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Introducing LangSmith Fleet: an enterprise workspace for creating, using, and managing your fleet of agents. Fleet agents have their own memory, access to a collection of tools and skills, and can be exposed through the communication channels your team uses every day. Fleet includes: → Agent identity and credential... show more

LangChain

270,682 subscribers

149,147 görüntüleme • 7 ay önce •via X (Twitter)

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Arel Avellino profil fotoğrafı

Arel Avellino7 ay önce

The memory-per-agent model is the right call. Shared memory across a fleet is where multi-agent systems fall apart - you get state collisions, context bleed, agents stepping on each other's work. Isolation first, then controlled sharing.

Fleeks profil fotoğrafı

Fleeks7 ay önce

Nice ship! We're focused on the other side of the "Fleet"- running autonomous agents locally via the CLI. Our v1.14.0 lets the terminal catch webhooks and fix bugs while the team is away. Local > Cloud for dev flow.

gideon onyewuenyi profil fotoğrafı

gideon onyewuenyi7 ay önce

Do we have AI agent managers yet ? Just like we have AI product managers @grok

Jeremy Frenay profil fotoğrafı

Jeremy Frenay7 ay önce

Agent Identities 🪪👌

keren koshman profil fotoğrafı

keren koshman7 ay önce

Can we on-board agents from various platforms?

Yaki Dias profil fotoğrafı

Yaki Dias7 ay önce

Mine

Ian Kiku profil fotoğrafı

Ian Kiku7 ay önce

memory and tools per agent is the right abstraction. giving each agent a coherent scope has always been the hard part. fleet as a managed primitive makes that tractable at org scale

Karrie Ross 🍌🥚💙USA profil fotoğrafı

Karrie Ross 🍌🥚💙USA7 ay önce

👁️👁️ Ai agent worksoace

AgentLabX profil fotoğrafı

AgentLabX7 ay önce

Everyone's excited about agent fleet management. But the real bottleneck isn't identity/permissions—it's knowing when agents should NOT run. Most teams are automating ambiguity. Give your agents clearer specs before you give them Slack bots.

JS profil fotoğrafı

JS7 ay önce

Fleet management is a symptom, not a solution. 15 agents with good rules outperform 150 with good dashboards. You don't scale by controlling more—you scale by designing systems that don't need you.

Kyle Tran profil fotoğrafı

Kyle Tran7 ay önce

Isolation first, then controlled sharing is the right sequencing for fleet scale

Companaro profil fotoğrafı

Companaro7 ay önce

exploring their use case in ai frens to ramp it up

Git Maxd profil fotoğrafı

Git Maxd7 ay önce

Whoa 🔥

Burny - Effective Curiosity profil fotoğrafı

Burny - Effective Curiosity7 ay önce

hmm i wonder if its actually good or claude code/codex with multiple agents is just better

Hutch, D.O. profil fotoğrafı

Hutch, D.O.7 ay önce

Can I be real with you? I’ve been doing like hard core data science with agents for the last 2+ years. Even gone so far as invented my own workflow manager that exceeds airflow and dagster in core data passing (4M row csv handled w/ ease) and now I’m in the UI layer -use to be pure python. I say all this to say that I didn’t draw for langchain because I haven’t seen one actual real use case I could adapt. Not from your own shop or from any of your customers. Can you post a video of one of you agents doing real useful work? I’ll do the same

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Introducing LangSmith Fleet. Agents for every team. → Build agents with natural language → Share and control who can edit, run, or clone each agent → Manage authentication with agent identity → Approve actions with human-in-the-loop → Track and audit actions with tracing in LangSmith Observability Try Fleet:

Introducing LangSmith Fleet. Agents for every team. → Build agents with natural language → Share and control who can edit, run, or clone each agent → Manage authentication with agent identity → Approve actions with human-in-the-loop → Track and audit actions with tracing in LangSmith Observability Try Fleet:

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Kylon launched a new chat app with AI agents like Codex or Claude being a part of the same team. Users on the same team can link an AI agent running on their own computer and let anyone on the team @ mention it in a thread to write code or sort files. > Agents can pass the next steps of a job to a coworker after finishing their part. > Apps can be built inside the conversation, like an invoice tracker, and published to their own web address. > Each agent can keep its own memory, with the team deciding which accounts it can use.

🚨 AI News | TestingCatalog

276,828 görüntüleme • 13 gün önce

oh my pi, meet oh my fleet. OMP is now supported inside Agent Orchestrator. Give every task its own isolated worktree, run multiple OMP sessions in parallel, and track the code, PRs, CI, and reviews from one live board. your favorite harness just got a control plane. Can Bölük x AO

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Now building is a team sport. Introducing Slack Code: a new way for your team and AI agents, like Anthropic's Claude Code, Claude Tag, Devin from Cognition, GitHub Copilot, ChatGPT, and Vercel agents, to write, review, and ship code together. Just mention an agent to spin up a channel, pull in your team, and start building. All in the open. All in Slack.

Slack

591,135 görüntüleme • 2 ay önce