Agentset vs Sentra: Which is Better in 2026?
A comprehensive comparison of Agentset and Sentra covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Agentset if:
- →You need a broader feature set (6 features vs 5)
- →You need end-to-end rag: ingestion, chunking, retrieval, agentic search or multimodal — images, graphs and tables retrieved like text
Choose Sentra if:
- →You want more affordable paid plans (from $16/mo)
- →You need one queryable memory graph shared by humans and agents or rest and mcp access from claude, chatgpt, cursor and windsurf
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Agentset vs Sentra: At a Glance
Pricing Comparison: Agentset vs Sentra
Understanding the pricing differences between Agentset and Sentra is crucial for making the right choice. Here's how their plans compare side by side.
💡 Pricing takeaway: Both Agentset and Sentra offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Agentset and Sentra stacks up.
What Makes Each Tool Unique
🔵 Unique to Agentset
Features available in Agentset but not in Sentra:
- ✓End-to-end RAG: ingestion, chunking, retrieval, agentic search
- ✓Multimodal — images, graphs and tables retrieved like text
- ✓Automatic citations on every answer for source inspection
- ✓Metadata filtering to scope answers to a data subset
- ✓22+ file formats with JavaScript and Python SDKs
- ✓Shareable preview links for external feedback
🟣 Unique to Sentra
Features available in Sentra but not in Agentset:
- ✓One queryable memory graph shared by humans and agents
- ✓REST and MCP access from Claude, ChatGPT, Cursor and Windsurf
- ✓Semantics resolved at ingestion against a per-org ontology
- ✓Captures interactions and decisions, not just finished artefacts
- ✓Meeting recording, pre-meeting briefs, risk radar and commitment tracking
Use Case Recommendations
Best for: Agentset
Agentset is an open-source platform for building AI chat and search over your own data without assembling a RAG stack yourself. It covers the whole path — ingestion and chunking across 22-plus file formats, embedding, retrieval, agentic search and answer generation — behind JavaScript and Python SDKs, so a team that needs a reliable answer engine on top of a document corpus can ship one without hiring for retrieval expertise. Three design choices distinguish it from a hand-rolled pipeline. Multimodal handling means images, graphs and tables inside documents are treated as first-class retrievable content rather than being dropped at parse time, which is where most naive pipelines quietly lose half a knowledge base. Citations are automatic, so every answer carries inspectable sources — the single most effective mitigation for hallucinated answers in a customer-facing deployment. And metadata filtering lets a query be scoped to a subset of the corpus, which is what makes per-tenant or per-permission answering possible. The project publishes benchmark positions on MultiHopQA and FinanceBench and provides customisable preview links so non-technical stakeholders can test a deployment and leave feedback without an account. Supported inputs include PDF, DOCX, XLSX, PPTX, HTML, CSV, Markdown, email formats and common image types.
Ideal use cases:
- •Teams or individuals who need end-to-end rag: ingestion, chunking, retrieval, agentic search
- •Teams or individuals who need multimodal — images, graphs and tables retrieved like text
- •Teams or individuals who need automatic citations on every answer for source inspection
- •Teams or individuals who need metadata filtering to scope answers to a data subset
- •Anyone focused on rag workflows
- •Anyone focused on open-source workflows
Best for: Sentra
Sentra is a unified memory layer — a company brain shared by a team and its AI agents. It captures interactions, decisions and drift and writes them into one queryable graph exposed over REST or MCP, so what you teach one agent every agent remembers. The technical argument is specific: vector search returns what is close, not what is correct, because embeddings are stored at write time and structure is guessed at query time, which means every request re-crawls Slack, email and docs to rediscover what a term means inside your organisation. Sentra instead resolves semantics at ingestion and builds the graph on demand at query time against an ontology unique to your company, treating meaning as a primitive rather than a side effect. It also argues that most tools capture artefacts — the CRM record, the Jira ticket, the Confluence page — and therefore capture only the output of a decision, not the interaction that produced it. In practice the product covers meeting recording and notes, pre-meeting briefs, deep research across all connected data, a commitment tracker, a risk radar and connection intelligence, plugging into Slack, Gmail, Calendar, Outlook, GitHub and Linear, and into Claude, ChatGPT, Cursor, Perplexity, Codex and Windsurf.
Ideal use cases:
- •Teams or individuals who need one queryable memory graph shared by humans and agents
- •Teams or individuals who need rest and mcp access from claude, chatgpt, cursor and windsurf
- •Teams or individuals who need semantics resolved at ingestion against a per-org ontology
- •Teams or individuals who need captures interactions and decisions, not just finished artefacts
- •Anyone focused on memory workflows
- •Anyone focused on mcp workflows
🤖 Other AI Agent Infrastructure Tools to Consider
Agentset and Sentra aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is Agentset better than Sentra?
It depends on your needs. Agentset offers 6 key features including End-to-end RAG: ingestion, chunking, retrieval, agentic search and Multimodal — images, graphs and tables retrieved like text, while Sentra provides 5 features including One queryable memory graph shared by humans and agents and REST and MCP access from Claude, ChatGPT, Cursor and Windsurf. Agentset uses a freemium model with a free tier, while Sentra is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Agentset cheaper than Sentra?
Sentra is cheaper, starting at $16/month compared to Agentset's $49/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Agentset and Sentra together?
Yes, many users combine Agentset and Sentra in their workflow. Agentset excels at end-to-end rag: ingestion, chunking, retrieval, agentic search, while Sentra shines with one queryable memory graph shared by humans and agents. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Agentset and Sentra?
While both are ai agent infrastructure tools, Agentset emphasizes end-to-end rag: ingestion, chunking, retrieval, agentic search, whereas Sentra is known for one queryable memory graph shared by humans and agents. The best choice depends on your specific workflow and feature priorities.
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