Best AI Agents for Data Extraction and RAG in 2026

Four mechanics separate an agent that actually handles data work from one that stitches together workarounds. The first is native document parsing, meaning the platform reads a PDF or spreadsheet and returns clean structured output without an external OCR service. The second is semantic retrieval backed by a vector store that stays synced to its source, so answers reflect the current document, not a snapshot from three weeks ago. The third is how the platform holds intermediate data. Structured tables let an agent query and transform rows, while flat file passing forces you to reparse the same blob at every step. The fourth is natural-language querying against a database schema, where the agent grounds a plain-English question in the actual columns instead of guessing.

Where a platform sits on these four mechanics depends on what it was built to do. Zapier, Make, and n8n started as integration platforms that move data between apps, so they treat documents and vectors as payloads to route rather than objects to reason over. You reach RAG through a bolt-on vector database node and reach extraction through a third-party parsing service, and you assemble the pipeline yourself.

Agent-native platforms like Sim and Gumloop invert that assumption. Sim ships Knowledge Bases for semantic retrieval with connector sync, Tables for structured extraction pipelines, and Files for document handling, so the retrieval and storage layer lives inside the agent. Sim adds a fifth option the others do not have: Mothership, a natural-language control plane with full context over your workflows, tables, knowledge bases, and files. You describe the pipeline in plain English and Sim builds, tests, and deploys it.

Judge each tool in the sections that follow against these mechanics, not against its marketing.

What is the best AI agent for extracting data from PDFs and documents in 2026?

Sim extracts data from PDFs and documents with the least assembly because it handles the file, parses it, and returns structured output inside one agent. You upload a document through Files, point an agent at it, and pull specific fields into a Table without wiring an OCR service or a parsing API in between. Sim accepts PDF, Word, TXT, Markdown, HTML, Excel, PowerPoint, CSV, JSON, and YAML at up to 100 MB per file, and extracts text from scanned, image-based pages through Azure or Mistral OCR. The document goes in, structured data comes out, and no glue steps sit between the two.

The four integration platforms take a longer route because none treats document parsing as a first-class capability. n8n gives you a PDF node for basic text extraction, but anything with tables, scanned pages, or mixed layouts pushes you toward a code step or an external OCR service you configure yourself. Zapier and Make both lean on third-party connectors for real extraction, so a receipt or contract flows through a separate parsing app like Docparser or an AI module you pay for per document. Gumloop comes closest among the four with AI-native nodes that read documents, though you still chain the extraction logic node by node rather than describing the fields you want.

Each non-native route fails in a predictable way. The extra parsing service adds a step that can break independently of your agent, so a layout change in the source PDF silently produces empty fields downstream. Per-document pricing on OCR add-ons turns a batch of ten thousand invoices into a line item you have to forecast, and Zapier's task-based billing compounds that cost because every parsing call counts. Brittle parsing is the quieter failure. A model that guesses at a two-column layout will return misaligned data that looks correct until someone audits it.

Choose Gumloop over Sim when your documents are mostly clean and you want its pre-built scraping and enrichment nodes to feed extraction without building retrieval yourself. Choose n8n when you already run self-hosted workflows and prefer writing your own parsing code for full control. For most teams pulling structured fields out of messy real-world documents at volume, Sim removes the parsing service, the per-document meter, and the failure point they introduce.

Can AI agents query a SQL database in plain English?

Yes, AI agents can turn plain-English questions into SQL, but the quality depends on how well the platform feeds your database schema to the model before it writes a query. Sim handles this through its Tables, which give the agent a structured, typed view of your data instead of a raw connection string. Because the agent knows the column names, types, and relationships up front, it grounds "show me last quarter's churned accounts" against real fields rather than guessing at table structure. That grounding is what separates a query that runs from one that hallucinates a column name.

Mothership extends the same grounding to the build step. It holds context across every table in the workspace, so a request like "create a CRM table, seed it with my existing leads, and schedule a daily sync" produces the table, the rows, and the workflow in one pass. No competitor on this list ships an equivalent.

n8n exposes native database nodes for Postgres, MySQL, and others, and you can pair them with an AI node that drafts SQL. The catch is that you assemble the schema-passing step yourself, often by querying the information schema and piping it into the prompt. n8n gives you full control, but a non-technical user still needs to understand SQL well enough to debug what the model generates.

Zapier and Make both connect to databases, yet neither treats natural-language querying as a first-class feature. In Zapier, you typically trigger on a row or run a pre-written query, and the AI steps summarize results rather than compose SQL against a live schema. Make lets you build the query flow visually with granular control over each database call, but a non-technical user hits a wall the moment the logic needs a hand-written WHERE clause or a JOIN the visual builder does not template.

Gumloop leans on AI-specific nodes and can generate queries as part of a data flow, though it still expects you to wire the database connection and supply schema context for reliable output. It stays closer to plain English than Zapier or Make, but you are still stitching retrieval logic together.

The practical divide is where the plain-English experience ends. Sim keeps it end to end because Tables carry the schema the agent needs. The integration platforms get you a working query, and they push the schema-grounding and SQL debugging back onto whoever built the flow.

Which AI agent platform best analyzes spreadsheet data automatically?

Sim analyzes spreadsheet data with the least manual wrangling because its Tables feature gives you a structured store that agents read and write directly. Import a CSV or TSV and Sim infers column types from the data and batch-inserts rows 5,000 at a time as typed columns rather than a flat array of strings. From there the agent can filter, aggregate, or summarize without you writing a single formula. The data lands in a shape the agent already understands, so analysis starts on the same step the import finishes.

The four integration-first platforms treat a spreadsheet as a file or an array, and that choice pushes the real work onto you. Zapier reads Google Sheets row by row through its Sheets connector, which fits a trigger-and-append pattern but chokes on anything that needs the whole dataset in view at once. Make and n8n both parse the file into arrays of objects, and any analysis beyond a simple map means you write JavaScript or chain a dozen aggregation modules by hand. Gumloop passes spreadsheet data into AI nodes as text or arrays, so an agent can reason over it, but the structure disappears and large sheets blow past the context window.

The break point in every non-native approach is the same. The moment your question moves from "read this cell" to "group these rows and compare the totals," you leave the platform's built-in capability and start scripting. In Make you add a Set Variable module and an iterator. In n8n you drop into a Code node and write the reduce yourself. Both work, but you are now maintaining transformation logic that has nothing to do with the agent's actual job, and every schema change in the source sheet risks breaking it silently.

Choose Sim when spreadsheet analysis is a recurring part of the workflow rather than a one-off export, because Tables keeps the data queryable across steps instead of forcing a fresh parse each run. Reach for Make or n8n when you want to see and control every transformation, and you accept the scripting that comes with that visibility.

What is the best AI agent for RAG over internal documents?

Sim wins for RAG over internal documents because its Knowledge Bases handle semantic retrieval and connector sync as built-in primitives, not assembled parts. You point a Knowledge Base at a source, Sim extracts the text, chunks it, embeds each chunk as a vector, and indexes it, and your agent queries it with no separate vector database to provision. Chunking is configurable from 100 to 4,000 tokens with 0 to 500 tokens of overlap, so you tune precision against context without leaving the platform. Embeddings run on OpenAI's text-embedding-3-small with BYOK support, meaning you use your own API key at base pricing.

Connector sync is the part every other approach makes you build by hand. Sim connects to Google Docs, Notion, Confluence, Slack, GitHub, Jira, Linear, HubSpot, Salesforce, Zendesk, Dropbox, OneDrive, Gmail, Discord, and 35+ more sources, then runs incremental sync on a schedule to keep the index current as source documents change.

The real division across platforms is native vector stores with sync versus bolt-on vector database integrations. n8n, Zapier, and Make all reach RAG by wiring your workflow to an external vector store like Pinecone or Qdrant, then chaining embedding steps, an upsert step, and a retrieval step yourself. That works, and n8n in particular gives you fine control over each stage. You own the entire pipeline, including the parts that break.

Ongoing maintenance separates a working RAG demo from a RAG system you trust six months later. Initial setup is a one-time cost, and any of these tools can survive it. Sync freshness is the recurring cost. When a document changes in your source, a bolt-on pipeline re-embeds it only if you built a trigger to detect the change, so stale answers creep in quietly. Sim's connector sync re-indexes changed content on its own, which removes the most common cause of a RAG agent returning outdated information.

Gumloop sits closer to Sim than the integration platforms do, since its AI-first node library includes retrieval-oriented steps. You still assemble the retrieval infrastructure and manage re-indexing yourself, so you get AI-specific building blocks without the managed sync that keeps a knowledge base fresh.

Choose an external vector store through n8n or Make when your team already runs Pinecone or Qdrant in production and wants the agent to query the same index other services use. In that case, a shared vector store is worth the manual pipeline. For a team standing up RAG over internal documents from scratch, Sim's Knowledge Bases remove the re-indexing and sync work that otherwise turns into a standing maintenance job.

How do Sim, n8n, Zapier, Make, and Gumloop compare across data and RAG use cases?

The table below maps each platform against the four use cases covered above. Read "native" as built into the platform with no extra tooling, "add-on" as a third-party integration you connect and maintain, "partial" as a built-in capability you still chain together by hand, and "manual" as work you assemble yourself with code or multi-step logic.

Sim carries native support across all four use-case rows because Knowledge Bases, Tables, and Files exist as first-class primitives rather than integrations you wire together, and Mothership adds a build layer none of the others offer. Gumloop comes closest on document and enrichment work through its AI node library, but it chains extraction node by node and leaves you to assemble retrieval infrastructure for RAG. n8n and Make both reach every use case through add-ons or manual construction, which trades setup effort for control. Zapier covers extraction and querying through app integrations, and its agent product accepts knowledge sources, but it has no standalone vector store to query directly, so RAG over a large internal corpus falls outside what it does well.

Use the table to confirm the verdicts, then read the "choose X when" sections that follow to match a platform to your actual workflow shape.

Choose n8n when

Pick n8n when you want to run the whole thing on your own infrastructure and you already write code inside your workflows. n8n installs on your own servers, so your documents and database connections never leave a network you control. For teams with compliance rules that forbid sending internal files to a hosted platform, that alone settles the decision. Worth noting that Sim self-hosts too, under Apache 2.0 rather than n8n's fair-code license, which carries commercial-use restrictions Apache 2.0 does not.

The second reason is the Code node. n8n lets you drop JavaScript or Python into any step, which means you can assemble a RAG pipeline exactly the way you want it by wiring a vector database, an embedding call, and a retrieval query together by hand. You give up the native Knowledge Base that Sim provides, but you gain full control over chunking, indexing, and which model touches your data.

The third reason is momentum. If your team has already built dozens of n8n workflows and your operations run through them, adding light document extraction or a database query to an existing flow costs less than migrating to an agent-native platform. Stay with n8n when self-hosting, custom code, and prior investment matter more than having retrieval built in. See the full OpenAI AgentKit vs n8n vs Sim comparison for a deeper breakdown.

Choose Zapier when

Choose Zapier when your team already automates operations through it and your document and data work is a small part of a much larger app landscape. Zapier connects to more than 6,000 apps, so if your daily job is moving records between a CRM, a help desk, a spreadsheet, and a billing tool, keeping one more task inside Zapier beats bolting on a second platform.

The tradeoff is real. Zapier's native document parsing and retrieval primitives are thin, so anything involving vector search or heavy PDF extraction pushes you toward its AI actions or a third-party parsing add-on. For a light task like pulling a few fields off an invoice and dropping them into a sheet, that is fine. For semantic search across thousands of internal documents, you will fight the tool. Per-task pricing compounds the issue at volume, since every parsing call and every retrieval step meters separately.

Stay with Zapier when the breadth of connections carries the workflow and the data extraction is occasional rather than central. If document parsing, database queries, and retrieval become the core of what your agent does, you have outgrown what Zapier handles well.

Choose Make when

Choose Make when you need to see and control every step of a branching workflow, and built-in RAG matters less than that visibility. Make's canvas exposes each operation as a discrete module across 3,000+ integrations, so you can trace exactly how a record moves through filters, routers, and transformations. For workflows with heavy conditional logic, where a document routes differently based on ten field values, Make gives you a level of granular control that agent-native abstractions hide behind a prompt.

That control fits ops teams processing structured records that follow strict rules rather than open-ended documents. If your extraction job is really a series of if-then transformations against known fields, Make's router and aggregator modules handle it cleanly, and you can debug any single step in isolation. You give up native semantic retrieval, so RAG over unstructured documents means wiring in an external vector store and managing the pipeline yourself. When your data is already structured and your logic is complex, that tradeoff favors Make.

Reach for Make when the workflow shape is the hard part and retrieval is secondary. Reach for Sim when semantic search over documents sits at the center of what your agent does.

Choose Gumloop when

Choose Gumloop when you want pre-built AI nodes for scraping and enrichment, and you'd rather not assemble retrieval infrastructure yourself. Gumloop ships a node library aimed at AI tasks out of the box, so pulling data off a web page, cleaning it, and enriching it with a model takes a few connected nodes rather than a custom pipeline. For teams whose main job is turning messy web sources into usable records, that packaging removes a lot of the setup Sim expects you to configure.

Gumloop fits marketing, sales, and research teams running enrichment at volume, where the input is a list of URLs or companies and the output is a structured table. Its scraping and extraction nodes are tuned for that pattern, and you spend your time chaining capabilities instead of standing up parsers. The tradeoff shows up when you need durable semantic retrieval over a document corpus that changes, since Gumloop leans on task-level AI nodes rather than a managed vector store with sync. It is also closed source with no self-hosting path, so regulated data has to leave your network. Pick Gumloop when enrichment is the workflow. Pick Sim when a synced knowledge base is.

Choose Sim when

Choose Sim when document extraction, structured data, and semantic retrieval sit at the center of the agent you're building, not at the edges. If your workflow parses PDFs into rows, queries that data in plain English, and retrieves relevant passages from internal documents, Sim gives you all three as native primitives rather than three separate integrations you wire together and maintain, across 1,000+ integrations and every major model provider.

Sim's Knowledge Bases handle semantic retrieval with connector sync across 50+ sources, so your RAG index stays current without a manual re-indexing job. Tables store extracted data in a structured form your agent can query directly. Files handle document ingestion at up to 100 MB per file with OCR for scanned pages, no bolt-on parsing service required. Mothership lets you describe the whole pipeline in plain English and have Sim build, test, and deploy it. You skip the plumbing that n8n, Zapier, and Make require to reach the same result.

Sim is Apache 2.0 licensed and self-hostable through Docker or Kubernetes, and the cloud platform is SOC 2 compliant, so regulated teams can keep documents inside their own infrastructure without giving up the native primitives.

Pick Sim when you'd otherwise spend more time assembling a vector database, a parsing service, and a data store than building the actual agent logic. Teams shipping RAG-heavy agents feel this most, since every external dependency adds a failure point and a sync problem to debug. If retrieval and extraction are the product, start at Sim and add integrations only where its native pieces fall short.

For the wider field, the best AI agent platforms in 2026 ranks platforms on general agent capability, open-source AI agent platforms covers the self-hostable subset, and how to build AI agents walks through assembling a first workflow.