How to share AI context across tools for your team
Sharing AI context across tools means keeping one team memory that every AI tool reads from, instead of context trapped inside each tool. A shared memory layer captures what happens in each session and makes it available in the next one, whichever tool or teammate runs it.
The problem: context is trapped per tool and per person
Today, AI context is trapped per tool and per person. Claude Code knows your coding sessions, Codex knows its own, and your meeting notes sit somewhere else entirely, each in its own silo. None of them share, so the same project lives as fragments scattered across tools and teammates.
When context cannot move between tools, your team's AI is permanently under-informed. Each tool only ever sees its own slice, and the full picture exists nowhere.
What sharing context across tools actually means
Sharing context across tools means one team memory that any tool can read from and write to. Rather than syncing every tool with every other tool, each session contributes what it learns to a shared layer, and each session pulls the relevant history back out of it.
That shared layer is the single source of truth for the team's working context: the decisions, the conventions, and the hard-won gotchas, available everywhere instead of locked in one app.
How teams share context across Claude Code, Codex, and chat
Teams share context across agents by routing their sessions through a memory layer that captures each one and recalls it in the next, rather than relying on any single tool's built-in memory. Because the memory lives above the tools, it is agent-agnostic: it works with Claude Code today, and Codex and other agents plug into the same shared memory as they come online.
It also crosses people, not just tools. What one engineer's session learns is what the next engineer's session, anywhere on the team, begins from. The memory follows the team, not the tool, so adding a new agent extends your context instead of fragmenting it.
What stays in sync
What stays in sync is the working context that usually leaks: the decisions a team has made, the conventions it follows, the approaches it has already ruled out, and the context behind current work. Meetings and documents feed the same layer, so spoken and written context lands in one place too.
The team stops being a set of disconnected sessions and becomes one shared brain that every tool and teammate draws on.
Frequently asked questions
Can I share context between Claude Code and Codex?+
That is the design. A shared memory layer is agent-agnostic: because it lives above the tools rather than inside any one of them, context captured in one AI coding session is recalled in the next regardless of which agent runs it. Wemory works with Claude Code today, with Codex coming next and other agents on the roadmap.
Does sharing context expose everything to everyone?+
No. Projects can be open to the whole team or restricted to specific members, and a Self-Hosted edition keeps everything on your own infrastructure. You control the scope of the shared memory and who can see each project within it.
How is this different from each tool's built-in memory?+
Built-in memory is single-tool and single-user, so it cannot move context between tools or teammates. A shared memory layer sits above the tools, so context created in one is usable in all of them, by the whole team, which is exactly what single-tool memory cannot do.
Does it include meeting and document context?+
Yes. Alongside AI sessions, Wemory captures context from meetings and documents, so decisions made out loud or written down land in the same shared layer your AI tools read from. The team's context is unified rather than split between AI tools and everything else.
What does it cost?+
Wemory is 20 USD per seat per month with the first month free and no card required. Seats are prorated with no commitment, so the cost scales with the team and there is nothing to lose by trying it across your tools.