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You do not need a dedicated vector database to build retrieval for an AI agent. Depending on the questions your agent answers and the systems you already operate, a BM25 or full-text search engine, a relational database, graph retrieval, or an agent that iteratively calls search tools may fit better. The right choice is the one that retrieves supporting evidence reliably at acceptable latency and cost—not a category that wins in the abstract.
For text-heavy collections with exact names or identifiers, start with a lexical-search baseline. Consider a relational system when retrieval belongs alongside structured application data, a graph when answers depend on explicit relationships, and agentic search when the agent needs to refine or combine searches. Reranking can improve a multi-stage design, but it adds work that must be measured. The comparison below is a practical evaluation order, not a universal product ranking.
At a glance
| Name | Best for | Key spec | Checked |
|---|---|---|---|
| BM25 and full-text search | Text-heavy corpora, exact terms, names, and identifiers | Lexical first-stage retrieval; can be combined with vector search or reranking | Nov 2026 |
| Relational database retrieval | Keeping application data and retrieval in an existing database environment | PostgreSQL with pgvector is one documented option | Nov 2026 |
| Graph retrieval | Questions that rely on entities, relationships, or connected facts | Can complement vector similarity with explicit graph relationships | Nov 2026 |
| Agentic search | Questions that require iterative searching, query refinement, or source selection | The agent can select and repeat searches rather than depend on one retrieval pass | Nov 2026 |
| Hybrid retrieval and reranking | Workloads needing more than one kind of evidence or a stronger final ordering | Combine a first-stage retriever with a second-stage ranking step | Nov 2026 |
What are the best alternatives to a vector database for AI agent retrieval?
The most useful alternatives are full-text search, a relational database with retrieval capabilities, graph queries, and agentic search. They solve different problems; many production designs combine them instead of replacing vector retrieval with one other technology.
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There is no evidence-backed universal winner among these categories. Vendor guidance describes supported capabilities and architecture choices, while published surveys and studies have their own scope and limitations. Evaluate the options against your own corpus, queries, permissions, freshness needs, and operational constraints.
When is BM25 or full-text search a better fit?
Start with BM25 or full-text search when finding the exact wording in a text collection matters as much as, or more than, finding conceptually similar passages. It is a useful baseline for names, identifiers, technical terms, and other lexical clues.
BM25 is a lexical-ranking method: in broad terms, it scores documents based on how well their words match a query, with adjustments for how common terms are and how long documents are. Unlike vector similarity, it does not depend on representing the text and query as embedding vectors. This makes lexical search a natural candidate when a user knows the words likely to appear in the source. The 2025 VLDB workshop paper describes BM25 as a strong first-stage baseline for agent retrieval (VLDB workshop paper).
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That does not make lexical search the best choice for every question. A query phrased differently from the source text can miss relevant content if the terms do not match. The practical test is whether the approach finds evidence for representative queries, including paraphrases as well as exact phrases. Search-engine systems may also offer vector or hybrid capabilities; Haystack’s document-store guidance describes mature full-text support and notes that some integrations provide vector capabilities too (Haystack’s document-store guidance).
For many teams, the decision is not “BM25 or vectors forever.” A sensible sequence is to establish a lexical baseline, inspect the misses, and add semantic retrieval only if the workload shows that lexical matching is not finding enough relevant evidence. In a hybrid design, lexical and semantic candidates can supply different kinds of matches. Measure whether that extra retrieval path improves answer-supporting evidence enough to justify its complexity.
Can an existing relational database handle agent retrieval?
It can be a strong candidate if the application already relies on a relational database and its data, filters, and retrieval need to work together. PostgreSQL with pgvector is one documented route; the trade-off is that you must validate its performance and operational fit for your particular workload.
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Keeping application records and retrieval in a familiar database environment may reduce the number of separate systems the team has to operate. It can also be a natural fit when questions involve structured fields or when retrieval results must be checked against application data. Haystack lists PostgreSQL with pgvector and related managed options among its document-store choices (Haystack documentation), and AWS identifies Aurora PostgreSQL with pgvector as a custom retrieval path for organizations that already have PostgreSQL vector infrastructure (AWS retrieval-architecture guidance).
Using an existing database is not automatically simpler or faster. Assess index behavior, corpus size, query patterns, update frequency, workload scale, and the limits of the deployment you have. A database that works well for application transactions may still need careful evaluation for retrieval workloads. Consider how metadata filters, joins, and permissions are expressed, and whether the chosen retrieval method can return the right evidence under those constraints.
If you already operate PostgreSQL, compare the database-based path with the cost and complexity of adding another retrieval service. Count not just infrastructure, but also index maintenance, data synchronization, troubleshooting, and the expertise needed to run the system. The comparison is specific to your environment; no general scale or latency threshold is established at which one approach becomes better.
When should an agent use graph retrieval?
Evaluate graph retrieval when the answer depends on explicit connections between entities—for example, following a chain of relationships or combining facts linked across records. A graph can complement semantic search rather than replace it.
In a knowledge graph, entities and their relationships are represented as structured connections. That structure can help with questions whose answer depends not just on which passage resembles the query, but on how people, events, systems, or concepts relate. A vector index can help locate semantically relevant material; a graph can represent connections that need to be traversed or queried directly. The VLDB workshop paper describes hybrid storage in which vectors support semantic similarity and a graph stores structured relationships (VLDB workshop paper).
Graph retrieval adds its own modeling and maintenance demands. The team must decide which entities and relationships matter, keep that representation current, and test whether the graph answers the questions users actually ask. It is not a shortcut for an unstructured corpus in which relationships are unclear or rarely needed. A graph-plus-search design can be more appropriate when the corpus contains both unstructured text and meaningful, queryable relationships.
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Cloud architecture guidance offers concrete examples rather than a category-wide verdict: AWS names Neptune Analytics for combining vector search with graph queries, alongside other retrieval options in its guidance (AWS architecture guidance). Treat such documentation as evidence of an available design path, not as an independent benchmark or a recommendation for every deployment.
What is agentic search, and when does it help?
Agentic search lets an agent choose and iterate retrieval actions: it can select a source, reformulate or decompose a query, inspect intermediate results, and search again. Consider it when a question requires multiple steps or sources, while accounting for the additional latency and cost of those calls.
Traditional retrieval commonly sends a query to a retriever and supplies the resulting passages to a model. Agentic retrieval can make source selection and subsequent searches part of the workflow. Microsoft describes this iterative selection among sources as agentic RAG (Microsoft’s information-retrieval guidance). This approach may be useful when a question needs decomposition, when one search reveals terms for a better next search, or when the agent must inspect several sources before answering.
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A 2026 arXiv preprint reports that its agentic keyword-search implementation reached over 90% of performance metrics compared with traditional RAG in the authors’ evaluation (the authors’ preprint). This is a study-specific result, not a general benchmark across systems or tasks, and it does not guarantee that agentic keyword search will match a vector-based system on your workload. Use it as a reason to evaluate the approach, not as a prediction of your outcome.
Should you add reranking?
Reranking is worth evaluating when the first-stage retriever returns plausible candidates but puts the most useful evidence too low in the list. It can improve ordering after retrieval, but its relevance gain should be compared with its added latency and cost.
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A common multi-stage pattern is to use a relatively fast retriever to create a candidate set, then apply a second-stage ranking step to order those candidates. The first stage might be BM25 or a hybrid retriever; the reranker then concentrates on which retrieved items are most relevant to the query. Microsoft describes semantic ranking as a second stage after BM25 or hybrid retrieval and advises measuring relevance and latency on the target workload (Microsoft retrieval guidance). The VLDB workshop paper also describes two-stage retrieval as a common pattern (VLDB workshop paper).
Reranking cannot rescue evidence the first stage never retrieved. If relevant passages are absent from the candidate pool, improve the retrieval or query strategy before expecting a ranking step to solve the problem. Compare first-stage and reranked results on the same held-out query set, and record both evidence quality and end-to-end latency. Keep reranking only when its measured benefit matters for the agent’s answers.
How should you choose among retrieval approaches?
Use a representative evaluation set and compare evidence quality, latency, cost, freshness, structure, and operational fit. The best design is the least complicated approach that reliably supports the answers your agent must give.
- Collect realistic questions and documents. Include exact names and identifiers, paraphrased requests, questions that need metadata filters, and questions requiring connected facts or multiple sources. Use the intended corpus rather than a generic benchmark.
- Define what counts as a successful retrieval. For each question, identify the source passages, records, or relationships that actually support a correct answer. A result that sounds related is not enough if it omits the evidence the agent needs.
- Establish a BM25 or full-text baseline. This is especially important for text-heavy collections and queries with identifiable terms. Record misses separately for exact-match failures, paraphrase failures, and multi-step questions.
- Test the simplest existing-system path. If the application already uses a relational database, assess its retrieval capabilities and how filters, joins, and permissions work. If the answer depends on explicit relationships, include a graph or graph-plus-search candidate.
- Try hybrid retrieval or reranking when the misses justify it. Add a second method to address an observed shortcoming, not simply because a more elaborate architecture is available. Keep the candidate set and test queries consistent when comparing results.
- Evaluate iterative agentic search on multi-step questions. Measure the total number of calls, elapsed response time, and whether later searches bring in useful evidence. Set stopping conditions and account for the added complexity.
- Include freshness, permissions, and operations. Check how quickly updates become searchable, whether access controls are enforced for every retrieval path, and how much synchronization and maintenance the design needs.
- Choose using the complete workload. Compare answer-supporting retrieval quality alongside end-to-end latency and operating cost. A result that improves one narrow query type but worsens the questions users ask most may not be a worthwhile trade.
Microsoft’s guidance explicitly recommends measuring relevance and latency before production (Microsoft information-retrieval guidance). The vector-database survey also highlights hybrid queries that combine attributes with vector similarity as a design challenge (survey of vector database management systems). Together, these points argue for testing the actual combination of content, filters, and answer needs rather than choosing from a feature list alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which approach fits common agent-retrieval scenarios?
Match the retrieval design to the dominant failure you need to prevent: missed exact terms, disconnected structured data, missing relationship paths, or questions that need several searches. A mixed workload may need a combined architecture.
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| If your workload is… | Start by evaluating… | Watch for… |
|---|---|---|
| Text-heavy, with exact terminology or identifiers | BM25 or full-text search | Paraphrases or concept matches that do not share source wording |
| Closely tied to structured application data | The relational database already in use, including its retrieval capabilities | Index performance, update behavior, workload limits, and filter requirements |
| Built around connected entities or multi-hop facts | Graph retrieval or graph-plus-search | The effort to model, update, and validate the relationships |
| Likely to require query decomposition or repeated source selection | Agentic retrieval | Extra calls, latency, cost, and clear stopping criteria |
| First-stage results contain good evidence but rank it poorly | A reranking stage | Whether the first stage retrieves the needed candidates at all |
These are starting points, not exclusive assignments. Exact terminology can coexist with semantic questions; structured records can link to documents; a graph can supply relationships while search retrieves supporting text. The VLDB workshop paper describes vectors and graphs serving complementary roles, while Microsoft’s guidance describes hybrid retrieval and semantic ranking as stages that can be evaluated together. Let the query set reveal whether the combination is useful.
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What are the common mistakes when replacing vector search?
The biggest mistake is treating a technology category as an answer before identifying what retrieval failures matter. Another is comparing systems on a narrow search-speed measure while ignoring evidence quality, updates, filters, and the complete agent workflow.
- Assuming lexical search handles every paraphrase. BM25 is a strong baseline, not proof that word matching will cover conceptually different wording. Include paraphrased queries and inspect the missing evidence.
- Assuming an existing database is automatically the cheapest option. Fewer systems can simplify operations, but index tuning, workload contention, and maintenance still have costs. Evaluate the actual deployment.
- Adding a graph without a relationship problem. Graph structure is valuable when relationships are central to the question. If users mainly seek passages by wording, graph modeling may add work without improving retrieval.
- Counting agentic calls as free. Each additional search can add time and operational cost. Measure the complete path and establish a stopping rule.
- Expecting reranking to find missing documents. A second-stage ranker can reorder candidates; it cannot rank evidence that the first stage did not retrieve.
- Optimizing a single metric. Retrieval quality, latency, cost, freshness, permissions, and operational constraints can pull in different directions. Select against the production use case, not a standalone metric.
How we picked the approaches in this comparison
The order reflects a practical evaluation sequence, not an overall ranking of technologies. It begins with the lexical baseline highlighted as useful for agent retrieval, then considers existing relational infrastructure and relationship-focused graph retrieval, followed by iterative agentic search and the multi-stage option of reranking. The criteria are retrieval quality, latency and cost, update freshness, structure and filtering, operational fit, and the kinds of questions each approach serves.
Product documentation in the source set establishes documented capabilities and decision guidance, not independent comparative performance. The 2024 survey provides broad context on vector-database design, and the 2026 keyword-search preprint reports a result specific to its own evaluation. Neither supports a universal claim that one alternative is faster, cheaper, or more accurate for all agents.
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Can an AI agent retrieve documents without embeddings?
Yes. A lexical search system can retrieve passages using query terms without embedding vectors, and an agent can call search tools repeatedly or combine different sources. Whether that is enough depends on how often the relevant evidence uses wording unlike the query.
Does BM25 understand the meaning of a query?
BM25 scores lexical matches rather than reasoning about meaning in the way people do. It may still work well when the words in the question closely match the source, but paraphrase-heavy queries should be included in evaluation.
What is the difference between hybrid search and agentic search?
Hybrid search combines retrieval methods, such as lexical and semantic search, within a retrieval design. Agentic search describes an iterative workflow in which the agent can choose or refine searches based on what it has found; a system can use both.
Is the reported 90% result a guarantee that keyword search can replace vector retrieval?
No. The figure comes from the authors’ evaluation of one agentic keyword-search implementation against traditional RAG, as reported in a 2026 preprint. It should not be read as a general result across tasks, corpora, or production systems.
