Capacitor
Shared memory for coding agents
Capacitor launched on June 6, 2026 with 6 votes, #2,066 of 22,647 launches that month and more than 75% of that year's launches.
What it does
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.
Kurrent Capacitor records Claude Code, Codex, Cursor, Gemini, and Copilot sessions as shared team memory. Continue lost work, switch agents, recall decisions, review PRs with the reasoning. Free for five seats.
Capacitor records every coding-agent session — so you can continue it, hand it to another agent, search it, and learn from it. Recording is opt-in, and your first import gives you a searchable history in minutes. Try the dashboard in the browser first. Install the CLI when you want sessions recorded. Tried mocking the clock first — abandoned after the race persisted. Root cause: token cache TTL rounding. Patched in auth/cache.ts , added regression test. Two approaches rejected, one shipped. Every session is recorded as it runs. Reopen it, read the recap, continue where the agent stopped — on any machine, even the one at home. Capacitor is vendor-neutral. The session's context follows you…from kurrent.io
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AgentmemoryMay 2026 · agent-memory.dev · ▲310Persistent memory for Claude Code, Codex & coding agents


Claude-MemDec 2025 · cmem.ai · ▲115AI agent memory, everywhere. claude-mem is open-source memory for Claude Code; cmem links it to any MCP client — Cursor, Codex, more — via one private link.
20+ Claude Code agents coordinating on real work (open source)Feb 2026 · github.com · ▲53Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Fast-agent – Compose MCP enabled Agents and Workflows in minutes2025 · github.com · ▲29Hello, HN. I've created fast-agent to make building my own products easier - and remove the friction between defining Prompts, MCP Servers and their composition. It uses a simple, declarative style that's easy to work with and source control - with inbuilt support for the patterns in the Building Effective Agents paper. Because you can "warm-up" and interact with Agents before, during or after the workflows, it's easy to diagnose and tune Agent prompts and behaviour for later runs. Being able to set these workflows up makes LLM Context Management and Tool Selection a lot easier and can…
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