Relational databases, CRMs, ERPs, operational systems — the entities agents need to reason about (customers, orders, accounts, inventory)
JSON, event streams, Kafka topics, API responses, logs — real-time signals and operational state changes
Documents, PDFs, support transcripts, knowledge base articles, policies — content best accessed via vector similarity search
Session history, prior decisions, user preferences, workflow state — the context agents generate themselves as they work
Change data capture from relational and NoSQL sources. Keeps Redis continuously synchronized with systems of record — no batch jobs, no stale reads, no custom ETL pipelines
Unstructured content vectorized at ingest time and written to Redis vector indexes — ready for semantic search without runtime embedding latency
Kafka, event APIs, and webhook feeds — real-time operational signals written directly into Redis for immediate agent access
Vector, full-text, and hybrid search over structured and unstructured data in a single query. Powers RAG over documents and semantic similarity over entity fields
Short-term: per-session working memory across agent turns. Long-term: LLM-extracted durable facts and preferences retrieved via vector similarity. Agents get smarter over time without manual state management
Semantic cache-as-a-service — routes semantically equivalent queries to cached responses before the agent pipeline runs. Reduces LLM cost and response latency for repeated or similar requests
Schema-first MCP tool generation — define your data model once and Context Retriever auto-compiles a governed set of MCP tools the agent can call directly. No hand-written tool code, no text-to-SQL, no raw database access. The agent navigates your data in business language, not query language.
LangGraph, CrewAI, OpenAI Agents SDK, Microsoft Agent Framework, Google ADK — any framework that supports MCP tool use and memory injection
GPT-4o, Claude, Gemini, Llama — receives enriched prompts including session memory, long-term preferences, and Context Retriever tool results. Reasons over real data, not static documents
Auto-generated by Context Retriever from the entity schema. The agent calls governed, structured interfaces — not raw queries. Access controls enforced server-side at the Context Retriever layer
Agent answers are based on live entity data and retrieved context — not hallucinated or stale. Every response is traceable to a Context Retriever tool call
Downstream actions triggered by agent decisions — API calls, workflow updates, CRM writes, notifications — all driven by context assembled in Redis
Agent Memory records session outcomes and extracts durable facts. LangCache stores the response for future semantic cache hits. The system compounds with every interaction