Perplexity Developers

@jeffgrimes9 super cool! thanks perplexity's very own finance guru!!

Dashboard with results: sp500-ai-agents.pplx.app Recap of how the API tackled this: prompt, the row schema, and the actual code: sp500-ai-agents.staging.pplx.app/how.html Docs for the wide-research preset: docs.perplexity.ai/docs/agent-api…

I used the new wide-research preset in the Perplexity API to check the most recent earnings call for every company in the S&P 500 for mentions of "AI agents", and pull the corresponding YoY revenue growth for each. The agent pulled historical financials and earnings call transcripts for 500 companies, with every output row cited. Computer outputted the results to a dashboard, and deployed it publicly. Results: 66 companies mentioned AI agents, with median YoY growth +12.7% for that cohort, compared to +8.1% for the rest. Good example of how the Perplexity API's wide-research preset can tackle an in-depth task for an n=500 use case.

@beffjezos indeed, indeed... i n d e e d

W I D E research is the way

@farango77 good question! it's absolutely for non-coding agents to use too. Search as Code is just the architecture we use to run search inside our Agent API. As a user, you don't need to think about all that! Just call our wide research preset + your agent gets wide/deep research results out of the box :)

@perplexitydevs So this isn’t something that non-coding agent use is gonna be used for right now?

Narrow search is essentially solved. That's why simple browse/search benchmarks are so saturated. The frontier is wide research: find every qualifying result and back each one with evidence. Today we launched WANDR, a 500-task benchmark built on real knowledge work. It's difficult even for today's most powerful models. Perplexity Agent API's Search as Code architecture excels on WANDR because it allows the model to design the research once and then deterministically execute it at scale without overwhelming the model’s context. Using wide research in your existing agent research workflows? Try out our wide-research preset in Agent API: docs.perplexity.ai/docs/agent-api… WANDR Research Article: research.perplexity.ai/articles/wandr… Github Repo: github.com/perplexityai/w…

@morganlinton Excited for you to try it out @morganlinton!

@perplexitydevs Super interesting!

@LloydPasha ty! we think so too! let us know if you end up trying :)

@perplexitydevs This is cool

The GPT-5.6 model family is now available on Perplexity's Agent API. Every gain at the frontier compounds through Perplexity's API stack. The GPT-5.6 family sits on the Pareto frontier of our agentic research evals: higher accuracy at lower cost. As such, we've updated our Agent API Presets. xHigh, High, and Medium (our pre-configured presets for long-running research workloads) are now better, faster and cheaper on the GPT-5.6 model family. Check it out today: docs.perplexity.ai/docs/agent-api…

This agent drafts a cited VC investment memo in ~90 seconds for $0.40. 1️⃣ 4 LangGraph nodes powered by the @Perplexity_AI Agent API research financials, product, and market in parallel 2️⃣ A tool-less synthesizer writes the memo from only what they found Deep dive: langchain.com/blog/build-an-…

Perplexity's Agent API presets have been refreshed: faster, smarter, cheaper while maintaining full computer and usage transparency. Most agentic API endpoints charge a fixed per-request rate in the name of simplicity. This masks what's actually happening under the hood. Providers can use as little computer as possible on your request and you pay the same (regardless of what actually ran). On Agent API, it's simple and transparent: > Pick preset mode: fast / low / medium / high depending on task complexity > Pay only for the inference and tools you actually use (direct model provider pricing) > No manual tuning + no model selection required Try it for yourself: docs.perplexity.ai/docs/agent-api…

GLM-5.2 is available in Perplexity's Agent API. Just tested it, and it's powerful when paired with the Search SDK inside a sandbox. - Spin up a sandbox environment - Call the web search tool (people search, finance search, etc. also work) - Pretty good for wide & deep research

@Zai_org's flagship model, GLM-5.2, is now available in Perplexity's Agent API. GLM-5.2 is one of the strongest open-source models for long-horizon coding and agentic workflows. It shines in Agent API, making particularly effective use of our Search as Code architecture. Combine frontier reasoning with real-time programmatic search with just one API call. OpenAI-compatible interface and first-party pricing with no markup. Get started: docs.perplexity.ai/docs/agent-api…

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 Agent API docs: docs.perplexity.ai/docs/agent-api…

Search as Code is now available in the Perplexity Agent API. Search as Code matches or beats every competing system across all five leading benchmarks and sets a new cost-performance frontier.

🌐 @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/integrations/p… 📖 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.