AI Frameworks · jina-ai

reader

Reader is a free API service that converts any URL into LLM-friendly markdown output, handling web pages, PDFs, and Office documents. It also provides web search functionality that fetches and processes the top results automatically, eliminating the need for LLMs to handle rendering and JavaScript execution.

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Source: GitHub — github.com/jina-ai/reader

11.5k

GitHub stars

845

Forks

TypeScript

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	jina-ai/reader
Owner	jina-ai
Primary language	TypeScript
License	Apache-2.0 — OSI-approved
Stars	11.5k
Forks	845
Open issues	25
Latest release	Unknown
Last updated	2026-05-22
Source	https://github.com/jina-ai/reader

What reader is

TypeScript-based open-source proxy service that intelligently routes between headless Chrome (for JavaScript-heavy sites) and curl-impersonate (for lightweight fetching), with support for PDF.js parsing, LibreOffice document conversion, and vision-model image captioning. Stateless architecture with optional MinIO/S3 caching; MongoDB storage layer excluded from OSS branch.

Quickstart

Get the reader source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/jina-ai/reader.git2cd reader3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

RAG and Agent Context Enrichment

Automatically fetch and clean web content for retrieval-augmented generation pipelines without building custom crawlers. Pass URLs directly to Reader via prefix and receive structured markdown ready for embedding and retrieval.

Web Search Integration for LLM Agents

Enable agents to search the web and access top-5 result content automatically, not just titles and snippets. Reader handles browser rendering, JavaScript execution, and content extraction—critical for agents requiring current knowledge.

PDF and Document Processing Pipelines

Convert PDFs, Word, Excel, and PowerPoint documents to markdown via simple HTTP requests. Useful for document-heavy workflows (compliance, research, knowledge bases) where markdown output integrates cleanly with text-based LLM systems.

Implementation considerations

Request headers (x-respond-with, x-engine, x-timeout, x-max-tokens) allow fine-grained control over output format and latency/completeness trade-offs; integrate these into your client logic for optimal results.
Token budgeting (x-max-tokens, x-token-budget) is essential for cost control when feeding fixed-size context windows; truncation vs. rejection behavior should be tested against your specific LLM constraints.
Stateless SaaS mode (r.jina.ai, s.jina.ai) requires no setup but sends data externally; OSS self-hosting adds DevOps overhead (Docker, MinIO, optional MongoDB for caching) but keeps content on-premises.
CSS selectors (x-target-selector, x-wait-for-selector) enable content extraction refinement when automatic readability filtering misses critical sections; build fallback logic for sites with non-standard DOM structures.
Image captioning via vision-language model introduces variable latency and quality; test against your image-heavy use cases to confirm output utility for your downstream LLM.

When to avoid it — and what to weigh

Strict Data Residency Requirements — Reader's SaaS endpoints (r.jina.ai, s.jina.ai) send content to Jina AI servers. If data must remain on-premises or in specific jurisdictions, self-hosting the OSS branch locally is required but adds operational complexity.
Authenticated Content Access — Reader does not natively support cookies, session tokens, or login-protected content. Fetching paywalled articles, authenticated APIs, or user-specific pages will fail unless pre-signed URLs or alternative mechanisms are provided.
Real-Time Content Guarantee — Default caching (3600s) means responses may be stale. While x-no-cache bypasses this, latency becomes unpredictable. Avoid for applications requiring sub-second freshness or strict response time SLAs.
High-Volume Commercial Deployments Without Clarity — README states Reader is 'free and stable' but omits concrete rate limits and SLA guarantees. Large-scale production use should verify current rate limiting at jina.ai/reader#pricing before committing.

License & commercial use

Licensed under Apache License 2.0, a permissive OSI-approved license permitting commercial use, modification, and distribution with minimal restrictions (must retain copyright/license notice, provide copy of license, and state material changes).

Apache-2.0 permits commercial use of the source code itself. The hosted SaaS endpoints (r.jina.ai, s.jina.ai) are offered as free services by Jina AI with rate limiting; the README states 'Feel free to use Reader API in production' but does not provide explicit SLA or uptime guarantee. For commercial production use, verify current rate limits and availability terms at jina.ai/reader#pricing before deployment. Self-hosting the OSS branch incurs your own infrastructure costs and operational responsibility.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

SaaS mode sends web content to Jina AI servers; evaluate data sensitivity before using for proprietary, regulated, or confidential content. Self-hosting keeps data local but requires securing your own Docker/Kubernetes deployment and dependency updates. Reader uses headless Chrome and curl-impersonate for fetching; verify these tools' vulnerability status for your threat model. No explicit mention of input validation (URL injection), output sanitization, or SSRF protections; test against malicious URLs and compromised websites before deploying to untrusted environments.

Alternatives to consider

Firecrawl (https://www.firecrawl.dev)

Similar URL-to-markdown conversion with headless browser support; may offer different pricing, SLA guarantees, and enterprise features. Evaluate if you need commercial support or stricter SLAs.

Apache Tika + custom crawlers

Open-source document processing library; requires building your own HTTP wrapper and crawler logic. More control but higher engineering effort; suitable if you need offline processing or extreme customization.

LLM-integrated APIs (OpenAI, Anthropic, Claude plugins)

Some LLMs now include built-in web browsing or document reading; if your workflow is already LLM-centric, native integrations may reduce external dependencies, though with less granular control.

Software development agency

Build on reader with DEV.co software developers

Try the free r.jina.ai endpoint now, or review the self-hosted Docker setup in the GitHub repository. Verify rate limits at jina.ai/reader#pricing before production deployment.

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reader FAQ

Do I need to self-host Reader or can I use the free SaaS?

Both options exist. r.jina.ai and s.jina.ai are free SaaS endpoints maintained by Jina AI with no explicit rate-limit or SLA guarantee stated in the README. Self-hosting via Docker Compose is documented for on-premises or private deployments but requires DevOps setup.

Can Reader handle authenticated or paywalled content?

Not natively. Reader fetches URLs as an anonymous user and does not support cookies, session tokens, or login flows. Pre-signed URLs or service accounts with public URLs are required as workarounds.

What happens if content exceeds my token budget?

Use x-max-tokens to truncate output or x-token-budget to reject the request. x-max-tokens will trim markdown; x-token-budget will fail the request if estimated tokens would exceed the limit. Choose based on whether partial content is acceptable.

Is Reader suitable for high-volume production use?

Unknown without explicit rate limits. The README says Reader is 'stable and scalable' and 'actively maintained as a core product', but concrete rate limits, uptime SLA, and cost structure are not disclosed. Verify at jina.ai/reader#pricing before committing large-scale deployments.

Custom software development services

DEV.co helps companies turn open-source tools like reader into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your ai frameworks stack.

Start Using Reader for Your LLM Pipeline

Try the free r.jina.ai endpoint now, or review the self-hosted Docker setup in the GitHub repository. Verify rate limits at jina.ai/reader#pricing before production deployment.

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