Agent memory is a crowded space, filled with people talking about fundamentally different things. Shared context across agents and agentic systems is a different problem—I know because it's the one we work on. Memory helps one agent remember. Shared context keeps every agent, in every tool, working from the same source of truth. It eliminates the confusion that tanks agent accuracy and token spend.
About a year ago, our company PromptOwl was focused on a harness for agents, and while we were doing that a bunch of us were running mini second brains, each with a whole host of agents doing all sorts of things, and none of it was coordinated. It hit marketing and strategy first. My agents would come up with content and approaches wildly different from engineering's agents, and a shared memory wasn't going to fix that.
So we built ContextNest, a wiki-style, markdown based, governed context management solution meant for agents and people to cowork. It is open source for one user, but for communities of agents and people with different roles you will want to look at the paid versions.
We recently recorded some of this history in a walkthrough of the ContextNest technology.
Like Notion, but a lot more slick
If you've used Notion, you already know how it feels. A nest is folders and markdown files. You edit them, you leave comments on them, and people on your team can work in it without learning anything new. The graph builds itself from your tagging, your ontology, and your links, and if you want to get fancy you can put edge definitions on it. Notion is still better at polished editing and real-time co-editing, and we say so on our Notion comparison. Keep writing there if you like, and distill the pages your agents should rely on into ContextNest.
The slick part is underneath. Every single change is hash-chained, so every edit leaves an immutable record, and you can run comparison reviews as you go. You won't feel it as an end user, and it's nowhere near as cumbersome as a business user working in GitHub. When an agent runs against an old version, that run gets stored and logged. You can go back and see who approved it, when, how, and what the draft status was. For regulated work, that's the audit trail you need.
It's also agent native. You connect through the CLI, a desktop app that works as a harness, MCP, or an API, and you scope each token to a single nest, the whole vault, a user, or a specific agent. Most of the time you're not even in the interface. You're working in Claude, or Antigravity, or whatever your team already uses, and ContextNest is what keeps them all reading the same thing.
And no, this isn't SharePoint again. Wikis failed because people had to maintain them. Here the agent does most of the writing. In the video I was running a weekly report, changed one thing, and the agent told me it now conflicted with another document and showed me every place I would have forgotten to update by hand.
The video goes further. It covers why we passed on RAG, how we separate the context that should evolve freely from the context a human has to approve, and how we keep sensitive material in a local vault. Watch it here: https://youtu.be/WAkic4Z1ERs
How it compares
We keep an honest comparison page, including where the other tools win. A few places to start:
- Memory tools: Mem0, Zep, and AI memory vs. governed context. Memory serves one agent or app. Governed context holds the approved knowledge every agent reads. The two work together.
- Wikis and knowledge tools: Notion, SharePoint and Copilot, and Glean.
- Building it yourself: LangChain, CrewAI, and the rest.
- Everything else: promptowl.ai/compare.
Try it
For anything with more than one person or one agent in it, start with the hosted version. It's built for teams, with shared nests, review and approval, and the audit trail you saw above. You can also run it yourself if your data has to stay in your environment.
- Start with Magic Context on the hosted version: app.promptowl.ai/magic-context
- Pricing: promptowl.ai/pricing
If you want to poke at the engine on your own first, the CLI, engine, and MCP server are open source under AGPL-3.0, and the spec is Apache-2.0. The CLI is a single-user, local tool, so it's great for your own vault, but it won't give you team approvals.
Originally published at promptowl.ai.
Top comments (0)