ICYMI: @LangChain Deep Agents on @nvidia Nemotron 3 Ultra. frontier open-model agents at ~10x lower cost than closed. Run on Fireworks, then post-train it into specialized intelligence you own.

🧵 TL;DR: Long-running AI agents rack up token costs fast. Deep Agents turns on prompt caching with zero config, cutting up to 80% of cost on real tasks. Title: Prompt Caching with Deep Agents URL: Key points 💸 Every request reprocesses the full history, system prompt, and tool defs — costs compound ⚡ Prompt caching reuses the compute for static content and only processes the new delta 🧩 Explicit cache breakpoints keep partial hits even when the prompt prefix changes a bit 🤖 Deep Agents auto-applies 3 strategies: explicit breakpoints / provider-side implicit caching / cache-maximizing prompt structure 📊 Measured: Claude Haiku 4.5 -77%, GPT-5.4-mini -80%, Gemini 3.5-Flash -49% 🔭 LangSmith surfaces cache-read tokens per call so you can measure and optimize savings ⏳ The longer the conversation, the bigger the win; short runs see little benefit Abstracting away provider differences with zero config is what makes this land in production. #LangChain# #AIAgents#

Coding agents fire dozens of API calls per task, so a single developer can quietly burn thousands of dollars a week before anyone notices — here's how LangChain killed that "spend unpredictability" internally 💸 The key was folding budget control into the same place as observability. Title: How LangChain Made Coding Agent Spend Predictable URL: 💸 Overview An LLM Gateway built into LangSmith gives a company-wide, minute-level view of model spend and manages budgets centrally. Rather than a bolt-on proxy, it sits on the same foundation as existing tracing, evaluation, and user management. ❓ Challenges Solved Model usage spread from a few teams to the whole company, and premium model prices rose, so costs spiked. ・Coding agents trigger dozens of API calls per task ・Individual developers ran up thousands of dollars a week, unnoticed until month-end 💡 Methodology & Proposed Approach Budgets can be set across multiple layers. ・Caps at the organization, workspace, user, and API-key level ・Default monthly, weekly, daily, and hourly windows for all employees, with exceptions for heavy projects ・Covers agents accessed via Claude Code, Codex, and LangChain Deep Agents ・Deployed via MDM so no one has to set it up manually ・Runs are traced and tied to a user and API key; overspend can be diagnosed by inspecting the trace with evaluation data 🌍 Use Cases Engineering leaders can set team-level limits while still letting people use agents without fear of a surprise bill. The practical value is replacing the month-end billing shock with real-time monitoring. 📊 Lessons & Outcomes ・Static price tables go stale fast, so pricing must be handled dynamically, including caching and tier differences ・Cursor and Claude Desktop didn't route cleanly, so they measured the delta between Gateway-captured traffic and provider settings to correct for it ・Hard limits alone block real work, so they evolved into early-warning alerts and auditable budget-increase requests ・Since internal rollout, LLM costs have stayed within budget #CodingAgents# #LLMOps#