Monitor changes the web from something you search into something that talks back.
Proactive agents that know when something changed before you even ask.
What are the craziest use cases you’d build with this? 👇
Today, we’re announcing the general availability of the Parallel Monitor API.
The web is shifting from pull to push, and agents are coming online. This release marks a major step towards a more proactive model, where the web pushes updates directly to your background agent.
Monitor now includes:
- Lite and Base processors
- Event streams and snapshots
- Rich attribution (Basis) on every event
- Advanced domain filtering
- Interactions for persistent follow-on research
Claude Opus 5.5 with Parallel Fast is now #1 on the Vals Web Search Index, scoring 57.5% overall against 53.8% for the same model with native search a gain of 3.8pp.
Parallel Decisions is now on OpenRouter. Parallel Decisions delivers the highest accuracy on web-grounded decisions for System One models like Jev. Get web-grounded answers 13× faster at 1/16 the cost of OpenAI’s GPT Luna with native web search.
Ask questions about the live web and get structured answers with probabilities and citations in under a second.
Parallel MCP 2.0 is live.
It's the most powerful web search and retrieval MCP, launching today with four tools for agentic search:
- Web search
- Page extract
- Web-grounded decisions
- Deep research
Parallel MCP is free and keyless by default, with auth for advanced features and higher limits.
It's also the most configurable web data MCP, designed for admins to define web research standards across an entire fleet of agents.
Add it to any MCP client: https://t.co/sla9L3nEib
Learn more: https://t.co/5C2CHSj6IV
Introducing Parallel in Slack.
Tell Parallel what to monitor on the web, and the agent posts in the channel when there's a hit.
Or tag it with a question to get a cited answer based on the context in your thread.
Available today on the Slack Marketplace.
Introducing Parallel in Slack.
Tell Parallel what to monitor on the web, and the agent posts in the channel when there's a hit.
Or tag it with a question to get a cited answer based on the context in your thread.
Available today on the Slack Marketplace.
Introducing the Parallel Decisions API: the first web-grounded decisions engine.
Decision models like Jev can answer in an instant, but they lack the recency of current news. Now you can get both in a single call.
Decisions API is live today at $0.001 per request.
Try it with 5,000 free requests: https://t.co/tu4CidUdxV
Here are some examples we've been experimenting with:
- Fact-check content (@Grok is this true?)
- Refresh stale banking records
- Classify companies and leads with fresh signals
- Backtest trading strategies
- Track competitor e-commerce inventory
Typesafe's CEO @CompleteSkeptic does not trust public benchmarks.
Neither do I, Diogo.
So @p0 and I wrote a blog evaluating 8 decision models on real company training datasets we have personally audited and iterated on.
For what it's worth though, Jev is still on top 👑
https://t.co/Qd2g0Hwp4V
Great to have rigorous independent benchmarking for search that measures end-to-end quality, cost, latency for agents.
Parallel search is
(1) Highest quality
(2) Quality vs. cost Pareto frontier
(3) Quality vs. latency Pareto frontier
Today, @ArtificialAnlys announced their new Search Index, ranking leading web search APIs across quality, speed, and cost.
On an equal-weighted mean of DeepSearchQA F1, BrowseComp accuracy, and AA-Omniscience accuracy, Parallel is ranked #1.
How to monitor the web and research what comes back in 300 lines of code.
I want to buy some cheap products on Amazon, but I want to grab them when prices drop.
I'm using Parallel @p0 here, which is a really easy way to ground your agentic code.
Here is how this works:
1. Set up a monitoring event with Parallel for the products
2. If there's a hit, I get a webhook event with the details
3. My script immediately kicks off a Deep Research task
4. The final result tells me whether the product is worth buying
I probably spent 1 hour building all of this with Claude Code + the Parallel Agent Skills. I'm linking to the GitHub repo below.
Just think about what you could do with this. Here is an easy example (I also set this up):
• Monitor news for public tech companies
• Analyze the news + price
• Assess whether it's worth investing
You can do this, put it on autopilot, and get an email anytime there's something worth your time.
Here is the video I recorded.
We first launched Monitor last November. Since then, the reception has far exceeded our expectations.
Developers are monitoring everything from drug commercialization pipelines to government RFPs to GTM signals.