Alternatives
Products that do what AgentLens-Observe does
Open-source observability for AI agents — self-hosted
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Everything in OpenClaw's terminal, you can now do visually
Apr 2026 · rectify.so
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Scan your website to see how ready it is for AI agents.
Apr 2026 · isitagentready.com
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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
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Hi HN, I forked chromium and built agent-browser-protocol (ABP) after noticing that most browser-agent failures aren’t really about the model misunderstanding the page. Instead, the problem is that the model is reasoning from a stale state. ABP is designed to keep the acting agent synchronized with the browser at every step. After each action (click, type, etc), it freezes JavaScript execution and rendering, then captures the resulting state. It also compiles the notable events that occurred during that action loop, such as navigation, file pickers, permission prompts, alerts, and downloads,…
Mar 2026 · github.com
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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
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Mar 2026 · github.com
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I’ve spent the last two months building AgenticSeek, a privacy-focused alternative to cloud-based AI tools like ManusAI. It runs entirely on your machine—no API calls, no data leaks. Why AgenticSeek? Optimized for local LLMs (developed mostly on an RTX 3060 running deepseek r1 14b). Truly private: All components (TTS, STT, planner) run locally. More responsive than alternatives (we respond fast to issues + active Discord). Designed to be fun—think JARVIS-like voice control, multi-agent workflows, and a slick web UI. Current Features: Web browsing (research + form filling), code…
2025 · github.com
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Hey HN, Robert from Laminar (lmnr.ai) here. We built Index - new SOTA Open Source browser agent. It reached 92% on WebVoyager with Claude 3.7 (extended thinking). o1 was used as a judge, also we manually double checked the judge. At the core is same old idea - run simple JS script in the browser to identify interactable elements -> draw bounding boxes around them on a screenshot of a browser window -> feed it to the LLM. What made Index so good: 1. We essentially created browser agent observability. We patched Playwright to record the entire browser session while the agent operates,…
2025 · github.com
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16d ago · github.com
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We recently started to use agents to update some documentation across our codebase on a weekly basis, and everything quickly turned into cron jobs, logs, and terminal output. it worked, but was hard to tell what agents were doing, why something failed, or whether a workflow was actually progressing. We thought it would be more interesting to treat agents as long-lived workers with state and responsibilities and explicit handoffs. Something you can actually see and reason about, instead of just tailing logs. So we built Clawe, a small coordination layer on top of OpenClaw that lets agent…
Feb 2026 · github.com
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I'm Josh, founder of Synth. We've been working on coding agent optimization with method like GEPA and MIPRO (the latter of which, I helped to originally develop), agent evaluation via methods like RLMs, and large scale deployment for training and inference. We've also worked on patterns for memory, processing live context, and managing agent actions, combining it all in a single stack called Horizons. With the release of OpenAI's Frontier and the consumer excitement around OpenClaw, we think the timing is right to release a v0. It integrates with our sdk for evaluation and optimization but…
Feb 2026 · github.com
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Build autonomous Python agents with native Agent-to-Agent (A2A) communication - protolink/examples/ai_courtroom at main · nMaroulis/protolink
28d ago · github.com
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