Capacitor
Shared memory for coding agents
23 followers
Shared memory for coding agents
23 followers
Agents code fast. Capacitor helps your team understand them just as fast. It's a shared session layer for coding agents: hand off sessions from Claude Code to Codex, learn from past mistakes with an eval loop, review PRs with the agent's reasoning, not just the diff through a dashboard, CLI, and MCP tools your agents query directly.
This is the 2nd launch from Capacitor. View more
Capacitor is live!
Launching today
Capacitor is now out of private preview and going live!
The shared coding agent memory enables collaboration across your team, PR review with session context, multi-agent handoffs, multi-player sessions and vendor neutral switching between Claude Code, Codex, Cursor and now Kiro, Gemini CLI, PI and more. Check the comment section for more info.
Free
Launch Team / Built With
Hey Product Hunt π
I'm Lokhesh, part of the Product & AI team at Kurrent.io. Excited (and a little nervous) to announce that Capacitor is out of private preview (everyone can sign up for free): https://capacitor.kurrent.io/
The problem we kept hitting: coding agents are incredible, but they forget everything. You spend 40 turns in Claude Code ruling out approaches, hit a wall, switch to Codex and now you're re-explaining every dead-end from scratch. Multiply that across a team and it's chaos: your teammate's agent rediscovers a bug you fixed last quarter, the reasoning behind a PR lives in a transcript that's already gone, and nobody knows what anyone's agent actually tried.
So we built shared memory for your team's coding agents.
Capacitor records every coding agent session automatically which means no record commands, no per-session config. Every turn, tool call, test run, and reasoning block streams to your team's Capacitor server in real time, gets indexed, and links back to the repo and PR it belongs to. One setup, and your coding agents have memory. π§
Six things it unlocks:
π€ Collaboration - drop a live session link; a teammate opens it, sees the reasoning in progress, and contributes directly. No reconstructing context from message fragments.
π Multi-agent handoffs - any agent picks up exactly where another left off, with full context of what was tried and decided. Switch models mid-task, run agents in parallel, or bring in a second when the first gets stuck.
π PR review with session context - reviewers (and their agents) pull the tests, attempts, and reasoning behind a diff at review time. Changes become explainable, not just visible.
π Evals β Every session your agents run can be scored against 13 specific quality and safety questions by an LLM-as-judge: βDid the agent run destructive commands?β, βDid it write tests when appropriate?β, βWere there repeated failed attempts at the same operation?β and the findings flow back into a per-repo signal your next session can act on.
π₯ Multi-player sessions - launch any agent from the dashboard, share the link, and your whole team drives together in real time from the browser.
βͺ Active session memory - ask "have we worked on this before?" and your agent searches the full team history via built-in MCP tools, then drills into the exact turn where the decision was made.
It's vendor-neutral by design so it works with Claude Code, Codex, Gemini CLI, Cursor, Copilot CLI, Pi, OpenCode, and Kiro.
We just came out of private preview, so you can jump in today.
π Get started for free: https://capacitor.kurrent.io/
π Docs: https://capacitor.kurrent.io/doc...
I'll be hanging out in the comments all day would genuinely love your feedback, especially from the multi-agent crowd. Does the handoff-without-re-explaining thing resonate, or is your workflow solving this some other way? Roast and questions both very welcome. π
Thanks for checking it out!
(For context: we're the team behind KurrentDB. Events and streams in production are our whole thing and it turns out agent sessions are just a spicy new kind of event stream.)
How does the eval loop actually work in practice, like do I need to label failed runs manually or does it pick up patterns from my own review feedback over time?