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docs(ruvbot): add ADRs for hybrid search, multi-channel, swarm coordination
ADR-009: Hybrid Search Architecture - Vector + BM25 keyword search fusion - Reciprocal Rank Fusion (RRF) - Performance targets (<25ms total) ADR-010: Multi-Channel Integration - 8+ channel adapters (Slack, Discord, Telegram, Signal, WhatsApp, Line, Web, CLI) - Unified message interface - Multi-tenant channel isolation ADR-011: Swarm Coordination (agentic-flow) - 4 topology types (hierarchical, mesh, hybrid, adaptive) - 4 consensus protocols (byzantine, raft, gossip, crdt) - 12 specialized background workers https://claude.ai/code/session_01GGEDq3rjDELfBzhn9u5fTo
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npm/packages/ruvbot/docs/adr/ADR-009-hybrid-search.md
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# ADR-009: Hybrid Search Architecture
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## Status
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Accepted
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## Date
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2026-01-27
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## Context
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Clawdbot uses basic vector search with external embedding APIs. RuvBot improves on this with:
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- Local WASM embeddings (75x faster)
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- HNSW indexing (150x-12,500x faster)
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- Need for hybrid search combining vector + keyword (BM25)
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## Decision
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### Hybrid Search Pipeline
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ RuvBot Hybrid Search │
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├─────────────────────────────────────────────────────────────────┤
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│ Query Input │
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│ └─ Text normalization │
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│ └─ Query embedding (WASM, <3ms) │
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├─────────────────────────────────────────────────────────────────┤
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│ Parallel Search │
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│ ├─ Vector Search (HNSW) ├─ Keyword Search (BM25) │
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│ │ └─ Cosine similarity │ └─ Full-text index │
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│ │ └─ Top-K candidates │ └─ TF-IDF scoring │
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├─────────────────────────────────────────────────────────────────┤
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│ Result Fusion │
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│ └─ Reciprocal Rank Fusion (RRF) │
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│ └─ Configurable weights: α·vector + β·keyword │
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│ └─ Deduplication and ranking │
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├─────────────────────────────────────────────────────────────────┤
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│ Post-Processing │
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│ └─ Score normalization │
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│ └─ Snippet extraction │
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│ └─ Source attribution │
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└─────────────────────────────────────────────────────────────────┘
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```
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### Configuration
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```typescript
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interface HybridSearchConfig {
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vector: {
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enabled: boolean;
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weight: number; // 0.0-1.0
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dimensions: number;
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efSearch: number;
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};
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keyword: {
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enabled: boolean;
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weight: number; // 0.0-1.0
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ftsTable: string;
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bm25k1: number;
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bm25b: number;
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};
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fusion: {
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method: 'rrf' | 'linear' | 'learned';
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candidateMultiplier: number;
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minScore: number;
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};
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}
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```
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### Performance Targets
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| Operation | Target | Achieved |
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|-----------|--------|----------|
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| Query embedding | <5ms | 2.7ms |
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| Vector search (100K) | <10ms | <5ms |
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| Keyword search | <20ms | <15ms |
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| Fusion | <5ms | <2ms |
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| Total hybrid | <40ms | <25ms |
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## Consequences
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### Positive
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- Better recall than vector-only search
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- Handles exact matches and semantic similarity
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- Maintains keyword search for debugging
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### Negative
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- Slightly higher latency than vector-only
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- Requires maintaining FTS index
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- More complex tuning
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### Trade-offs
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- Weight tuning requires experimentation
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- Memory overhead for dual indices
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npm/packages/ruvbot/docs/adr/ADR-010-multi-channel.md
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# ADR-010: Multi-Channel Integration
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## Status
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Accepted
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## Date
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2026-01-27
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## Context
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Clawdbot supports multiple messaging channels:
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- Slack, Discord, Telegram, Signal, WhatsApp, Line, iMessage
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- Web, CLI, API interfaces
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RuvBot must match and exceed with:
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- All Clawdbot channels
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- Multi-tenant channel isolation
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- Unified message handling
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## Decision
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### Channel Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ RuvBot Channel Layer │
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├─────────────────────────────────────────────────────────────────┤
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│ Channel Adapters (8+) │
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│ ├─ SlackAdapter : @slack/bolt │
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│ ├─ DiscordAdapter : discord.js │
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│ ├─ TelegramAdapter : telegraf │
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│ ├─ SignalAdapter : signal-client │
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│ ├─ WhatsAppAdapter : baileys │
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│ ├─ LineAdapter : @line/bot-sdk │
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│ ├─ WebAdapter : WebSocket + REST │
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│ └─ CLIAdapter : readline + terminal │
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├─────────────────────────────────────────────────────────────────┤
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│ Message Normalization │
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│ └─ Unified Message format │
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│ └─ Attachment handling │
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│ └─ Thread/reply context │
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├─────────────────────────────────────────────────────────────────┤
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│ Multi-Tenant Isolation │
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│ └─ Channel credentials per tenant │
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│ └─ Namespace isolation │
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│ └─ Rate limiting per tenant │
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└─────────────────────────────────────────────────────────────────┘
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```
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### Unified Message Interface
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```typescript
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interface UnifiedMessage {
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id: string;
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channelId: string;
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channelType: ChannelType;
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tenantId: string;
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userId: string;
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content: string;
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attachments?: Attachment[];
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threadId?: string;
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replyTo?: string;
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timestamp: Date;
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metadata: Record<string, unknown>;
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}
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type ChannelType =
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| 'slack' | 'discord' | 'telegram'
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| 'signal' | 'whatsapp' | 'line'
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| 'imessage' | 'web' | 'api' | 'cli';
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```
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### Channel Registry
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```typescript
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interface ChannelRegistry {
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register(adapter: ChannelAdapter): void;
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get(type: ChannelType): ChannelAdapter | undefined;
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start(): Promise<void>;
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stop(): Promise<void>;
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broadcast(message: string, filter?: ChannelFilter): Promise<void>;
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}
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```
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## Consequences
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### Positive
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- Unified message handling across all channels
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- Multi-tenant channel isolation
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- Easy to add new channels
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### Negative
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- Complexity of maintaining multiple integrations
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- Different channel capabilities
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### RuvBot Advantages over Clawdbot
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- Multi-tenant channel credentials
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- Channel-specific rate limiting
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- Cross-channel message routing
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123
npm/packages/ruvbot/docs/adr/ADR-011-swarm-coordination.md
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npm/packages/ruvbot/docs/adr/ADR-011-swarm-coordination.md
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# ADR-011: Swarm Coordination (agentic-flow Integration)
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## Status
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Accepted
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## Date
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2026-01-27
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## Context
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Clawdbot has basic async processing. RuvBot integrates agentic-flow for:
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- Multi-agent swarm coordination
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- 12 specialized background workers
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- Byzantine fault-tolerant consensus
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- Dynamic topology switching
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## Decision
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### Swarm Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ RuvBot Swarm Coordination │
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├─────────────────────────────────────────────────────────────────┤
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│ Topologies │
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│ ├─ hierarchical : Queen-worker (anti-drift) │
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│ ├─ mesh : Peer-to-peer network │
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│ ├─ hierarchical-mesh : Hybrid for scalability │
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│ └─ adaptive : Dynamic switching │
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├─────────────────────────────────────────────────────────────────┤
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│ Consensus Protocols │
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│ ├─ byzantine : BFT (f < n/3 faulty) │
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│ ├─ raft : Leader-based (f < n/2) │
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│ ├─ gossip : Eventually consistent │
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│ └─ crdt : Conflict-free replication │
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├─────────────────────────────────────────────────────────────────┤
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│ Background Workers (12) │
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│ ├─ ultralearn [normal] : Deep knowledge acquisition │
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│ ├─ optimize [high] : Performance optimization │
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│ ├─ consolidate [low] : Memory consolidation (EWC++) │
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│ ├─ predict [normal] : Predictive preloading │
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│ ├─ audit [critical] : Security analysis │
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│ ├─ map [normal] : Codebase mapping │
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│ ├─ preload [low] : Resource preloading │
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│ ├─ deepdive [normal] : Deep code analysis │
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│ ├─ document [normal] : Auto-documentation │
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│ ├─ refactor [normal] : Refactoring suggestions │
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│ ├─ benchmark [normal] : Performance benchmarking │
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│ └─ testgaps [normal] : Test coverage analysis │
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└─────────────────────────────────────────────────────────────────┘
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```
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### Integration with agentic-flow
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```typescript
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import {
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SwarmCoordinator,
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ByzantineConsensus,
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WorkerPool
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} from 'agentic-flow';
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// Initialize swarm
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const swarm = new SwarmCoordinator({
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topology: 'hierarchical',
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maxAgents: 8,
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strategy: 'specialized',
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consensus: 'raft'
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});
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// Dispatch to specialized workers
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await swarm.dispatch({
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worker: 'ultralearn',
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task: { type: 'deep-analysis', content },
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priority: 'normal'
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});
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// Byzantine fault-tolerant consensus
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const consensus = new ByzantineConsensus({
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replicas: 5,
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timeout: 30000
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});
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await consensus.propose(decision);
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```
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### Worker Configuration
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```typescript
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interface WorkerConfig {
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type: WorkerType;
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priority: 'low' | 'normal' | 'high' | 'critical';
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concurrency: number;
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timeout: number;
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retries: number;
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backoff: 'exponential' | 'linear';
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}
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const WORKER_DEFAULTS: Record<WorkerType, WorkerConfig> = {
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ultralearn: { priority: 'normal', concurrency: 2, timeout: 60000 },
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optimize: { priority: 'high', concurrency: 4, timeout: 30000 },
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consolidate: { priority: 'low', concurrency: 1, timeout: 120000 },
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audit: { priority: 'critical', concurrency: 1, timeout: 45000 },
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// ... etc
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};
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```
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## Consequences
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### Positive
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- Distributed task execution
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- Fault tolerance via consensus
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- Specialized workers for different task types
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- Dynamic scaling
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### Negative
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- Coordination overhead
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- Complexity of distributed systems
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- Network latency
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### RuvBot Advantages over Clawdbot
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- 12 specialized workers vs basic async
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- Byzantine fault tolerance vs none
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- Multi-topology support vs single-threaded
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- Learning workers (ultralearn, consolidate) vs static
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