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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 次观看 • 7 个月前 •via X (Twitter)

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15 条评论

Arel Avellino 的头像

Arel Avellino7 个月前

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 的头像

Fleeks7 个月前

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 的头像

gideon onyewuenyi7 个月前

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

Jeremy Frenay 的头像

Jeremy Frenay7 个月前

Agent Identities 🪪👌

keren koshman 的头像

keren koshman7 个月前

Can we on-board agents from various platforms?

Yaki Dias 的头像

Yaki Dias7 个月前

Mine

Ian Kiku 的头像

Ian Kiku7 个月前

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 的头像

Karrie Ross 🍌🥚💙USA7 个月前

👁️👁️ Ai agent worksoace

AgentLabX 的头像

AgentLabX7 个月前

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 的头像

JS7 个月前

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 的头像

Kyle Tran7 个月前

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

Companaro 的头像

Companaro7 个月前

exploring their use case in ai frens to ramp it up

Git Maxd 的头像

Git Maxd7 个月前

Whoa 🔥

Burny - Effective Curiosity 的头像

Burny - Effective Curiosity7 个月前

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

Hutch, D.O. 的头像

Hutch, D.O.7 个月前

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:

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