/2026]: ## Context Fabric Architecture — FINAL (July 2026)
### Core Concept
A workspace is a **context fabric** — a bounded context environment made up of different zones: grounding, working memory, episodic memory, knowledge graph, and optional shared/global memory.
### Tenant Isolation Model (Locked)
- **Namespace** = client boundary (`USE NS {client_id}`)
- **Database** = workspace boundary (`USE NS {client_id} DB {workspace_id}`)
- **Logical context zones** = tables within workspace DB
- **Shared memory** = physically separate (`USE NS shared DB global_knowledge`)
### Context Zones (Minimal Phase 1)
1. **Grounding** — documents, chunks, embeddings (vector search)
2. **Working Memory** — messages, query logs
3. **Episodic** — conversations, events
4. **Knowledge Graph** — entities, relations, facts (graph edges as proper relations)
### Future Zones (Phase 2+)
- Policies (read-only)
- Procedures (workflows)
- Computation (formulas)
### Agent Architecture
- **Context Assembler** — separate role from Main Agent, same base model, different prompts
- **Main Agent** — reasons over assembled context, generates responses
- **No compaction needed** — context assembled via SurrealQL queries, not full history
### Key Design Decisions (Locked)
1. LSP-aware schema tooling (LSP for dev-time, INFO for runtime)
2. Live queries via Worker broker (WebSocket + LIVE SELECT, SDK lifecycle)
3. Graph edges as proper `TYPE RELATION IN ... OUT ...`
4. Template-based workspace creation (platform-controlled)
5. Provenance metadata on all data (source, creator, timestamp, confidence, derivation, lineage, visibility)
6. Schema as TypeScript modules → deterministic SurrealQL
### Implementation Order
1. Core schema, analyzers, indexes, permissions
2. Document ingestion and grounding retrieval
3. Provenance and memory writes
4. LSP-aware Context Assembler
5. Main Agent loop
6. Worker-brokered live-query UI
7. Shared memory and templates
### Status: Implementation in progress — Step 1 (Core Schema)
[Update 7/20/2026]: ## Step 1 Complete: Core Schema, Analyzers, Indexes, Permissions
### Files Created
- `worker/src/schema/constants.ts` — Platform constants (dimensions, analyzers, thresholds)
- `worker/src/schema/analyzers.ts` — SurrealQL analyzer definitions (english, keyword)
- `worker/src/schema/core.ts` — workspace, thread tables
- `worker/src/schema/grounding.ts` — document, chunk tables + HNSW/BM25 indexes
- `worker/src/schema/knowledge.ts` — entity, relation, fact tables + graph edges
- `worker/src/schema/working-memory.ts` — message, query_log tables
- `worker/src/schema/episodic.ts` — conversation, event tables
- `worker/src/schema/permissions.ts` — Zone-based permission rules
- `worker/src/schema/shared.ts` — Shared memory schema (separate namespace)
- `worker/src/schema/index.ts` — Schema orchestrator (combines all modules)
- `worker/src/db/client.ts` — SurrealDB client factory (workspace + shared)
- `worker/src/db/bootstrap.ts` — Schema bootstrap/migration script
### Schema Architecture
- **Analyzers** defined first (english, keyword)
- **Core tables** (workspace, thread) defined second
- **Zone tables** defined third (grounding, knowledge, working_memory, episodic)
- **Permissions** defined last (zone-based access control)
- **Graph edges** use proper `TYPE RELATION IN ... OUT ...` syntax
- **Embeddings** use platform constants (768 dimensions, cosine distance)
- **Indexes** include HNSW for vectors and BM25 for full-text search
### Key Patterns
- Schema as TypeScript modules → deterministic SurrealQL
- Platform constants centralized in `constants.ts`
- Schema orchestrator orders statements correctly
- Bootstrap script executes schema against scoped namespace/database
- Client factory creates workspace-scoped and shared memory clients
### Status: Step 1 COMPLETE — ready for Step 2 (Document Ingestion)
[Update 7/20/2026]: ## Implementation Complete: All 7 Steps (July 2026)
### Step 1: Core Schema, Analyzers, Indexes, Permissions
- `schema/constants.ts` — Platform constants
- `schema/analyzers.ts` — SurrealQL analyzers (english, keyword)
- `schema/core.ts` — workspace, thread tables
- `schema/grounding.ts` — document, chunk tables + HNSW/BM25 indexes
- `schema/knowledge.ts` — entity, relation, fact tables + graph edges
- `schema/working-memory.ts` — message, query_log tables
- `schema/episodic.ts` — conversation, event tables
- `schema/permissions.ts` — Zone-based permission rules
- `schema/shared.ts` — Shared memory schema
- `schema/index.ts` — Schema orchestrator
- `db/client.ts` — SurrealDB client factory
- `db/bootstrap.ts` — Schema bootstrap/migration
### Step 2: Document Ingestion and Grounding Retrieval
- `ingestion/document.ts` — Document upload/extraction/dedup/chunking/embedding
- `ingestion/chunking.ts` — Document chunking with overlap
- `ingestion/embedding.ts` — Workers AI embedding generation
- `retrieval/hybrid-search.ts` — Vector + BM25 + RRF fusion
- `retrieval/grounding.ts` — Multi-zone grounding retrieval
### Step 3: Provenance and Memory Writes
- `agent/provenance.ts` — Provenance metadata creation/extension
- `agent/memory.ts` — Entity, relation, fact creation with provenance
### Step 4: LSP-aware Context Assembler
- `context/lsp-aware.ts` — Schema discovery via INFO FOR DB/TABLE
- `context/assembler.ts` — Multi-zone context assembly
### Step 5: Main Agent Loop
- `agent/main-agent.ts` — Agent loop with context assembly, reasoning, knowledge persistence
### Step 6: Worker-brokered Live-query UI
- `index.ts` — Worker entry point with API routes and WebSocket support
### Step 7: Shared Memory and Templates
- `db/templates.ts` — Template-based workspace creation, proposal, listing
### Architecture Summary
- **Namespace** = client boundary
- **Database** = workspace boundary
- **Logical context zones** = tables within workspace DB
- **Shared memory** = physically separate (namespace: shared)
- **Agent loop** = Context Assembler + Main Agent (same model, separate roles)
- **Provenance** = source, creator, timestamp, confidence, derivation, lineage, visibility
- **Live queries** = Worker-brokered WebSocket with SurrealDB LIVE SELECT
### Status: All 7 steps COMPLETE — ready for testing and deployment
[Update 7/20/2026]: ## SurrealDB Testing Complete (July 2026)
### Environment
- SurrealDB 3.2 running locally on port 8000 (no auth)
- LSP wrapper on port 8001 (GLIBC compatibility issue, not critical)
- Namespace: docuclear, Database: default_workspace
### Schema Bootstrap Results
- All 15 tables created successfully
- Graph edges working (contains_entity, relates_to, supports, contradicts)
- Full-text search indexes working (english BM25)
- Document ingestion working (SCHEMALESS for flexible provenance)
- Chunk creation with embeddings working
- Entity creation with attributes working
- Graph traversal working (document → entity)
### Key Findings
1. **SCHEMAFULL vs SCHEMALESS**: Use SCHEMALESS for tables with flexible nested objects (provenance, attributes) to avoid validation errors
2. **Field syntax**: SurrealDB 3.2 requires `ON TABLE` in DEFINE FIELD statements
3. **FULLTEXT ANALYZER**: Use `FULLTEXT ANALYZER` instead of `SEARCH ANALYZER` (renamed in 3.0.0)
4. **Vector storage**: Vectors stored as `array<number>` type
5. **Graph edges**: `TYPE RELATION IN ... OUT ...` syntax works correctly
### Test Results
- Document creation: ✅ Working
- Chunk creation with embedding: ✅ Working
- Entity creation with attributes: ✅ Working
- Graph edge creation: ✅ Working
- Graph traversal: ✅ Working
- Full-text search: Ready to test (index created)
### Status: Schema validated and working — ready for integration
[Update 7/20/2026]: ## Embedding Function Setup (July 2026)
### Two Approaches for Embedding
1. **Worker Runtime** (preferred for Phase 1):
- Use `ai.run(PLATFORM.EMBEDDING_MODEL, { text })` directly
- Workers AI binding handles the API call
- No need for SurrealDB function
2. **SurrealDB Function** (for runtime queries):
- Define `fn::embed($text)` using `http::post()`
- Calls Workers AI via AI Gateway REST API
- Requires API token (placeholder for now)
- Useful for queries that need embeddings at runtime
### AI Gateway Configuration
- Account ID: c79b62572bcaf9d3640f40de89ad49a2
- Gateway ID: mike
- Embedding model: @cf/baai/bge-base-en-v1.5
- Dimensions: 768
### Status: Embedding function defined, ready for API token configuration
[Update 7/20/2026]: ## External Services Status (July 2026)
### Xberg (Document Intelligence)
- Running on port 9000 (MCP server)
- Version: kreuzberg-mcp 4.9.9
- Status: MCP-only, no REST API
- Phase 0: Using simple chunking (deferred to Phase 1)
### Crawlberg (Web Scraping)
- Running on port 9001 (REST API)
- Version: 1.0.7
- Status: REST API working (/v1/scrape endpoint)
- Phase 0: Not used yet (deferred to Phase 1)
### AI Gateway
- Account ID: c79b62572bcaf9d3640f40de89ad49a2
- Gateway ID: mike
- Embedding model: @cf/baai/bge-base-en-v1.5
- API Token: cfut_F3VoD1sciaHVfYYdNeejQHAWCbYzYHctgPV6aUeD64a65d35
### Phase 0 Approach
- Simple chunking for document ingestion
- Workers AI for embeddings (direct binding)
- SurrealDB functions defined for future Xberg MCP integration
### Status: Phase 0 pipeline working with simple chunking — Xberg MCP integration deferred to Phase 1
[Update 7/20/2026]: ## Kreuzberg REST Integration Complete (July 2026)
### Configuration
- Kreuzberg REST API running on port 9000
- Crawlberg REST API running on port 9001
- Docker compose at C:\CoderFiles\xbergdocker\dockercompose.yml
### Endpoints Working
- `GET /health` — Returns status, version, plugins
- `POST /chunk` — Chunks text into segments
### Test Results
- Kreuzberg chunking: ✅ Working
- Document ingestion with chunking: ✅ Working
- Graph traversal: ✅ Working
- SurrealDB schema: ✅ Working
### Integration
- `worker/src/ingestion/document.ts` — Uses Kreuzberg REST API for chunking
- `worker/src/schema/functions.ts` — Defines SurrealDB functions for embedding
- Fallback to simple chunking if Kreuzberg is unavailable
### Status: Kreuzberg REST integration complete — ready for full testing
[Update 7/20/2026]: ## Full Pipeline Testing Complete (July 2026)
### What's Working
1. **Worker API** — Running on port 8787
2. **Document ingestion** — Kreuzberg chunking + SurrealDB storage + embedding
3. **Knowledge graph** — Entity creation, graph traversal
4. **Context assembly** — Multi-zone retrieval (grounding + knowledge)
5. **Agent loop** — LLM reasoning with context, citations
### Test Results
- Document ingestion: ✅ Working (creates document + chunks)
- Hybrid search: ✅ Working (vector + BM25)
- Context assembly: ✅ Working (queries grounding + knowledge zones)
- Agent response: ✅ Working (returns answer with citations)
### Example Query
**User:** "What is the expense policy for meals?"
**Agent:** "The expense policy for meals is that they are limited to $50 per day [Source 1: Chunk 1, Source 2: Chunk 3]. This policy is also stated in Expense Policy v2, which requires pre-approval for all expenses [Source 3: Chunk 3]."
### Key Learnings
1. SurrealDB client requires `.collect()` to get query results
2. Context assembler needs better keyword matching for zone selection
3. Hybrid search works with vector + BM25 + RRF fusion
4. Agent loop successfully assembles context and generates responses
### Status: Full pipeline tested and working — ready for Phase 1
[Update 7/20/2026]: ## Phase 1 Complete (July 2026)
### What's Been Built
#### 1. SurrealQL Tools (schema management)
- `agent/tools.ts` — defineTable, defineField, runQuery, listTables, listFields
- Allows LLM to create tables, fields, and run arbitrary queries
#### 2. Knowledge Tools (entity/relation/fact management)
- `agent/knowledge-tools.ts` — createEntityTool, createRelationTool, createFactTool, searchEntitiesTool, searchFactsTool
- Allows LLM to create entities, relations, and facts with provenance
#### 3. Workflow Tools (workflow management)
- `agent/workflow-tools.ts` — createWorkflowTool, transitionStepTool, getWorkflowStatusTool
- Allows LLM to create workflows with steps and transitions
#### 4. Frontend Integration
- `frontend/app/api/chat/route.ts` — Proxies to Worker API
- `frontend/app/assistant.tsx` — assistant-ui with useChatRuntime
- `frontend/.env.local` — Worker URL configuration
### API Endpoints
| Endpoint | Method | Purpose |
|----------|--------|---------|
| `/api/bootstrap` | GET | Bootstrap schema |
| `/api/schema` | GET | Get schema info |
| `/api/chat` | POST | Agent loop (context assembly + LLM) |
| `/api/documents` | POST | Document ingestion |
| `/api/knowledge` | POST | Knowledge graph queries |
| `/api/schema/define` | POST | Define new tables |
| `/api/schema/fields` | GET | List table fields |
| `/api/workflows` | POST | Create workflows |
| `/api/workflows/status` | GET | Get workflow status |
| `/api/live` | WebSocket | Live queries |
### Architecture Summary
```
Frontend (assistant-ui)
↓
Worker API (/api/chat, /api/documents, /api/schema, /api/workflows)
↓
Context Assembler (zones: grounding, knowledge, working_memory, episodic)
↓
Hybrid Search (vector + BM25 + RRF)
↓
SurrealDB (15 tables, graph edges, full-text search)
↓
Kreuzberg (chunking)
↓
Workers AI (embeddings)
```
### Status: Phase 1 COMPLETE — ready for Phase 2 (frontend testing and deployment)
[Update 7/20/2026]: ## Phase 1 Testing Complete (July 2026)
### All Endpoints Working
| Endpoint | Test | Result |
|----------|------|--------|
| `/api/chat` | "What is the expense policy for meals?" | ✅ Response with citations |
| `/api/documents` | Ingest travel-policy.txt | ✅ Document created, 1 chunk |
| `/api/knowledge` | SELECT * FROM document | ✅ 4 documents found |
| `/api/schema/define` | Create custom_entity table | ✅ Table created |
| `/api/workflows` | Create test workflow | ✅ Workflow created |
| `/api/workflows/status` | Get workflow status | ✅ Workflow found |
### Frontend Status
- Running on port 3000 ✅
- Connected to Worker on port 8787 ✅
- Chat interface working ✅
- Document ingestion working ✅
- Knowledge graph queries working ✅
- Schema definition working ✅
- Workflow creation and status working ✅
### Key Learnings
1. SurrealDB client requires `.collect()` to get query results
2. Workflow ID format needs string conversion for queries
3. Frontend proxies to Worker for all API calls
4. All tools (SurrealQL, knowledge, workflow) are functional
### Status: Phase 1 COMPLETE and TESTED — ready for deployment
Tags: phase-0, mvp, plan, cf-agents, assistant-ui, project:docuclear_clearspace, kind:semantic, status:canonical — Source: claude — 2026-07-19 15:53:11 UTC