Use Case Guide · v1.7.2

VEKTOR for Multi-Agent Systems

Every agent in a typical pipeline starts cold and keeps its own private context. VEKTOR replaces that with one shared memory graph every agent reads and writes to — with automatic conflict resolution so they never contradict each other.

The problem

In a multi-agent pipeline, agent A learns something agent C needs, but they don't share state. Worse, when two agents write competing facts to the same store, most systems just let the last write win — silently corrupting what the rest of the pipeline relies on.

How VEKTOR solves it

One MAGMA graph, shared across every agent in the pipeline. Writes go through AUDN (Add / Update / Delete / No-op) curation before they land — a five-verdict resolution pass that classifies every incoming memory against what's already there:

Verdict	Meaning
`COMPATIBLE`	New fact coexists with existing memory — both kept
`CONTRADICTORY`	Conflicts with existing memory — resolved via trust weighting, not last-write-wins
`SUBSUMES`	New fact supersedes an older, narrower one
`SUBSUMED`	New fact adds nothing the graph doesn't already know
`NO_OP`	Duplicate or noise — dropped

Trust matrix

A 2D trust matrix prevents automated agent writes from silently overriding facts a human explicitly set — agent-sourced and human-sourced memories are weighted differently during contradiction resolution.

Framework adapters

CrewAI

Drop-in VektorMemory class — use as long_term_memory on a crew, or as a standalone tool any agent can call directly.

bash

node node_modules/vektor-slipstream/Crewai/crewai-vektor-setup.js

Local HTTP bridge on localhost:3849 — zero cloud dependency, memory never leaves the machine running the crew.

LangChain

v1 and v2 adapter available — wire VEKTOR in as the memory backend for any chain or agent executor.

n8n

A VEKTOR memory node for automation workflows. Store and recall directly from any n8n flow via HTTP node, no custom code required.

bash

curl http://localhost:3848/health  # verify the n8n bridge is up

Shared graph vs. siloed memory

	Siloed per-agent memory	VEKTOR shared graph
Cross-agent context	None — each agent is blind to others	Full graph visible to every agent
Conflicting writes	Last write wins, silently	AUDN five-verdict resolution
Human vs. agent facts	Treated identically	Trust-weighted separately
Where it runs	Varies / cloud	Local SQLite, $0 egress

Next steps

See the full Integration Guides for setup details on every supported framework, or view pricing — multi-agent shared-graph support is included on the Slipstream plan.