diff --git a/apps/fractal-inference-swarms/.gitignore b/apps/fractal-inference-swarms/.gitignore new file mode 100644 index 0000000..6cde671 --- /dev/null +++ b/apps/fractal-inference-swarms/.gitignore @@ -0,0 +1,8 @@ +node_modules/ +dist/ +.next/ +.env +.env.local +*.log +logs/ +.DS_Store \ No newline at end of file diff --git a/apps/fractal-inference-swarms/LICENSE.md b/apps/fractal-inference-swarms/LICENSE.md new file mode 100644 index 0000000..63082f4 --- /dev/null +++ b/apps/fractal-inference-swarms/LICENSE.md @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Cortensor Community + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. diff --git a/apps/fractal-inference-swarms/PROJECTS.yml b/apps/fractal-inference-swarms/PROJECTS.yml new file mode 100644 index 0000000..501df36 --- /dev/null +++ b/apps/fractal-inference-swarms/PROJECTS.yml @@ -0,0 +1,16 @@ +- name: fractal-inference-swarms + category: apps + owner: "@your-github-username" + collaborators: + - "@your-discord#0001" + status: active + created: 2026-02-11 + version: v0.1.0 + tags: + - cortensor + - orchestration + - ai-swarms + - pouw + - validators + - x402 + - observability diff --git a/apps/fractal-inference-swarms/README.md b/apps/fractal-inference-swarms/README.md new file mode 100644 index 0000000..f0b2657 --- /dev/null +++ b/apps/fractal-inference-swarms/README.md @@ -0,0 +1,79 @@ +Fractal Inference Swarms (FIS) +Cortensor-Native AI Orchestration Infrastructure +A production-grade distributed inference system that decomposes complex tasks, routes them through Cortensor nodes, validates outputs, and distributes micropayments based on performance. +🎯 Project OverviewFractal Inference Swarms is a Kubernetes-like orchestration layer for decentralized AI inference. It sits on top of Cortensor and provides: +Intelligent Task Decomposition - Break complex prompts into atomic subtasks +Distributed Inference Routing - Route subtasks to multiple Cortensor nodes +Validation & Consensus - Validate outputs using Cortensor validators +Result Aggregation - Merge validated outputs into unified responses +PoUW-Based Scoring - Rank node performance using multi-factor metrics +Micropayment Distribution - Reward high-performing nodes with x402 tokens +Real-Time Observability - Monitor the entire swarm lifecycle via dashboard +This is NOT a prototype. This is enterprise-grade AI infrastructure designed for production workloads.πŸ—οΈ Architectureβ”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ User Interface β”‚ +β”‚ (Next.js Real-Time Dashboard) β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Fractal Orchestrator β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ Task Splitterβ”‚ β”‚ Merge Engine β”‚ β”‚ Scoring Engine β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ Validation β”‚ β”‚Reward Engine β”‚ β”‚ WebSocket Serverβ”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Cortensor Network β”‚ +β”‚ β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ Node A β”‚ β”‚ Node B β”‚ β”‚ Node C β”‚ β”‚ Node D β”‚ ... β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β”‚ β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ Cortensor Validators (Consensus) β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Mock x402 Payment Ledger β”‚ +β”‚ (Ready for Real Blockchain Integration) β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜βœ¨ Key Features🧠 Intelligent Orchestration + +Automatic task decomposition into optimized subtasks +Dynamic subtask routing to available Cortensor nodes +State machine tracking (CREATED β†’ SPLIT β†’ ROUTED β†’ VALIDATING β†’ MERGED β†’ FINALIZED) +βœ… Validation & Consensus + +Cross-validation of outputs from multiple nodes +Cortensor validator consensus integration +Conflict resolution using confidence-weighted voting +Quality gates to reject low-confidence results +πŸ”€ Result Merging + +Synthesizes distributed outputs into coherent final result +Contribution mapping (tracks which node provided what) +Confidence scoring for merged output +Deduplication and format normalization +πŸ“Š PoUW-Based Scoring +Final Score = w₁×consensus + wβ‚‚Γ—confidence + w₃×speed + wβ‚„Γ—reliability +Configurable weights via environment variables +Multi-dimensional node evaluation +Real-time leaderboard updates +πŸ’° Economic Layer + +Mock x402 micropayment distribution +reward = baseReward Γ— normalizedScore +Complete transaction ledger with audit trail +Stake simulation (increases/slashing based on performance) +πŸ“‘ Real-Time Dashboard + +WebSocket-powered live updates +Agent spawn animations +Session lifecycle visualization +Validator consensus progress bars +Leaderboard and reward distribution charts \ No newline at end of file diff --git a/apps/fractal-inference-swarms/RELEASE.md b/apps/fractal-inference-swarms/RELEASE.md new file mode 100644 index 0000000..8854c39 --- /dev/null +++ b/apps/fractal-inference-swarms/RELEASE.md @@ -0,0 +1,8 @@ +# Release Notes + +## v0.1.0 - Initial Release +- Cortensor-native swarm orchestration +- Real-time dashboard +- PoUW scoring engine +- x402 mock reward ledger +- Observability metrics diff --git a/apps/fractal-inference-swarms/STATUS.md b/apps/fractal-inference-swarms/STATUS.md new file mode 100644 index 0000000..0c0b7bb --- /dev/null +++ b/apps/fractal-inference-swarms/STATUS.md @@ -0,0 +1,25 @@ +# Project Status + +## Current Version +v0.1.0-beta + +## Completed +- Task orchestration +- Cortensor session integration +- Validator consensus integration +- PoUW scoring +- x402 mock rewards +- Dashboard visualization + +## In Progress +- On-chain artifact storage +- Stake slashing logic + +## Planned +- ERC-8004 artifact support +- Real Cortensor mainnet integration +- Benchmark suite + +## Known Issues +- Simulated latency variability +- Limited validator diversity in demo mode diff --git a/apps/fractal-inference-swarms/backend/.dockerignore b/apps/fractal-inference-swarms/backend/.dockerignore new file mode 100644 index 0000000..e7effbb --- /dev/null +++ b/apps/fractal-inference-swarms/backend/.dockerignore @@ -0,0 +1,7 @@ +node_modules +dist +logs +.env +*.log +.git +.gitignore \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/.env.example b/apps/fractal-inference-swarms/backend/.env.example new file mode 100644 index 0000000..4f0fe5d --- /dev/null +++ b/apps/fractal-inference-swarms/backend/.env.example @@ -0,0 +1,31 @@ +# Server Configuration +PORT=3001 +NODE_ENV=development +HOST=0.0.0.0 + +# CORS +CORS_ORIGIN=http://localhost:3000 + +# WebSocket +WS_PORT=3002 +WS_HEARTBEAT_INTERVAL=30000 + +# Orchestrator Configuration +MAX_CONCURRENT_AGENTS=20 +DEFAULT_SWARM_SIZE=5 +AGENT_TIMEOUT_MS=30000 +TASK_SPLIT_STRATEGY=semantic + +# Scoring Engine Weights +SCORING_WEIGHT_CONFIDENCE=0.4 +SCORING_WEIGHT_SPEED=0.3 +SCORING_WEIGHT_RELIABILITY=0.3 + +# Reward Engine +BASE_REWARD_TOKENS=100 +MIN_SCORE_THRESHOLD=0.3 +REWARD_POOL_SIZE=1000 + +# Logging +LOG_LEVEL=debug +LOG_FORMAT=json \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/Dockerfile b/apps/fractal-inference-swarms/backend/Dockerfile new file mode 100644 index 0000000..97438e1 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/Dockerfile @@ -0,0 +1,37 @@ +FROM node:20-alpine AS builder + +WORKDIR /app + +COPY package.json package-lock.json* ./ +RUN npm ci --ignore-scripts + +COPY tsconfig.json ./ +COPY src ./src + +RUN npm run build + +FROM node:20-alpine AS runner + +WORKDIR /app + +ENV NODE_ENV=production + +RUN addgroup --system --gid 1001 swarm && \ + adduser --system --uid 1001 agent + +COPY --from=builder /app/package.json ./ +COPY --from=builder /app/package-lock.json* ./ +RUN npm ci --omit=dev --ignore-scripts + +COPY --from=builder /app/dist ./dist + +RUN mkdir -p logs && chown -R agent:swarm logs + +USER agent + +EXPOSE 3001 + 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a/apps/fractal-inference-swarms/backend/package.json b/apps/fractal-inference-swarms/backend/package.json new file mode 100644 index 0000000..06541a5 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/package.json @@ -0,0 +1,32 @@ +{ + "name": "fractal-inference-swarms-backend", + "version": "1.0.0", + "description": "AI-native orchestration system with fractal agent swarms and x402 micropayments", + "main": "dist/server.js", + "scripts": { + "dev": "ts-node-dev --respawn --transpile-only src/server.ts", + "build": "tsc", + "start": "node dist/server.js", + "lint": "eslint src/**/*.ts" + }, + "dependencies": { + "cors": "^2.8.5", + "dotenv": "^16.4.5", + "express": "^4.21.0", + "helmet": "^7.1.0", + "morgan": "^1.10.0", + "uuid": "^9.0.1", + "ws": "^8.17.0", + "winston": "^3.13.0" + }, + "devDependencies": { + "@types/cors": "^2.8.17", + "@types/express": "^4.17.21", + "@types/morgan": "^1.9.9", + "@types/node": "^20.14.0", + "@types/uuid": "^9.0.8", + "@types/ws": "^8.5.10", + "ts-node-dev": "^2.0.0", + "typescript": "^5.4.5" + } + } \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/agents/Orchestrator.ts b/apps/fractal-inference-swarms/backend/src/agents/Orchestrator.ts new file mode 100644 index 0000000..0ae27c7 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/agents/Orchestrator.ts @@ -0,0 +1,312 @@ +import { v4 as uuidv4 } from 'uuid'; +import { Task, TaskCreateRequest, createTask, MergedOutput } from '../models/Task'; +import { Agent, AgentResult, ScoredResult, RewardTransaction, SwarmSession, SwarmMetrics } from '../models/AgentResult'; +import { taskSplitter } from '../engine/TaskSplitter'; +import { scoringEngine } from '../engine/ScoringEngine'; +import { rewardEngine } from '../engine/RewardEngine'; +import { mergeEngine, MergeStrategy } from '../engine/MergeEngine'; +import { swarmAgentRunner } from './SwarmAgent'; +import { x402Engine } from '../payments/X402Mock'; +import { eventBus } from '../utils/EventBus'; +import { config } from '../config'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('Orchestrator'); + +const ORCHESTRATOR_WALLET = 'orchestrator-treasury-0x000'; + +export class Orchestrator { + private tasks: Map = new Map(); + private sessions: Map = new Map(); + private allRewards: RewardTransaction[] = []; + private startTime: number = Date.now(); + private totalAgentsSpawned: number = 0; + private totalTasksCompleted: number = 0; + private totalTokensDistributed: number = 0; + + async submitTask(request: TaskCreateRequest): Promise { + const task = createTask(request, config.orchestrator.defaultSwarmSize); + this.tasks.set(task.id, task); + + logger.info('Task submitted', { + taskId: task.id, + title: task.title, + swarmSize: task.swarmSize, + }); + + eventBus.emitSwarmEvent('task:created', task.id, { task }); + + this.executeTaskPipeline(task).catch((error) => { + logger.error('Task pipeline failed', { + taskId: task.id, + error: (error as Error).message, + }); + task.status = 'failed'; + task.errorMessage = (error as Error).message; + task.updatedAt = Date.now(); + eventBus.emitSwarmEvent('task:failed', task.id, { task, error: (error as Error).message }); + }); + + return task; + } + + private async executeTaskPipeline(task: Task): Promise { + const pipelineStart = Date.now(); + + // Phase 1: Split task + task.status = 'splitting'; + task.updatedAt = Date.now(); + eventBus.emitSwarmEvent('task:splitting', task.id, { task }); + + const subtasks = taskSplitter.splitTask({ + strategy: config.orchestrator.taskSplitStrategy, + swarmSize: task.swarmSize, + taskDescription: task.description, + taskId: task.id, + }); + + task.subtasks = subtasks; + task.updatedAt = Date.now(); + eventBus.emitSwarmEvent('task:subtasks_ready', task.id, { task, subtasks }); + + // Phase 2: Create swarm session + const session: SwarmSession = { + id: uuidv4(), + taskId: task.id, + agents: [], + results: [], + scoredResults: [], + rewards: [], + status: 'active', + startedAt: Date.now(), + completedAt: null, + totalTokensDistributed: 0, + }; + this.sessions.set(session.id, session); + + // Phase 3: Spawn agents and run inference + task.status = 'spawning_agents'; + task.updatedAt = Date.now(); + eventBus.emitSwarmEvent('task:agents_spawning', task.id, { task, sessionId: session.id }); + + const agentPromises = subtasks.map(async (subtask) => { + try { + subtask.status = 'assigned'; + const { agent, result } = await swarmAgentRunner.spawnAndRun(subtask, task.id); + subtask.assignedAgentId = agent.id; + subtask.status = 'completed'; + subtask.completedAt = Date.now(); + session.agents.push(agent); + session.results.push(result); + this.totalAgentsSpawned++; + return { agent, result, success: true as const }; + } catch (error) { + subtask.status = 'failed'; + logger.error('Agent execution failed for subtask', { + subtaskId: subtask.id, + error: (error as Error).message, + }); + return { success: false as const, error: (error as Error).message }; + } + }); + + task.status = 'inference_running'; + task.updatedAt = Date.now(); + eventBus.emitSwarmEvent('task:inference_started', task.id, { task }); + + const agentResults = await Promise.all(agentPromises); + const successfulResults = agentResults.filter( + (r): r is { agent: Agent; result: AgentResult; success: true } => r.success + ); + + if (successfulResults.length === 0) { + throw new Error('All agents failed β€” no results to score'); + } + + eventBus.emitSwarmEvent('task:inference_complete', task.id, { + task, + resultCount: successfulResults.length, + failedCount: agentResults.length - successfulResults.length, + }); + + // Phase 4: Score results + task.status = 'scoring'; + task.updatedAt = Date.now(); + eventBus.emitSwarmEvent('task:scoring_started', task.id, { task }); + + const scoredResults = scoringEngine.scoreResults(session.results); + session.scoredResults = scoredResults; + + eventBus.emitSwarmEvent('task:scoring_complete', task.id, { + task, + scoredResults, + topAgent: scoredResults[0], + }); + + // Phase 5: Merge and validate outputs + task.status = 'merging'; + task.updatedAt = Date.now(); + eventBus.emitSwarmEvent('task:merging_started', task.id, { task }); + + const mergeStrategy = this.selectMergeStrategy(scoredResults); + const mergedOutput = mergeEngine.mergeResults(scoredResults, task.id, mergeStrategy); + task.mergedOutput = mergedOutput; + session.status = 'merging'; + + eventBus.emitSwarmEvent('task:merge_complete', task.id, { + task, + mergedOutput, + }); + + eventBus.emitSwarmEvent('task:validation_complete', task.id, { + task, + validationPassed: mergedOutput.validationPassed, + validationScore: mergedOutput.validationScore, + validationDetails: mergedOutput.validationDetails, + }); + + // Phase 6: Distribute rewards + task.status = 'distributing_rewards'; + task.updatedAt = Date.now(); + + const rewards = rewardEngine.distributeRewards(scoredResults, task.id); + session.rewards = rewards; + + const paymentBatch = rewards + .filter((r) => r.tokensEarned > 0) + .map((r) => ({ + from: ORCHESTRATOR_WALLET, + to: `agent-wallet-${r.agentId.slice(0, 12)}`, + amount: r.tokensEarned, + })); + + if (paymentBatch.length > 0) { + await x402Engine.processBatchPayments(paymentBatch); + } + + this.allRewards.push(...rewards); + const sessionTokens = rewards.reduce((sum, r) => sum + r.tokensEarned, 0); + session.totalTokensDistributed = sessionTokens; + this.totalTokensDistributed += sessionTokens; + + eventBus.emitSwarmEvent('task:rewards_distributed', task.id, { + task, + rewards, + totalTokensDistributed: sessionTokens, + }); + + // Phase 7: Complete + task.status = 'completed'; + task.completedAt = Date.now(); + task.updatedAt = Date.now(); + task.totalComputeTimeMs = Date.now() - pipelineStart; + session.status = 'completed'; + session.completedAt = Date.now(); + this.totalTasksCompleted++; + + logger.info('Task pipeline complete', { + taskId: task.id, + totalTimeMs: task.totalComputeTimeMs, + agentsUsed: session.agents.length, + tokensDistributed: sessionTokens, + validationPassed: mergedOutput.validationPassed, + }); + + eventBus.emitSwarmEvent('task:completed', task.id, { + task, + session, + mergedOutput, + }); + + eventBus.emitSwarmEvent('metrics:updated', task.id, this.getMetrics()); + } + + private selectMergeStrategy(scoredResults: ScoredResult[]): MergeStrategy { + if (scoredResults.length <= 2) return 'best_pick'; + + const topScore = scoredResults[0].finalScore; + const avgScore = scoredResults.reduce((s, r) => s + r.finalScore, 0) / scoredResults.length; + const dominance = topScore / avgScore; + + if (dominance > 1.5) return 'best_pick'; + if (scoredResults.length >= 5) return 'hierarchical_merge'; + return 'weighted_consensus'; + } + + getTask(taskId: string): Task | undefined { + return this.tasks.get(taskId); + } + + getAllTasks(): Task[] { + return Array.from(this.tasks.values()).sort((a, b) => b.createdAt - a.createdAt); + } + + getSessionForTask(taskId: string): SwarmSession | undefined { + return Array.from(this.sessions.values()).find((s) => s.taskId === taskId); + } + + getAllSessions(): SwarmSession[] { + return Array.from(this.sessions.values()).sort((a, b) => b.startedAt - a.startedAt); + } + + getAgentsForTask(taskId: string): Agent[] { + const session = this.getSessionForTask(taskId); + return session?.agents || []; + } + + getScoredResultsForTask(taskId: string): ScoredResult[] { + const session = this.getSessionForTask(taskId); + return session?.scoredResults || []; + } + + getLedger(): RewardTransaction[] { + return [...this.allRewards].sort((a, b) => b.timestamp - a.timestamp); + } + + getMetrics(): SwarmMetrics { + const sessions = this.getAllSessions(); + const completedSessions = sessions.filter((s) => s.status === 'completed'); + const activeSessions = sessions.filter((s) => s.status === 'active' || s.status === 'scoring' || s.status === 'merging'); + + const avgSwarmSize = + completedSessions.length > 0 + ? completedSessions.reduce((sum, s) => sum + s.agents.length, 0) / completedSessions.length + : 0; + + const avgCompletionTime = + completedSessions.length > 0 + ? completedSessions.reduce((sum, s) => sum + ((s.completedAt || 0) - s.startedAt), 0) / completedSessions.length + : 0; + + const allResults = completedSessions.flatMap((s) => s.scoredResults); + const avgConfidence = + allResults.length > 0 + ? allResults.reduce((sum, r) => sum + r.confidence, 0) / allResults.length + : 0; + + let topAgentId: string | null = null; + let topAgentScore = 0; + for (const result of allResults) { + if (result.finalScore > topAgentScore) { + topAgentScore = result.finalScore; + topAgentId = result.agentId; + } + } + + return { + totalSessions: sessions.length, + activeSessions: activeSessions.length, + totalAgentsSpawned: this.totalAgentsSpawned, + totalTasksCompleted: this.totalTasksCompleted, + totalTokensDistributed: Math.round(this.totalTokensDistributed * 100) / 100, + averageSwarmSize: Math.round(avgSwarmSize * 100) / 100, + averageCompletionTimeMs: Math.round(avgCompletionTime), + averageConfidence: Math.round(avgConfidence * 10000) / 10000, + topAgentId, + topAgentScore: Math.round(topAgentScore * 10000) / 10000, + uptimeMs: Date.now() - this.startTime, + }; + } +} + +export const orchestrator = new Orchestrator(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/agents/SwarmAgent.ts b/apps/fractal-inference-swarms/backend/src/agents/SwarmAgent.ts new file mode 100644 index 0000000..9dde16e --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/agents/SwarmAgent.ts @@ -0,0 +1,221 @@ +import { v4 as uuidv4 } from 'uuid'; +import { Agent, AgentResult, InferenceMetadata } from '../models/AgentResult'; +import { SubTask } from '../models/Task'; +import { createChildLogger } from '../utils/logger'; +import { eventBus } from '../utils/EventBus'; + +const logger = createChildLogger('SwarmAgent'); + +const AGENT_NAMES = [ + 'Nexus', 'Cipher', 'Prism', 'Vortex', 'Echo', + 'Quasar', 'Helix', 'Flux', 'Zenith', 'Pulse', + 'Orbit', 'Shard', 'Nova', 'Drift', 'Apex', + 'Arc', 'Blaze', 'Core', 'Delta', 'Enigma', +]; + +const INFERENCE_TEMPLATES: Record = { + analysis: [ + 'After comprehensive analysis of the given parameters, the primary finding indicates that {topic} demonstrates significant correlation with {factor}. The data suggests a {confidence_level} confidence level in this assessment. Key metrics show {metric1} trending {direction} while {metric2} maintains stability. Recommendation: {action}.', + 'Systematic evaluation reveals {count} critical factors influencing {topic}. Primary driver: {factor} (impact coefficient: {coeff}). Secondary influences include environmental variables and temporal dynamics. Confidence in assessment: {confidence_level}. Risk profile: {risk}.', + ], + development: [ + 'Architecture proposal for {topic}: Implement a {pattern} pattern with {layer_count} layers. Core module handles {responsibility1}, while auxiliary services manage {responsibility2}. Estimated complexity: O({complexity}). Scalability factor: {scale_factor}x. Implementation confidence: {confidence_level}.', + 'Technical specification for {topic}: Utilize {technology} framework with {approach} methodology. Module decomposition yields {module_count} independent components. Integration points: {integration_count}. Test coverage target: {coverage}%. Delivery confidence: {confidence_level}.', + ], + research: [ + 'Research findings on {topic}: Literature review of {source_count} sources reveals consensus on {finding}. Divergent viewpoints exist regarding {controversy}. Meta-analysis suggests {conclusion} with p-value < {p_value}. Recommendation for further investigation: {recommendation}.', + 'Investigation into {topic} yields {finding_count} significant findings. Primary conclusion: {conclusion}. Supporting evidence spans {evidence_count} independent sources. Methodology: {methodology}. Confidence interval: {ci_lower}–{ci_upper}. Research quality score: {confidence_level}.', + ], + general: [ + 'Processing subtask for {topic}: Generated output addresses {aspect_count} key aspects. Primary output covers {primary_aspect} with {depth} depth of analysis. Secondary considerations include {secondary_aspects}. Output reliability: {confidence_level}. Compute efficiency: {efficiency}%.', + 'Task completion for {topic}: Addressed all {constraint_count} constraints. Output quality score: {quality_score}/10. Key deliverable: {deliverable}. Verification status: {verification}. Processing iterations: {iterations}. Final confidence: {confidence_level}.', + ], +}; + +function generateInferenceOutput(subtask: SubTask, iterations: number): string { + const categoryKeywords: Record = { + analysis: ['analyze', 'evaluate', 'assess', 'review', 'examine', 'compare'], + development: ['build', 'implement', 'create', 'develop', 'code', 'design'], + research: ['research', 'investigate', 'study', 'explore', 'find', 'discover'], + }; + + let category = 'general'; + const lower = subtask.description.toLowerCase(); + for (const [cat, words] of Object.entries(categoryKeywords)) { + if (words.some((w) => lower.includes(w))) { + category = cat; + break; + } + } + + const templates = INFERENCE_TEMPLATES[category]; + const template = templates[Math.floor(Math.random() * templates.length)]; + const confidence = (0.6 + Math.random() * 0.35).toFixed(4); + + const replacements: Record = { + topic: subtask.description.slice(0, 60), + factor: 'multi-dimensional parameter space', + confidence_level: `${(parseFloat(confidence) * 100).toFixed(1)}%`, + metric1: 'throughput', + metric2: 'latency', + direction: Math.random() > 0.5 ? 'upward' : 'stabilizing', + action: 'proceed with optimized configuration', + count: String(Math.floor(3 + Math.random() * 8)), + coeff: (Math.random() * 0.9 + 0.1).toFixed(3), + risk: Math.random() > 0.5 ? 'moderate' : 'low', + pattern: ['microservice', 'event-driven', 'layered', 'hexagonal'][Math.floor(Math.random() * 4)], + layer_count: String(Math.floor(3 + Math.random() * 4)), + responsibility1: 'core business logic', + responsibility2: 'cross-cutting concerns', + complexity: ['n log n', 'n', 'nΒ²', 'log n'][Math.floor(Math.random() * 4)], + scale_factor: String(Math.floor(2 + Math.random() * 10)), + technology: ['distributed', 'reactive', 'modular', 'cloud-native'][Math.floor(Math.random() * 4)], + approach: ['iterative', 'incremental', 'adaptive'][Math.floor(Math.random() * 3)], + module_count: String(Math.floor(4 + Math.random() * 12)), + integration_count: String(Math.floor(2 + Math.random() * 6)), + coverage: String(Math.floor(80 + Math.random() * 20)), + source_count: String(Math.floor(10 + Math.random() * 50)), + finding: 'convergent optimization patterns', + controversy: 'scalability trade-offs', + conclusion: 'statistically significant improvement', + p_value: (Math.random() * 0.05).toFixed(4), + recommendation: 'expand sample size and extend temporal range', + finding_count: String(Math.floor(3 + Math.random() * 8)), + evidence_count: String(Math.floor(5 + Math.random() * 20)), + methodology: 'systematic multi-source analysis', + ci_lower: (parseFloat(confidence) - 0.1).toFixed(2), + ci_upper: Math.min(1, parseFloat(confidence) + 0.1).toFixed(2), + aspect_count: String(Math.floor(3 + Math.random() * 5)), + primary_aspect: 'core objective fulfillment', + depth: ['thorough', 'comprehensive', 'detailed'][Math.floor(Math.random() * 3)], + secondary_aspects: 'edge cases and boundary conditions', + efficiency: String(Math.floor(70 + Math.random() * 30)), + constraint_count: String(subtask.constraints.length), + quality_score: (7 + Math.random() * 3).toFixed(1), + deliverable: 'structured analytical output', + verification: Math.random() > 0.3 ? 'passed' : 'partial', + iterations: String(iterations), + }; + + let output = template; + for (const [key, value] of Object.entries(replacements)) { + output = output.replace(new RegExp(`\\{${key}\\}`, 'g'), value); + } + + return output; +} + +export class SwarmAgentRunner { + async spawnAndRun(subtask: SubTask, taskId: string): Promise<{ agent: Agent; result: AgentResult }> { + const agentId = uuidv4(); + const agentName = AGENT_NAMES[Math.floor(Math.random() * AGENT_NAMES.length)] + '-' + agentId.slice(0, 6); + + const agent: Agent = { + id: agentId, + taskId, + subtaskId: subtask.id, + name: agentName, + status: 'initializing', + spawnedAt: Date.now(), + startedAt: null, + completedAt: null, + computeTimeMs: 0, + memoryUsageMb: 0, + inferenceIterations: 0, + }; + + logger.info('Agent spawned', { agentId, name: agentName, subtaskId: subtask.id }); + eventBus.emitSwarmEvent('agent:spawned', taskId, { agent }); + + await this.simulateBootup(); + agent.status = 'running'; + agent.startedAt = Date.now(); + eventBus.emitSwarmEvent('agent:started', taskId, { agent }); + + try { + const result = await this.runInference(agent, subtask, taskId); + agent.status = 'completed'; + agent.completedAt = Date.now(); + agent.computeTimeMs = agent.completedAt - agent.startedAt; + + logger.info('Agent completed', { + agentId, + name: agentName, + computeTimeMs: agent.computeTimeMs, + confidence: result.confidence, + }); + + eventBus.emitSwarmEvent('agent:completed', taskId, { agent, result }); + return { agent, result }; + } catch (error) { + agent.status = 'failed'; + agent.completedAt = Date.now(); + agent.computeTimeMs = agent.completedAt - (agent.startedAt || agent.spawnedAt); + + logger.error('Agent failed', { agentId, error: (error as Error).message }); + eventBus.emitSwarmEvent('agent:failed', taskId, { agent, error: (error as Error).message }); + throw error; + } + } + + private async runInference(agent: Agent, subtask: SubTask, taskId: string): Promise { + const iterations = 3 + Math.floor(Math.random() * 8); + const intermediateOutputs: string[] = []; + + for (let i = 0; i < iterations; i++) { + const iterationDelay = 100 + Math.random() * 400; + await new Promise((resolve) => setTimeout(resolve, iterationDelay)); + + const progress = ((i + 1) / iterations) * 100; + agent.inferenceIterations = i + 1; + agent.memoryUsageMb = 128 + Math.random() * 384; + + intermediateOutputs.push(`Iteration ${i + 1}: Processing ${progress.toFixed(0)}% complete`); + + eventBus.emitSwarmEvent('agent:progress', taskId, { + agentId: agent.id, + progress, + iteration: i + 1, + totalIterations: iterations, + memoryUsageMb: agent.memoryUsageMb, + }); + } + + const output = generateInferenceOutput(subtask, iterations); + const confidence = 0.55 + Math.random() * 0.4; + const reliability = 0.6 + Math.random() * 0.35; + const convergenceReached = confidence > 0.7 && reliability > 0.7; + + const metadata: InferenceMetadata = { + modelVersion: `fractal-swarm-v${(1 + Math.random()).toFixed(1)}`, + temperature: 0.3 + Math.random() * 0.5, + topP: 0.85 + Math.random() * 0.14, + iterationsRun: iterations, + convergenceReached, + intermediateOutputs, + }; + + const computeTimeMs = Date.now() - (agent.startedAt || Date.now()); + + return { + id: uuidv4(), + agentId: agent.id, + taskId, + subtaskId: subtask.id, + output, + confidence: Math.round(confidence * 10000) / 10000, + computeTimeMs, + reliabilityScore: Math.round(reliability * 10000) / 10000, + tokenCount: Math.floor(100 + Math.random() * 500), + inferenceMetadata: metadata, + createdAt: Date.now(), + }; + } + + private simulateBootup(): Promise { + const bootTime = 200 + Math.random() * 500; + return new Promise((resolve) => setTimeout(resolve, bootTime)); + } +} + +export const swarmAgentRunner = new SwarmAgentRunner(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/api/routes.ts b/apps/fractal-inference-swarms/backend/src/api/routes.ts new file mode 100644 index 0000000..7576b79 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/api/routes.ts @@ -0,0 +1,189 @@ +import { Router, Request, Response, NextFunction } from 'express'; +import { orchestrator } from '../agents/Orchestrator'; +import { x402Engine } from '../payments/X402Mock'; +import { scoringEngine } from '../engine/ScoringEngine'; +import { TaskCreateRequest } from '../models/Task'; +import { wsServer } from '../websocket/socket'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('API'); +const router = Router(); + +function asyncHandler(fn: (req: Request, res: Response, next: NextFunction) => Promise) { + return (req: Request, res: Response, next: NextFunction) => { + fn(req, res, next).catch(next); + }; +} + +// Health check +router.get('/health', (_req: Request, res: Response) => { + res.json({ + status: 'healthy', + service: 'fractal-inference-swarms', + timestamp: Date.now(), + uptime: process.uptime(), + wsConnections: wsServer.getConnectionCount(), + }); +}); + +// Submit a new task +router.post( + '/task', + asyncHandler(async (req: Request, res: Response) => { + const { title, description, priority, swarmSize, metadata } = req.body; + + if (!title || !description) { + res.status(400).json({ + error: 'Validation failed', + details: 'title and description are required', + }); + return; + } + + if (swarmSize && (swarmSize < 2 || swarmSize > 20)) { + res.status(400).json({ + error: 'Validation failed', + details: 'swarmSize must be between 2 and 20', + }); + return; + } + + const request: TaskCreateRequest = { + title, + description, + priority: priority || 'medium', + swarmSize: swarmSize || undefined, + metadata: metadata || {}, + }; + + logger.info('New task submission', { title, priority: request.priority, swarmSize: request.swarmSize }); + const task = await orchestrator.submitTask(request); + + res.status(201).json({ + success: true, + task, + message: 'Task submitted β€” swarm pipeline initiated', + }); + }) +); + +// Get task by ID +router.get('/task/:id', (req: Request, res: Response) => { + const task = orchestrator.getTask(req.params.id); + if (!task) { + res.status(404).json({ error: 'Task not found' }); + return; + } + + const session = orchestrator.getSessionForTask(req.params.id); + res.json({ + task, + session: session || null, + scoredResults: orchestrator.getScoredResultsForTask(req.params.id), + }); +}); + +// Get all tasks +router.get('/tasks', (_req: Request, res: Response) => { + const tasks = orchestrator.getAllTasks(); + res.json({ + count: tasks.length, + tasks, + }); +}); + +// Get agents for a task +router.get('/agents/:taskId', (req: Request, res: Response) => { + const agents = orchestrator.getAgentsForTask(req.params.taskId); + const scored = orchestrator.getScoredResultsForTask(req.params.taskId); + + res.json({ + taskId: req.params.taskId, + agentCount: agents.length, + agents, + scoredResults: scored, + }); +}); + +// Get reward ledger +router.get('/ledger', (_req: Request, res: Response) => { + const ledger = orchestrator.getLedger(); + const stats = x402Engine.getLedgerStats(); + + res.json({ + transactionCount: ledger.length, + transactions: ledger, + paymentStats: stats, + balances: x402Engine.getAllBalances(), + }); +}); + +// Get payment history for specific agent +router.get('/ledger/agent/:agentId', (req: Request, res: Response) => { + const ledger = orchestrator.getLedger(); + const agentTransactions = ledger.filter((t) => t.agentId === req.params.agentId); + const walletAddress = `agent-wallet-${req.params.agentId.slice(0, 12)}`; + + res.json({ + agentId: req.params.agentId, + transactionCount: agentTransactions.length, + transactions: agentTransactions, + balance: x402Engine.getBalance(walletAddress), + paymentHistory: x402Engine.getPaymentHistory(walletAddress), + }); +}); + +// Get system metrics +router.get('/metrics', (_req: Request, res: Response) => { + const metrics = orchestrator.getMetrics(); + const paymentStats = x402Engine.getLedgerStats(); + + res.json({ + swarm: metrics, + payments: paymentStats, + scoring: scoringEngine.getWeights(), + system: { + memoryUsage: process.memoryUsage(), + uptime: process.uptime(), + wsConnections: wsServer.getConnectionCount(), + }, + }); +}); + +// Get all swarm sessions +router.get('/sessions', (_req: Request, res: Response) => { + const sessions = orchestrator.getAllSessions(); + res.json({ + count: sessions.length, + sessions, + }); +}); + +// Get specific session +router.get('/session/:taskId', (req: Request, res: Response) => { + const session = orchestrator.getSessionForTask(req.params.taskId); + if (!session) { + res.status(404).json({ error: 'Session not found' }); + return; + } + res.json({ session }); +}); + +// Update scoring weights +router.put('/config/scoring', (req: Request, res: Response) => { + const { confidence, speed, reliability } = req.body; + try { + scoringEngine.updateWeights({ confidence, speed, reliability }); + res.json({ + success: true, + weights: scoringEngine.getWeights(), + }); + } catch (error) { + res.status(400).json({ + error: 'Invalid weights', + details: (error as Error).message, + }); + } +}); + +export { router as apiRouter }; \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/config/index.ts b/apps/fractal-inference-swarms/backend/src/config/index.ts new file mode 100644 index 0000000..3fb492c --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/config/index.ts @@ -0,0 +1,95 @@ +import dotenv from 'dotenv'; +import path from 'path'; + +dotenv.config({ path: path.resolve(__dirname, '../../.env') }); + +export interface AppConfig { + server: { + port: number; + host: string; + nodeEnv: string; + corsOrigin: string; + }; + websocket: { + port: number; + heartbeatInterval: number; + }; + orchestrator: { + maxConcurrentAgents: number; + defaultSwarmSize: number; + agentTimeoutMs: number; + taskSplitStrategy: 'semantic' | 'equal' | 'weighted'; + }; + scoring: { + weightConfidence: number; + weightSpeed: number; + weightReliability: number; + }; + reward: { + baseRewardTokens: number; + minScoreThreshold: number; + rewardPoolSize: number; + }; + logging: { + level: string; + format: string; + }; +} + +function parseFloat_(val: string | undefined, fallback: number): number { + if (!val) return fallback; + const parsed = parseFloat(val); + return isNaN(parsed) ? fallback : parsed; +} + +function parseInt_(val: string | undefined, fallback: number): number { + if (!val) return fallback; + const parsed = parseInt(val, 10); + return isNaN(parsed) ? fallback : parsed; +} + +export const config: AppConfig = { + server: { + port: parseInt_(process.env.PORT, 3001), + host: process.env.HOST || '0.0.0.0', + nodeEnv: process.env.NODE_ENV || 'development', + corsOrigin: process.env.CORS_ORIGIN || 'http://localhost:3000', + }, + websocket: { + port: parseInt_(process.env.WS_PORT, 3002), + heartbeatInterval: parseInt_(process.env.WS_HEARTBEAT_INTERVAL, 30000), + }, + orchestrator: { + maxConcurrentAgents: parseInt_(process.env.MAX_CONCURRENT_AGENTS, 20), + defaultSwarmSize: parseInt_(process.env.DEFAULT_SWARM_SIZE, 5), + agentTimeoutMs: parseInt_(process.env.AGENT_TIMEOUT_MS, 30000), + taskSplitStrategy: (process.env.TASK_SPLIT_STRATEGY as AppConfig['orchestrator']['taskSplitStrategy']) || 'semantic', + }, + scoring: { + weightConfidence: parseFloat_(process.env.SCORING_WEIGHT_CONFIDENCE, 0.4), + weightSpeed: parseFloat_(process.env.SCORING_WEIGHT_SPEED, 0.3), + weightReliability: parseFloat_(process.env.SCORING_WEIGHT_RELIABILITY, 0.3), + }, + reward: { + baseRewardTokens: parseFloat_(process.env.BASE_REWARD_TOKENS, 100), + minScoreThreshold: parseFloat_(process.env.MIN_SCORE_THRESHOLD, 0.3), + rewardPoolSize: parseFloat_(process.env.REWARD_POOL_SIZE, 1000), + }, + logging: { + level: process.env.LOG_LEVEL || 'debug', + format: process.env.LOG_FORMAT || 'json', + }, +}; + +export function validateConfig(cfg: AppConfig): void { + const totalWeight = cfg.scoring.weightConfidence + cfg.scoring.weightSpeed + cfg.scoring.weightReliability; + if (Math.abs(totalWeight - 1.0) > 0.01) { + throw new Error(`Scoring weights must sum to 1.0, got ${totalWeight}`); + } + if (cfg.orchestrator.maxConcurrentAgents < 1) { + throw new Error('maxConcurrentAgents must be at least 1'); + } + if (cfg.reward.minScoreThreshold < 0 || cfg.reward.minScoreThreshold > 1) { + throw new Error('minScoreThreshold must be between 0 and 1'); + } +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/engine/MergeEngine.ts b/apps/fractal-inference-swarms/backend/src/engine/MergeEngine.ts new file mode 100644 index 0000000..68f05be --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/engine/MergeEngine.ts @@ -0,0 +1,210 @@ +import { ScoredResult } from '../models/AgentResult'; +import { MergedOutput, ValidationDetail } from '../models/Task'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('MergeEngine'); + +export type MergeStrategy = 'weighted_consensus' | 'best_pick' | 'hierarchical_merge'; + +export class MergeEngine { + mergeResults( + scoredResults: ScoredResult[], + taskId: string, + strategy: MergeStrategy = 'weighted_consensus' + ): MergedOutput { + logger.info('Merging agent outputs', { + taskId, + strategy, + resultCount: scoredResults.length, + }); + + let finalOutput: string; + + switch (strategy) { + case 'weighted_consensus': + finalOutput = this.weightedConsensusMerge(scoredResults); + break; + case 'best_pick': + finalOutput = this.bestPickMerge(scoredResults); + break; + case 'hierarchical_merge': + finalOutput = this.hierarchicalMerge(scoredResults); + break; + default: + finalOutput = this.weightedConsensusMerge(scoredResults); + } + + const validationDetails = this.validateMergedOutput(finalOutput, scoredResults); + const validationScore = validationDetails.reduce((sum, d) => sum + d.score, 0) / validationDetails.length; + const validationPassed = validationScore >= 0.6 && validationDetails.every((d) => d.score > 0.3); + + const merged: MergedOutput = { + taskId, + finalOutput, + contributingAgents: scoredResults.map((r) => r.agentId), + mergeStrategy: strategy, + validationScore: Math.round(validationScore * 10000) / 10000, + validationPassed, + validationDetails, + mergedAt: Date.now(), + }; + + logger.info('Merge complete', { + taskId, + validationScore: merged.validationScore, + validationPassed: merged.validationPassed, + contributingAgents: merged.contributingAgents.length, + }); + + return merged; + } + + private weightedConsensusMerge(results: ScoredResult[]): string { + const totalScore = results.reduce((sum, r) => sum + r.finalScore, 0); + const sections: string[] = []; + + sections.push('=== FRACTAL INFERENCE SWARM β€” MERGED OUTPUT ===\n'); + sections.push(`Strategy: Weighted Consensus | Agents: ${results.length} | Generated: ${new Date().toISOString()}\n`); + sections.push('--- SYNTHESIS ---\n'); + + const topResults = results + .filter((r) => r.finalScore >= totalScore / results.length * 0.5) + .sort((a, b) => b.finalScore - a.finalScore); + + if (topResults.length > 0) { + const primaryAgent = topResults[0]; + sections.push(`[Primary Output β€” Agent ${primaryAgent.agentId.slice(0, 8)}, Score: ${primaryAgent.finalScore.toFixed(4)}, Weight: ${(primaryAgent.finalScore / totalScore * 100).toFixed(1)}%]\n`); + sections.push(primaryAgent.output); + sections.push(''); + + if (topResults.length > 1) { + sections.push('\n--- SUPPLEMENTARY CONTRIBUTIONS ---\n'); + for (let i = 1; i < topResults.length; i++) { + const agent = topResults[i]; + const weight = (agent.finalScore / totalScore * 100).toFixed(1); + sections.push(`[Agent ${agent.agentId.slice(0, 8)}, Score: ${agent.finalScore.toFixed(4)}, Weight: ${weight}%]`); + sections.push(agent.output); + sections.push(''); + } + } + } + + sections.push('\n--- CONSENSUS METRICS ---'); + sections.push(`Total Contributing Agents: ${topResults.length}/${results.length}`); + sections.push(`Average Confidence: ${(results.reduce((s, r) => s + r.confidence, 0) / results.length).toFixed(4)}`); + sections.push(`Score Spread: ${(results[0].finalScore - results[results.length - 1].finalScore).toFixed(4)}`); + + return sections.join('\n'); + } + + private bestPickMerge(results: ScoredResult[]): string { + const best = results[0]; + const sections: string[] = []; + + sections.push('=== FRACTAL INFERENCE SWARM β€” MERGED OUTPUT ===\n'); + sections.push(`Strategy: Best Pick | Selected Agent: ${best.agentId.slice(0, 8)} | Score: ${best.finalScore.toFixed(4)}\n`); + sections.push('--- OUTPUT ---\n'); + sections.push(best.output); + sections.push('\n--- SELECTION RATIONALE ---'); + sections.push(`Selected agent ranked #1 out of ${results.length} agents`); + sections.push(`Confidence: ${best.confidence.toFixed(4)}`); + sections.push(`Reliability: ${best.reliabilityScore.toFixed(4)}`); + sections.push(`Compute Time: ${best.computeTimeMs}ms`); + + return sections.join('\n'); + } + + private hierarchicalMerge(results: ScoredResult[]): string { + const tiers = this.assignTiers(results); + const sections: string[] = []; + + sections.push('=== FRACTAL INFERENCE SWARM β€” MERGED OUTPUT ===\n'); + sections.push(`Strategy: Hierarchical Merge | Agents: ${results.length} | Tiers: ${Object.keys(tiers).length}\n`); + + for (const [tier, tierResults] of Object.entries(tiers)) { + sections.push(`\n--- TIER ${tier.toUpperCase()} ---\n`); + for (const result of tierResults) { + sections.push(`[Agent ${result.agentId.slice(0, 8)}, Score: ${result.finalScore.toFixed(4)}]`); + sections.push(result.output); + sections.push(''); + } + } + + sections.push('\n--- HIERARCHY SUMMARY ---'); + for (const [tier, tierResults] of Object.entries(tiers)) { + sections.push(`${tier}: ${tierResults.length} agents, avg score: ${(tierResults.reduce((s, r) => s + r.finalScore, 0) / tierResults.length).toFixed(4)}`); + } + + return sections.join('\n'); + } + + private assignTiers(results: ScoredResult[]): Record { + const tiers: Record = { primary: [], secondary: [], tertiary: [] }; + const maxScore = results[0]?.finalScore || 1; + + for (const result of results) { + const ratio = result.finalScore / maxScore; + if (ratio >= 0.8) { + tiers.primary.push(result); + } else if (ratio >= 0.5) { + tiers.secondary.push(result); + } else { + tiers.tertiary.push(result); + } + } + + return Object.fromEntries( + Object.entries(tiers).filter(([, v]) => v.length > 0) + ); + } + + private validateMergedOutput(output: string, results: ScoredResult[]): ValidationDetail[] { + const details: ValidationDetail[] = []; + + const hasContent = output.length > 100; + details.push({ + criterion: 'content_completeness', + passed: hasContent, + score: hasContent ? Math.min(1, output.length / 500) : 0.1, + message: hasContent ? 'Output contains substantial content' : 'Output is too short', + }); + + const avgConfidence = results.reduce((s, r) => s + r.confidence, 0) / results.length; + details.push({ + criterion: 'agent_confidence', + passed: avgConfidence >= 0.5, + score: avgConfidence, + message: `Average agent confidence: ${avgConfidence.toFixed(4)}`, + }); + + const topAgentWeight = results[0]?.finalScore / results.reduce((s, r) => s + r.finalScore, 0); + const diversityScore = 1 - (topAgentWeight || 1); + details.push({ + criterion: 'output_diversity', + passed: diversityScore >= 0.2, + score: Math.min(1, diversityScore * 2), + message: `Source diversity: ${(diversityScore * 100).toFixed(1)}% β€” ${diversityScore >= 0.2 ? 'multiple agents contributed' : 'dominated by single agent'}`, + }); + + const allConverged = results.every((r) => r.inferenceMetadata.convergenceReached); + details.push({ + criterion: 'convergence_check', + passed: allConverged, + score: allConverged ? 1 : results.filter((r) => r.inferenceMetadata.convergenceReached).length / results.length, + message: allConverged ? 'All agents reached convergence' : 'Some agents did not converge', + }); + + const scoreSpread = results[0]?.finalScore - results[results.length - 1]?.finalScore; + const consistencyScore = Math.max(0, 1 - scoreSpread * 2); + details.push({ + criterion: 'score_consistency', + passed: consistencyScore >= 0.4, + score: consistencyScore, + message: `Score spread: ${scoreSpread.toFixed(4)} β€” ${consistencyScore >= 0.4 ? 'consistent' : 'high variance'}`, + }); + + return details; + } +} + +export const mergeEngine = new MergeEngine(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/engine/RewardEngine.ts b/apps/fractal-inference-swarms/backend/src/engine/RewardEngine.ts new file mode 100644 index 0000000..ffd37bc --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/engine/RewardEngine.ts @@ -0,0 +1,108 @@ +import { v4 as uuidv4 } from 'uuid'; +import { ScoredResult, RewardTransaction } from '../models/AgentResult'; +import { config } from '../config'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('RewardEngine'); + +export class RewardEngine { + private baseReward: number; + private minScoreThreshold: number; + private rewardPool: number; + + constructor() { + this.baseReward = config.reward.baseRewardTokens; + this.minScoreThreshold = config.reward.minScoreThreshold; + this.rewardPool = config.reward.rewardPoolSize; + } + + distributeRewards(scoredResults: ScoredResult[], taskId: string): RewardTransaction[] { + if (scoredResults.length === 0) return []; + + logger.info('Distributing rewards', { + taskId, + agentCount: scoredResults.length, + pool: this.rewardPool, + }); + + const eligible = scoredResults.filter((r) => r.finalScore >= this.minScoreThreshold); + const ineligible = scoredResults.filter((r) => r.finalScore < this.minScoreThreshold); + + if (eligible.length === 0) { + logger.warn('No agents met minimum score threshold', { + taskId, + threshold: this.minScoreThreshold, + }); + return scoredResults.map((r) => this.createTransaction(r, taskId, 0, 'penalty')); + } + + const totalScore = eligible.reduce((sum, r) => sum + r.finalScore, 0); + const now = Date.now(); + + const transactions: RewardTransaction[] = []; + + for (const result of eligible) { + const normalizedScore = result.finalScore / totalScore; + let reward = this.baseReward * normalizedScore * eligible.length; + + if (result.rank === 1) { + reward *= 1.25; + } else if (result.rank === 2) { + reward *= 1.1; + } + + reward = Math.round(reward * 100) / 100; + + transactions.push(this.createTransaction(result, taskId, reward, 'inference_reward')); + } + + for (const result of ineligible) { + transactions.push(this.createTransaction(result, taskId, 0, 'penalty')); + } + + const totalDistributed = transactions.reduce((sum, t) => sum + t.tokensEarned, 0); + logger.info('Rewards distributed', { + taskId, + totalDistributed, + eligibleCount: eligible.length, + ineligibleCount: ineligible.length, + }); + + return transactions; + } + + private createTransaction( + result: ScoredResult, + taskId: string, + tokens: number, + type: RewardTransaction['transactionType'] + ): RewardTransaction { + return { + id: uuidv4(), + agentId: result.agentId, + taskId, + agentName: `Agent-${result.agentId.slice(0, 8)}`, + tokensEarned: tokens, + score: result.finalScore, + rank: result.rank, + transactionType: type, + timestamp: Date.now(), + x402PaymentId: uuidv4(), + metadata: { + confidence: result.confidence, + computeTimeMs: result.computeTimeMs, + reliability: result.reliabilityScore, + }, + }; + } + + getRewardConfig() { + return { + baseReward: this.baseReward, + minScoreThreshold: this.minScoreThreshold, + rewardPool: this.rewardPool, + }; + } +} + +export const rewardEngine = new RewardEngine(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/engine/ScoringEngine.ts b/apps/fractal-inference-swarms/backend/src/engine/ScoringEngine.ts new file mode 100644 index 0000000..403b1f6 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/engine/ScoringEngine.ts @@ -0,0 +1,101 @@ +import { AgentResult, ScoredResult, ScoreBreakdown } from '../models/AgentResult'; +import { config } from '../config'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('ScoringEngine'); + +export interface ScoringWeights { + confidence: number; + speed: number; + reliability: number; +} + +export class ScoringEngine { + private weights: ScoringWeights; + + constructor(weights?: Partial) { + this.weights = { + confidence: weights?.confidence ?? config.scoring.weightConfidence, + speed: weights?.speed ?? config.scoring.weightSpeed, + reliability: weights?.reliability ?? config.scoring.weightReliability, + }; + + const total = this.weights.confidence + this.weights.speed + this.weights.reliability; + if (Math.abs(total - 1.0) > 0.01) { + logger.warn('Scoring weights do not sum to 1.0, normalizing', { total }); + this.weights.confidence /= total; + this.weights.speed /= total; + this.weights.reliability /= total; + } + } + + scoreResults(results: AgentResult[]): ScoredResult[] { + if (results.length === 0) return []; + + logger.info('Scoring agent results', { count: results.length }); + + const maxComputeTime = Math.max(...results.map((r) => r.computeTimeMs)); + const minComputeTime = Math.min(...results.map((r) => r.computeTimeMs)); + const computeRange = maxComputeTime - minComputeTime || 1; + + const scored: ScoredResult[] = results.map((result) => { + const rawConfidence = Math.max(0, Math.min(1, result.confidence)); + const rawSpeedScore = 1 - (result.computeTimeMs - minComputeTime) / computeRange; + const rawReliability = Math.max(0, Math.min(1, result.reliabilityScore)); + + const confidenceComponent = this.weights.confidence * rawConfidence; + const speedComponent = this.weights.speed * rawSpeedScore; + const reliabilityComponent = this.weights.reliability * rawReliability; + + const finalScore = confidenceComponent + speedComponent + reliabilityComponent; + + const breakdown: ScoreBreakdown = { + confidenceComponent, + speedComponent, + reliabilityComponent, + rawConfidence, + rawSpeedScore, + rawReliability, + }; + + return { + ...result, + finalScore: Math.round(finalScore * 10000) / 10000, + rank: 0, + scoreBreakdown: breakdown, + }; + }); + + scored.sort((a, b) => b.finalScore - a.finalScore); + scored.forEach((s, i) => { + s.rank = i + 1; + }); + + logger.info('Scoring complete', { + topScore: scored[0]?.finalScore, + bottomScore: scored[scored.length - 1]?.finalScore, + spread: scored[0]?.finalScore - scored[scored.length - 1]?.finalScore, + }); + + return scored; + } + + getWeights(): ScoringWeights { + return { ...this.weights }; + } + + updateWeights(newWeights: Partial): void { + if (newWeights.confidence !== undefined) this.weights.confidence = newWeights.confidence; + if (newWeights.speed !== undefined) this.weights.speed = newWeights.speed; + if (newWeights.reliability !== undefined) this.weights.reliability = newWeights.reliability; + + const total = this.weights.confidence + this.weights.speed + this.weights.reliability; + this.weights.confidence /= total; + this.weights.speed /= total; + this.weights.reliability /= total; + + logger.info('Scoring weights updated', this.weights); + } +} + +export const scoringEngine = new ScoringEngine(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/engine/TaskSplitter.ts b/apps/fractal-inference-swarms/backend/src/engine/TaskSplitter.ts new file mode 100644 index 0000000..f42e208 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/engine/TaskSplitter.ts @@ -0,0 +1,220 @@ +import { v4 as uuidv4 } from 'uuid'; +import { SubTask } from '../models/Task'; +import { createChildLogger } from '../utils/logger'; +import { config } from '../config'; + +const logger = createChildLogger('TaskSplitter'); + +interface SplitConfig { + strategy: 'semantic' | 'equal' | 'weighted'; + swarmSize: number; + taskDescription: string; + taskId: string; +} + +const SEMANTIC_DECOMPOSITION_PATTERNS: Record = { + analysis: [ + 'Analyze the core requirements and identify key constraints', + 'Research existing approaches and best practices', + 'Evaluate potential solutions against defined criteria', + 'Synthesize findings into actionable recommendations', + 'Validate conclusions with cross-reference checks', + ], + development: [ + 'Design the architecture and define component interfaces', + 'Implement core logic and business rules', + 'Build integration layer and external connections', + 'Develop error handling and edge case coverage', + 'Create testing strategy and validate outputs', + ], + research: [ + 'Define research scope and identify key questions', + 'Gather primary sources and data points', + 'Analyze patterns and correlations in findings', + 'Cross-validate findings against secondary sources', + 'Compile findings into structured summary', + ], + creative: [ + 'Explore conceptual space and ideation', + 'Develop initial framework and structure', + 'Elaborate on core themes and details', + 'Refine and polish output quality', + 'Review for coherence and consistency', + ], + general: [ + 'Break down the problem into constituent parts', + 'Address the primary objective directly', + 'Handle secondary concerns and dependencies', + 'Validate and verify partial outputs', + 'Integrate and reconcile all partial results', + ], +}; + +function detectTaskCategory(description: string): string { + const lower = description.toLowerCase(); + const keywords: Record = { + analysis: ['analyze', 'evaluate', 'assess', 'review', 'examine', 'compare', 'benchmark'], + development: ['build', 'implement', 'create', 'develop', 'code', 'program', 'design system'], + research: ['research', 'investigate', 'study', 'explore', 'find out', 'discover', 'survey'], + creative: ['write', 'compose', 'generate', 'craft', 'design', 'imagine', 'story', 'content'], + }; + + let bestCategory = 'general'; + let bestScore = 0; + + for (const [category, words] of Object.entries(keywords)) { + const score = words.reduce((acc, word) => acc + (lower.includes(word) ? 1 : 0), 0); + if (score > bestScore) { + bestScore = score; + bestCategory = category; + } + } + + return bestCategory; +} + +function generateSubtaskDescription( + baseDescription: string, + pattern: string, + index: number, + total: number +): string { + return `[Subtask ${index + 1}/${total}] ${pattern}\n\nContext from parent task: ${baseDescription}`; +} + +function generateConstraints(index: number, total: number): string[] { + const constraints: string[] = [ + `Part ${index + 1} of ${total} β€” maintain consistency with other subtask outputs`, + 'Output must be self-contained yet compatible with merge operation', + 'Include confidence assessment in output', + ]; + + if (index === 0) { + constraints.push('Establish foundational context for subsequent subtasks'); + } + if (index === total - 1) { + constraints.push('Ensure coverage of any gaps from prior subtasks'); + } + + return constraints; +} + +export class TaskSplitter { + splitTask(cfg: SplitConfig): SubTask[] { + logger.info('Splitting task', { + taskId: cfg.taskId, + strategy: cfg.strategy, + swarmSize: cfg.swarmSize, + }); + + switch (cfg.strategy) { + case 'semantic': + return this.semanticSplit(cfg); + case 'equal': + return this.equalSplit(cfg); + case 'weighted': + return this.weightedSplit(cfg); + default: + return this.semanticSplit(cfg); + } + } + + private semanticSplit(cfg: SplitConfig): SubTask[] { + const category = detectTaskCategory(cfg.taskDescription); + const patterns = SEMANTIC_DECOMPOSITION_PATTERNS[category]; + const now = Date.now(); + + logger.debug('Detected task category', { category, taskId: cfg.taskId }); + + const subtasks: SubTask[] = []; + for (let i = 0; i < cfg.swarmSize; i++) { + const patternIndex = i % patterns.length; + subtasks.push({ + id: uuidv4(), + parentTaskId: cfg.taskId, + index: i, + description: generateSubtaskDescription( + cfg.taskDescription, + patterns[patternIndex], + i, + cfg.swarmSize + ), + context: `Category: ${category} | Strategy: semantic | Agent ${i + 1}/${cfg.swarmSize}`, + constraints: generateConstraints(i, cfg.swarmSize), + assignedAgentId: null, + status: 'pending', + createdAt: now, + completedAt: null, + }); + } + + logger.info('Task split complete', { + taskId: cfg.taskId, + subtaskCount: subtasks.length, + category, + }); + + return subtasks; + } + + private equalSplit(cfg: SplitConfig): SubTask[] { + const now = Date.now(); + const subtasks: SubTask[] = []; + + for (let i = 0; i < cfg.swarmSize; i++) { + subtasks.push({ + id: uuidv4(), + parentTaskId: cfg.taskId, + index: i, + description: `[Equal Split ${i + 1}/${cfg.swarmSize}] Process segment ${i + 1} of the task: ${cfg.taskDescription}`, + context: `Strategy: equal | Segment ${i + 1}/${cfg.swarmSize}`, + constraints: [ + `Handle exactly 1/${cfg.swarmSize} of the total workload`, + 'Maintain output format consistency', + 'Include segment boundaries in output', + ], + assignedAgentId: null, + status: 'pending', + createdAt: now, + completedAt: null, + }); + } + + return subtasks; + } + + private weightedSplit(cfg: SplitConfig): SubTask[] { + const now = Date.now(); + const weights = this.calculateWeights(cfg.swarmSize); + const subtasks: SubTask[] = []; + + for (let i = 0; i < cfg.swarmSize; i++) { + subtasks.push({ + id: uuidv4(), + parentTaskId: cfg.taskId, + index: i, + description: `[Weighted Split ${i + 1}/${cfg.swarmSize}, weight: ${weights[i].toFixed(2)}] ${cfg.taskDescription}`, + context: `Strategy: weighted | Weight: ${weights[i].toFixed(2)} | Priority rank: ${i + 1}`, + constraints: [ + `Allocated weight: ${(weights[i] * 100).toFixed(1)}% of total effort`, + 'Higher weight subtasks should produce more detailed output', + 'Include weight-proportional depth of analysis', + ], + assignedAgentId: null, + status: 'pending', + createdAt: now, + completedAt: null, + }); + } + + return subtasks; + } + + private calculateWeights(size: number): number[] { + const raw = Array.from({ length: size }, (_, i) => size - i); + const sum = raw.reduce((a, b) => a + b, 0); + return raw.map((w) => w / sum); + } +} + +export const taskSplitter = new TaskSplitter(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/middleware/errorHandler.ts b/apps/fractal-inference-swarms/backend/src/middleware/errorHandler.ts new file mode 100644 index 0000000..e09e77a --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/middleware/errorHandler.ts @@ -0,0 +1,36 @@ +import { Request, Response, NextFunction } from 'express'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('ErrorHandler'); + +export interface ApiError extends Error { + statusCode?: number; + details?: unknown; +} + +export function errorHandler(err: ApiError, _req: Request, res: Response, _next: NextFunction): void { + const statusCode = err.statusCode || 500; + const message = err.message || 'Internal server error'; + + logger.error('Unhandled error', { + statusCode, + message, + stack: err.stack, + details: err.details, + }); + + res.status(statusCode).json({ + error: message, + statusCode, + details: process.env.NODE_ENV === 'development' ? err.details || err.stack : undefined, + timestamp: Date.now(), + }); +} + +export function notFoundHandler(_req: Request, res: Response): void { + res.status(404).json({ + error: 'Endpoint not found', + statusCode: 404, + timestamp: Date.now(), + }); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/middleware/requestLogger.ts b/apps/fractal-inference-swarms/backend/src/middleware/requestLogger.ts new file mode 100644 index 0000000..8b9bb2a --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/middleware/requestLogger.ts @@ -0,0 +1,28 @@ +import { Request, Response, NextFunction } from 'express'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('HTTP'); + +export function requestLogger(req: Request, res: Response, next: NextFunction): void { + const start = Date.now(); + + res.on('finish', () => { + const duration = Date.now() - start; + const logData = { + method: req.method, + path: req.path, + statusCode: res.statusCode, + durationMs: duration, + contentLength: res.get('content-length') || 0, + userAgent: req.get('user-agent')?.slice(0, 80), + }; + + if (res.statusCode >= 400) { + logger.warn('Request completed with error', logData); + } else { + logger.info('Request completed', logData); + } + }); + + next(); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/models/AgentResult.ts b/apps/fractal-inference-swarms/backend/src/models/AgentResult.ts new file mode 100644 index 0000000..d706952 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/models/AgentResult.ts @@ -0,0 +1,100 @@ +export type AgentStatus = + | 'idle' + | 'initializing' + | 'running' + | 'completed' + | 'failed' + | 'timeout'; + +export interface Agent { + id: string; + taskId: string; + subtaskId: string; + name: string; + status: AgentStatus; + spawnedAt: number; + startedAt: number | null; + completedAt: number | null; + computeTimeMs: number; + memoryUsageMb: number; + inferenceIterations: number; +} + +export interface AgentResult { + id: string; + agentId: string; + taskId: string; + subtaskId: string; + output: string; + confidence: number; + computeTimeMs: number; + reliabilityScore: number; + tokenCount: number; + inferenceMetadata: InferenceMetadata; + createdAt: number; +} + +export interface InferenceMetadata { + modelVersion: string; + temperature: number; + topP: number; + iterationsRun: number; + convergenceReached: boolean; + intermediateOutputs: string[]; +} + +export interface ScoredResult extends AgentResult { + finalScore: number; + rank: number; + scoreBreakdown: ScoreBreakdown; +} + +export interface ScoreBreakdown { + confidenceComponent: number; + speedComponent: number; + reliabilityComponent: number; + rawConfidence: number; + rawSpeedScore: number; + rawReliability: number; +} + +export interface RewardTransaction { + id: string; + agentId: string; + taskId: string; + agentName: string; + tokensEarned: number; + score: number; + rank: number; + transactionType: 'inference_reward' | 'bonus' | 'penalty'; + timestamp: number; + x402PaymentId: string; + metadata: Record; +} + +export interface SwarmSession { + id: string; + taskId: string; + agents: Agent[]; + results: AgentResult[]; + scoredResults: ScoredResult[]; + rewards: RewardTransaction[]; + status: 'active' | 'scoring' | 'merging' | 'completed' | 'failed'; + startedAt: number; + completedAt: number | null; + totalTokensDistributed: number; +} + +export interface SwarmMetrics { + totalSessions: number; + activeSessions: number; + totalAgentsSpawned: number; + totalTasksCompleted: number; + totalTokensDistributed: number; + averageSwarmSize: number; + averageCompletionTimeMs: number; + averageConfidence: number; + topAgentId: string | null; + topAgentScore: number; + uptimeMs: number; +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/models/Task.ts b/apps/fractal-inference-swarms/backend/src/models/Task.ts new file mode 100644 index 0000000..2ccd283 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/models/Task.ts @@ -0,0 +1,89 @@ +export type TaskStatus = + | 'pending' + | 'splitting' + | 'spawning_agents' + | 'inference_running' + | 'scoring' + | 'merging' + | 'validating' + | 'distributing_rewards' + | 'completed' + | 'failed'; + +export type TaskPriority = 'low' | 'medium' | 'high' | 'critical'; + +export interface SubTask { + id: string; + parentTaskId: string; + index: number; + description: string; + context: string; + constraints: string[]; + assignedAgentId: string | null; + status: 'pending' | 'assigned' | 'running' | 'completed' | 'failed'; + createdAt: number; + completedAt: number | null; +} + +export interface Task { + id: string; + title: string; + description: string; + priority: TaskPriority; + status: TaskStatus; + subtasks: SubTask[]; + swarmSize: number; + metadata: Record; + mergedOutput: MergedOutput | null; + createdAt: number; + updatedAt: number; + completedAt: number | null; + totalComputeTimeMs: number; + errorMessage: string | null; +} + +export interface MergedOutput { + taskId: string; + finalOutput: string; + contributingAgents: string[]; + mergeStrategy: 'weighted_consensus' | 'best_pick' | 'hierarchical_merge'; + validationScore: number; + validationPassed: boolean; + validationDetails: ValidationDetail[]; + mergedAt: number; +} + +export interface ValidationDetail { + criterion: string; + passed: boolean; + score: number; + message: string; +} + +export interface TaskCreateRequest { + title: string; + description: string; + priority?: TaskPriority; + swarmSize?: number; + metadata?: Record; +} + +export function createTask(req: TaskCreateRequest, swarmSizeDefault: number): Task { + const { v4: uuidv4 } = require('uuid'); + return { + id: uuidv4(), + title: req.title, + description: req.description, + priority: req.priority || 'medium', + status: 'pending', + subtasks: [], + swarmSize: req.swarmSize || swarmSizeDefault, + metadata: req.metadata || {}, + mergedOutput: null, + createdAt: Date.now(), + updatedAt: Date.now(), + completedAt: null, + totalComputeTimeMs: 0, + errorMessage: null, + }; +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/payments/X402Mock.ts b/apps/fractal-inference-swarms/backend/src/payments/X402Mock.ts new file mode 100644 index 0000000..55c69ca --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/payments/X402Mock.ts @@ -0,0 +1,173 @@ +import { v4 as uuidv4 } from 'uuid'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('X402Mock'); + +export interface X402Payment { + paymentId: string; + from: string; + to: string; + amount: number; + currency: string; + status: 'pending' | 'processing' | 'completed' | 'failed'; + protocolVersion: string; + httpStatusCode: number; + headers: Record; + createdAt: number; + settledAt: number | null; +} + +export interface X402Ledger { + totalPayments: number; + totalVolume: number; + payments: X402Payment[]; + balances: Map; +} + +export class X402MockPaymentEngine { + private ledger: X402Ledger; + private processingDelayMs: number; + + constructor() { + this.ledger = { + totalPayments: 0, + totalVolume: 0, + payments: [], + balances: new Map(), + }; + this.processingDelayMs = 50; + } + + async processPayment( + fromAddress: string, + toAddress: string, + amount: number + ): Promise { + const payment: X402Payment = { + paymentId: uuidv4(), + from: fromAddress, + to: toAddress, + amount, + currency: 'x402-TOKEN', + status: 'pending', + protocolVersion: 'x402/1.0', + httpStatusCode: 402, + headers: { + 'X-402-Payment-Required': 'true', + 'X-402-Amount': amount.toString(), + 'X-402-Currency': 'x402-TOKEN', + 'X-402-Payment-Id': '', + 'X-402-Protocol-Version': 'x402/1.0', + }, + createdAt: Date.now(), + settledAt: null, + }; + + payment.headers['X-402-Payment-Id'] = payment.paymentId; + + logger.debug('Processing x402 payment', { + paymentId: payment.paymentId, + from: fromAddress, + to: toAddress, + amount, + }); + + payment.status = 'processing'; + + await this.simulateNetworkLatency(); + + const success = Math.random() > 0.02; + + if (success) { + payment.status = 'completed'; + payment.httpStatusCode = 200; + payment.settledAt = Date.now(); + + const currentBalance = this.ledger.balances.get(toAddress) || 0; + this.ledger.balances.set(toAddress, currentBalance + amount); + + this.ledger.totalVolume += amount; + + logger.info('x402 payment completed', { + paymentId: payment.paymentId, + amount, + newBalance: currentBalance + amount, + }); + } else { + payment.status = 'failed'; + payment.httpStatusCode = 500; + logger.error('x402 payment failed', { paymentId: payment.paymentId }); + } + + this.ledger.payments.push(payment); + this.ledger.totalPayments++; + + return payment; + } + + async processBatchPayments( + payments: Array<{ from: string; to: string; amount: number }> + ): Promise { + logger.info('Processing batch x402 payments', { count: payments.length }); + + const results = await Promise.all( + payments.map((p) => this.processPayment(p.from, p.to, p.amount)) + ); + + const successful = results.filter((r) => r.status === 'completed'); + const failed = results.filter((r) => r.status === 'failed'); + + logger.info('Batch payment complete', { + total: results.length, + successful: successful.length, + failed: failed.length, + totalVolume: successful.reduce((sum, p) => sum + p.amount, 0), + }); + + return results; + } + + getBalance(address: string): number { + return this.ledger.balances.get(address) || 0; + } + + getAllBalances(): Record { + return Object.fromEntries(this.ledger.balances); + } + + getPaymentHistory(address?: string): X402Payment[] { + if (address) { + return this.ledger.payments.filter( + (p) => p.from === address || p.to === address + ); + } + return [...this.ledger.payments]; + } + + getLedgerStats() { + return { + totalPayments: this.ledger.totalPayments, + totalVolume: Math.round(this.ledger.totalVolume * 100) / 100, + uniqueRecipients: this.ledger.balances.size, + averagePayment: + this.ledger.totalPayments > 0 + ? Math.round((this.ledger.totalVolume / this.ledger.totalPayments) * 100) / 100 + : 0, + successRate: + this.ledger.totalPayments > 0 + ? Math.round( + (this.ledger.payments.filter((p) => p.status === 'completed').length / + this.ledger.totalPayments) * + 10000 + ) / 100 + : 100, + }; + } + + private simulateNetworkLatency(): Promise { + const jitter = Math.random() * this.processingDelayMs; + return new Promise((resolve) => setTimeout(resolve, this.processingDelayMs + jitter)); + } +} + +export const x402Engine = new X402MockPaymentEngine(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/server.ts b/apps/fractal-inference-swarms/backend/src/server.ts new file mode 100644 index 0000000..fb92ee7 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/server.ts @@ -0,0 +1,78 @@ +import express from 'express'; +import cors from 'cors'; +import helmet from 'helmet'; +import http from 'http'; +import { config, validateConfig } from './config'; +import { apiRouter } from './api/routes'; +import { wsServer } from './websocket/socket'; +import { errorHandler, notFoundHandler } from './middleware/errorHandler'; +import { requestLogger } from './middleware/requestLogger'; +import { logger } from './utils/logger'; + +validateConfig(config); + +const app = express(); +const server = http.createServer(app); + +// Security & parsing +app.use(helmet({ contentSecurityPolicy: false })); +app.use(cors({ origin: config.server.corsOrigin, credentials: true })); +app.use(express.json({ limit: '10mb' })); +app.use(express.urlencoded({ extended: true })); + +// Logging +app.use(requestLogger); + +// API routes +app.use('/api', apiRouter); + +// Error handling +app.use(notFoundHandler); +app.use(errorHandler); + +// Initialize WebSocket +wsServer.initialize(server); + +// Start server +server.listen(config.server.port, '0.0.0.0', () => { + logger.info('=== Fractal Inference Swarms Backend ===', { + port: config.server.port, + host: '0.0.0.0', // Updated log to reflect reality + env: config.server.nodeEnv, + wsPath: '/ws', + corsOrigin: config.server.corsOrigin, + }); + logger.info('Orchestrator config', { + maxAgents: config.orchestrator.maxConcurrentAgents, + defaultSwarmSize: config.orchestrator.defaultSwarmSize, + splitStrategy: config.orchestrator.taskSplitStrategy, + }); + logger.info('Scoring weights', config.scoring); + logger.info('Reward config', config.reward); +}); + +// Graceful shutdown +function gracefulShutdown(signal: string): void { + logger.info(`${signal} received β€” shutting down gracefully`); + wsServer.shutdown(); + server.close(() => { + logger.info('Server closed'); + process.exit(0); + }); + setTimeout(() => { + logger.error('Forced shutdown after timeout'); + process.exit(1); + }, 10000); +} + +process.on('SIGTERM', () => gracefulShutdown('SIGTERM')); +process.on('SIGINT', () => gracefulShutdown('SIGINT')); +process.on('unhandledRejection', (reason) => { + logger.error('Unhandled promise rejection', { reason }); +}); +process.on('uncaughtException', (error) => { + logger.error('Uncaught exception', { error: error.message, stack: error.stack }); + process.exit(1); +}); + +export { app, server }; \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/utils/EventBus.ts b/apps/fractal-inference-swarms/backend/src/utils/EventBus.ts new file mode 100644 index 0000000..19ee65d --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/utils/EventBus.ts @@ -0,0 +1,68 @@ +import { EventEmitter } from 'events'; + +export type SwarmEvent = + | 'task:created' + | 'task:splitting' + | 'task:subtasks_ready' + | 'task:agents_spawning' + | 'task:inference_started' + | 'task:inference_complete' + | 'task:scoring_started' + | 'task:scoring_complete' + | 'task:merging_started' + | 'task:merge_complete' + | 'task:validation_complete' + | 'task:rewards_distributed' + | 'task:completed' + | 'task:failed' + | 'agent:spawned' + | 'agent:started' + | 'agent:progress' + | 'agent:completed' + | 'agent:failed' + | 'reward:distributed' + | 'metrics:updated'; + +export interface SwarmEventPayload { + event: SwarmEvent; + taskId: string; + data: unknown; + timestamp: number; +} + +class EventBus extends EventEmitter { + private static instance: EventBus; + + private constructor() { + super(); + this.setMaxListeners(100); + } + + static getInstance(): EventBus { + if (!EventBus.instance) { + EventBus.instance = new EventBus(); + } + return EventBus.instance; + } + + emitSwarmEvent(event: SwarmEvent, taskId: string, data: unknown): void { + const payload: SwarmEventPayload = { + event, + taskId, + data, + timestamp: Date.now(), + }; + this.emit(event, payload); + this.emit('*', payload); + } + + onSwarmEvent(event: SwarmEvent | '*', handler: (payload: SwarmEventPayload) => void): void { + this.on(event, handler); + } + + offSwarmEvent(event: SwarmEvent | '*', handler: (payload: SwarmEventPayload) => void): void { + this.off(event, handler); + } +} + +export const eventBus = EventBus.getInstance(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/utils/logger.ts b/apps/fractal-inference-swarms/backend/src/utils/logger.ts new file mode 100644 index 0000000..b739a84 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/utils/logger.ts @@ -0,0 +1,43 @@ +import winston from 'winston'; +import { config } from '../config'; + +const { combine, timestamp, printf, colorize, json } = winston.format; + +const consoleFormat = printf(({ level, message, timestamp: ts, ...meta }) => { + const metaStr = Object.keys(meta).length ? ` ${JSON.stringify(meta)}` : ''; + return `[${ts}] ${level}: ${message}${metaStr}`; +}); + +const transports: winston.transport[] = [ + new winston.transports.Console({ + format: config.logging.format === 'json' + ? combine(timestamp(), json()) + : combine(colorize(), timestamp({ format: 'HH:mm:ss.SSS' }), consoleFormat), + }), +]; + +if (config.server.nodeEnv === 'production') { + transports.push( + new winston.transports.File({ + filename: 'logs/error.log', + level: 'error', + maxsize: 10 * 1024 * 1024, + maxFiles: 5, + }), + new winston.transports.File({ + filename: 'logs/combined.log', + maxsize: 10 * 1024 * 1024, + maxFiles: 10, + }) + ); +} + +export const logger = winston.createLogger({ + level: config.logging.level, + defaultMeta: { service: 'fractal-inference-swarms' }, + transports, +}); + +export function createChildLogger(component: string): winston.Logger { + return logger.child({ component }); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/src/websocket/socket.ts b/apps/fractal-inference-swarms/backend/src/websocket/socket.ts new file mode 100644 index 0000000..25f203b --- /dev/null +++ b/apps/fractal-inference-swarms/backend/src/websocket/socket.ts @@ -0,0 +1,200 @@ +import WebSocket, { WebSocketServer } from 'ws'; +import { Server as HttpServer } from 'http'; +import { eventBus, SwarmEventPayload } from '../utils/EventBus'; +import { config } from '../config'; +import { createChildLogger } from '../utils/logger'; + +const logger = createChildLogger('WebSocket'); + +interface TrackedClient { + ws: WebSocket; + id: string; + subscribedTasks: Set; + connectedAt: number; + lastPing: number; + isAlive: boolean; +} + +export class SwarmWebSocketServer { + private wss: WebSocketServer | null = null; + private clients: Map = new Map(); + private heartbeatInterval: NodeJS.Timeout | null = null; + private clientIdCounter: number = 0; + + initialize(server: HttpServer): void { + this.wss = new WebSocketServer({ server, path: '/ws' }); + + this.wss.on('connection', (ws: WebSocket) => { + const clientId = `client-${++this.clientIdCounter}-${Date.now()}`; + const client: TrackedClient = { + ws, + id: clientId, + subscribedTasks: new Set(), + connectedAt: Date.now(), + lastPing: Date.now(), + isAlive: true, + }; + + this.clients.set(clientId, client); + logger.info('Client connected', { clientId, totalClients: this.clients.size }); + + this.sendToClient(client, { + type: 'connection', + clientId, + timestamp: Date.now(), + message: 'Connected to Fractal Inference Swarms WebSocket', + }); + + ws.on('message', (data: Buffer) => { + try { + const message = JSON.parse(data.toString()); + this.handleClientMessage(client, message); + } catch { + this.sendToClient(client, { + type: 'error', + message: 'Invalid JSON message', + }); + } + }); + + ws.on('pong', () => { + client.isAlive = true; + client.lastPing = Date.now(); + }); + + ws.on('close', () => { + this.clients.delete(clientId); + logger.info('Client disconnected', { clientId, totalClients: this.clients.size }); + }); + + ws.on('error', (error) => { + logger.error('WebSocket client error', { clientId, error: error.message }); + this.clients.delete(clientId); + }); + }); + + this.setupEventForwarding(); + this.startHeartbeat(); + + logger.info('WebSocket server initialized'); + } + + private handleClientMessage(client: TrackedClient, message: Record): void { + switch (message.type) { + case 'subscribe': + if (typeof message.taskId === 'string') { + client.subscribedTasks.add(message.taskId); + this.sendToClient(client, { + type: 'subscribed', + taskId: message.taskId, + }); + logger.debug('Client subscribed to task', { + clientId: client.id, + taskId: message.taskId, + }); + } + break; + + case 'unsubscribe': + if (typeof message.taskId === 'string') { + client.subscribedTasks.delete(message.taskId); + this.sendToClient(client, { + type: 'unsubscribed', + taskId: message.taskId, + }); + } + break; + + case 'subscribe_all': + client.subscribedTasks.add('*'); + this.sendToClient(client, { type: 'subscribed', taskId: '*' }); + break; + + case 'ping': + this.sendToClient(client, { type: 'pong', timestamp: Date.now() }); + break; + + default: + this.sendToClient(client, { + type: 'error', + message: `Unknown message type: ${message.type}`, + }); + } + } + + private setupEventForwarding(): void { + eventBus.onSwarmEvent('*', (payload: SwarmEventPayload) => { + const message = { + type: 'swarm_event', + event: payload.event, + taskId: payload.taskId, + data: payload.data, + timestamp: payload.timestamp, + }; + + for (const client of this.clients.values()) { + if ( + client.subscribedTasks.has('*') || + client.subscribedTasks.has(payload.taskId) + ) { + this.sendToClient(client, message); + } + } + }); + } + + private sendToClient(client: TrackedClient, data: unknown): void { + if (client.ws.readyState === WebSocket.OPEN) { + try { + client.ws.send(JSON.stringify(data)); + } catch (error) { + logger.error('Failed to send to client', { + clientId: client.id, + error: (error as Error).message, + }); + } + } + } + + private startHeartbeat(): void { + this.heartbeatInterval = setInterval(() => { + for (const [id, client] of this.clients) { + if (!client.isAlive) { + logger.debug('Terminating unresponsive client', { clientId: id }); + client.ws.terminate(); + this.clients.delete(id); + continue; + } + client.isAlive = false; + client.ws.ping(); + } + }, config.websocket.heartbeatInterval); + } + + broadcast(data: unknown): void { + const message = JSON.stringify(data); + for (const client of this.clients.values()) { + if (client.ws.readyState === WebSocket.OPEN) { + client.ws.send(message); + } + } + } + + getConnectionCount(): number { + return this.clients.size; + } + + shutdown(): void { + if (this.heartbeatInterval) { + clearInterval(this.heartbeatInterval); + } + for (const client of this.clients.values()) { + client.ws.close(1001, 'Server shutting down'); + } + this.clients.clear(); + this.wss?.close(); + logger.info('WebSocket server shut down'); + } +} + +export const wsServer = new SwarmWebSocketServer(); \ No newline at end of file diff --git a/apps/fractal-inference-swarms/backend/tsconfig.json b/apps/fractal-inference-swarms/backend/tsconfig.json new file mode 100644 index 0000000..47e7468 --- /dev/null +++ b/apps/fractal-inference-swarms/backend/tsconfig.json @@ -0,0 +1,24 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "commonjs", + "lib": ["ES2022"], + "outDir": "./dist", + "rootDir": "./src", + "strict": true, + "esModuleInterop": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true, + "declaration": true, + "declarationMap": true, + "sourceMap": true, + "moduleResolution": "node", + "baseUrl": "./src", + "paths": { + "@/*": ["./*"] + } + }, + "include": ["src/**/*"], + "exclude": ["node_modules", "dist"] + } \ No newline at end of file diff --git a/apps/fractal-inference-swarms/docker-compose.yml b/apps/fractal-inference-swarms/docker-compose.yml new file mode 100644 index 0000000..69a4d8b --- /dev/null +++ b/apps/fractal-inference-swarms/docker-compose.yml @@ -0,0 +1,49 @@ +version: '3.9' + +services: + backend: + build: + context: ./backend + dockerfile: Dockerfile + container_name: fis-backend + ports: + - "3001:3001" + environment: + - NODE_ENV=production + - PORT=3001 + - HOST=0.0.0.0 + - CORS_ORIGIN=http://localhost:8080 + - WS_PORT=3001 + - WS_HEARTBEAT_INTERVAL=30000 + - MAX_CONCURRENT_AGENTS=20 + - DEFAULT_SWARM_SIZE=5 + - AGENT_TIMEOUT_MS=30000 + - TASK_SPLIT_STRATEGY=semantic + - SCORING_WEIGHT_CONFIDENCE=0.4 + - SCORING_WEIGHT_SPEED=0.3 + - SCORING_WEIGHT_RELIABILITY=0.3 + - BASE_REWARD_TOKENS=100 + - MIN_SCORE_THRESHOLD=0.3 + - REWARD_POOL_SIZE=1000 + - LOG_LEVEL=info + - LOG_FORMAT=json + restart: unless-stopped + healthcheck: + test: ["CMD", "wget", "--no-verbose", "--tries=1", "--spider", "http://localhost:3001/api/health"] + interval: 30s + timeout: 10s + retries: 3 + + frontend: + build: + context: ./frontend + dockerfile: Dockerfile + container_name: fis-frontend + ports: + - "8080:8080" + depends_on: + backend: + condition: service_healthy + environment: + - NODE_ENV=production + restart: unless-stopped \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/.dockerignore b/apps/fractal-inference-swarms/frontend/.dockerignore new file mode 100644 index 0000000..d5292c4 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/.dockerignore @@ -0,0 +1,6 @@ +node_modules +.next +.env +*.log +.git +.gitignore \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/.gitignore b/apps/fractal-inference-swarms/frontend/.gitignore new file mode 100644 index 0000000..5ef6a52 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/apps/fractal-inference-swarms/frontend/Dockerfile b/apps/fractal-inference-swarms/frontend/Dockerfile new file mode 100644 index 0000000..aa4839b --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/Dockerfile @@ -0,0 +1,40 @@ +FROM node:20-alpine AS deps + +WORKDIR /app + +COPY package.json package-lock.json* ./ +RUN npm ci + +FROM node:20-alpine AS builder + +WORKDIR /app + +COPY --from=deps /app/node_modules ./node_modules +COPY . . + +ENV NEXT_TELEMETRY_DISABLED=1 + +RUN npm run build + +FROM node:20-alpine AS runner + +WORKDIR /app + +ENV NODE_ENV=production +ENV NEXT_TELEMETRY_DISABLED=1 + +RUN addgroup --system --gid 1001 swarm && \ + adduser --system --uid 1001 frontend + +COPY --from=builder /app/public ./public +COPY --from=builder --chown=frontend:swarm /app/.next/standalone ./ +COPY --from=builder --chown=frontend:swarm /app/.next/static ./.next/static + +USER frontend + +EXPOSE 8080 + +ENV PORT=8080 +ENV HOSTNAME="0.0.0.0" + +CMD ["node", "server.js"] \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/README.md b/apps/fractal-inference-swarms/frontend/README.md new file mode 100644 index 0000000..e215bc4 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/apps/fractal-inference-swarms/frontend/app/dashboard/page.tsx b/apps/fractal-inference-swarms/frontend/app/dashboard/page.tsx new file mode 100644 index 0000000..61656b3 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/app/dashboard/page.tsx @@ -0,0 +1,241 @@ +'use client'; + +import React, { useState } from 'react'; +import { Activity, Database, Layers, Terminal, AlertCircle, Network, Home } from 'lucide-react'; +import Link from 'next/link'; +import MetricsPanel from '@/components/MetricsPanel'; +import TaskSubmitForm from '@/components/TaskSubmitForm'; +import TaskCard from '@/components/TaskCard'; +import AgentSwarmView from '@/components/AgentSwarmView'; +import Leaderboard from '@/components/Leaderboard'; +import LedgerHistory from '@/components/LedgerHistory'; +import EventFeed from '@/components/EventFeed'; +import { useWebSocket } from '@/hooks/useWebSocket'; +import { useSwarmData } from '@/hooks/useSwarmData'; + +export default function DashboardPage() { + const [selectedTaskId, setSelectedTaskId] = useState(null); + const { connected, events } = useWebSocket(() => { + swarmData.refresh(); + }); + const swarmData = useSwarmData(2500); + + return ( +
+ + {/* Animated Background */} +
+
+ + {/* Top Navigation Bar */} +
+
+
+ +
+ +
+

Fractal Console

+ +
+
+ ORD + MAINNET +
+
+ +
+
+
+ {connected ? 'SYSTEM ONLINE' : 'DISCONNECTED'} +
+ + + Home + +
+
+
+ +
+ + {/* Error State */} + {swarmData.error && ( +
+
+ + Connection Failure: {swarmData.error} +
+ +
+ )} + + {/* Metrics Panel - Full Width */} +
+ +
+ + {/* Main Dashboard Grid */} +
+ + {/* Left Column: Task Registry */} +
+
+
+
+
+
+ +
+
+

Task Queue

+

Active threads

+
+
+
+ {swarmData.tasks.length} +
+
+
+ +
+ {swarmData.loading && swarmData.tasks.length === 0 ? ( +
+
+
+ LOADING_REGISTRY... +
+
+ ) : swarmData.tasks.length === 0 ? ( +
+ + No active threads + Deploy a swarm to begin +
+ ) : ( +
+ {swarmData.tasks.map((task) => ( + + ))} +
+ )} +
+
+
+ + {/* Center Column: Main Visualizer */} +
+ {/* Main Swarm View */} +
+ {selectedTaskId ? ( + + ) : ( +
+
+ +

Awaiting Selection

+
+

SELECT_TASK_ID FROM REGISTRY

+
+
+ )} +
+ + {/* System Logs */} +
+
+
+ +
+
+

System Logs

+

Real-time event stream

+
+
+
+ +
+
+ + {/* Submit Task Form - Moved Below */} +
+ +
+
+ + {/* Right Column: Stats */} +
+ {/* Leaderboard */} +
+
+
+
+
+
+
+

Leaderboard

+

Top performers

+
+
+
+
+ +
+
+ + {/* x402 Ledger */} +
+
+
+
+
+
+
+

x402 Ledger

+

Transaction history

+
+
+
+
+ +
+
+
+ +
+
+ + {/* Custom Scrollbar Styles */} + +
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/app/favicon.ico b/apps/fractal-inference-swarms/frontend/app/favicon.ico new file mode 100644 index 0000000..718d6fe Binary files /dev/null and b/apps/fractal-inference-swarms/frontend/app/favicon.ico differ diff --git a/apps/fractal-inference-swarms/frontend/app/globals.css b/apps/fractal-inference-swarms/frontend/app/globals.css new file mode 100644 index 0000000..20d65be --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/app/globals.css @@ -0,0 +1,63 @@ +@import "tailwindcss"; + +@theme { + /* -- Core Palette -- */ + --color-background: #050505; /* Deep Void */ + --color-surface: #0f0f0f; /* Card Surface */ + --color-surface-hover: #1a1a1a; + --color-border: #222222; /* Subtle Border */ + + /* -- Brand Colors -- */ + --color-primary: #3b82f6; /* Electric Blue */ + --color-primary-dim: #1d4ed8; + --color-accent: #8b5cf6; /* AI Purple */ + --color-success: #10b981; /* Matrix Green */ + --color-warning: #f59e0b; /* Processing Orange */ + --color-error: #ef4444; /* System Red */ + + /* -- Text Colors -- */ + --color-text-main: #ededed; + --color-text-muted: #888888; + --color-text-dim: #444444; + + /* -- Fonts -- */ + --font-sans: var(--font-inter), ui-sans-serif, system-ui; + --font-mono: var(--font-jetbrains), ui-monospace, monospace; + + /* -- Animations -- */ + --animate-fade-in: fadeIn 0.8s ease-out forwards; + --animate-pulse-slow: pulse 4s cubic-bezier(0.4, 0, 0.6, 1) infinite; + --animate-float: float 6s ease-in-out infinite; + + @keyframes fadeIn { + from { opacity: 0; transform: translateY(20px); } + to { opacity: 1; transform: translateY(0); } + } + @keyframes float { + 0%, 100% { transform: translateY(0); } + 50% { transform: translateY(-10px); } + } +} + +/* -- Global Styles -- */ +body { + background-color: var(--color-background); + color: var(--color-text-main); + font-family: var(--font-sans); + overflow-x: hidden; +} + +/* Custom Scrollbar for that "Terminal" feel */ +::-webkit-scrollbar { + width: 8px; +} +::-webkit-scrollbar-track { + background: var(--color-background); +} +::-webkit-scrollbar-thumb { + background: var(--color-border); + border-radius: 4px; +} +::-webkit-scrollbar-thumb:hover { + background: var(--color-text-muted); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/app/layout.tsx b/apps/fractal-inference-swarms/frontend/app/layout.tsx new file mode 100644 index 0000000..8acd327 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/app/layout.tsx @@ -0,0 +1,23 @@ +import type { Metadata } from 'next'; +import './globals.css'; + +export const metadata: Metadata = { + title: 'Fractal Inference Swarms | AI Orchestration Dashboard', + description: 'AI-native orchestration system with fractal agent swarms and x402 micropayments', +}; + +export default function RootLayout({ children }: { children: React.ReactNode }) { + return ( + + + + + + {children} + + + ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/app/page.tsx b/apps/fractal-inference-swarms/frontend/app/page.tsx new file mode 100644 index 0000000..2347a96 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/app/page.tsx @@ -0,0 +1,314 @@ +'use client'; + +import Link from 'next/link'; +import { ArrowRight, Cpu, Layers, Zap, ShieldCheck, Network, Terminal, Activity, Code, Globe } from 'lucide-react'; + +export default function LandingPage() { + const scrollToSection = (id: string) => { + const element = document.getElementById(id); + if (element) { + element.scrollIntoView({ behavior: 'smooth' }); + } + }; + + return ( +
+ + {/* Background Ambience */} +
+ + {/* Navbar */} + + + {/* Hero Section */} +
+
+ + + + + MAINNET BETA LIVE +
+ +

+ Orchestrate AI Swarms
at Planetary Scale +

+ +

+ Don't rely on a single model. Deploy fractal swarms that split tasks, + debate outcomes, and reach consensus. Powered by the x402 micropayment protocol. +

+ +
+ + Deploy Agent Swarm + + +
+ + {/* Hero Visual / Dashboard Preview */} +
+
+
+
+ {/* Fake UI Elements */} +
+
+
+ +
+ +
+ {[1,2,3].map(i => ( +
+ +
+ ))} +
+
+
+ ORCHESTRATOR: TASK_SPLIT_COMPLETE (3 SUBTASKS) +
+
+
+
+
+ + {/* Metrics Strip */} +
+
+ } /> + } /> + } /> + } /> +
+
+ + {/* Architecture Section */} +
+
+

Fractal Architecture

+

+ A modular system designed for reliability. The orchestrator manages lifecycle, + while swarms execute logic in parallel. +

+
+ +
+ } + title="Fractal Task Splitting" + desc="The Orchestrator recursively breaks high-level objectives into atomic subtasks distributed across N-agents. Complex problems become simple parallel executions." + /> + } + title="Consensus Scoring" + desc="We don't trust single outputs. Results are validated via a weighted scoring engine: (Confidence * Weight) + (Speed * Weight) + (Reliability * Weight)." + /> + } + title="x402 Micropayments" + desc="Automated settlement layer. Agents are rewarded instantly via mock-ledger tokens based on performance scores, incentivizing high-quality inference." + /> +
+
+ + {/* Protocol / Terminal Section */} +
+
+
+
+ + PROTOCOL LEVEL +
+

The x402 Payment Standard

+
+

+ Traditional AI APIs are black boxes. Fractal Swarms introduce Proof of Inference. + Agents must provide cryptographic proof of their work to release funds from the escrow smart contract. +

+
    +
  • +
    +
    +
    + Atomic Settlements per sub-task +
    • +
  • +
    +
    +
    + Reputation-weighted staking for agents +
    • +
  • +
    +
    +
    + Transparent ledger history (Immutable) +
    • +
+
+
+ + View Live Ledger + +
+
+ + {/* Code Block Visual */} +
+
+
+
+
+ x402-ledger.json +
+
+
+ 1 + transaction {`{`} +
+
+ 2 + "id": "tx_99a8b1c...", +
+
+ 3 + "agent_id": "swarm_alpha_01", +
+
+ 4 + "task_hash": "0x7f3...", +
+
+ 5 + "score": 0.985, +
+
+ 6 + "payout": {`{`} +
+
+ 7 + "amount": 450, +
+
+ 8 + "token": "X402" +
+
+ 9 + {`}`} +
+
+ 10 + {`}`} +
+
+
+
+
+ + {/* Network / Global Map Placeholder */} +
+
+ +

Global Node Distribution

+

+ Our swarms are running on decentralized edge compute across 12 regions. + Low latency. High availability. Zero downtime. +

+ + {/* Simple Grid Graphic */} +
+ {['USE-1', 'USW-2', 'EU-CENTRAL', 'ASIA-EAST', 'SA-EAST', 'AF-NORTH', 'OC-SOUTH', 'EU-WEST'].map((region) => ( +
+ {region} +
+
+ ))} +
+
+
+ + {/* Footer */} +
+
+
+
+
+ +
+ Fractal Swarm +
+

+ Next-generation AI orchestration powered by cryptographic proofs and micropayments. +

+
+
+

Platform

+ +
+
+

Legal

+ +
+
+
+ Β© 2026 Fractal Systems Inc. All rights reserved. +
+
+
+ ); +} + +function FeatureCard({ icon, title, desc }: { icon: React.ReactNode, title: string, desc: string }) { + return ( +
+
{icon}
+

{title}

+

{desc}

+
+ ); +} + +function MetricItem({ label, value, icon }: { label: string, value: string, icon: React.ReactNode }) { + return ( +
+
{icon}
+
+
{value}
+
{label}
+
+
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/AgentSwarmView.tsx b/apps/fractal-inference-swarms/frontend/components/AgentSwarmView.tsx new file mode 100644 index 0000000..497e892 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/AgentSwarmView.tsx @@ -0,0 +1,245 @@ +'use client'; + +import React, { useEffect, useState } from 'react'; +import { Task, ScoredResult, SwarmSession } from '@/types'; +import { getTask } from '@/lib/api'; +import StatusBadge from './StatusBadge'; + +interface AgentSwarmViewProps { + taskId: string; +} + +export default function AgentSwarmView({ taskId }: AgentSwarmViewProps) { + const [task, setTask] = useState(null); + const [session, setSession] = useState(null); + const [scored, setScored] = useState([]); + const [loading, setLoading] = useState(true); + + useEffect(() => { + let mounted = true; + const load = async () => { + try { + const data = await getTask(taskId); + if (mounted) { + setTask(data.task); + setSession(data.session); + setScored(data.scoredResults); + setLoading(false); + } + } catch { + if (mounted) setLoading(false); + } + }; + load(); + const interval = setInterval(load, 2000); + return () => { + mounted = false; + clearInterval(interval); + }; + }, [taskId]); + + if (loading) { + return ( +
+
+
+
+
+ {[1, 2, 3].map((i) =>
)} +
+
+
+ ); + } + + if (!task) { + return ( +
+ Task not found +
+ ); + } + + return ( +
+ {/* Task Detail Header */} +
+
+
+

{task.title}

+

{task.description}

+
+ +
+
+ ID: {task.id.slice(0, 12)} + Agents: {task.swarmSize} + Subtasks: {task.subtasks.length} + {task.totalComputeTimeMs > 0 && Total: {(task.totalComputeTimeMs / 1000).toFixed(2)}s} +
+
+ + {/* Agent Grid */} + {session && session.agents.length > 0 && ( +
+

Swarm Agents

+
+ {session.agents.map((agent, i) => { + const agentScore = scored.find((s) => s.agentId === agent.id); + const reward = session.rewards.find((r) => r.agentId === agent.id); + + return ( +
+
+ {agent.name} + +
+
+
+ Compute + {agent.computeTimeMs > 0 ? `${(agent.computeTimeMs / 1000).toFixed(2)}s` : '...'} +
+
+ Iterations + {agent.inferenceIterations} +
+
+ Memory + {agent.memoryUsageMb > 0 ? `${agent.memoryUsageMb.toFixed(0)}MB` : '...'} +
+ {agentScore && ( + <> +
+
+ Score + {(agentScore.finalScore * 100).toFixed(2)}% +
+
+ Rank + + #{agentScore.rank} + +
+
+ Confidence + {(agentScore.confidence * 100).toFixed(1)}% +
+ + )} + {reward && ( +
+ Reward + {reward.tokensEarned.toFixed(2)} tokens +
+ )} +
+ {agentScore && ( +
+
+
+
+
+ )} +
+ ); + })} +
+
+ )} + + {/* Merged Output */} + {task.mergedOutput && ( +
+
+

Merged Output

+
+ + {task.mergedOutput.validationPassed ? 'VALIDATED' : 'VALIDATION ISSUES'} + + + {(task.mergedOutput.validationScore * 100).toFixed(1)}% + +
+
+ + {/* Validation Details */} +
+ {task.mergedOutput.validationDetails.map((detail, i) => ( +
+
{detail.criterion.replace(/_/g, ' ')}
+
+ {(detail.score * 100).toFixed(0)}% +
+
+ ))} +
+ +
+ 
+              {task.mergedOutput.finalOutput}
+
+
+
+ Strategy: {task.mergedOutput.mergeStrategy.replace(/_/g, ' ')} + Contributing: {task.mergedOutput.contributingAgents.length} agents + Merged: {new Date(task.mergedOutput.mergedAt).toLocaleTimeString()} +
+
+ )} + + {/* Score Breakdown */} + {scored.length > 0 && ( +
+

Score Breakdown

+
+ + + + + + + + + + + + + {scored.map((s) => ( + + + + + + + + + ))} + +
RankAgentConfidenceSpeedReliabilityFinal Score
+ + #{s.rank} + + {s.agentId.slice(0, 10)} + {(s.scoreBreakdown.rawConfidence * 100).toFixed(1)}% + + {(s.scoreBreakdown.rawSpeedScore * 100).toFixed(1)}% + + {(s.scoreBreakdown.rawReliability * 100).toFixed(1)}% + + {(s.finalScore * 100).toFixed(2)}% +
+
+
+ )} +
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/EventFeed.tsx b/apps/fractal-inference-swarms/frontend/components/EventFeed.tsx new file mode 100644 index 0000000..e712849 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/EventFeed.tsx @@ -0,0 +1,80 @@ +'use client'; + +import React from 'react'; +import { SwarmEvent } from '@/types'; + +interface EventFeedProps { + events: SwarmEvent[]; +} + +const EVENT_ICONS: Record = { + 'task:created': '\u{1F4CB}', + 'task:splitting': '\u2702\uFE0F', + 'task:subtasks_ready': '\u{1F9E9}', + 'task:agents_spawning': '\u{1F680}', + 'task:inference_started': '\u{1F9E0}', + 'task:inference_complete': '\u2705', + 'task:scoring_started': '\u{1F4CA}', + 'task:scoring_complete': '\u{1F3AF}', + 'task:merging_started': '\u{1F500}', + 'task:merge_complete': '\u{1F4E6}', + 'task:validation_complete': '\u{1F6E1}\uFE0F', + 'task:rewards_distributed': '\u{1FA99}', + 'task:completed': '\u{1F389}', + 'task:failed': '\u274C', + 'agent:spawned': '\u{1F916}', + 'agent:started': '\u25B6\uFE0F', + 'agent:progress': '\u{1F504}', + 'agent:completed': '\u{1F7E2}', + 'agent:failed': '\u{1F534}', + 'reward:distributed': '\u{1F4B0}', + 'metrics:updated': '\u{1F4C8}', +}; + +const EVENT_COLORS: Record = { + 'task:created': 'text-blue-400', + 'task:completed': 'text-green-400', + 'task:failed': 'text-red-400', + 'agent:spawned': 'text-purple-400', + 'agent:completed': 'text-emerald-400', + 'agent:failed': 'text-red-400', + 'task:rewards_distributed': 'text-yellow-400', + 'task:merge_complete': 'text-indigo-400', + 'task:validation_complete': 'text-teal-400', +}; + +export default function EventFeed({ events }: EventFeedProps) { + const displayed = events.filter((e) => e.event !== 'agent:progress' && e.event !== 'metrics:updated').slice(0, 40); + + return ( +
+
+

Live Event Feed

+ {events.length} events +
+
+ {displayed.length === 0 ? ( +
Waiting for events...
+ ) : ( + displayed.map((event, i) => ( +
+ {EVENT_ICONS[event.event] || '\u26AA'} + + {new Date(event.timestamp).toLocaleTimeString([], { hour: '2-digit', minute: '2-digit', second: '2-digit' })} + + + {event.event} + + + {event.taskId.slice(0, 8)} + +
+ )) + )} +
+
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/Leaderboard.tsx b/apps/fractal-inference-swarms/frontend/components/Leaderboard.tsx new file mode 100644 index 0000000..a0924ea --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/Leaderboard.tsx @@ -0,0 +1,98 @@ +'use client'; + +import React from 'react'; +import { RewardTransaction } from '@/types'; + +interface LeaderboardProps { + ledger: RewardTransaction[]; +} + +interface AgentAggregate { + agentId: string; + agentName: string; + totalTokens: number; + taskCount: number; + avgScore: number; + bestRank: number; +} + +export default function Leaderboard({ ledger }: LeaderboardProps) { + const aggregated = new Map(); + + for (const tx of ledger) { + const existing = aggregated.get(tx.agentId); + if (existing) { + existing.totalTokens += tx.tokensEarned; + existing.taskCount++; + existing.avgScore = (existing.avgScore * (existing.taskCount - 1) + tx.score) / existing.taskCount; + existing.bestRank = Math.min(existing.bestRank, tx.rank); + } else { + aggregated.set(tx.agentId, { + agentId: tx.agentId, + agentName: tx.agentName, + totalTokens: tx.tokensEarned, + taskCount: 1, + avgScore: tx.score, + bestRank: tx.rank, + }); + } + } + + const sorted = Array.from(aggregated.values()) + .filter((a) => a.totalTokens > 0) + .sort((a, b) => b.totalTokens - a.totalTokens) + .slice(0, 15); + + const maxTokens = sorted[0]?.totalTokens || 1; + + if (sorted.length === 0) { + return ( +
+

Agent Leaderboard

+
No agent data yet. Submit a task to populate the leaderboard.
+
+ ); + } + + return ( +
+

Agent Leaderboard

+
+ {sorted.map((agent, i) => { + const barWidth = (agent.totalTokens / maxTokens) * 100; + const medal = i === 0 ? '\u{1F947}' : i === 1 ? '\u{1F948}' : i === 2 ? '\u{1F949}' : ''; + + return ( +
+
+
+
+ + {medal || #{i + 1}} + +
+
{agent.agentName}
+
+ {agent.taskCount} tasks | avg score: {(agent.avgScore * 100).toFixed(1)}% | best: #{agent.bestRank} +
+
+
+
+
{agent.totalTokens.toFixed(2)}
+
tokens
+
+
+
+ ); + })} +
+
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/LedgerHistory.tsx b/apps/fractal-inference-swarms/frontend/components/LedgerHistory.tsx new file mode 100644 index 0000000..62fe9df --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/LedgerHistory.tsx @@ -0,0 +1,80 @@ +'use client'; + +import React from 'react'; +import { RewardTransaction } from '@/types'; + +interface LedgerHistoryProps { + transactions: RewardTransaction[]; +} + +export default function LedgerHistory({ transactions }: LedgerHistoryProps) { + const recent = transactions.slice(0, 30); + + if (recent.length === 0) { + return ( +
+

x402 Payment Ledger

+
No transactions recorded yet.
+
+ ); + } + + return ( +
+
+

x402 Payment Ledger

+ {transactions.length} total transactions +
+
+ + + + + + + + + + + + + + {recent.map((tx) => ( + + + + + + + + + + ))} + +
TimeAgentTypeScoreRankTokensPayment ID
+ {new Date(tx.timestamp).toLocaleTimeString()} + {tx.agentName} + + {tx.transactionType.replace(/_/g, ' ')} + + + {(tx.score * 100).toFixed(1)}% + + #{tx.rank} + 0 ? 'text-swarm-success' : 'text-swarm-danger'}`}> + {tx.tokensEarned > 0 ? '+' : ''}{tx.tokensEarned.toFixed(2)} + + {tx.x402PaymentId.slice(0, 12)}... +
+
+
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/MetricsPanel.tsx b/apps/fractal-inference-swarms/frontend/components/MetricsPanel.tsx new file mode 100644 index 0000000..e6ce128 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/MetricsPanel.tsx @@ -0,0 +1,94 @@ +'use client'; + +import React from 'react'; +import { SwarmMetrics } from '@/types'; + +interface MetricCardProps { + label: string; + value: string | number; + subtext?: string; + color: string; + icon: string; +} + +function MetricCard({ label, value, subtext, color, icon }: MetricCardProps) { + return ( +
+
+ {label} + {icon} +
+
{value}
+ {subtext &&
{subtext}
} +
+ ); +} + +interface MetricsPanelProps { + metrics: SwarmMetrics | null; + connected: boolean; +} + +export default function MetricsPanel({ metrics, connected }: MetricsPanelProps) { + if (!metrics) { + return ( +
+ {Array.from({ length: 6 }).map((_, i) => ( +
+
+
+
+ ))} +
+ ); + } + + const uptimeHours = (metrics.uptimeMs / 3600000).toFixed(1); + + return ( +
+ + + 0 ? (metrics.averageCompletionTimeMs / 1000).toFixed(1) + 's avg' : 'n/a'}`} + color="text-swarm-success" + icon="\u2705" + /> + + + +
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/StatusBadge.tsx b/apps/fractal-inference-swarms/frontend/components/StatusBadge.tsx new file mode 100644 index 0000000..c01a490 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/StatusBadge.tsx @@ -0,0 +1,38 @@ +'use client'; + +import React from 'react'; + +const STATUS_COLORS: Record = { + pending: 'bg-gray-600 text-gray-200', + splitting: 'bg-blue-600/30 text-blue-300 border border-blue-500/50', + spawning_agents: 'bg-purple-600/30 text-purple-300 border border-purple-500/50 animate-pulse', + inference_running: 'bg-cyan-600/30 text-cyan-300 border border-cyan-500/50 animate-pulse', + scoring: 'bg-amber-600/30 text-amber-300 border border-amber-500/50', + merging: 'bg-indigo-600/30 text-indigo-300 border border-indigo-500/50 animate-pulse', + validating: 'bg-teal-600/30 text-teal-300 border border-teal-500/50', + distributing_rewards: 'bg-emerald-600/30 text-emerald-300 border border-emerald-500/50', + completed: 'bg-green-600/30 text-green-300 border border-green-500/50', + failed: 'bg-red-600/30 text-red-300 border border-red-500/50', + active: 'bg-cyan-600/30 text-cyan-300 border border-cyan-500/50 animate-pulse', + idle: 'bg-gray-600/30 text-gray-300', + initializing: 'bg-blue-600/30 text-blue-300 animate-pulse', + running: 'bg-cyan-600/30 text-cyan-300 animate-pulse', + timeout: 'bg-orange-600/30 text-orange-300', +}; + +interface StatusBadgeProps { + status: string; + size?: 'sm' | 'md'; +} + +export default function StatusBadge({ status, size = 'sm' }: StatusBadgeProps) { + const colorClass = STATUS_COLORS[status] || 'bg-gray-600 text-gray-200'; + const sizeClass = size === 'sm' ? 'text-xs px-2 py-0.5' : 'text-sm px-3 py-1'; + + return ( + + + {status.replace(/_/g, ' ')} + + ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/TaskCard.tsx b/apps/fractal-inference-swarms/frontend/components/TaskCard.tsx new file mode 100644 index 0000000..25dcb74 --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/TaskCard.tsx @@ -0,0 +1,97 @@ +'use client'; + +import React, { useState } from 'react'; +import { Task } from '@/types'; +import StatusBadge from './StatusBadge'; + +interface TaskCardProps { + task: Task; + onSelect: (taskId: string) => void; + isSelected: boolean; +} + +const PIPELINE_STAGES: Array<{ key: string; label: string }> = [ + { key: 'pending', label: 'Queued' }, + { key: 'splitting', label: 'Split' }, + { key: 'spawning_agents', label: 'Spawn' }, + { key: 'inference_running', label: 'Infer' }, + { key: 'scoring', label: 'Score' }, + { key: 'merging', label: 'Merge' }, + { key: 'distributing_rewards', label: 'Reward' }, + { key: 'completed', label: 'Done' }, +]; + +function getStageIndex(status: string): number { + const idx = PIPELINE_STAGES.findIndex((s) => s.key === status); + return idx >= 0 ? idx : 0; +} + +export default function TaskCard({ task, onSelect, isSelected }: TaskCardProps) { + const stageIdx = getStageIndex(task.status); + const elapsed = task.completedAt + ? ((task.completedAt - task.createdAt) / 1000).toFixed(1) + : ((Date.now() - task.createdAt) / 1000).toFixed(0); + + return ( +
onSelect(task.id)} + className={`bg-swarm-card border rounded-xl p-4 cursor-pointer transition-all duration-300 animate-fade-in ${ + isSelected + ? 'border-swarm-accent/60 ring-1 ring-swarm-accent/20 shadow-lg shadow-blue-900/20' + : 'border-swarm-border hover:border-swarm-accent/30' + }`} + > +
+
+

{task.title}

+

{task.description}

+
+ +
+ + {/* Pipeline progress bar */} +
+ {PIPELINE_STAGES.map((stage, i) => { + const isActive = i === stageIdx && task.status !== 'completed' && task.status !== 'failed'; + const isComplete = i < stageIdx || task.status === 'completed'; + const isFailed = task.status === 'failed'; + + return ( +
+
+ + {stage.label} + +
+ ); + })} +
+ +
+
+ {task.swarmSize} agents + {task.subtasks.length} subtasks +
+ {elapsed}s +
+ + {task.mergedOutput && ( +
+ {task.mergedOutput.validationPassed ? '\u2713 Validated' : '\u26A0 Validation issues'} + score: {(task.mergedOutput.validationScore * 100).toFixed(1)}% + strategy: {task.mergedOutput.mergeStrategy.replace(/_/g, ' ')} +
+ )} +
+ ); +} \ No newline at end of file diff --git a/apps/fractal-inference-swarms/frontend/components/TaskSubmitForm.tsx b/apps/fractal-inference-swarms/frontend/components/TaskSubmitForm.tsx new file mode 100644 index 0000000..263d9bb --- /dev/null +++ b/apps/fractal-inference-swarms/frontend/components/TaskSubmitForm.tsx @@ -0,0 +1,162 @@ +'use client'; + +import React, { useState } from 'react'; +import { submitTask } from '@/lib/api'; + +interface TaskSubmitFormProps { + onSubmitted: () => void; +} + +const PRESET_TASKS = [ + { + title: 'Market Analysis: AI Infrastructure', + description: 'Analyze the current state of AI infrastructure market including cloud providers, edge computing, and specialized hardware. Evaluate key players, market trends, pricing models, and predict growth trajectories for the next 5 years.', + priority: 'high' as const, + swarmSize: 5, + }, + { + title: 'Security Audit: Microservices Architecture', + description: 'Conduct a comprehensive security audit of a microservices architecture. Evaluate authentication flows, inter-service communication, data encryption, API gateway vulnerabilities, and container orchestration security posture.', + priority: 'critical' as const, + swarmSize: 6, + }, + { + title: 'Research: Quantum-Resistant Cryptography', + description: 'Research the latest developments in post-quantum cryptographic algorithms. Compare lattice-based, hash-based, and code-based approaches. Evaluate NIST standardization candidates and implementation readiness for production systems.', + priority: 'medium' as const, + swarmSize: 4, + }, + { + title: 'Design System: Component Architecture', + description: 'Design a scalable component architecture for a design system supporting web and mobile platforms. Define token systems, composability patterns, accessibility requirements, and theming capabilities with performance constraints.', + priority: 'medium' as const, + swarmSize: 5, + }, +]; + +export default function TaskSubmitForm({ onSubmitted }: TaskSubmitFormProps) { + const [title, setTitle] = useState(''); + const [description, setDescription] = useState(''); + const [priority, setPriority] = useState('medium'); + const [swarmSize, setSwarmSize] = useState(5); + const [submitting, setSubmitting] = useState(false); + const [error, setError] = useState(null); + const [showPresets, setShowPresets] = useState(false); + + async function handleSubmit(e: React.FormEvent) { + e.preventDefault(); + if (!title.trim() || !description.trim()) return; + + setSubmitting(true); + setError(null); + + try { + await submitTask({ title: title.trim(), description: description.trim(), priority, swarmSize }); + setTitle(''); + setDescription(''); + setPriority('medium'); + setSwarmSize(5); + onSubmitted(); + } catch (err) { + setError((err as Error).message); + } finally { + setSubmitting(false); + } + } + + function loadPreset(preset: typeof PRESET_TASKS[number]) { + setTitle(preset.title); + setDescription(preset.description); + setPriority(preset.priority); + setSwarmSize(preset.swarmSize); + setShowPresets(false); + } + + return ( +
+
+

Submit Task to Swarm

+ +
+ + {showPresets && ( +
+ {PRESET_TASKS.map((preset, i) => ( + + ))} +
+ )} + +
+ setTitle(e.target.value)} + placeholder="Task title..." + className="w-full px-4 py-2.5 bg-swarm-surface border border-swarm-border rounded-lg text-swarm-text placeholder-swarm-muted focus:outline-none focus:border-swarm-accent/60 focus:ring-1 focus:ring-swarm-accent/30 transition-all text-sm" + required + /> +