claude-flow
Version:
Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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text/typescript
import "https://deno.land/x/xhr@0.1.0/mod.ts";
import { serve } from "https://deno.land/std@0.168.0/http/server.ts";
const corsHeaders = {
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Headers': 'authorization, x-client-info, apikey, content-type',
};
interface OptimizeConfigRequest {
preset: string;
currentGoal?: string;
}
serve(async (req) => {
if (req.method === 'OPTIONS') {
return new Response(null, { headers: corsHeaders });
}
try {
const { preset, currentGoal }: OptimizeConfigRequest = await req.json();
console.log('Optimize config request:', { preset, currentGoal });
const LOVABLE_API_KEY = Deno.env.get('LOVABLE_API_KEY');
if (!LOVABLE_API_KEY) {
throw new Error('LOVABLE_API_KEY is not configured');
}
const systemPrompt = `You are an expert research workflow architect specializing in GOAP (Goal-Oriented Action Planning) configuration optimization.
Generate optimized research configuration settings based on the given preset/objective. Your configuration should maximize research effectiveness for the specific use case.
Consider:
- Research depth appropriate for the objective
- Source types and quality thresholds matching the domain
- Execution parameters balancing speed and thoroughness
- Perspective and focus areas relevant to the preset
- GOAP settings for optimal planning and replanning
Be specific and practical - these settings will directly control AI research behavior.`;
const presetPrompts: Record<string, string> = {
'academic-deep': `Optimize for: Academic/Scientific Deep Research
- Maximum depth and rigor
- Academic and peer-reviewed sources prioritized
- High confidence thresholds (90%+)
- Comprehensive analysis with extensive cross-referencing
- Focus: Methodology, citations, reproducibility
- Timeframe: Include seminal works, not just recent
Goal: ${currentGoal || 'Scientific research with publication-grade rigor'}`,
'industry-quick': `Optimize for: Industry Quick Scan
- Speed and actionable insights prioritized
- Industry reports, market data, business sources
- Moderate confidence acceptable (75%+)
- Surface to moderate depth
- Focus: Practical applications, ROI, trends
- Timeframe: Recent only (past 6-12 months)
Goal: ${currentGoal || 'Fast industry insights for business decisions'}`,
'competitive-analysis': `Optimize for: Competitive Intelligence & Analysis
- Comprehensive competitor research
- Industry reports, news, company filings, social media
- Focus: Market positioning, strategies, strengths/weaknesses
- Moderate to deep depth
- Business and strategic perspective
- Parallel execution for multiple competitors
Goal: ${currentGoal || 'Competitive landscape analysis'}`,
'technical-feasibility': `Optimize for: Technical Feasibility Study
- Technical and engineering focus
- Academic papers, technical documentation, GitHub
- Deep analysis of implementation details
- Focus: Architecture, performance, limitations, trade-offs
- High confidence for technical claims (85%+)
- Technical perspective with practical considerations
Goal: ${currentGoal || 'Technical implementation feasibility assessment'}`,
'market-trends': `Optimize for: Market Trends & Predictions
- Trend analysis and future predictions
- Industry reports, market research, financial data
- Focus: Growth patterns, emerging opportunities, disruptions
- Moderate depth with broad coverage
- Business and analytical perspective
- Recent timeframe with historical context
Goal: ${currentGoal || 'Market trend analysis and forecasting'}`,
'medical-clinical': `Optimize for: Medical/Clinical Research
- Medical journals, clinical trials, PubMed prioritized
- Very high confidence required (90%+)
- Deep analysis with safety/efficacy focus
- Focus: Clinical evidence, patient outcomes, safety profiles
- Academic and clinical perspective
- Exclude non-peer-reviewed sources
Goal: ${currentGoal || 'Clinical research with evidence-based analysis'}`,
'startup-validation': `Optimize for: Startup/Business Idea Validation
- Market size, competition, customer needs
- Industry reports, surveys, competitor analysis
- Practical and business perspective
- Focus: Market gaps, validation metrics, go-to-market
- Moderate depth, broad coverage
- Cost-effective with parallel research
Goal: ${currentGoal || 'Startup idea validation and market assessment'}`,
'policy-regulatory': `Optimize for: Policy & Regulatory Research
- Government sources, legal documents, policy papers
- High accuracy and recency critical
- Focus: Compliance, legal frameworks, regulatory trends
- Deep analysis with risk assessment
- Academic and legal perspective
- Exclude opinion pieces, prioritize official sources
Goal: ${currentGoal || 'Policy and regulatory compliance research'}`
};
const userPrompt = presetPrompts[preset.toLowerCase()] || `Optimize research settings for: ${preset}. Goal: ${currentGoal || 'general research'}`;
const response = await fetch('https://ai.gateway.lovable.dev/v1/chat/completions', {
method: 'POST',
headers: {
'Authorization': `Bearer ${LOVABLE_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'google/gemini-2.5-flash',
messages: [
{ role: 'system', content: systemPrompt },
{ role: 'user', content: userPrompt }
],
tools: [
{
type: "function",
function: {
name: "generate_optimized_config",
description: "Generate optimized research configuration for the given preset",
parameters: {
type: "object",
properties: {
researchGuidance: {
type: "object",
properties: {
focusAreas: {
type: "array",
items: { type: "string" },
description: "Specific topics to emphasize (2-4 items)"
},
excludeTopics: {
type: "array",
items: { type: "string" },
description: "Topics to avoid (0-3 items)"
},
depth: {
type: "string",
enum: ["surface", "moderate", "deep"],
description: "Research depth level"
},
perspective: {
type: "string",
description: "Research perspective (technical/business/academic/practical)"
},
timeframe: {
type: "string",
description: "Time focus (recent/current-year/past-year/past-2-years/all-time)"
}
},
required: ["depth", "perspective", "timeframe"]
},
prompts: {
type: "object",
properties: {
systemPrompt: {
type: "string",
description: "Custom system prompt for AI (2-3 paragraphs)"
}
}
},
parameters: {
type: "object",
properties: {
maxSources: {
type: "number",
minimum: 5,
maximum: 25,
description: "Number of sources per step"
},
minConfidence: {
type: "number",
minimum: 70,
maximum: 95,
description: "Minimum confidence threshold (%)"
},
maxSteps: {
type: "number",
minimum: 5,
maximum: 10,
description: "Maximum research steps"
},
parallelAgents: {
type: "number",
minimum: 1,
maximum: 5,
description: "Number of parallel agents"
},
timeout: {
type: "number",
minimum: 60,
maximum: 300,
description: "Timeout in seconds"
}
},
required: ["maxSources", "minConfidence", "maxSteps"]
},
filters: {
type: "object",
properties: {
dateRange: {
type: "string",
description: "Date range filter (recent/current-year/past-year/past-2-years/all-time)"
},
sourceTypes: {
type: "array",
items: { type: "string" },
description: "Preferred source types (academic/technical/industry/news)"
},
excludeDomains: {
type: "array",
items: { type: "string" },
description: "Domains to exclude (0-3 items)"
}
},
required: ["dateRange", "sourceTypes"]
},
goapConfig: {
type: "object",
properties: {
executionMode: {
type: "string",
enum: ["focused", "closed", "open"],
description: "GOAP execution mode"
},
enableReplanning: {
type: "boolean",
description: "Enable adaptive replanning"
},
costOptimization: {
type: "boolean",
description: "Optimize for cost efficiency"
},
parallelExecution: {
type: "boolean",
description: "Enable parallel agent execution"
}
},
required: ["executionMode", "enableReplanning"]
}
},
required: ["researchGuidance", "parameters", "filters", "goapConfig"],
additionalProperties: false
}
}
}
],
tool_choice: { type: "function", function: { name: "generate_optimized_config" } }
}),
});
if (!response.ok) {
if (response.status === 429) {
return new Response(JSON.stringify({ error: "Rate limits exceeded. Please try again later." }), {
status: 429,
headers: { ...corsHeaders, 'Content-Type': 'application/json' },
});
}
if (response.status === 402) {
return new Response(JSON.stringify({ error: "AI usage limit reached. Please add credits to continue." }), {
status: 402,
headers: { ...corsHeaders, 'Content-Type': 'application/json' },
});
}
const errorText = await response.text();
console.error('AI gateway error:', response.status, errorText);
throw new Error(`AI gateway error: ${response.status}`);
}
const data = await response.json();
console.log('AI response received');
const toolCall = data.choices?.[0]?.message?.tool_calls?.[0];
if (!toolCall) {
throw new Error('No tool call in AI response');
}
const config = JSON.parse(toolCall.function.arguments);
console.log('Generated optimized config:', config);
return new Response(JSON.stringify({ config }), {
headers: { ...corsHeaders, 'Content-Type': 'application/json' },
});
} catch (error) {
console.error('Error in optimize-research-config function:', error);
return new Response(
JSON.stringify({
error: error instanceof Error ? error.message : 'Unknown error occurred',
details: 'Failed to optimize research configuration'
}),
{
status: 500,
headers: { ...corsHeaders, 'Content-Type': 'application/json' },
}
);
}
});