Polygres
Postgres extensions for retrieval-augmented generation: graph search, multi-path hybrid retrieval, and token-budgeted context assembly, self-hosted or managed.
✅ Pros / Advantages
- • pgGraph 图检索引擎
- • pgContext 十路融合检索
- • 混合检索 + 上下文组装
- • Token 预算控制
- • 无额外基础设施
❌ Cons / Limitations
💰 Pricing Plans
Freemium (Self-hosted free / Managed $16–$4,096/mo)
Pricing details are gathered from public sources and are subject to change. Please visit the official website for real-time rates.
Last updated: July 2026 · 9bests editorial review
✅ Who should use Polygres
- • pgGraph 图检索引擎
- • pgContext 十路融合检索
- • 混合检索 + 上下文组装
- • Token 预算控制
- • 无额外基础设施
⚠️ Who should look elsewhere
🎯 Common use cases
Web scraping and extraction
Building AI data pipelines
Enrichment and cleaning
⚖️ Polygres vs Crawl4AI
| Polygres | Crawl4AI | |
|---|---|---|
| Rating | 4/5 | 4.3/5 |
| Pricing | Freemium (Self-hosted free / Managed $16–$4,096/mo) | Free (Open Source) |
| Key strength | pgGraph 图检索引擎 | LLM-first output format |
See the full head-to-head in our Polygres vs Crawl4AI comparison.
❓ Frequently asked questions
Is Polygres free?
+
Polygres offers a free tier (Freemium (Self-hosted free / Managed $16–$4,096/mo)). Paid plans unlock higher limits and advanced features.
What is Polygres best for?
+
Polygres is best for pgGraph 图检索引擎 and pgContext 十路融合检索. Postgres extensions for retrieval-augmented generation: graph search, multi-path hybrid retrieval, and token-budgeted context assembly, self-hosted or managed.
How does Polygres compare to Crawl4AI?
+
Polygres (4/5) and Crawl4AI (4.3/5) serve overlapping needs. Polygres stands out for pgGraph 图检索引擎, while Crawl4AI is stronger at LLM-first output format. Choose based on your priority.
🔄 Top Alternatives to Polygres
Related ToolsCrawl4AI
Open-source web crawler designed for LLMs and AI agents with structured extraction and browser automation.
Atlas
Open-source local-first cognitive memory system implementing AGM-compatible belief revision that automatically re-evaluates downstream beliefs when facts change, with SHA-256 hash chain for data integrity.
ParseHawk
ParseHawk is a fully local document AI processing toolkit — no data leaves your machine. It ships with an API server, CLI, and Web UI, making it easy to integrate into existing workflows or use standalone for document parsing, chunking, OCR, and Q&A over documents.
Adaptive Recall
Adaptive Recall is a hosted memory system for AI applications that goes far beyond simple vector search. It stores, recalls, and manages long-term memory for agents and apps over MCP or a plain REST API, and — unlike a static embeddings store — it actively learns. Four retrieval strategies run in parallel (vector similarity, temporal recency, full-text keyword, and knowledge-graph traversal), and the system learns which to prioritize for each query type. Results are ranked with ACT-R cognitive scoring from 30 years of cognitive-science research, factoring in recency, access frequency, entity connections, and validated confidence. A knowledge graph is built automatically from stored memories, memories move through a confidence-based lifecycle and fade when unused, and an ML pipeline trains on your usage patterns — validating every parameter change against real query history before adopting it. A simple eight-tool API (store, recall, update, forget, graph, status, snapshot, feedback) covers everything, with Bearer-token auth and JSON in/out. Free, Starter, Pro, and Business plans are available.