I have not, and I assume you made it seamless for your users 🙂 Activegraph is an experiment in a new agent primitive (log centric vs LLM centric). very early as a concept, but aligned with a lot of the ideas in your original post so thought you might find it interesting
AGENTS
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Summoning AI help via sag.sh when distracted
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I told codex to use http://
sag.sh whenever I'm distracted and it needs my help to be unblocked, and ever once it a while I hear it talking to me, and it's the coolest thing ever. (e.g. for releases, that needs npm and is 1Password-gated) -
Resume restores persisted conversation history but not live processes
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Resume restores the persisted thread: conversation/run history plus thread settings/config that are saved with it. It does not resurrect live tool/process state. So if Codex changed files, those filesystem changes are still there. But open shell processes, transient handles,
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Codex Python SDK Released for Embedding in Python Apps
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We just released the Codex Python SDK You can now embed Codex directly into your Python apps and workflows! > Start threads
> Run turns > Stream progress > Resume sessions
> Pass images > Control sandbox access All whilst reusing your existing Codex auth. pip install -
ActiveGraph agents have automatic first-class tracing, not bolted on
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if you build any agent on activegraph, the trace is automatic and first-class, not bolted on
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Parallel experiment building coding agent on ActiveGraph AI with event log trace
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a parallel experiment building a coding agent on top of @activegraphai
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Technical breakdown of agent analyzing 2025 EU GDP data
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Get a full technical breakdown of this agent, and see what happened when we rain it ran it against 2025 GDP data for all 27 EU member states. https://
langchain.com/blog/financial
-ai-that-investigates-macro-trends-eu-economic-analysis-with-you-com-and-langchain
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LangSmith preserves decision logs for explainable financial AI
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In financial services, the ability to explain how a conclusion was reached matters as much as the conclusion itself. This agent uses LangSmith to preserve that decision log: every query issued, every response received, and every intermediate result produced before the final
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LangChain agent analyzes GDP data with AI and research API
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This macroeconomic research agent powered by Deep Agents, LangSmith, and the @youdotcom Finance Research API: Analyzes GDP data Detects anomalies Investigates structural & cyclical drivers at the sector level Produces structured, cited briefings
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Rippling AI shipped millions using Deep Agents and LangSmith in 6 months
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@Rippling AI runs on Deep Agents and LangSmith. Here’s how they shipped to millions of users in 6 months.