llm-service-integration
Guide for integrating with LLM providers in the DEVS platform. Use this when asked to add LLM functionality, create AI-powered features, or work with the LLM service.
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- 16 de janeiro de 2026 às 23:22
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- llm-service-integration
- description
- Guide for integrating with LLM providers in the DEVS platform. Use this when asked to add LLM functionality, create AI-powered features, or work with the LLM service.
# LLM Service Integration for DEVS
When working with LLM functionality in the DEVS platform, always use the abstracted LLM service layer. Never call provider APIs directly.
## Core Principle
DEVS is provider-agnostic. The `LLMService` in `src/lib/llm/` abstracts multiple providers:
- OpenAI
- Anthropic (Claude)
- Google Gemini
- Mistral
- Ollama (local)
- Custom endpoints
## Basic Usage
```typescript
import { LLMService } from '@/lib/llm'
import type { Message } from '@/types'
async function generateResponse(userPrompt: string): Promise<string> {
const messages: Message[] = [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: userPrompt },
]
const response = await LLMService.chat(messages, {
temperature: 0.7,
maxTokens: 2000,
})
return response.content
}
```
## Message Types
```typescript
interface Message {
role: 'system' | 'user' | 'assistant'
content: string
name?: string // For multi-agent conversations
}
```
## Configuration Options
```typescript
interface LLMConfig {
temperature?: number // 0-1, default 0.7
maxTokens?: number // Max response tokens
model?: string // Override default model
stream?: boolean // Enable streaming
topP?: number // Nucleus sampling
frequencyPenalty?: number // Reduce repetition
presencePenalty?: number // Encourage new topics
}
```
## Streaming Responses
For real-time UI updates:
```typescript
import { LLMService } from '@/lib/llm'
async function streamResponse(
messages: Message[],
onChunk: (text: string) => void,
): Promise<string> {
let fullResponse = ''
await LLMService.streamChat(messages, {
onChunk: (chunk) => {
fullResponse += chunk
onChunk(chunk)
},
temperature: 0.7,
})
return fullResponse
}
```
## Error Handling
Always wrap LLM calls in try/catch:
```typescript
import { LLMService } from '@/lib/llm'
import { toast } from '@/lib/toast'
async function safeGenerate(prompt: string): Promise<string | null> {
try {
const response = await LLMService.chat([{ role: 'user', content: prompt }])
return response.content
} catch (error) {
console.error('LLM call failed:', error)
toast.error('Failed to generate response. Please try again.')
return null
}
}
```
## Agent Context Integration
When generating responses for agents, include their instructions:
```typescript
import { LLMService } from '@/lib/llm'
import { getAgentById } from '@/stores/agentStore'
import type { Agent, Message } from '@/types'
async function generateAgentResponse(
agentId: string,
conversationHistory: Message[],
userMessage: string,
): Promise<string> {
const agent = getAgentById(agentId)
if (!agent) throw new Error('Agent not found')
const messages: Message[] = [
{
role: 'system',
content: buildAgentSystemPrompt(agent),
},
...conversationHistory,
{ role: 'user', content: userMessage },
]
const response = await LLMService.chat(messages, {
temperature: agent.temperature ?? 0.7,
})
return response.content
}
function buildAgentSystemPrompt(agent: Agent): string {
return `You are ${agent.name}, ${agent.role}.
${agent.instructions}
Always stay in character and respond according to your role and expertise.`
}
```
## Structured Output (JSON)
For extracting structured data:
```typescript
import { LLMService } from '@/lib/llm'
interface ExtractedData {
title: string
summary: string
keywords: string[]
}
async function extractStructuredData(text: string): Promise<ExtractedData> {
const response = await LLMService.chat(
[
{
role: 'system',
content: `Extract information from the text and return as JSON:
{
"title": "string",
"summary": "string",
"keywords": ["string"]
}
Return ONLY valid JSON, no other text.`,
},
{ role: 'user', content: text },
],
{
temperature: 0.3, // Lower for more deterministic output
},
)
// Parse with error handling
try {
return JSON.parse(response.content)
} catch {
// Attempt to extract JSON from response
const jsonMatch = response.content.match(/\{[\s\S]*\}/)
if (jsonMatch) {
return JSON.parse(jsonMatch[0])
}
throw new Error('Failed to parse LLM response as JSON')
}
}
```
## Cost & Usage Tracking
The platform tracks LLM usage via the traces feature. Include metadata when relevant:
```typescript
import { LLMService } from '@/lib/llm'
const response = await LLMService.chat(messages, {
metadata: {
feature: 'task-analysis',
agentId: agent.id,
taskId: task.id,
},
})
```
## Testing LLM Integration
Mock the LLM service in tests:
```typescript
import { describe, it, expect, vi, beforeEach } from 'vitest'
import { LLMService } from '@/lib/llm'
vi.mock('@/lib/llm', () => ({
LLMService: {
chat: vi.fn(),
streamChat: vi.fn(),
},
}))
describe('MyFeature', () => {
beforeEach(() => {
vi.clearAllMocks()
})
it('should process LLM response correctly', async () => {
vi.mocked(LLMService.chat).mockResolvedValue({
content: '{"result": "success"}',
usage: { promptTokens: 100, completionTokens: 50 },
})
const result = await myFunction()
expect(result).toEqual({ result: 'success' })
})
})
```
## Common Patterns
### Task Analysis
See `src/lib/task-analyzer.ts` for breaking down complex prompts.
### Conversation Title Generation
See `src/lib/conversation-title-generator.ts` for generating chat titles.
### Memory Learning
See `src/lib/memory-learning-service.ts` for extracting learnable facts from conversations.
### Requirement Validation
See `src/lib/requirement-validator.ts` for validating task deliverables.
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