AI · alternatives · 2026
24 alternatives to Willder
AI agents coordinate. People collaborate. One memory.
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 4 we have checked on their own sites, 4 of them free or with a free tier.
- 1
Pylar▲502Hey HN! We're Hoshang & Vishal, the team behind Pylar - a governed access layer between databases and LLMs. We previously led data and AI and we kept seeing the same problem across teams using LLMs internally: agents are great with unstructured data, but the moment you want them touching your actual systems of record — Snowflake, Postgres, CRMs, product DBs — everything becomes fragile, risky, or outright unsafe. Two issues show up every single time: 1. Agents over-querying They don’t understand cost. They’ll happily generate queries that blow up your warehouse bill. 2. Accidental data…
Dec 2025 · pylar.ai · its alternatives →
- 2
Clark▲264Hey folks, I've been building Clark solo, with the aim to match Manus agent (I really like it!) in features and capabilities, but also in its relentless ability to just complete tasks while using browser, computer etc. So far I think I've achieved 1:1 core feature parity: 1) computer use agent 2) browser use in same thread 3) wide and deep research - clark calls clark 4) integration with google tools Would love to have community feedback and for you to try it out, a couple thousand people already using it daily for very broad use cases. You can use this referral link, that hopefully works…
Jul 2026 · clarkchat.com · its alternatives →
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Venn.ai▲336Delegate real work to AI agents with safety guardrails
Mar 2026 · venn.ai · its alternatives →
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N71free tier, from $49/mo▲141Give all your AI agents one shared context
Jul 2026 · n71.ai · its alternatives →
- 5
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 · its alternatives →
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Spacesfree tier, from $10.99/mo▲77One shared space where your team and AI agents work
Sep 2026 · spaces.ez-ai.io · its alternatives →
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Eggshell▲74Local memory for AI agents to reuse work, spend fewer tokens
Sep 2026 · github.com · its alternatives →
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Trails▲81Hey all, Justin here. I previously built Phind, the AI search engine for developers. One of the biggest problems we had there was figuring out what went wrong with bad searches. We had tons of searches per day, but less than 1% of users gave any explicit feedback. So we were either manually digging through searches or making general system improvements and hoping they helped. This problem gets harder with agents. Traces are longer and more complex. It takes more effort to review them, so I'm building a tool that lets you analyze LLM outputs directly to help developers of LLM apps and agents…
Jan 2026 · trails-red.vercel.app · its alternatives →
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M9Rfree▲56Multiplayer space for your AI coding agents and teams
Sep 2026 · app.m9r.workers.dev · its alternatives →
- 10
Alder▲22Optimizing Data Warehouse Query Performance via AI Agents
2025 · alderintelligence.com · its alternatives →
- 11
Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com · its alternatives →
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- 13PS
I didn't want to buy a standalone computer or repurpose a laptop to run constantly so I could maintain a system to sync my LLMs, so I built this. It's a simple overview of my system, laid out in a way easy to unpack and replicate for yourself. The project is meant to be configured individually, and uniquely, since one solution might not be what's best for another. If anything, maybe it gives you some ideas on how to implement things for your own project. Best wishes, Ryan.
Aug 2026 · pacslate.com · its alternatives →
- 14
An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience.
Aug 2026 · pinglin.tw · its alternatives →
- 15
Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
2025 · github.com · its alternatives →
- 16
AgentID▲7Shared memory, identity and tasks. Lower token costs.
Apr 2026 · agentid.live · its alternatives →
- 17
A file-based protocol for multi-agent AI workflows — portable, auditable, human-in-command. Cross-agent communication and persistent shared memory across runtimes, projects, and machines. No daemons, no central server.
May 2026 · oacp.dev · its alternatives →
- 18
Shared memory & coordination layer for AI agent teams
Jul 2026 · agenthelm.online · its alternatives →
- 19MC
Hi HN, I’ve been building AI agents and copilots, and kept running into a frustrating problem: they don’t fail loudly, they forget things quietly. Users re-explain preferences, agents contradict earlier responses, and context resets without any clear visibility into why. I built Memograph CLI as a debugging tool to analyze conversation transcripts and show: - what the agent forgot - where continuity broke - contradictions and repeated context - estimated token waste due to re-prompting It works locally and supports plain text or JSON transcripts. Example: $ memograph Output: Cognitive Drift…
Feb 2026 · its alternatives →
- 20
Cursor published a great post last week on scaling long-running autonomous coding agents to build a browser. We’ve been exploring a similar idea, but applied to math proofs We show how a swarm of agents, coordinated via shared memory, can generate a Lean proof for a non-trivial multi-step math problem-Putnan A2-that a single agent struggled with. We were feeling fancy so we wrote a full post and captured the runtime. Harness will be public soon(tm) for you to run it yourself.
Mar 2026 · ensue.dev · its alternatives →
- 21
Hello there HN I experimented with agentic coding recently and I felt the need to track more contextual data by project. Also I felt the need to be able to go beyond the 1D chat to communicate with agents. So I created a local document memory, that is discoverable by agents themselves. The CLI is designed to be easy to pick up by agents. It allows humans to collaborate too by reading / searching / editing documents in the store. I have a Mac native GUI in the review process, I hope it will show up in the App Store soon. You can try it easily, instructions here:…
Jun 2026 · metabrain.eu · its alternatives →
- 22
Mingle▲3I built an autonomous AI agent running 24/7 on a Mac Mini (16 tools, persistent memory, proactive scheduler). Kept hitting token budget walls on ambitious tasks — agent starts something good, runs out of tokens, stops. So I wrote a protocol for agents to collaborate: discover each other, propose projects, vote on resource allocation (one agent = one vote, cryptographically bound to one owner), pool tokens, execute together. Key decisions: reputation-weighted not wealth-weighted governance, privacy-first (share knowledge never owner data), protocol evolves through agent voting.
Mar 2026 · aeoess.com · its alternatives →
- 23WB
Humans compete to improve their AI agents on benchmarks. But what if agents could collaborate and compete on their own? We built Hive, a crowdsourced platform where agents can evolve solutions together. One agent begins to tackle a task, iteratively improving its code. Then other agents join. They read each other’s runs, fork the best ideas, propose new ones, and push the solution forward together. We already have agents working on benchmarks like Tau2-Bench, Terminal-Bench, and ARC-AGI-2, with more tasks coming soon. We also support the new OpenAI Parameter Golf Challenge, and you can…
Mar 2026 · hive.rllm-project.com · its alternatives →
- 24
Hey HN, I've made a groundbreaking discovery: procrastination can lead to questionable projects! While avoiding real work, I somehow created a directory of 130+ AI agents and frameworks. It's like I tried to organize a robot party and everyone showed up. What's inside: - A list of AI agents - Frameworks to build more agents So, HN, before I spiral into an existential crisis: did I accidentally create something useful, or should I go touch grass? P.S. If this somehow becomes the next big thing, I promise to pretend it was intentional all along.
2024 · aiagentsdirectory.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →