Multi Model Agent FAQ & Answers

Use semantic router with task-specific routes: from semantic_router import Route, RouteLayer; from langchain_openai import ChatOpenAI; from langchain_anthropic import ChatAnthropic; routes = [Route(name='code', utterances=['write function', 'debug code', 'implement algorithm']), Route(name='creative', utterances=['write story', 'compose email', 'draft article'])]; router = RouteLayer(routes=routes); route = router(query).name; model = ChatOpenAI(model='gpt-4') if route == 'code' else ChatAnthropic(model='claude-3-7-sonnet-20250219'). Alternative: use LLM-assisted routing where a lightweight model (GPT-3.5) classifies task type, then routes to specialist models. Benefits: 10x cost reduction (GPT-3.5 for simple, GPT-4 for complex), improved quality (specialized models). Production: create reference prompts for each task type, use embeddings for semantic matching. Monitor routing accuracy - adjust routes based on performance metrics.

Use layered architecture with proposers + aggregator: proposers = [ChatOpenAI(model='gpt-4o'), ChatAnthropic(model='claude-3-7-sonnet'), ChatGoogleGenerativeAI(model='gemini-2.0-flash')]; responses = [model.invoke(query) for model in proposers]; aggregator = ChatAnthropic(model='claude-3-7-sonnet'); final = aggregator.invoke(f'Synthesize these responses: {responses}'). For multi-layer MoA: layer1 = [proposer1, proposer2, proposer3]; layer1_out = [m.invoke(query) for m in layer1]; layer2 = [m.invoke(layer1_out) for m in layer1]; final = aggregator.invoke(layer2). MoA achieves 65.1% on AlpacaEval using only open-source models. Benefits: higher quality than single model, diverse perspectives, reduced bias. Production: use async.gather for parallel proposer calls, cache intermediate results. Together.ai provides 50-line reference implementation. Works best when proposers have different strengths.

All LangChain chat models share same interface: from langchain_openai import ChatOpenAI; from langchain_anthropic import ChatAnthropic; from langchain_google_genai import ChatGoogleGenerativeAI; models = {'openai': ChatOpenAI(model='gpt-4o'), 'anthropic': ChatAnthropic(model='claude-3-7-sonnet-20250219'), 'google': ChatGoogleGenerativeAI(model='gemini-2.0-flash-exp')}; model = models[provider]; response = model.invoke(messages). Dynamic switching with ConfigurableField: model = ChatOpenAI(model='gpt-4').configurable_alternatives(ConfigurableField(id='llm'), default_key='openai', anthropic=ChatAnthropic(model='claude-3-7-sonnet')); result = model.with_config(configurable={'llm': 'anthropic'}).invoke(input). All support: invoke(), ainvoke(), stream(), astream(), batch(), abatch(). Benefits: provider flexibility, A/B testing, cost optimization. Use environment variables for API keys. Switch based on task, cost, latency, or availability.

Use with_fallbacks() for automatic model fallback: from langchain_openai import ChatOpenAI; from langchain_anthropic import ChatAnthropic; primary = ChatOpenAI(model='gpt-4o'); backup = ChatAnthropic(model='claude-3-7-sonnet-20250219'); llm = primary.with_fallbacks([backup]). Multiple fallbacks in order: llm = gpt4.with_fallbacks([claude, gemini, gpt35]). Combine with retries: llm = gpt4.with_retry(stop_after_attempt=2).with_fallbacks([claude]). Pass exception context to fallbacks: llm.with_fallbacks([backup], exception_key='error'). Use cases: API outages, rate limits, regional availability. Benefits: 99.9% uptime, automatic failover, no code changes needed. Production: monitor fallback frequency, alert on primary failures, test fallback models regularly. Fallbacks tried sequentially until success or all fail. Works with any Runnable - chains, retrievers, tools.

Classify query complexity then route to appropriate model: from langchain_openai import ChatOpenAI; def classify_complexity(query): classifier = ChatOpenAI(model='gpt-3.5-turbo'); prompt = f'Is this complex (yes/no): {query}'; return classifier.invoke(prompt).content.lower() == 'yes'; complexity = classify_complexity(user_query); model = ChatOpenAI(model='gpt-4o') if complexity else ChatOpenAI(model='gpt-3.5-turbo'); response = model.invoke(user_query). Alternative: use query length, keywords, or embeddings for complexity scoring. Cost savings: Claude 3 Haiku ($0.00025/1K tokens) vs Claude 3.5 Sonnet ($0.003/1K tokens) = 12x difference. Best practice: route 80% to cheap model, 20% to expensive - achieves 90% quality at 10% cost. Monitor: accuracy per model, routing precision, cost per query. Use semantic router or Portkey for production routing. GPT-4 Turbo for reasoning/code, GPT-3.5 for summaries/chat.

Query multiple models and vote on answers: from langchain_openai import ChatOpenAI; from langchain_anthropic import ChatAnthropic; from langchain_google_genai import ChatGoogleGenerativeAI; from collections import Counter; models = [ChatOpenAI(model='gpt-4o'), ChatAnthropic(model='claude-3-7-sonnet-20250219'), ChatGoogleGenerativeAI(model='gemini-2.0-flash-exp')]; responses = [model.invoke(query).content for model in models]; votes = Counter(responses); consensus = votes.most_common(1)[0][0]. For numeric answers: use median or mean. For structured decisions: majority_vote = max(set(responses), key=responses.count). Use cases: financial decisions, medical diagnosis, legal analysis. Benefits: reduced hallucinations, higher accuracy, bias mitigation. Research shows 5+ smaller models can match larger model quality via voting. Production: use odd number of models (3, 5, 7), async.gather for parallel calls, cache results. Full consensus for irreversible actions, simple majority for low-stakes tasks.

Model selection by strength: GPT-4o/GPT-4.1 - best for code generation, complex reasoning, function calling, mathematical proofs, structured outputs. Claude 3.7/4 Sonnet - best for long-context analysis (200K tokens), creative writing, nuanced instructions, extended thinking, citations. Gemini 2.0 Flash - best for multimodal tasks (video/audio), real-time applications, low latency, cost efficiency, agentic workflows. Specific use cases: GPT-4 for debugging/refactoring, Claude for document analysis/content creation, Gemini for image understanding/live conversations. Performance: GPT-4.1 leads on MMLU (91.8%), Claude 3.7 on context window, Gemini 2.0 on speed. Cost: Gemini Flash cheapest, Claude Haiku mid-range, GPT-4 most expensive. Production: benchmark on your tasks, A/B test, use routing for cost optimization. 2025 trend: hybrid agents using multiple models for different subtasks.