Give every agent the same memory
ScientiaMesh is a living context layer for humans and AI agents. Capture once, keep it evergreen, and let your team plus ChatGPT, Claude, OpenClaw, and MCP-native agents reason from the same source of truth.
Built for real agent workflows
The goal is not better prompt gymnastics. The goal is durable memory that flows across people, tools, and agents with clean boundaries and source traceability.
Shared Context Bus
Every capture lands in one living memory graph. Human teammates and AI agents query the same evolving context instead of fragmented notes.
Capture at Runtime Speed
Text, files, voice, links, and events ingest fast, then get normalized for retrieval so your agents can use fresh context without manual formatting.
Retrieval that Cites Sources
Answers point back to original captures. You keep trust and auditability while agents move faster.
Scoped Memory Boundaries
Separate work, personal, and shared meshes with explicit permissions. Agents only see the context they are allowed to see.
Human + Agent Collaboration
Capture once, then let teammates and assistants co-build on top of the same memory rather than re-explaining history every sprint.
Memory That Scales With You
No hard ceiling on useful context. As your graph grows, discovery gets sharper and your agent workflows get more capable.
From capture to agent memory
Three steps: ingest, structure, execute. This is the loop that makes agentic systems actually compound over time.
Ingest from anywhere
Capture notes, files, voice, links, and tool output from any device or workflow. No manual filing step.
Synapses build structure
ScientiaMesh extracts entities, links related context, and keeps memory queryable through a consistent graph model.
Humans and agents execute
Teams and AI agents query the same memory with scoped access, then act with better context and less repeated prompting.
Who this is built for
ScientiaMesh fits teams and builders who treat context as infrastructure, not chat history. If you run multiple agents, this becomes your memory backbone.
For Agent Builders
OpenClaw, Claude Code, MCP servers, and custom loops
- Give every agent the same memory contract through MCP instead of bespoke adapters
- Feed captures and execution output back into the graph so each run gets smarter
- Preserve source lineage and replayability when debugging multi-agent behavior
- Drop your current tools in place, no full stack rewrite required
For Product and Ops Teams
People coordinating work across docs, tickets, chat, and calls
- Keep institutional memory alive as projects and owners change
- Stop context switching between tools to answer straightforward operational questions
- Share scoped meshes with contractors, clients, or functional squads
- Use assistants as force multipliers without losing governance
For Neurodivergent Builders
ADHD minds and messy-but-structured thinkers
- Capture ideas fast without forcing perfect organization in the moment
- Let ScientiaMesh add structure later so loose notes become usable context
- Ask assistants to resurface buried threads when your focus shifts
- Keep one memory layer that supports your natural thinking style across tools
Integrate agents without glue-code fatigue
ScientiaMesh is MCP-native, OpenClaw-ready, and built to slot into your existing assistant stack. You can start simple, then scale to multi-agent workflows.
MCP by default
Connect compatible assistants once and let them query the same shared memory graph with no per-tool context duplication.
OpenClaw-ready
Use ScientiaMesh as persistent memory inside OpenClaw and Clawdbot-style agent workflows, with scoped access controls.
Evidence-backed outputs
Agent answers can include source links to your original captures so teams can verify and act with confidence.
Ready to give your agents durable memory?
Join preview and build a living context layer for your team, your tools, and your AI workflows.
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