Claude-code-plugins mistral-migration-deep-dive
install
source · Clone the upstream repo
git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/jeremylongshore/claude-code-plugins-plus-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/saas-packs/mistral-pack/skills/mistral-migration-deep-dive" ~/.claude/skills/jeremylongshore-claude-code-plugins-mistral-migration-deep-dive && rm -rf "$T"
manifest:
plugins/saas-packs/mistral-pack/skills/mistral-migration-deep-dive/SKILL.mdsource content
Mistral AI Migration Deep Dive
Current State
!
npm list openai @anthropic-ai/sdk @mistralai/mistralai 2>/dev/null | grep -E "openai|anthropic|mistral" || echo 'No AI SDKs found'
Overview
Comprehensive migration guide from OpenAI or Anthropic to Mistral AI using the adapter pattern with feature-flag controlled rollout. Covers model mapping, API differences, prompt adjustments, validation testing, and rollback procedures.
Prerequisites
- Current AI integration documented
- Mistral AI SDK installed (
)@mistralai/mistralai - Feature flag infrastructure (env vars or LaunchDarkly)
- Rollback plan tested
Migration Complexity
| Migration | Effort | Duration | Risk |
|---|---|---|---|
| Fresh install (no existing AI) | Low | Days | Low |
| OpenAI to Mistral | Medium | 1-2 weeks | Medium |
| Anthropic to Mistral | Medium | 1-2 weeks | Medium |
| Multi-provider to Mistral | High | 2-4 weeks | Medium |
Instructions
Step 1: Assessment — Find All AI Touchpoints
set -euo pipefail # Count integration points echo "=== AI Integration Assessment ===" echo "OpenAI imports: $(grep -r "from 'openai'" src/ --include='*.ts' -l 2>/dev/null | wc -l)" echo "Anthropic imports: $(grep -r "from '@anthropic'" src/ --include='*.ts' -l 2>/dev/null | wc -l)" echo "Chat completions: $(grep -r "chat\.completions\|messages\.create" src/ --include='*.ts' -c 2>/dev/null | wc -l)" echo "Embeddings: $(grep -r "embeddings\.create" src/ --include='*.ts' -c 2>/dev/null | wc -l)" echo "Streaming: $(grep -r "stream\|for await" src/ --include='*.ts' -c 2>/dev/null | wc -l)"
Step 2: Model Mapping
| OpenAI | Anthropic | Mistral | Notes |
|---|---|---|---|
| gpt-4o | claude-3-5-sonnet | | Complex reasoning |
| gpt-4o-mini | claude-3-5-haiku | | Fast, cheap |
| gpt-3.5-turbo | — | | General purpose |
| text-embedding-3-small | — | | 1024 dims (vs 1536) |
| — | — | | Code-specialized |
| gpt-4-vision | claude-3-5-sonnet | | Vision + text |
Step 3: Provider-Agnostic Adapter
// adapters/types.ts export interface Message { role: 'system' | 'user' | 'assistant' | 'tool'; content: string; } export interface ChatOptions { model?: string; temperature?: number; maxTokens?: number; stream?: boolean; } export interface ChatResponse { content: string; usage: { inputTokens: number; outputTokens: number }; model: string; } export interface AIAdapter { chat(messages: Message[], options?: ChatOptions): Promise<ChatResponse>; chatStream(messages: Message[], options?: ChatOptions): AsyncGenerator<string>; embed(texts: string[]): Promise<number[][]>; }
Step 4: Mistral Adapter
// adapters/mistral.adapter.ts import { Mistral } from '@mistralai/mistralai'; import type { AIAdapter, Message, ChatOptions, ChatResponse } from './types.js'; export class MistralAdapter implements AIAdapter { private client: Mistral; private defaultModel: string; constructor(apiKey: string, defaultModel = 'mistral-small-latest') { this.client = new Mistral({ apiKey }); this.defaultModel = defaultModel; } async chat(messages: Message[], options?: ChatOptions): Promise<ChatResponse> { const response = await this.client.chat.complete({ model: options?.model ?? this.defaultModel, messages, temperature: options?.temperature, maxTokens: options?.maxTokens, }); return { content: response.choices?.[0]?.message?.content ?? '', usage: { inputTokens: response.usage?.promptTokens ?? 0, outputTokens: response.usage?.completionTokens ?? 0, }, model: response.model ?? this.defaultModel, }; } async *chatStream(messages: Message[], options?: ChatOptions): AsyncGenerator<string> { const stream = await this.client.chat.stream({ model: options?.model ?? this.defaultModel, messages, temperature: options?.temperature, maxTokens: options?.maxTokens, }); for await (const event of stream) { const content = event.data?.choices?.[0]?.delta?.content; if (content) yield content; } } async embed(texts: string[]): Promise<number[][]> { const response = await this.client.embeddings.create({ model: 'mistral-embed', inputs: texts, }); return response.data.map(d => d.embedding); } }
Step 5: Feature-Flag Controlled Rollout
// adapters/factory.ts import { MistralAdapter } from './mistral.adapter.js'; import { OpenAIAdapter } from './openai.adapter.js'; export function createAdapter(): AIAdapter { const rolloutPercent = parseInt(process.env.MISTRAL_ROLLOUT_PERCENT ?? '0'); const useMistral = Math.random() * 100 < rolloutPercent; if (useMistral) { console.log('[AI] Using Mistral'); return new MistralAdapter(process.env.MISTRAL_API_KEY!); } console.log('[AI] Using OpenAI (legacy)'); return new OpenAIAdapter(process.env.OPENAI_API_KEY!); }
Step 6: Gradual Rollout Plan
| Phase | Rollout % | Duration | Criteria to Advance |
|---|---|---|---|
| 0. Validation | 0% | 1-2 days | A/B tests pass |
| 1. Canary | 5% | 2-3 days | Error rate < 1%, latency OK |
| 2. Partial | 25% | 3-5 days | Quality metrics match |
| 3. Majority | 50% | 5-7 days | Cost reduction confirmed |
| 4. Full | 100% | — | Remove old adapter code |
# Advance rollout export MISTRAL_ROLLOUT_PERCENT=5 # Canary export MISTRAL_ROLLOUT_PERCENT=25 # Partial export MISTRAL_ROLLOUT_PERCENT=100 # Full migration export MISTRAL_ROLLOUT_PERCENT=0 # Emergency rollback
Step 7: A/B Validation Testing
async function validateMigration(adapter1: AIAdapter, adapter2: AIAdapter) { const testPrompts = [ 'Summarize: TypeScript adds static typing to JavaScript.', 'Classify: "The app crashes on login" — bug, feature, or question?', 'What is 2+2?', ]; for (const prompt of testPrompts) { const messages = [{ role: 'user' as const, content: prompt }]; const [r1, r2] = await Promise.all([ adapter1.chat(messages, { temperature: 0 }), adapter2.chat(messages, { temperature: 0 }), ]); console.log(`Prompt: ${prompt.slice(0, 50)}...`); console.log(` Provider 1: ${r1.content.slice(0, 100)} (${r1.usage.outputTokens} tokens)`); console.log(` Provider 2: ${r2.content.slice(0, 100)} (${r2.usage.outputTokens} tokens)`); console.log(); } }
Key API Differences
| Feature | OpenAI | Mistral |
|---|---|---|
| SDK import | | |
| Chat method | | |
| Stream events | | |
| Embeddings | | (same) |
| Tool calling | Identical JSON Schema format | Identical JSON Schema format |
| JSON mode | | |
| Vision | Base64 in content array | Same approach with models |
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Different output quality | Model differences | Adjust prompts, tune temperature |
| Embedding dimension mismatch | 1536 vs 1024 | Re-embed all vectors, update vector DB config |
| Missing feature | Not supported by Mistral | Implement fallback in adapter |
| Cost increase | Token counting differs | Monitor and optimize prompts |
Resources
Output
- Integration assessment with effort estimation
- Provider-agnostic adapter interface
- Mistral adapter implementation
- Feature-flag controlled gradual rollout
- Model mapping and API difference reference
- A/B validation test suite
- Rollback procedure (set MISTRAL_ROLLOUT_PERCENT=0)