kontexta
Save your tokens. Remember Forever
kontexta launched on June 2, 2026 with 4 votes, #3,322 of 22,647 launches that month and more than 75% of that year's launches.
What it does
Persistent context, governed execution, and cross-agent continuity for Claude Code, Cursor, Gemini, and more. Local-first. No cloud subscriptions. Surgical context economy for expensive Cloud APIs, or Safe, private memory for Local LLMs. Some features include: Cut API Costs Eliminate “Context Fog” Built for Small Context Windows Safer Command Execution Fully Offline & Private Cross-Agent Handoff And much more possibilities. Spin up the local server and see if for yourself!
Your agents have memory. It just isn't yours. kontexta gives Claude Code, Cursor, Codex, Gemini, Copilot and every other agent one shared local vault: searchable, versioned, portable. No cloud.
Claude Code , Cursor , Codex , Gemini , Copilot and every other agent keep their own silo. kontexta gives them one shared vault on your machine: searchable, versioned, and portable across every tool. Claude Code fixes the bug and journals it. Hermes , a different tool with zero prior context, recalls it from the same local vault. Wire up your clients → Each tool remembers only what it saw. Switch agents and the context stays behind. One page from the same log, before kontexta and after. Without kontexta, a different agent cannot see the decisions made in another tool's session; they are effectively redacted. With kontexta, the next agent recovers the same decisions from the shared journal.…from kontexta.dev
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Context GatewayMar 2026 · github.com · ▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
ReplicasJun 2026 · replicas.dev · ▲239Hey HN! I'm Connor, creator of Replicas. I'm a heavy Codex/Claude Code user, and I wanted to hand off tasks to agents like I would to an engineer - kick something off before work, let it scaffold while I'm at lunch, even come back to a ready PR. The problem: existing alternatives use workspace VMs that are black boxes. I have no access to any of them - they can't run tests, read logs, or use tools like the Playwright MCP (which I use to verify agents actually fixed what they claim to fix). Replicas gives agents real cloud workspaces where they can: - Run your test suite and read the…
A local-first memory store for LLM agents (SQLite)Dec 2025 · github.com · ▲48Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc. - CaviraOSS/LongMemory
Kontext CLI – Credential broker for AI coding agents in GoApr 2026 · github.com · ▲70We built the Kontext CLI because AI coding agents need access to GitHub, Stripe, databases, and dozens of other services — and right now most teams handle this by copy-pasting long-lived API keys into .env files, or the actual chat interface, whilst hoping for the best. The problem isn't just secret sprawl. It's that there's no lineage of access. You don't know which developer launched which agent, what it accessed, or whether it should have been allowed to. The moment you hand raw credentials to a process, you've lost the ability to enforce policy, audit access, or rotate without pain. The…
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…
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