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Short answer: these are different architectural layers, not three interchangeable database products. A vector database retrieves semantically similar passages. A knowledge graph stores entities and explicit relationships that can be traversed. A context engine is a broader system that decides what an AI agent should remember, how that information is governed and maintained, and which retrieval tools assemble it at runtime. Many useful systems combine all three.
Start with ordinary or hybrid retrieval-augmented generation (RAG) when a relevant passage usually answers the question. Add graph structures when the answer depends on explicit, multi-step connections among people, products, events, or records. Add context-engine capabilities when an agent must assemble permitted, current context from business data, interaction memory, and execution traces.
What each term means
Vector database: semantic retrieval
A vector database indexes embedding representations of documents, passages, images, or other items. During a query, an embedding model converts the user’s request into a vector; similarity search returns nearby vectors, and the application places the corresponding source text in the model’s prompt. The common document pipeline is to extract text, split it into chunks, embed and index those chunks while retaining their source mapping, then embed a user query and retrieve semantically similar chunks. AWS describes this flow in its knowledge-base documentation.
That makes a vector index a retrieval mechanism, not a complete memory policy. Your application still has to decide what is worth storing, how records are scoped to a user or session, when they expire, how corrections are applied, and what a caller is authorized to see. Similarity can find text that sounds related, but it does not by itself create a dependable map of who owns what, which event preceded another, or whether two records refer to the same entity.
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Knowledge graph: explicit, queryable relationships
A knowledge graph represents entities and the links among them: for example, a customer, a contract, a product, and a support incident connected by typed relationships. Graph retrieval can follow those links to answer relational or multi-hop questions. Vector search is often useful for finding an entry point or matching a user’s wording to the right node or passage.
In Google Cloud’s GraphRAG reference design, source files are processed into graph nodes and vector embeddings. Similarity search identifies relevant nodes, graph traversal gathers connected information, and a ranking and summarization stage prepares the result for the model. The architecture is documented by Google Cloud. The same page gives an important boundary condition: “However, if the source data that you use for augmenting prompts doesn’t have complex inter-relationships, then RAG might be an appropriate choice.” It is guidance from a vendor reference architecture, reviewed July 1, 2025, rather than a universal benchmark.
Graphs introduce real ingestion work. Data must be parsed, entities resolved, relationships extracted or modeled, and changes propagated. In the cited design, a generative model and a processing function perform extraction and graph construction before storage. A graph is therefore not a free accuracy upgrade; its value has to justify its modeling and maintenance cost.
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Context engine: orchestration and lifecycle
“Context engine” is broader than “vector database.” Elastic defines it as a component that gathers and builds context from business data and interactions. Its FAQ states, “A context engine builds on vector databases and memory stores.” In that framing, the vector database provides storage and retrieval, while a memory store captures interaction context; the context engine decides what enters those stores, how it is structured and maintained, how it is governed and secured, and which tools expose it for retrieval. See Elastic’s context-engine explanation (page accessed October 13, 2026; the page does not state a publication date).
Elastic also describes an AI index containing “knowledge indicators” from data sources and memories from agent interactions and traces, with provenance attached. Those are product descriptions, not an independent taxonomy. A useful translation is that context engineering is the application and systems work of selecting, shaping, securing, and refreshing context; a context engine is a product or service that may implement part of that work.
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AI memory: persistence policy, not a storage format
Memory describes what persists and for how long, independently of the retrieval technology. MongoDB’s agent documentation distinguishes short-term memory—recent turns and active task information—from long-term memory that persists across sessions, such as earlier conversations or preferences. MongoDB documents both scopes here.
A sound memory design specifies ownership, scope, retention, correction, deletion, freshness, provenance, and authorization. Saving every conversation is neither automatically useful nor safe. A vector index can retrieve a stored memory, but it cannot decide whether that memory should exist or whether the current user may read it.
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How the approaches differ in practice
| Dimension | Vector database | Knowledge graph | Context engine |
|---|---|---|---|
| Primary question shape | “Which passages mean something like this query?” | “How are these entities connected, and what follows across several links?” | “Which permitted facts, memories, and tools should this agent use now?” |
| Typical data | Chunked text, images, transcripts, or other embedded items | Entities, typed edges, attributes, and linked source evidence | Business records, indexes, memories, traces, policies, and tool metadata |
| Memory scope | Whatever records the application indexes | Durable relationships and their history, if modeled | Session state plus durable user, task, or organizational context |
| Ingestion and updates | Chunk, embed, index, and refresh changed items | Extract or model entities and relationships; resolve identities; maintain edges | Select, summarize, classify, govern, and refresh context across systems |
| Retrieval | Vector similarity, often combined with keyword or metadata filters | Graph traversal, often seeded by keyword or vector search | A router or workflow that may call vector, keyword, graph, SQL, memory, and business tools |
| Governance concerns | Source mapping, tenant isolation, filtering, deletion, and access checks | Entity-level permissions, edge visibility, provenance, and update consistency | Policy enforcement across every source, tool, memory type, and prompt assembly step |
| Operational burden | Embedding-model, index, chunking, and freshness management | All vector concerns plus schema, extraction, entity resolution, and graph maintenance | Coordination among stores, policies, retrieval tools, observability, and lifecycle jobs |
| Best evidence of success | Recall and precision of relevant passages, supported answers, latency, and cost | Correct entities and paths, multi-hop answer support, freshness, latency, and cost | Task success, groundedness, permission correctness, freshness, latency, cost, and safe memory behavior |
No reviewed source establishes a universal speed, accuracy, or cost winner. Compare systems on your own representative queries, data shape, update rate, permissions, latency target, and operating budget.
When a vector database is the right first step
Use vector or hybrid RAG when users mainly ask for explanations, summaries, or lookups that can be answered by relevant passages in a document collection and explicit links among entities are not central. Typical examples include an internal policy assistant, a product-manual search tool, or a support bot retrieving troubleshooting paragraphs.
A practical vector-RAG pipeline
- Collect and normalize sources. Preserve document identifiers, section titles, timestamps, owners, and access-control metadata alongside the text.
- Split for retrieval. Choose chunks that preserve enough local meaning to answer a question, and retain the exact source mapping for citations.
- Embed and index. Store embeddings with metadata that supports tenant, department, language, version, and deletion filters.
- Retrieve broadly enough. Use vector similarity, keyword search, or a hybrid query when names, codes, and exact phrases matter.
- Filter before generation. Enforce authorization and freshness constraints before passages enter the model context.
- Generate with evidence. Instruct the model to answer from retrieved material, identify uncertainty, and expose source references.
- Measure the workload. Track retrieval recall, answer support, latency, token use, cost, and failures on a fixed evaluation set.
Google’s conventional RAG architecture discusses trade-offs among managed vector and database choices, including the balance between configuration control and management effort. Its reference architecture is here. The choice of managed or self-operated infrastructure is an operational decision, not evidence that one retrieval category always performs better.
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When a knowledge graph or GraphRAG earns its complexity
Add a graph when the answer requires joining records or traversing relationships that are important enough to model explicitly. Signals include questions about ownership chains, dependencies, organizational hierarchies, timelines, supplier-to-component links, or the impact of a change across connected systems.
GraphRAG retrieval flow
- Define entities and edge types. Decide what counts as a customer, contract, component, event, or other node, and give relationships precise meanings.
- Extract and resolve. Parse source material, identify entities, link mentions to canonical records, and record confidence and provenance.
- Store source evidence. Keep the passage, document version, timestamp, and permissions that justify each node or edge.
- Find entry points. Use keyword, vector, or hybrid search to map the user’s wording to likely entities and source passages.
- Traverse selectively. Follow only relationship types and hop counts relevant to the question; unconstrained traversal creates noise and latency.
- Rank and summarize. Combine node, edge, and source relevance, then provide the model with a compact, cited subgraph.
- Refresh continuously. Reprocess changed records, retire obsolete edges, and monitor extraction or identity-resolution errors.
Google Cloud’s newer multimodal GraphRAG example, reviewed April 6, 2026, illustrates orchestration choices for combining retrieval and tools; it is an architecture example, not a universal recipe. Read the multimodal orchestration reference.
Cases where a graph is unnecessary
If the corpus is mostly independent passages and questions rarely require connecting entities, graph construction may add failure points without improving answers. Google Cloud’s guidance explicitly leaves ordinary RAG as the appropriate choice for data without complex interrelationships. Start with a measured baseline rather than creating a graph because “GraphRAG” sounds more advanced.
When you need a context-engine layer
A context-engine layer becomes valuable when an agent must combine more than document snippets: current business data, prior turns, durable preferences, task state, execution traces, and specialized retrieval or action tools. The layer should decide what context is relevant, permitted, fresh enough, and affordable for the current task.
Core responsibilities
- Context selection: route a request to vector, keyword, graph, SQL, APIs, memory stores, or several of them.
- Memory policy: classify candidate memories, assign scope and retention, merge corrections, and support deletion.
- Provenance: preserve source, timestamp, transformation, and confidence so an answer can be audited.
- Authorization: apply identity, tenant, role, and record-level permissions before retrieval and again before tool execution.
- Freshness: distinguish a durable preference from volatile operational state and refresh each at an appropriate cadence.
- Context budgeting: rank, compress, deduplicate, and limit material to fit latency and model-token constraints.
- Observability: log which sources, memories, policies, and tools contributed to each response without leaking protected content.
These functions can be implemented as application services rather than purchased as one product. Calling the result a context engine is useful only if the system actually performs this orchestration and lifecycle work.
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How do AI agents remember previous conversations?
They remember only what the application deliberately persists and makes retrievable. During a session, the agent can carry recent turns and active task state in a short-term context. For later sessions, the application may write selected facts—such as a user preference, an approved project detail, or an unresolved task—to a durable memory store, then retrieve those facts when the user returns. The MongoDB distinction between short-term and long-term memory is a useful model, but the storage choice can be a document store, relational table, graph, vector index, or combination.
Memory policy checklist
- Define who owns a memory and whether it is personal, team-wide, or organizational.
- Record why it was stored, its source, confidence, and last verification time.
- Set retention and deletion behavior, including user-visible correction and export paths.
- Separate ephemeral task state from durable preferences and regulated records.
- Apply authorization at retrieval time; similarity does not grant access.
- Resolve contradictions by preferring newer, authoritative evidence or asking the user.
- Test whether a memory improves task success instead of assuming that more history is better.
Hybrid designs are often the practical answer
Vector and graph retrieval solve different parts of the search problem. A hybrid flow can use embeddings to locate relevant passages or entities, traverse explicit relationships to gather connected evidence, and rerank the combined results before generation. A context layer can then add session state, durable memories, permissions, and tool results.
A 2025 VLDB workshop survey describes vector indexes for semantic similarity alongside graphs for structured relationships, including staged retrieval with reranking. The survey is available from VLDB Workshops. It is an overview, not a controlled comparison proving that hybrid storage is best for every application.
Example hybrid request
For “Which customers are affected by the component change announced last week, and what contractual notice is required?”, vector search can find the announcement and contract clauses; entity resolution can identify the component and customers; graph traversal can follow component-to-product-to-customer links; a policy or contract service can verify current obligations; and the context layer can enforce tenant permissions, attach dates, and fit the evidence into the model’s prompt.
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- Characterize the question. If semantic passage matching is enough, begin with vector or hybrid RAG. If the question is about connected entities or multiple hops, test a graph. If it spans sources, interactions, and tools, design a context layer.
- Map the data. List unstructured documents, structured records, relationship data, session state, durable memories, and traces. Note owners, update rates, and permissions for each.
- Build the smallest baseline. Evaluate ordinary RAG before adding extraction or orchestration complexity.
- Add only demonstrated capabilities. Introduce graph retrieval when baseline errors come from missed relationships; introduce memory when cross-session continuity is a requirement; introduce context orchestration when routing and governance cross system boundaries.
- Define operating controls. Specify ingestion jobs, consistency expectations, deletion workflows, access checks, audit logs, model versions, and rollback procedures.
- Evaluate representative tasks. Measure answer support, retrieval recall and precision, multi-hop correctness, permission violations, freshness, latency, token consumption, and cost.
- Reassess as data changes. A corpus that begins as documents may later acquire important relationships or interaction history; architecture should allow incremental adoption.
Common failure modes and fixes
“The vector result is related but not the answer”
Improve chunk boundaries and metadata, add keyword or exact-match retrieval for identifiers, filter by version and permissions, and evaluate recall on questions containing names, codes, and dates.
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“The graph contains plausible but wrong links”
Strengthen entity resolution, require provenance and confidence for extracted edges, validate high-impact relationships against authoritative records, and monitor stale or contradictory links.
“The agent remembers something it should have forgotten”
Separate session state from durable memory, assign retention classes, provide deletion and correction operations, and test retrieval after revocation rather than merely testing insertion.
“The answer leaks another user’s context”
Enforce tenant and identity filters in every retrieval path, including graph traversal and memory lookup. Do not rely on the model to hide unauthorized passages after they have entered the prompt.
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“More context makes answers worse”
Rank and deduplicate evidence, cap hop counts, summarize long histories, and expose only the fields needed for the task. Log context selection so irrelevant or stale material can be removed systematically.
“The system is too hard to operate”
Count every index, graph, extraction job, memory policy, synchronization path, and model dependency. Prefer a simpler architecture when its measured task quality meets the requirement; sophistication without operational ownership is a liability.
What to measure before choosing
Use a workload-specific test set containing ordinary lookups, exact identifiers, multi-hop relationship questions, cross-session requests, updates, deletions, and unauthorized access attempts. Report:
- Retrieval quality: relevant-passage recall and precision, correct graph paths, and source coverage.
- Answer quality: factual support, citation correctness, abstention when evidence is missing, and task completion.
- Memory behavior: useful recall, stale-memory rate, correction success, deletion success, and cross-tenant isolation.
- Freshness: time from source change to retrievable update and behavior during partial failure.
- Operations: p50 and tail latency, token use, infrastructure and model cost, indexing time, and maintenance effort.
- Safety and governance: authorization accuracy, provenance availability, audit completeness, and prompt-injection resilience.
Do not publish a category-wide speed, accuracy, or cost claim from a vendor diagram or an unrelated benchmark. The reviewed material contains no independent, comparable head-to-head figure for vector databases, knowledge graphs, and context engines.
Bottom line
Choose a vector database for semantic passage retrieval, a knowledge graph for explicit and traversable relationships, and a context-engine layer for selecting, governing, and assembling context across data, memory, and tools. Treat AI memory as a policy problem as much as a storage problem. Begin with the simplest RAG baseline that can answer your real questions, add graph structures when relationship errors justify them, and add context orchestration when lifecycle, permissions, provenance, and multi-source agent behavior become first-class requirements.
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