Alternatives
Products that do what TraceBack（溯·源） does
TraceBack（溯·源） 是一款基于多智能体技术的因果回溯分析引擎,开源的是基础版。
- 1
- 2
- 3
- 4TO
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
- 5
Session reports for Claude Code & Codex to improve your code
Jun 2026 · backplanes.com
- 6
- 7DF
2019 · github.com
- 8
- 9
- 10
- 11
- 12
- 13
- 14TA
Hi HN, There’s been a lot of discussion lately around context graphs, decision traces, and how AI systems reason. One thing we kept running into: when AI agents make real decisions, the why behind those decisions often disappears. The context is scattered across prompts, tools, policies, and approvals. Logs show what happened, but not why it was allowed. TraceMem is an attempt to make decision context durable. It records the reasoning, authority, and context behind AI actions as a system of record, not as monitoring data, but as memory. Happy to share more details or answer questions. - Tommi
Jan 2026 · tracemem.com
- 15
- 16GF
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
- 17
- 18
- 19MA
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
- 20
- 21CY
2019 · clearbrain.com
- 22TA
Traces is a new way to share and discover agent traces. You can setup a personal or team account, and share publicly or privately (in a team). We use Traces internally to capture and share our agent conversations. Every PR has a Traces link attached to it. We even have skills that can automate that for you (run 'traces setup' after install). You might ask: 1) Why would I share traces? Well, we have found ourselves wanting to learn from each other on how to prompt different models and agents. We built Traces as a tool for teams to learn that together, and for us to learn that as an open…
Mar 2026 · traces.com
- 23TC
Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!
Jun 2026 · atomprophet.io
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →