Context Graph for Agents
Give your AI agents persistent memory and structured reasoning context.
Built for production. Auditable by default.
Get running in minutes.
Start with AI Agents Evaluation
Before adding memory, baseline your agent's reliability. Evaluation is the entry point: measure hallucinations, identify failure modes, and establish the foundation that makes persistent context safe to deploy.
Learn about AI Agents Evaluation →Why context breaks in production
AI agents lose context between sessions, across tools, and at scale. The result: repeated questions, inconsistent answers, and hallucinations that compound with every interaction. Without structured memory, reliability degrades and continuity disappears.
What the Context Graph is
The Rippletide Context Graph is a persistent, structured memory layer for AI agents. It stores entities, relations, and facts as a graph, not flat embeddings. Each agent operates in an isolated namespace, with full provenance on every piece of context. Built to ship, not to prototype.
What you get
Cross-session memory
Agents retain context across sessions. No repeated onboarding, no lost history.
Structured recall
Entities, relations, and facts are stored as a graph, not flat text. Queries return precise, relevant context.
Full provenance
Every fact in the graph is traceable to its source. Audit any decision back to the data that informed it.
Agent-level isolation
Each agent operates in its own graph namespace. No data leakage between agents or tenants.
From tool call to persistent graph
Connect via MCP
Your client (Cursor, Claude, VS Code, or any MCP-compatible tool) connects to the Rippletide MCP server. One config file, zero boilerplate.
Call remember, recall, relate
Three tool calls are all you need. remember stores facts, recall retrieves relevant context, and relate maps connections between entities.
Persist and isolate
Every fact is written to a persistent graph, scoped to the agent. Context survives restarts, scales across sessions, and never leaks between tenants.
Built for real agent workloads
Customer support agents
Remember customer preferences, past issues, and resolution history across every interaction.
Coding assistants
Maintain awareness of project architecture, conventions, and prior decisions across sessions.
Workflow agents
Track multi-step processes, dependencies, and state transitions with full context continuity.
Multi-agent teams
Share structured context between agents while maintaining strict isolation boundaries.
Isolation by design
Every agent operates in its own graph namespace. There is no shared state between agents or tenants unless you explicitly configure it. Context boundaries are enforced at the infrastructure level, not by convention. This means no accidental leakage, no cross-tenant contamination, and full auditability on every read and write.
Context Graph for Agents
Give your agents the memory they need.
Persistent, structured, auditable context for every agent interaction. Get started in minutes or talk to our team about your architecture.
- Cross-session persistent memory
- Structured entity and relation graph
- Full provenance and auditability
- Agent-level isolation by default