Agents

LangGraph for tokenmaxxing

Stateful graphs help keep agents from wandering through expensive loops. Fewer accidental tool calls, more deliberate context.

36.7K starslangchain-ai/langgraph
6.2K forksGitHub metadata checked 2026-07-07
MITTokenmaxxing in spirit

What it does

A framework for building resilient stateful agents with explicit graphs, persistence, human-in-the-loop flows, and controllable execution.

Why it belongs here

Stateful graphs help keep agents from wandering through expensive loops. Fewer accidental tool calls, more deliberate context.

Best use case

Agent workflows that need explicit state, resumability, human review, branching, and clearer control over multi-step execution.

How to use it

Model the workflow as a graph, add stopping rules and review nodes, and log each model and tool step as part of the agent trace.

Limits

A graph can make behavior easier to inspect, but it does not make every task a good fit for agent automation.

Tags

agentsstateworkflows
Related feed

Source notes connected to this use case

Anthropic source artwork
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Introducing Claude Sonnet 5

Anthropic launched Claude Sonnet 5 on June 30, priced at $2/$10 per million input/output tokens through Aug 31, then $3/$15. It pitches the model as approaching Opus 4.8 quality at a lower price.

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IT Pro source artwork
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‘What we’re seeing right now is just rapid escalation in AI token spend’: Accenture tells staff to stop using AI for unnecessary tasks amid surging costs

Leaked internal audio, reported by IT Pro via 404 Media, shows Accenture telling staff to stop burning AI tokens on low-value work like turning PDFs into slide decks, as its agentic-AI lead flags a sharp jump in token spend.

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Generated Tokenmaxxing editorial thumbnail for Anthropic’s Economic Index maps the daily cadences of token use
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Anthropic’s Economic Index maps the daily cadences of token use

Anthropic’s June 2026 Economic Index ties Claude use to real-world rhythms: 93% of chats yield an artifact, marketing-manager sessions burn ~2.5x the tokens of editors, and app-building runs over 3x the median conversation.

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Generated Tokenmaxxing editorial thumbnail for AI cost challenges mount as agent use gets more complex: KPMG
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AI cost challenges mount as agent use gets more complex: KPMG

KPMG’s Q2 AI Pulse (204 US leaders at $1B+ firms) finds twice as many companies now running fleets of coordinated agents — up to 18% from 9% — yet only 26% can see in real time what AI at scale actually costs them.

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