Is Synorb a knowledge graph?

Synorb behaves like a temporal context graph. It connects Source Channels, Streams, Manifests, Signals, Briefs, Records, stable IDs, and source provenance so agents can reason over what changed.

How is this different from vector search?

Vector search helps retrieve similar text. Synorb adds source-grounded events, typed tags, provenance, published dates, delivery state, and stable IDs so agents can monitor and cite current context.

How do agents connect?

Agents can use Synorb MCP for interactive discovery and Manifest pulls. Shipped applications usually call Synorb REST, receive webhooks, or use S3 archive exports for historical backfills.

AI operator infrastructure

Context graph for AI agents.

Q: What is a context graph for AI agents?

A context graph for AI agents is a structured layer of entities, events, source links, time, and provenance that an agent can query before it reasons, searches, or acts.

A context graph gives an agent a structured map of what changed, where it came from, when it happened, and how it connects to entities, topics, and sources. Synorb's graph is temporal: Source Channels feed Streams, Streams produce Manifests, and Manifests carry Briefs, Signals, Records, stable IDs, and provenance.

Definition

The graph starts before the prompt.

Agents fail when they begin every task from a cold search box. A context graph keeps current source events organized before the agent asks a question. That lets the agent retrieve from known coverage, cite source URLs, inspect freshness, and then search only for gaps.

Inputs

Source Channels

Examplesfilings, reports, blogs, releases, data pages, podcasts, docs

Kept withsource URL, source name, published date, capture date

Outputs

Manifests

IncludeBrief, Signal, Record metadata, tags, stable IDs, citations

Delivered byREST, MCP, webhooks, and S3 archive exports

Agent payload

What an agent receives.

Synorb does not hand the agent an undifferentiated list of links. It returns structured Manifests that can be routed into retrieval, alerts, dashboards, review queues, or agent memory.

{ "manifest_id": "1777525429698648000", "stream_names": ["ai-infrastructure"], "source": { "name": "Observed Source Channel", "url": "https://source.example/update", "published_date": "2026-06-17" }, "brief": { "headline": "Source-grounded update" }, "signals": [{ "claim": "Atomic claim from the source event" }], "tags": [{ "type": "organization", "name": "Example Co" }] }

When to use it

Use a context graph when the coverage repeats.

Company and market monitors

Keep current events tied to companies, people, products, filings, and source surfaces.

Research dashboards

Give apps and agents the same source-backed state, with citations and stable IDs.

Agent apps

Feed agent workflows with structured context instead of asking them to crawl from scratch.

FAQ

Short answers.

What is a context graph for AI agents?

A structured layer of entities, events, source links, time, and provenance that an agent can query before it reasons, searches, or acts.

Is Synorb replacing search?

No. Synorb gives agents a maintained context layer for known coverage areas. Search is still useful for unknown or long-tail gaps.

How do I connect it?

Use MCP while building, REST and webhooks for live production delivery, and S3 archive exports for historical backfills. Credentials are available from /connect or https://synorb.com/connect.

Start

Give the agent context it can cite.

Test

Test Synorb feeds for free.

Want to connect to Synorb's graph to test source-grounded feeds for free? Start with free test credentials, then connect through Core MCP or REST.

Free test credentialscurl

curl -s https://synorb.com/connect