Alternatives
Products that do what agent-coherence does
Stop AI agents from silently corrupting shared state
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Jan 2026 · arxiv.org
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Single-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…
Feb 2026 · github.com
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Most multi-agent systems fail the same way: agents drift apart across handoffs. By turn 3 they are working in different realities. By turn 5 they are repeating each other's mistakes and calling it parallelism. WUPHF is an open-source local-first office where AI coworkers run on your laptop, around a shared markdown + git LLM wiki the agents build. The wiki is the collective memory. The office around it keeps the team on the same shared context across thousands of handoffs. What actually stops drift is not the wiki. It is the agents reviewing each other's work. The CRO catching the CMO's…
May 2026 · wuphf.team
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We now write most of our code with agents. For a while, PRs piled up, causing review fatigue, and we had this sinking feeling that standards were slipping. Consistency is tough at this volume. I’m sharing the solution we found, which has become our main product. Continue (https://docs.continue.dev) runs AI checks on every PR. Each check is a source-controlled markdown file in `.continue/checks/` that shows up as a GitHub status check. They run as full agents, not just reading the diff, but able to read/write files, run bash commands, and use a browser. If it finds…
Feb 2026 · docs.continue.dev
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May 2026 · oacp.dev
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AI agents accumulate stale tool results — file reads, web fetches, bash outputs — in their context window. Every one sits there for the entire conversation, consuming tokens and degrading quality. The standard fix is auto-compaction: wait until full, then drop content indiscriminately. Context Surgeon gives the agent three operations — evict, replace, and restore — so it can manage its own context. It works as a transparent local proxy that intercepts API requests, assigns IDs to content blocks, and applies eviction directives before forwarding. The agent calls the tools via bash. The proxy…
Apr 2026 · github.com
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We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent.…
Apr 2026 · github.com
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Open Source Context Infrastructure for AI Agents
May 2026 · ravbyte-ai.github.io
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AGENTS.lock keeps AI agent skills, instructions, and MCP servers in sync across Claude, Codex, Gemini, and Copilot CLIs using a single TOML lockfile as the source of truth. Instead of manually copying skills and configs between tools, you declare everything once in AGENTS.lock and run `al sync`. GitHub: https://github.com/luml-ai/AGENTS.lock
Jan 2026 · github.com
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Recently I've been running more and more agents in parallel however I noticed that they have no task context of what the other agents are doing even when a lot of work is interconnected It's like taking Slack away from a team. Agents duplicate work, make conflicting changes, and step on each others' toes simply because they can't talk to each other. Concord is an MCP + CLI that lets coding agents claim work, see what other agents are doing, and message each other live.
10d ago · github.com
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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