# Agentset vs Polygres

Agentset records 6,469 monthly visits with -29.3% month-over-month growth and Freemium, Contact for Pricing pricing. Polygres records 4,018 monthly visits with -52.3% growth and Freemium pricing.

| Metric | [Agentset](https://openaitools.app/tool/agentset) | [Polygres](https://openaitools.app/tool/polygres) |
| :--- | :--- | :--- |
| Website URL | https://agentset.ai | https://polygres.com |
| Categories | AI Developer Tools, AI App Builder, AI Search Engine, AI Chatbot | AI Developer Tools, AI Agent, AI Knowledge Graph, AI API |
| Pricing Model | Freemium, Contact for Pricing | Freemium |
| Monthly Visits | 6,469 | 4,018 |
| MoM Growth Rate | -29.3% | -52.3% |
| Primary Market | United States | Global |
| Direct Visits | 2,569 | 1,605 |
| Review Score | 4.6 | 4.1 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Agentset

Agentset packages everything a production RAG system needs into one platform. Documents are ingested through JavaScript and Python SDKs with support for 22+ file formats, including scans, spreadsheets and images, then chunked, embedded and stored in a vector database of your choosing. At query time the system combines agentic reasoning with hybrid search and reranking, returns grounded answers, and automatically attaches citations so end users can inspect the sources behind each response.

The platform is deliberately model agnostic. Teams can pair Agentset with OpenAI, Anthropic, Google AI, xAI, Mistral, Cohere, Qwen and other providers, and can select from multiple vector databases. Features such as metadata filtering, preview links for external feedback, a Model Context Protocol server and an AI SDK integration make it straightforward to embed the resulting knowledge assistant into existing products or developer tooling.

Agentset targets teams that have outgrown demo-quality RAG. It is used in demanding verticals such as legal publishing, healthcare research and public-sector content, where accuracy, multimodal handling of tables and graphs, and traceable sourcing matter. The core platform is open source, with a managed option and enterprise deployment choices for organizations that need more control.

**Pricing & Plans:** Free/open-source core with paid plans; pricing page lists a freemium entry point and enterprise options available on request. Check the official pricing page for current details.

- **Agentic RAG engine:** Combines agentic reasoning with hybrid search and reranking to produce accurate answers even on large, messy document sets.
- **Automatic citations:** Every answer includes references to the source passages, so users can verify where an answer came from.
- **Multimodal ingestion:** Indexes text plus images, charts, tables and 22+ file formats such as PDF, DOCX, XLSX, PPTX, HTML and EML via ready-made SDKs.
- **Model and database agnostic:** Plug in your own LLM, embedding model and vector database, including OpenAI, Anthropic, Google, Cohere, Pinecone and Qdrant.
- **MCP server and preview links:** Expose your knowledge base to external AI clients through an MCP server, and collect structured feedback via shareable preview chats.

**Strengths:** Open-source core with self-hosting options, so there is no hard lock-in; Strong accuracy story on benchmarks such as MultiHopQA and FinanceBench; Model, embedding and vector database choices stay with the developer; Handles multimodal documents (images, graphs, tables) not just plain text

**Limitations:** Pricing details require contacting sales for higher tiers; Best results still need thoughtful document preparation and metadata hygiene; The breadth of configuration options can be a lot for small teams to digest

[Profile (Markdown)](https://openaitools.app/tool/agentset.md)

## Polygres

Polygres is a retrieval platform that treats PostgreSQL as the backbone for AI-native applications. Rather than forcing teams to migrate to a new vector-only database, it virtualises a GraphRAG-style retrieval layer on top of Postgres, so existing relational records, embeddings and text indexes stay where they are while agents gain the ability to search by meaning, follow relationships and match exact wording in one request.

The platform bundles four complementary retrieval modes: semantic vector search for meaning, graph traversal for multi-hop relationships and shortest-path style queries, full-text search for exact terms, and hybrid search that blends them. Developers reach these capabilities through a hosted playground, a CLI for managing projects and imports, and a typed Python SDK for production applications.

Behind the managed platform are two open-source engines, pgGraph for graph traversal inside Postgres and pgContext for AI search, which teams can inspect, benchmark and self-host. Polygres supports both a managed database hosted by the company and connecting an existing Postgres instance, and it markets itself around agent memory, grounded answers with sources, product semantic search and recommendation systems.

**Pricing & Plans:** Polygres follows a freemium model with a free tier for getting started and paid managed plans for production workloads. Exact tiers are published on the company's pricing page, so check there for current numbers before budgeting.

- **Unified retrieval API:** Query graph, vector, full-text and hybrid retrieval through one consistent interface instead of stitching multiple systems together.
- **Graph traversal on Postgres:** The open-source pgGraph engine follows relationships across your records, enabling multi-hop and shortest-path retrieval without moving data.
- **Vector and hybrid search:** Semantic search over embeddings combines with sparse and full-text matching so queries that mix meaning and exact terms still work.
- **Developer toolkit:** A dashboard playground, CLI for imports and configuration, and a typed Python SDK make it straightforward to prototype and then ship.
- **Managed or bring-your-own Postgres:** Start on a Polygres-hosted database or connect an existing Postgres instance and extend what you already run.

**Strengths:** Keeps your existing PostgreSQL instead of forcing a migration to a specialised vector store; Combines four retrieval modes in one platform, which reduces the number of moving parts in a RAG stack; Core engines pgGraph and pgContext are open source, so the internals are inspectable and self-hostable; Familiar Postgres tooling, SQL knowledge and connection patterns carry over for most teams

**Limitations:** Team and community are relatively early-stage, so long-term support and ecosystem maturity are still developing; Being a newer product, third-party tutorials and integrations are thinner than for established vector databases; Fully managed convenience depends on using the hosted offering rather than the open-source engines alone

[Profile (Markdown)](https://openaitools.app/tool/polygres.md)

## Frequently Asked Questions

### How do Agentset and Polygres compare in monthly traffic?

Agentset has 6,469 monthly visits, while Polygres has 4,018. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Agentset has a month-over-month growth rate of -29.3%; Polygres has -52.3%.

### How does pricing compare?

Agentset: Freemium, Contact for Pricing. Free/open-source core with paid plans; pricing page lists a freemium entry point and enterprise options available on request. Check the official pricing page for current details. Polygres: Freemium. Polygres follows a freemium model with a free tier for getting started and paid managed plans for production workloads. Exact tiers are published on the company's pricing page, so check there for current numbers before budgeting.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Give your AI agents secure, authenticated access to 1,700+ apps | [.md](https://openaitools.app/tool/composio.md) |
| 2 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Web search API and crawler built for AI agents | [.md](https://openaitools.app/tool/exa-ai.md) |
| 3 | [You.com](https://openaitools.app/tool/you) | 948,349 | +7% | Freemium | Search and inference APIs that give AI models real-time web data | [.md](https://openaitools.app/tool/you.md) |
| 4 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Build, deploy, and govern production-ready agentic AI workflows | [.md](https://openaitools.app/tool/dify-ai.md) |
| 5 | [Algolia](https://openaitools.app/tool/algolia) | 689,970 | -11% | Freemium, Contact for Pricing | AI search and retrieval platform for modern apps and commerce | [.md](https://openaitools.app/tool/algolia.md) |
| 6 | [LlamaIndex](https://openaitools.app/tool/llamaindex) | 387,910 | +19.9% | Freemium, Contact for Pricing | Turn any document into AI-ready context with agentic parsing and extraction | [.md](https://openaitools.app/tool/llamaindex.md) |
| 7 | [ERNIE Agent Platform (AgentBuilder)](https://openaitools.app/tool/ernie-agent-platform) | 371,311 | +9.3% | Contact for Pricing | Baidu's one-stop platform for building, distributing and monetizing AI agents | [.md](https://openaitools.app/tool/ernie-agent-platform.md) |
| 8 | [Pinecone](https://openaitools.app/tool/pinecone) | 286,732 | -31% | Freemium, Paid, Contact for Pricing | The managed vector database and knowledge engine for production AI | [.md](https://openaitools.app/tool/pinecone.md) |
| 9 | [supermemory](https://openaitools.app/tool/supermemory-1) | 156,579 | -7.7% | Contact for Pricing | A memory and continual-learning engine that gives any AI agent persistent context | [.md](https://openaitools.app/tool/supermemory-1.md) |
| 10 | [Yep.com](https://openaitools.app/tool/yep-com) | 155,834 | +6.2% | Free | Independent web search and agent grounding API built on the Ahrefs index | [.md](https://openaitools.app/tool/yep-com.md) |

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Source: https://openaitools.app/compare/agentset-vs-polygres

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