Today we're announcing that hybrid agentic inference is coming to Perplexity Computer. Computer can split tasks between a local model running on your machine and frontier models in the cloud. This keeps private data on your device and maximizes token efficiency. Coming soon.

🌐 @perplexity_ai is now integrated with @Haystack_AI! - `PerplexityWebSearch` to fetch fresh web results directly into your pipelines - `PerplexityChatGenerator` to power agent conversations with Perplexity's OpenAI-compatible LLM API - `PerplexityTextEmbedder` and `PerplexityDocumentEmbedder` for semantic embeddings The web search component is especially useful when your agents need real-time information beyond their training data. Just instantiate it with your API key, set `top_k` results, and pass your query. You get back both documents and source links in a single call. Perplexity's API combines language models with live web access, making it a strong choice for building knowledge-intensive applications that need current information. 🐍 pip install perplexity-haystack 🧩 haystack.deepset.ai/integrat…… 📖 docs.perplexity.ai

MiniMax Agent now uses @perplexity_ai Search. We benchmarked 3 AI-native search providers across 700 agent tasks. Perplexity delivered the best combination of answer quality and snippet density: more useful evidence per token, less irrelevant context, and lower end-to-end cost. Compared with Serper, our previous default: ⚡Tool calls per task: 17.8 vs 32.6 (45% reduction) 💰Token usage: 94.6M vs. 162.3M (42% reduction) 🔍Pass rate: 2% increase Total cost: 27% decrease in total cost savings In agent workflows, search is not a one-shot lookup. It is a loop. Better snippets mean better grounding. Better grounding means fewer searches, less context, better answers, and lower cost. One good search can save 14 bad ones. Now shipping in MiniMax Agent. agent.minimax.io/

We've productionized query-aware compression for faster, cleaner, more-accurate search. Better context is better than more context. Our system cuts context tokens up to 70% while improving answer quality.

NationGraph uses Perplexity APIs to map public-sector data across government sites, PDFs, filings, and meeting notes. So far, they’ve run 500,000 queries and saved users 10,000 hours of research. Read more: perplexity.ai/api-platform/c…

We’ve developed our own inference engine Runtime-Optimized Serving Engine (ROSE) to serve models ranging from embeddings to trillion-parameter LLMs. With CuTeDSL integrated into our inference engine, Perplexity can build the specialized GPU kernels faster to bring models up to peak performance on NVIDIA Hopper and Blackwell GPUs.

Perplexity is now a native node in @n8n_io. Add real-time web search, AI agents, and embeddings to your automation workflows with zero code. You get full API coverage, including Agent API, Search API, Embeddings API, and Sonar. Get started: docs.perplexity.ai/docs/gett…

Perplexity now powers Samsung’s Browsing Assist in the Samsung Browser on Galaxy Android and Windows. Read more: perplexity.ai/hub/blog/perpl…