Agents fail because of context, not models. SurrealDB + Spectron give AI agents structured context, persistent memory, and transactional consistency in one system.
THE PROBLEM
The context wall
Every AI team hits the same wall. The model is capable, but the agent cannot remember what happened two turns ago, cannot connect a user preference to a product entity, and cannot guarantee that concurrent writes stay consistent.
Today's teams assemble five or six systems - a vector database, a document store, a graph database, a cache, a queue, and a memory middleware layer - then spend months writing glue code to keep them consistent. Every seam is a place where context leaks.
Context leakage
When data flows between separate systems, agents lose context. Relationships, history, and metadata get fragmented.
No atomicity
Without unified transactions, an agent can update memory in one system but fail to update state in another.
Latency spikes
Multi-system pipelines add round trips. Each hop introduces latency that compounds under load.
Operational overhead
Five systems means five failure modes, five monitoring setups, five sets of credentials to manage.
THE SOLUTION
The read-think-write loop
Agents operate in a continuous cycle: read context, reason over it, and write results back. The context layer keeps the entire loop inside one transactional system.
Read
Query structured data, traverse knowledge graphs, perform vector similarity search, and retrieve temporal facts - all in a single SurrealQL statement.
Think
The agent reasons over rich, multi-model context. Documents, relationships, embeddings, and history arrive together - no stitching from separate systems.
Write
Persist decisions, update entities, append new facts, and trigger downstream events - all within a single ACID transaction.
CONTEXT ENGINEERING
Context engineering in one query
SurrealQL lets you combine graph traversal, vector similarity, structured filters, and temporal queries in a single statement. One round trip gives the agent everything it needs.
1-- Context engineering: combine graph, vector, and temporal data 2LET$user = user:jaime; 3LET$query_vec = fn::embed("What products does this user like?"); 4 5SELECT 6->purchased->productASpurchase_history, 7->reviewed->product[WHEREvector::similarity::cosine(embedding, $query_vec) >0.8]ASrelevant_products, 8->preferences[WHEREvalid_at<=time::now()]AScurrent_preferences 9FROMONLY$user;
MULTI-AGENT
Multi-agent coordination
Multiple agents share the same knowledge graph and memory. Coordination happens through shared context with ACID guarantees - no message passing, no race conditions.
Shared memory
Agents read from and write to the same Spectron memory graph. Knowledge accumulates across the team.
ACID consistency
Concurrent writes from multiple agents are serialised. No conflicts, no lost updates.
Event-driven handoffs
Live queries and events trigger downstream agents when relevant data changes.
Unified permissions
RBAC and record-level permissions apply consistently across all agents accessing the system.
THE STACK
SurrealDB + Spectron
SurrealDB provides the multi-model database for structured context. Spectron provides persistent memory for AI agents. Together, they form the context layer - one stack, one transaction boundary, one permission model.
LangGraph, CrewAI, and AutoGen orchestrate agent logic - the control flow. SurrealDB provides the persistent context layer beneath - the data, the memory, the knowledge graph. They are complementary. Connect through MCP, SDKs, or direct API calls.
LangChain & LangGraph
Use SurrealDB as the persistent context store for LangGraph agents. Memory, state, and knowledge in one system.
CrewAI & AutoGen
Multi-agent frameworks get shared memory and transactional coordination through SurrealDB and Spectron.
Official SDKs for Python, JavaScript, Rust, Go, Java, .NET, and PHP. Build custom integrations with any framework.
RAG retrieves chunks of text using vector similarity. The context layer provides structured, multi-modal context - graphs, documents, vectors, temporal facts, and agent memory - in one transactional system. RAG is one retrieval pattern within the context layer.
Yes. SurrealDB provides the multi-model database for context, graph traversal, vector search, and real-time subscriptions. Spectron adds structured memory on top. You can start with SurrealDB and add Spectron when you need agent memory.
SurrealDB integrates with LangChain, LlamaIndex, and any MCP-compatible client through SurrealMCP. Official SDKs are available for Python, JavaScript, Rust, Go, Java, .NET, and PHP.
GET STARTED
Build context-aware AI agents
The context layer for AI agents. Structured context, persistent memory, elastic storage. One stack.
