AI · alternatives · 2026
24 alternatives to Bloop – Answer questions about your code with an LLM agent
Hi HN! We launched bloop 10 weeks ago (https://news.ycombinator.com/item?id=35236275) and received a huge amount of feedback (both positive + constructive). We've undertaken a rewrite of the core search framework, which now acts as an LLM agent, significantly improving the number of queries that can be successfully answered. There's a bunch of hype surrounding LLM agents, but we're positive this is one of the first implementations of an agent that can deliver immediate value for engineers working on existing projects, especially larger ones. We'll do a full write up of how the…
Bloop is a developer assistant that uses an LLM agent to answer questions about codebases by searching local and remote repositories with natural language, regex, and filters. It is designed for engineers working on… 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 1 we have checked on their own sites, 1 of them free or with a free tier. Bloop – Answer questions about your code with an LLM agent launched in 2023; newer entries below may have overtaken it.
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Continual learning for every coding agent on your team.
2025 · deeplake.ai · its alternatives →
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I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…
Feb 2026 · github.com · its alternatives →
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Arch▲314Hey HN — I’m Adil from Katanemo (with Salman, Shuguang, and Meiyu) We previously shared an early version of this project as ArchGW. Based on customer feedback, the scope expanded from “LLM routing and model access” into something broader: delivery infrastructure for agentic applications. We renamed it to Plano and reworked the architecture accordingly. The problem On-the-ground AI practitioners will tell you that calling an LLM is not the hard part. The really hard part is delivering agentic applications to production quickly and reliably, then iterating without rewriting system code every…
2024 · github.com · its alternatives →
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Velozity▲450Multiplayer platform for your teams and AI Agents.
Sep 2026 · getvelozity.com · its alternatives →
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Felo▲562The New Work: Visual collaboration workspace with AI agents
2024 · felo.ai · its alternatives →
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I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com · its alternatives →
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Runtimefree tier, from $99/mo▲303Sandboxed coding agents for everyone on your team
May 2026 · runtm.com · its alternatives →
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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 · its alternatives →
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Bullet▲240Hi HN, excited to be sharing this with you guys today. TL;DR: Bullet is a fast coding agent. At our company, we were burning hours waiting on agent runs. The models weren’t the problem. The agent loops around them were slow (unnecessary planning, oversized context, repeated searches, and serial tool calls). So we built Bullet as a passion project, the goal being to help you iterate faster, without giving up quality: - It automatically selects the model and reasoning level for each prompt. - It runs independent searches, reads, and commands in parallel. - It uses targeted code search instead…
Aug 2026 · codewithbullet.com · its alternatives →
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I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
Apr 2026 · github.com · its alternatives →
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Preloop▲121Hey HN, I’m Yannis, co-founder of Preloop. We’ve built a proxy for the Model Context Protocol (MCP) that lets you add human approval gates to your AI agents without changing your agent code. We’re building agents that use tools (Claude Desktop, Cursor, etc.), but we were terrified to give them write-access to sensitive systems (Stripe, Prod DBs, AWS). We didn't want to rewrite our agents to wrap every tool call in complex "ask_user" logic, especially since we use different agent runtimes. We built Preloop as a middleware layer. It acts as a standard MCP server proxy.You point your agent to…
Jan 2026 · preloop.ai · its alternatives →
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Superflow AI▲270AI agents that QA your website before launch
Aug 2026 · usesuperflow.ai · its alternatives →
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SuperHQ▲95Hey HN, I built SuperHQ, an app that lets you run coding agents in local sandboxes (powered by Shuru). No custom UI wrapping the agents, they run as CLI/TUI like they were designed to. It just provides you the tools most of us (okay, maybe just me?) needed for running multiple coding agents in parallel without worrying about breaking your system or work environment. Each agent runs in its own microVM. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a unified diff view to accept or discard changes. API keys never enter the sandbox, they…
Apr 2026 · superhq.ai · its alternatives →
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agents-cli▲145The CLI your coding agent uses to ship agents
Jul 2026 · github.com · its alternatives →
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Matthew Blode is a Melbourne AI engineer at Linktree. He co-founded Fingertip and VenueSafe, sold both, made Forbes 30 Under 30, and ships open-source tools.
Feb 2026 · shareful.ai · its alternatives →
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Keen Code▲125Hello community! I am here to share a coding agent I have built solo from scratch using agentic engineering. Written in Go, it's a proper coding agent, has features you expect from a useful agent for your daily work, with a minimal and simple UI. I have named it Keen Code. The repo is here: https://github.com/mochow13/keen-code Even though it started as an experiment, it is now a full-fledged coding agent for real software engineering work. It supports multiple providers, skills, MCPs, multi-agent orchestration through subagents, automatic compaction, etc. I have been…
Jun 2026 · mochow13.github.io · its alternatives →
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Miniloop▲80Turn natural language into AI agents and automations
Feb 2026 · miniloop.ai · 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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MartinLoop▲79Control AI coding agents with limits, proof, + run receipts
Jun 2026 · martinloop.com · its alternatives →
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Hi HN, I built a CLI for uploading documents and querying them with an LLM agent that uses search tools rather than stuffing everything into the context window. I recorded a demo using the CrossFit 2025 rulebook that shows how this approach compares to traditional RAG and direct context injection[1]. The core insight is that LLMs running in loops with tool access are unreasonably effective at this kind of knowledge retrieval task[2]. Instead of hoping the right chunks make it into your context, the agent can iteratively search, refine queries, and reason about what it finds. The CLI handles…
2025 · github.com · its alternatives →
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Hi HN, I built my first project using an LLM in mid-2024. I've been excited ever since. But of course, at some point it all turns into a mess. You see, software is an intricate interwoven collection of tiny details. Good software gets many details right; and does not regress as it gains functionality. My bootstrapped startup, ApprovIQ (https://approviq.com) is trying to break into a mature market with multiple fully featured competitors. I need to get the details right: MVP quality won't sell. So I opted for Test-Driven Development, the classic red/green/refactor. Writing…
Feb 2026 · codeleash.dev · its alternatives →
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