October 6, 2025

# Building a real-time streaming task manager with Parallel

Build a task streaming playground that showcases Parallel's Task API with real-time Server-Sent Events (SSE).

Tags:Developers
Reading time: 4 min
GithubTry the App
Building a real-time streaming task manager with Parallel

This guide demonstrates how to build a complete task streaming playground that showcases Parallel's Task API[Parallel's Task API](https://docs.parallel.ai/task-api/task-quickstart) with real-time Server-Sent Events (SSE). By the end, you'll have a full-featured application that creates tasks, streams their execution progress, and displays results as they arrive. This can be helpful for developers building with the Task API, demonstrating how to recreate the user interfaces in our Playground[Playground](https://platform.parallel.ai).

Complete demo available at: https://oss.parallel.ai/tasks-sse/[https://oss.parallel.ai/tasks-sse/](https://oss.parallel.ai/tasks-sse/)

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/41b740035c5fb22bb1db114cbb255a8f3c02e44f-3924x2424.png)

## Key Features

  • - Task Manager for All Processors: From lite ($5/1K) to ultra8x ($2400/1K)
  • - Output Schemas: Text, JSON and Auto Schema
  • - Source Policy: Domain inclusion and exclusion
  • - Webhooks: HTTP notifications on task completion
  • - Streaming events view for each Task in the Task Manager
  • - OAuth flow for easy testing

## The Architecture

The task streaming playground we're building includes:

  • - OAuth2 authentication with Parallel's identity provider
  • - A comprehensive task creation form supporting all processor types and output schemas
  • - Real-time task progress streaming with Server-Sent Events
  • - Task history management with persistent localStorage
  • - Auto-reconnection for resilient streaming connections
  • - Rich event visualization with progress indicators and final outputs

## Our technology stack

  • - Parallel Task API for task execution
  • - Parallel OAuth Provider for secure authentication
  • - Server-Sent Events[Server-Sent Events](https://docs.parallel.ai/task-api/task-sse) for real-time streaming
  • - Cloudflare Workers[Cloudflare Workers](https://workers.cloudflare.com/) for deployment and CORS proxying
  • - Pure HTML/JavaScript/CSS for maximum compatibility

## Why this architecture

### Stateless Streaming Design

The key insight behind this implementation is that you don't need to maintain any backend state during streaming. The Parallel Task API's SSE endpoint provides the complete current state every time you connect, including:

  • - All previous events that occurred before connecting
  • - The latest progress statistics
  • - Current task status and metadata
  • - Final outputs when tasks complete

This stateless design means your backend can be incredibly simple - just a CORS proxy. All the complexity lives in the well-tested Parallel infrastructure.

### OAuth2 with PKCE Security

For production-ready authentication, we implement the complete OAuth2 flow with PKCE (Proof Key for Code Exchange):

  1. Dynamic Client Registration: Register OAuth client on-demand
  2. PKCE Challenge: Generate cryptographically secure code challenge
  3. Authorization Redirect: Send user to Parallel's OAuth server
  4. Token Exchange: Securely exchange authorization code for access token
  5. Persistent Storage: Store token in localStorage for session management

This provides enterprise-grade security without requiring pre-registration of OAuth clients.

Architecture diagram

Illustration demonstrating deep research API concepts, web search capabilities, or AI agent integration features
![](https://cdn.sanity.io/images/5hzduz3y/production/72fdd4e38f0a0717941be31929f27bbd3c7f8e9c-1600x1028.png)

## Implementation

### Real-Time Event Streaming

Server-Sent Events (SSE) provide the real-time updates for this Task manager that helps end-users understand the progress of the Parallel Task API. This is especially helpful for longer-running processors, like Pro and above. Unlike traditional polling approaches that repeatedly ask "are you done yet?", SSE creates a persistent connection that streams updates as they happen. However, implementing SSE correctly in the browser requires handling several complex challenges.

#### Why Manual SSE Implementation?

While browsers provide a built-in `EventSource` API for SSE, it has a critical limitation: **you cannot set custom headers**. Since Parallel's API requires authentication via the `x-api-key` header, we must implement SSE manually using the Fetch API and ReadableStreams.

SSE data arrives as a continuous stream of bytes, not discrete messages. Network packets can split messages in unpredictable ways:

Our implementation handles this with a streaming buffer pattern:

### Streaming buffer pattern
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const reader = response.body.getReader(); const decoder = new TextDecoder(); let buffer = ''; while (true) { const { done, value } = await reader.read(); if (done) break; // Add new data to buffer buffer += decoder.decode(value, { stream: true }); // Split into complete lines const lines = buffer.split('\n'); buffer = lines.pop(); // Keep incomplete line in buffer // Process complete lines for (const line of lines) { if (line.startsWith('data: ')) { const data = JSON.parse(line.substring(6)); handleEvent(data); } } }```
const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';
 
while (true) {
const { done, value } = await reader.read();
if (done) break;
 
// Add new data to buffer
buffer += decoder.decode(value, { stream: true });
// Split into complete lines
const lines = buffer.split('\n');
buffer = lines.pop(); // Keep incomplete line in buffer
// Process complete lines
for (const line of lines) {
if (line.startsWith('data: ')) {
const data = JSON.parse(line.substring(6));
handleEvent(data);
}
}
}
```

This pattern ensures we never lose data or attempt to parse incomplete JSON, regardless of how network packets arrive.

#### Resilient Connection Management

Task execution can take anywhere from seconds to 30+ minutes. Network connections inevitably drop during long operations, so robust reconnection logic is essential:

### Auto-retry logic for active tasks
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if (streamReconnectAttempts < MAX_RECONNECT_ATTEMPTS && currentTaskRun && ['queued', 'running'].includes(currentTaskRun.status)) { streamReconnectAttempts++; setTimeout(() => { console.log(`Reconnecting attempt ${streamReconnectAttempts}...`); startStream(); }, 2000); }```
if (streamReconnectAttempts < MAX_RECONNECT_ATTEMPTS &&
currentTaskRun && ['queued', 'running'].includes(currentTaskRun.status)) {
streamReconnectAttempts++;
setTimeout(() => {
console.log(`Reconnecting attempt ${streamReconnectAttempts}...`);
startStream();
}, 2000);
}
```

**Key resilience features:**

  • - Status-based reconnection: Only retry for tasks that might still be generating events
  • - Exponential backoff: 2-second delays prevent overwhelming the server
  • - Attempt limiting: Prevents infinite retry loops on permanent failures
  • - Stateless recovery: Each reconnection gets the complete current state

#### Understanding the Event Stream Format

Parallel's SSE stream follows the standard Server-Sent Events specification. Each event is prefixed with `data: ` and terminated with double newlines. The implementation strips the prefix and parses the JSON payload:

### Standard SEE specification
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for (const line of lines) { if (line.startsWith('data: ')) { try { const data = JSON.parse(line.substring(6)); // Remove 'data: ' prefix handleEvent(data); } catch (error) { console.error('Error parsing event data:', error, line); } } }```
for (const line of lines) {
if (line.startsWith('data: ')) {
try {
const data = JSON.parse(line.substring(6)); // Remove 'data: ' prefix
handleEvent(data);
} catch (error) {
console.error('Error parsing event data:', error, line);
}
}
}
 
```

#### Event Type Taxonomy

The stream delivers several categories of events, each serving a different purpose:

  • - `task_run.state`: Core lifecycle events (queued → running → completed/failed)
  • - `task_run.progress_stats`: Quantitative metrics (sources found, pages read, tokens used)
  • - `task_run.progress_msg.*`: Qualitative updates with timestamped reasoning steps
  • - `task_run.progress_msg.reasoning`: AI thought process
  • - `task_run.progress_msg.search`: Search query generation
  • - `task_run.progress_msg.analysis`: Content analysis steps
  • - `error`: Exception conditions with detailed error messages

This rich event taxonomy enables granular UI updates - progress bars for stats, reasoning displays for AI thinking, and error handling for failures.

#### Event Visualization and UI

The frontend renders different event types with appropriate styling:

### Displaying the events
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function handleEvent(data) { console.log(`event [${data.type}]`, data); let eventHtml = ''; let eventClass = ''; switch (data.type) { case 'task_run.state': eventClass = 'state'; eventHtml = ` <div class="event-type">TASK STATE</div> <div class="event-message"> Status: <span class="status ${data.run.status}">${data.run.status}</span> ${data.run.error ? `<br><span class="error">Error: ${data.run.error.message}</span>` : ''} </div> `; // Display final output if (data.output) { const outputHtml = formatOutput(data.output); eventHtml += `<div class="output-section"> <strong>Final Output:</strong> ${outputHtml} </div>`; } break; case 'task_run.progress_stats': eventClass = 'progress-stats'; const stats = data.source_stats; eventHtml = ` <div class="event-type">PROGRESS STATS</div> <div class="progress-stats"> <div class="stat">Sources Considered: ${stats.num_sources_considered || 'N/A'}</div> <div class="stat">Sources Read: ${stats.num_sources_read || 'N/A'}</div> </div> ${stats.sources_read_sample ? ` <div style="margin-top: 10px;"> <strong>Sample Sources:</strong> <ul style="margin-top: 5px; margin-left: 20px;"> ${stats.sources_read_sample.slice(0, 3).map(url => ```
function handleEvent(data) {
console.log(`event [${data.type}]`, data);
let eventHtml = '';
let eventClass = '';
 
switch (data.type) {
case 'task_run.state':
eventClass = 'state';
eventHtml = `
<div class="event-type">TASK STATE</div>
<div class="event-message">
Status: <span class="status ${data.run.status}">${data.run.status}</span>
${data.run.error ? `<br><span class="error">Error: ${data.run.error.message}</span>` : ''}
</div>
`;
 
// Display final output
if (data.output) {
const outputHtml = formatOutput(data.output);
eventHtml += `<div class="output-section">
<strong>Final Output:</strong>
${outputHtml}
</div>`;
}
break;
 
case 'task_run.progress_stats':
eventClass = 'progress-stats';
const stats = data.source_stats;
eventHtml = `
<div class="event-type">PROGRESS STATS</div>
<div class="progress-stats">
<div class="stat">Sources Considered: ${stats.num_sources_considered || 'N/A'}</div>
<div class="stat">Sources Read: ${stats.num_sources_read || 'N/A'}</div>
</div>
${stats.sources_read_sample ? `
<div style="margin-top: 10px;">
<strong>Sample Sources:</strong>
<ul style="margin-top: 5px; margin-left: 20px;">
${stats.sources_read_sample.slice(0, 3).map(url =>
```

### CORS Proxy Worker

A simple worker solves a fundamental web security challenge. Modern browsers implement the Same-Origin Policy, which prevents JavaScript running on `yourdomain.com` from making direct API calls to `api.parallel.ai`. This security feature protects users from malicious websites, but it also blocks legitimate applications from accessing external APIs.

Traditional solutions involve building a full backend API server that handles authentication, request validation, and response transformation. Instead, we use a much simpler proxy pattern that acts as a transparent middleman:

  • - Path Rewriting: `/tasks-sse/api/v1/tasks/runs` becomes `https://api.parallel.ai/v1/tasks/runs`
  • - Header Preservation: All original headers (including `x-api-key`) are forwarded
  • - CORS Headers: Added to all responses to enable browser access
  • - Static Serving: The same worker serves the HTML frontend
### CORS Proxy worker
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// @ts-ignore import indexHtml from "./index.html"; export default { async fetch(request, env, ctx) { const url = new URL(request.url); // Serve the frontend if (url.pathname === "/tasks-sse/") { return new Response(indexHtml, { headers: { "Content-Type": "text/html;charset=utf8" }, }); } // Proxy Parallel API requests if (url.pathname.startsWith("/tasks-sse/api/")) { // Remove /api prefix and forward to api.parallel.ai const targetPath = url.pathname.replace("/tasks-sse/api", ""); const targetUrl = `https://api.parallel.ai${targetPath}${url.search}`; // Clone the request but change the URL const modifiedRequest = new Request(targetUrl, { method: request.method, headers: request.headers, body: request.body, }); // Forward the request const response = await fetch(modifiedRequest); // Clone the response to modify headers const modifiedResponse = new Response(response.body, { status: response.status, statusText: response.statusText, headers: { ...Object.fromEntries(response.headers), // Add CORS headers "Access-Control-Allow-Origin": "*", "Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS", "Access-Control-Allow-Headers": "*", }, }); return modifiedResponse; } // Handle preflight requests if (request.method === "OPTIONS") { return new Response(null, { headers: { "Access-Control-Allow-Origin": "*", "Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS", "Access-Control-Allow-Headers": "*", }, }); } return new Response(null, { status: 302, headers: { Location: "/tasks-sse/" }, }); }, };```
// @ts-ignore
import indexHtml from "./index.html";
 
export default {
async fetch(request, env, ctx) {
const url = new URL(request.url);
 
// Serve the frontend
if (url.pathname === "/tasks-sse/") {
return new Response(indexHtml, {
headers: { "Content-Type": "text/html;charset=utf8" },
});
}
 
// Proxy Parallel API requests
if (url.pathname.startsWith("/tasks-sse/api/")) {
// Remove /api prefix and forward to api.parallel.ai
const targetPath = url.pathname.replace("/tasks-sse/api", "");
const targetUrl = `https://api.parallel.ai${targetPath}${url.search}`;
 
// Clone the request but change the URL
const modifiedRequest = new Request(targetUrl, {
method: request.method,
headers: request.headers,
body: request.body,
});
 
// Forward the request
const response = await fetch(modifiedRequest);
 
// Clone the response to modify headers
const modifiedResponse = new Response(response.body, {
status: response.status,
statusText: response.statusText,
headers: {
...Object.fromEntries(response.headers),
// Add CORS headers
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS",
"Access-Control-Allow-Headers": "*",
},
});
 
return modifiedResponse;
}
 
// Handle preflight requests
if (request.method === "OPTIONS") {
return new Response(null, {
headers: {
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS",
"Access-Control-Allow-Headers": "*",
},
});
}
 
return new Response(null, {
status: 302,
headers: { Location: "/tasks-sse/" },
});
},
};
```

### OAuth Authentication

The OAuth flow implementation handles dynamic client registration, as well as the complete PKCE security protocol:

### OAuth implementation
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async function startOAuth() { try { // Register client dynamically const reg = await fetch("https://platform.parallel.ai/getKeys/register", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ redirect_uris: [window.location.origin + window.location.pathname] }), }); const { client_id } = await reg.json(); // Generate PKCE challenge const cv = btoa(crypto.getRandomValues(new Uint8Array(32))).replace( /[+/=]/g, (m) => ({ "+": "-", "/": "_", "=": "" }[m]) ); localStorage.setItem('code_verifier', cv); const cc = btoa( String.fromCharCode( ...new Uint8Array( await crypto.subtle.digest( "SHA-256", new TextEncoder().encode(cv) ) ) ) ).replace(/[+/=]/g, (m) => ({ "+": "-", "/": "_", "=": "" }[m])); // Redirect to OAuth server const url = new URL("https://platform.parallel.ai/getKeys/authorize"); Object.entries({ client_id, redirect_uri: window.location.origin + window.location.pathname, response_type: "code", scope: "api", code_challenge: cc, code_challenge_method: "S256", state: Math.random().toString(36).substring(7) }).forEach(([k, v]) => url.searchParams.set(k, v)); window.location.href = url; } catch (error) { alert('OAuth error: ' + error.message); } }```
async function startOAuth() {
try {
// Register client dynamically
const reg = await fetch("https://platform.parallel.ai/getKeys/register", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
redirect_uris: [window.location.origin + window.location.pathname]
}),
});
 
const { client_id } = await reg.json();
 
// Generate PKCE challenge
const cv = btoa(crypto.getRandomValues(new Uint8Array(32))).replace(
/[+/=]/g, (m) => ({ "+": "-", "/": "_", "=": "" }[m])
);
localStorage.setItem('code_verifier', cv);
 
const cc = btoa(
String.fromCharCode(
...new Uint8Array(
await crypto.subtle.digest(
"SHA-256",
new TextEncoder().encode(cv)
)
)
)
).replace(/[+/=]/g, (m) => ({ "+": "-", "/": "_", "=": "" }[m]));
 
// Redirect to OAuth server
const url = new URL("https://platform.parallel.ai/getKeys/authorize");
Object.entries({
client_id,
redirect_uri: window.location.origin + window.location.pathname,
response_type: "code",
scope: "api",
code_challenge: cc,
code_challenge_method: "S256",
state: Math.random().toString(36).substring(7)
}).forEach(([k, v]) => url.searchParams.set(k, v));
 
window.location.href = url;
} catch (error) {
alert('OAuth error: ' + error.message);
}
}
```

## Resources

  • - App Source Code[App Source Code](https://github.com/parallel-web/parallel-cookbook/tree/main/typescript-recipes/parallel-tasks-sse)
  • - Live App Example[Live App Example](https://oss.parallel.ai/tasks-sse/)
  • - Parallel SSE Documentation[Parallel SSE Documentation](https://docs.parallel.ai/task-api/task-sse)

## Ready to get started?

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Try Parallel[Try Parallel](https://platform.parallel.ai/home)Contact sales[Contact sales](https://contact.parallel.ai/)
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Parallel avatar

By Parallel

October 6, 2025

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Tabstack + Parallel Case Study

Feb 23, 2026

- [How Tabstack by Mozilla enables agents to navigate the web with Parallel’s best-in-class web search](https://parallel.ai/blog/case-study-tabstack)

Tags:Customers
Author: By Parallel
Parallel | Vercel

Feb 4, 2026

- [Parallel Web Tools and Agents now available across Vercel AI Gateway, AI SDK, and Marketplace](https://parallel.ai/blog/vercel)

Tags:Product
Author: By Parallel
Product release: Authenticated page access for the Parallel Task API

Jan 28, 2026

- [Authenticated page access for the Parallel Task API](https://parallel.ai/blog/authenticated-page-access)

Tags:Product
Author: By Parallel
Introducing structured outputs for the Monitor API

Jan 21, 2026

- [Introducing structured outputs for the Monitor API](https://parallel.ai/blog/structured-outputs-monitor)

Tags:Product
Author: By Parallel
Product release: Research Models with Basis for the Parallel Chat API

Jan 15, 2026

- [Introducing research models with Basis for the Parallel Chat API](https://parallel.ai/blog/research-models-chat)

Tags:Product
Author: By Parallel
Parallel + Cerebras

Jan 8, 2026

- [Build a real-time fact checker with Parallel and Cerebras](https://parallel.ai/blog/cerebras-fact-checker)

Tags:Developers
Author: By Parallel
DeepSearch QA: Task API

Dec 17, 2025

- [Parallel Task API achieves state-of-the-art accuracy on DeepSearchQA](https://parallel.ai/blog/deepsearch-qa)

Tags:Benchmarks
Author: By Parallel
Product release: Granular Basis

Dec 16, 2025

- [Introducing Granular Basis for the Task API](https://parallel.ai/blog/granular-basis-task-api)

Tags:Product
Author: By Parallel
How Amp’s coding agents build better software with Parallel Search

Dec 11, 2025

- [How Amp’s coding agents build better software with Parallel Search](https://parallel.ai/blog/case-study-amp)

Tags:Customers
Author: By Parallel
Latency improvements on the Parallel Task API

Dec 10, 2025

- [Latency improvements on the Parallel Task API ](https://parallel.ai/blog/task-api-latency)

Tags:Product
Author: By Parallel
Product release: Extract

Nov 20, 2025

- [Introducing Parallel Extract](https://parallel.ai/blog/introducing-parallel-extract)

Tags:Product
Author: By Parallel
FindAll API - Product Release

Nov 18, 2025

- [Introducing Parallel FindAll](https://parallel.ai/blog/introducing-findall-api)

Tags:Product,Benchmarks
Author: By Parallel
Product release: Monitor API

Nov 13, 2025

- [Introducing Parallel Monitor](https://parallel.ai/blog/monitor-api-beta)

Tags:Product
Author: By Parallel
Parallel raises $100M Series A to build web infrastructure for agents

Nov 12, 2025

- [Parallel raises $100M Series A to build web infrastructure for agents](https://parallel.ai/blog/series-a)

Tags:Company
Author: By Parallel
How Macroscope reduced code review false positives with Parallel

Nov 11, 2025

- [How Macroscope reduced code review false positives with Parallel](https://parallel.ai/blog/case-study-macroscope)

Tags:Customers
Author: By Parallel
Product release - Parallel Search API

Nov 6, 2025

- [Introducing Parallel Search](https://parallel.ai/blog/parallel-search-api-beta)

Tags:Benchmarks
Author: By Parallel
Benchmarks: SealQA: Task API

Nov 3, 2025

- [Parallel processors set new price-performance standard on SealQA benchmark](https://parallel.ai/blog/benchmarks-task-api-sealqa)

Tags:Benchmarks
Author: By Parallel
Introducing LLMTEXT, an open source toolkit for the llms.txt standard

Oct 30, 2025

- [Introducing LLMTEXT, an open source toolkit for the llms.txt standard](https://parallel.ai/blog/LLMTEXT-for-llmstxt)

Tags:Product
Author: By Parallel
Starbridge + Parallel

Oct 23, 2025

- [How Starbridge powers public sector GTM with state-of-the-art web research](https://parallel.ai/blog/case-study-starbridge)

Tags:Customers
Author: By Parallel
Building a market research platform with Parallel Deep Research

Oct 22, 2025

- [Building a market research platform with Parallel Deep Research](https://parallel.ai/blog/cookbook-market-research-platform-with-parallel)

Tags:Developers
Author: By Parallel
How Lindy brings state-of-the-art web research to automation flows

Oct 17, 2025

- [How Lindy brings state-of-the-art web research to automation flows](https://parallel.ai/blog/case-study-lindy)

Tags:Customers
Author: By Parallel
Introducing the Parallel Task MCP Server

Oct 16, 2025

- [Introducing the Parallel Task MCP Server](https://parallel.ai/blog/parallel-task-mcp-server)

Tags:Product
Author: By Parallel
Introducing the Core2x Processor for improved compute control on the Task API

Oct 9, 2025

- [Introducing the Core2x Processor for improved compute control on the Task API](https://parallel.ai/blog/core2x-processor)

Tags:Product
Author: By Parallel
How Day AI merges private and public data for business intelligence

Oct 8, 2025

- [How Day AI merges private and public data for business intelligence](https://parallel.ai/blog/case-study-day-ai)

Tags:Customers
Author: By Parallel
Full Basis framework for all Task API Processors

Oct 7, 2025

- [Full Basis framework for all Task API Processors](https://parallel.ai/blog/full-basis-framework-for-task-api)

Tags:Product
Author: By Parallel
How Gumloop built a new AI automation framework with web intelligence as a core node

Sep 30, 2025

- [How Gumloop built a new AI automation framework with web intelligence as a core node](https://parallel.ai/blog/case-study-gumloop)

Tags:Customers
Author: By Parallel
Introducing the TypeScript SDK

Sep 16, 2025

- [Introducing the TypeScript SDK](https://parallel.ai/blog/typescript-sdk)

Tags:Product
Author: By Parallel
Building a serverless competitive intelligence platform with MCP + Task API

Sep 12, 2025

- [Building a serverless competitive intelligence platform with MCP + Task API](https://parallel.ai/blog/cookbook-competitor-research-with-reddit-mcp)

Tags:Developers
Author: By Parallel
Introducing Parallel Deep Research reports

Sep 11, 2025

- [Introducing Parallel Deep Research reports](https://parallel.ai/blog/deep-research-reports)

Tags:Product
Author: By Parallel
BrowseComp / DeepResearch: Task API

Sep 9, 2025

- [A new pareto-frontier for Deep Research price-performance](https://parallel.ai/blog/deep-research-benchmarks)

Tags:Benchmarks
Author: By Parallel
Building a Full-Stack Search Agent with Parallel and Cerebras

Sep 5, 2025

- [Building a Full-Stack Search Agent with Parallel and Cerebras](https://parallel.ai/blog/cookbook-search-agent)

Tags:Developers
Author: By Parallel
Webhooks for the Parallel Task API

Aug 21, 2025

- [Webhooks for the Parallel Task API](https://parallel.ai/blog/webhooks)

Tags:Product
Author: By Parallel
Introducing Parallel: Web Search Infrastructure for AIs

Aug 14, 2025

- [Introducing Parallel: Web Search Infrastructure for AIs ](https://parallel.ai/blog/introducing-parallel)

Tags:Benchmarks,Product
Author: By Parallel
Introducing SSE for Task Runs

Aug 7, 2025

- [Introducing SSE for Task Runs](https://parallel.ai/blog/sse-for-tasks)

Tags:Product
Author: By Parallel
A new line of advanced Processors: Ultra2x, Ultra4x, and Ultra8x

Aug 5, 2025

- [A new line of advanced Processors: Ultra2x, Ultra4x, and Ultra8x ](https://parallel.ai/blog/new-advanced-processors)

Tags:Product
Author: By Parallel
Introducing Auto Mode for the Parallel Task API

Aug 4, 2025

- [Introducing Auto Mode for the Parallel Task API](https://parallel.ai/blog/task-api-auto-mode)

Tags:Product
Author: By Parallel
A linear dithering of a search interface for agents

Jul 31, 2025

- [A state-of-the-art search API purpose-built for agents](https://parallel.ai/blog/search-api-benchmark)

Tags:Benchmarks
Author: By Parallel
Parallel Search MCP Server in Devin

Jul 31, 2025

- [Parallel Search MCP Server in Devin](https://parallel.ai/blog/parallel-search-mcp-in-devin)

Tags:Product
Author: By Parallel
Introducing Tool Calling via MCP Servers

Jul 28, 2025

- [Introducing Tool Calling via MCP Servers](https://parallel.ai/blog/mcp-tool-calling)

Tags:Product
Author: By Parallel
Introducing the Parallel Search MCP Server

Jul 14, 2025

- [Introducing the Parallel Search MCP Server ](https://parallel.ai/blog/search-mcp-server)

Tags:Product
Author: By Parallel
Starting today, Source Policy is available for both the Parallel Task API and Search API - giving you granular control over which sources your AI agents access and how results are prioritized.

Jul 8, 2025

- [Introducing Source Policy](https://parallel.ai/blog/source-policy)

Tags:Product
Author: By Parallel
The Parallel Task Group API

Jul 2, 2025

- [The Parallel Task Group API](https://parallel.ai/blog/task-group-api)

Tags:Product
Author: By Parallel
State of the Art Deep Research APIs

Jun 17, 2025

- [State of the Art Deep Research APIs](https://parallel.ai/blog/deep-research-browsecomp)

Tags:Benchmarks
Author: By Parallel
Introducing the Parallel Search API

Jun 10, 2025

- [Parallel Search API is now available in alpha](https://parallel.ai/blog/search-api-alpha)

Tags:Product
Author: By Parallel
Introducing the Parallel Chat API - a low latency web research API for web based LLM completions. The Parallel Chat API returns completions in text and structured JSON format, and is OpenAI Chat Completions compatible.

May 30, 2025

- [Introducing the Parallel Chat API ](https://parallel.ai/blog/chat-api)

Tags:Product
Author: By Parallel
Parallel Web Systems introduces Basis with calibrated confidences - a new verification framework for AI web research and search API outputs that sets a new industry standard for transparent and reliable deep research.

May 16, 2025

- [Introducing Basis with Calibrated Confidences ](https://parallel.ai/blog/introducing-basis-with-calibrated-confidences)

Tags:Product
Author: By Parallel
The Parallel Task API is a state-of-the-art system for automated web research that delivers the highest accuracy at every price point.

Apr 24, 2025

- [Introducing the Parallel Task API](https://parallel.ai/blog/parallel-task-api)

Tags:Product,Benchmarks
Author: By Parallel
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