nowfound

AI · alternatives · 2026

24 alternatives to RLM-based local debugger for AI agent traces

We built HALO (Hierarchal Agent Loop Optimizer), an open-source tool for debugging and optimizing AI agents using their execution traces. It’s a loop. Run your agent, feed the traces to HALO, get the report, apply the fixes, then re-run your agent. HALO takes in OTEL compliant traces from AI agents using tracing frameworks such as Langfuse, Arize/OpenInference, or even just plain JSONL. It uses an RLM (Recursive Language Model) to more efficiently break trace analysis into smaller subproblems in order to find recurring patterns across large amounts of data and fix systemic issues that…

HALO is an open-source debugging tool for AI agents that analyzes execution traces to identify and fix systemic issues. It accepts traces from frameworks like Langfuse and Arize in OTEL-compliant formats, then uses a… 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.

  1. 1

    Hi HN, I'm Brian, I spent the last few years at Vanta (YC W18), helping startups and enterprises become compliant and I recently started exploring what that might look like in a post-agentic world. The problem Halo solves is: when a company buys an AI agent from a vendor and gives it access to their data, they have no way to check what the agent did with that data. Vendors may have built observability dashboards and audit logs, but those are editable and partisan. SOC 2 and ISO 27001 audit a company's controls, but controls are less predictive when the software is agentic. TLDR: give an…

    Jul 2026 · github.com · its alternatives →

  2. 2

    Hello HN, I don't post on here much, but wanted to get some eyes on a new project I'm just launching. I think we definitely need one more AI code agent.. I'm a long-term C++ dev, and over 30+ years I've created some successful audio dev tools (JUCE, the Tracktion DAW, the Cmajor DSP language). All of these came from me getting annoyed with something I had to use, and deciding to have a go at my own take on whatever it was. So Juggler is my attempt at an AI code agent, after spending too many hours loving what the models could do, but hating the CLI experience, and having some opinions of…

    Jul 2026 · github.com · its alternatives →

  3. 3

    Hello everyone. I've been working on this experimental editor called Huzzah. I've been working almost exclusively with coding agents since January of this year, and over the past few months I began to feel utterly exhausted by them. They're great, but I'm finding it more and more tedious to write full sentences for every change I want. Not only that, but it seems there's a complexity limit for codebases - beyond a certain point the agent begins confusing itself. I'd like to go back to writing code, but I don't want to go all the way back to fully manual coding. So I've come up with this…

    Aug 2026 · danielvaughn.dev · its alternatives →

  4. 4
    Hyperprobefree tier, from $99/mo▲223

    Lets your AI agents debug production without redeploying

    Aug 2026 · hyperprobe.co · its alternatives →

  5. 5

    I've been working on CloudRouter, a skill + CLI that gives coding agents like Claude Code and Codex the ability to start cloud VMs and GPUs. When an agent writes code, it usually needs to start a dev server, run tests, open a browser to verify its work. Today that all happens on your local machine. This works fine for a single task, but the agent is sharing your computer: your ports, RAM, screen. If you run multiple agents in parallel, it gets a bit chaotic. Docker helps with isolation, but it still uses your machine's resources, and doesn't give the agent a browser, a desktop, or a GPU to…

    Feb 2026 · cloudrouter.dev · its alternatives →

  6. 6

    Hello! We just released freeact (https://github.com/gradion-ai/freeact), a lightweight agent library that empowers language models to act as autonomous agents through executable code actions. By enabling agents to express their actions directly in code rather than through constrained formats like JSON, freeact provides a flexible and powerful approach to solving complex, open-ended problems that require dynamic solution paths. * Supports dynamic installation and utilization of Python packages at runtime * Agents learn from feedback and store successful code actions as…

    2025 · github.com · its alternatives →

  7. 7

    Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.

    Aug 2026 · telerik.com · its alternatives →

  8. 8

    An open benchmark for AI agents that test APIs

    May 2026 · resources.kusho.ai · its alternatives →

  9. 9

    The CLI your coding agent uses to ship agents

    Jul 2026 · github.com · its alternatives →

  10. 10

    hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…

    May 2026 · github.com · its alternatives →

  11. 11
    Tracea▲80

    Datadog for AI agents with traces, RCA, and team memory

    May 2026 · tracea.dev · its alternatives →

  12. 12

    Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…

    Jul 2026 · oodle.ai · its alternatives →

  13. 13

    Hi HN! My name is Olaf, I work at CircleCI as a technology advisor in the CTO office, came in through the acquisition of my company Vamp.io (progressive delivery for microservices on k8s) in 2021. Wanted to hear the HN community feedback and thoughts on a project we think could be very interesting when adding AI coding agents to the SDLC and your CI pipelines. Our team at CircleCI built Chunk sidecars after repeatedly running into the same issue internally: by the time our CI catches a failure, the agent has already moved on and most of the useful context is gone. The basic idea of Chunk…

    May 2026 · circleci.com · its alternatives →

  14. 14
    SuperHQ▲95

    Hey 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 →

  15. 15

    I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here. Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly like this: 1. Take a screenshot 2. Have the model predict pixel coordinates 3. Click x,y 4. Take another screenshot 5. Repeat That works, but it's slow, expensive in tokens, and fragile. If the UI shifts a few pixels, things break. And the model still doesn't know what any element actually is. But the…

    May 2026 · github.com · its alternatives →

  16. 16

    AI powered cli coding agent that monitors your dev/test server and fixes errors and adds features - vysakh0/dravid

    2024 · github.com · its alternatives →

  17. 17

    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 →

  18. 18

    Hey Xinwei and Zecheng here, we are the authors of TraceRoot (https://github.com/traceroot-ai/traceroot). TraceRoot (https://traceroot.ai) is an open-source debugging platform that helps engineers fix production issues faster by combining structured traces, logs, source code contexts and discussions in Github PRs, issues and Slack channels, etc. with AI Agents. At the heart are our lightweight Python (https://github.com/traceroot-ai/traceroot-sdk) and TypeScript (https://github.com/traceroot-ai/traceroot-sdk-ts) SDKs -…

    2025 · github.com · its alternatives →

  19. 19

    This project (Agents Observe) started as an exploration into building automation harnesses around claude code. I needed a way to see exactly what teams of agents were doing in realtime and to filter and search their output. A few interesting learnings from building and using this: - Claude code hooks are blocking - performance degrades rapidly if you have a lot of plugins that use hooks - Hooks provide a lot more useful info than OTEL data - Claude's jsonl files provide the full picture - Lifecycle management of MCP processes started by plugins is a bit kludgy at best The biggest takeaway is…

    Apr 2026 · github.com · its alternatives →

  20. 20

    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 · its alternatives →

  21. 21

    I built OpenSwarm because I wanted an autonomous “AI dev team” that can actually plug into my real workflow instead of running toy tasks. OpenSwarm orchestrates multiple Claude Code CLI instances as agents to work on real Linear issues. It: • pulls issues from Linear and runs a Worker/Reviewer/Test/Documenter pipeline • uses LanceDB + multilingual-e5 embeddings for long‑term memory and context reuse • builds a simple code knowledge graph for impact analysis • exposes everything through a Discord bot (status, dispatch, scheduling, logs) • can auto‑iterate on existing PRs and…

    Feb 2026 · github.com · its alternatives →

  22. 22
    Spec27▲34

    Hi HN! We’re a team of ML validation specialists and we’ve been building /Spec27, a tool for testing whether AI agents still do their job safely and reliably as models, prompts, tools, and surrounding systems change. We started working on this because a lot of current LLM evaluation work seems aimed at scoring general model behavior, while many teams are deploying systems that have a specific mission to fulfill. Many of the tools also assume you have full access to the agent stack and traces so you can place SDKs and Gateways, but a lot of agents are being created on vendor platforms…

    Apr 2026 · spec27.ai · its alternatives →

  23. 23IN

    Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across…

    Jun 2026 · its alternatives →

  24. 24

    Tool-agnostic TypeScript library for observing coding agent sessions. Built-in Cursor provider, extensible to Claude Code, Codex, and OpenCode. - vtemian/agentprobe

    Mar 2026 · github.com · its alternatives →

Also compare

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 →