OpenCode Go is now supported on @LiteLLM AI Gateway.
Use your Console key and a Go model ID. LiteLLM routes requests to the right API, keeps sessions consistent across turns, and tracks estimated usage under @opencode Go.
The @LiteLLM team is hosting a 2-week workshop to help teams build cloud agents within their own infrastructure.
You can sign up here: https://t.co/ZnyBmkS9QX
We’ll work with your team to build an end-to-end cloud agent that users can trigger from Slack, Teams, or email to complete a task and open a PR.
The workshop starts October 19th and is remote-friendly (if you are based in SF, we will come and work with you in person).
By the end of the workshop, you’ll have a working cloud agent running on your infrastructure from a message in Slack to a PR, end to end.
We're open sourcing agentchat, it's a unified SDK that allows you to connect your agent to Slack, Teams, iMessage, email, and WhatsApp in 2 lines of code and works with any agent SDK
We noticed we kept on rebuilding the stack to connect our agents to different channels and ended up building the same shared layer. That's where agentchat came in.
This allows you as a developer to focus on your core agent's brain and let the SDK manage your connections with different channels.
Thrilled to announce Decision Models are now LIVE on @LiteLLM
Databricks, Azure AI, Perplexity, OpenRouter, Typesafe - multiple decision models are now live within LiteLLM. Available for use directly, as guardrails and within autorouter.
Read more: https://t.co/3wsR5BkYPx
meet @LiteLLM Lens, the next era of tracing and self improving agents.
• stop reading traces. let Lens investigate them
• get bug fixes and feature requests right in Slack
• see the evidence, replay the failures, measure the improvement
Microsoft 365 Copilot is now available through LiteLLM AI Gateway.
With LiteLLM and Copilot, it's now possible for your apps to answer questions like "summarize my emails and my last call”
https://t.co/Kl7A1MCJ3d
Our Usage dashboard just got a major refresh!
New charts and visuals for:
- Spend & token tracking: Break down cost, tokens, and requests across models, virtual keys, teams, and users.
- Agent attribution: See usage by agent (Claude Code, Codex, OpenCode, and more)
- Targeted insights: Filter by date range or user, then drill down into model, key, and MCP server activity.
We're open-sourcing Moyai, the cloud coding agent our team uses every day
It works with Claude Code, Codex, OpenCode and runs on 100+ providers through @LiteLLM
Before our Devin bills were $101,872 with Moyai we're now at $21,700 (79% less)
@LiteLLM Lens has to process thousands of agent traces, some larger than a model's context window, and identify repeated failures no matter how granular they are.
Here's how it works behind the scenes: https://t.co/iTz3zr3l7d
INTRODUCING LiteAdmin MCP 🚀
Now you can now manage and control your @LiteLLM using an Agent. Generate keys, set budgets, and check spend with a prompt so you can focus on building what matters!
Read more: https://t.co/JtA51opjlV
Get Started: https://t.co/478eRfHvvE
Introducing signal detection on @LiteLLM Lens: an auto-populated collection of the most common failure modes for agents
Problem: Before, Lens would only catch the failure modes you thought to define
Solution: We've curated the top agent failure modes based on our experience operating agents in production
Use Lens to build self-improvement loops for your agents.
Get started here: https://t.co/LlvkDAPnTF
Today we’re celebrating the community and a fresh new look for LiteLLM!
• 60k+ GitHub stars
• 89M+ monthly PyPI downloads
• 500M+ container pulls
• And an official new logo!
Thank you to the incredible community for building, contributing, and partnering with us every day.
https://t.co/UV2PpapQo7
Today we’re launching LiteLLM Lens.
@LiteLLM is already the chokepoint for 100% of your enterprise’s AI traffic. We believe the next era of the gateway is using the data flowing through it to help your agents improve.
We’re building for a future where developers run agentic swarms generating 200K+ traces.
We’re focused on two things ⬇️
h/t @Mokhalil01@yujonglee
When I was building Webhound, I'd spend hours a day manually clicking through supabase rows to see if it was behaving as expected.
Since joining LiteLLM Al Gateway, I've spoken to other developers building agents on top of our gateway and surprisingly, they're all doing the same thing.
LiteLLM Lens solves that problem. Hyped to be working on it.
Today we’re launching LiteLLM Lens.
@LiteLLM is already the chokepoint for 100% of your enterprise’s AI traffic. We believe the next era of the gateway is using the data flowing through it to help your agents improve.
We’re building for a future where developers run agentic swarms generating 200K+ traces.
We’re focused on two things ⬇️
h/t @Mokhalil01@yujonglee
Thrilled to announce a NEW AI ROI Calculator from @LiteLLM Labs
It reads spend per user from LiteLLM, and estimates the effort in each merged pull request with a model you choose.
https://t.co/toZGHTfHQt
If you’re building agents with @LiteLLM, I’d love to chat.
I’m working on making the SDK better and want to hear what’s working, what’s frustrating, and what you wish it could do.