Topic
Context Window & Token Management for Agents
13 posts on context window & token management for agents — part of ai agents & automation on the n4n AI blog.
Why bigger context windows don't fix agent forgetfulness
Bigger LLM context windows mask but don't solve agent memory failures. Learn why context window forgetfulness persists and how to architect real agent memory.
Token budgeting for multi-agent systems: a practical guide
A practical guide to token budgeting multi-agent systems: set global ceilings, allocate per-agent limits, truncate context, and meter usage to control cost.
Sliding window vs summarization: managing agent memory
A pragmatic head-to-head of sliding window vs summarization for agent memory: compare cost, latency, and retention to pick the right strategy for your LLM agent.
Managing tool output overflow in agent context windows
Practical steps to stop tool output context overflow in LLM agents: enforce limits, compress, schema-bound, tier retrieval, and meter usage.
How to compress conversation history without losing accuracy
Practical steps to compress conversation history LLM context for agents: summarize, prune, and rehydrate state without losing task accuracy. Includes Python code.
How to chunk documents for long-running research agents
Practical steps to chunk documents for research agents: structure-aware splitting, token budgets, metadata, and context rotation for long-running LLM workflows.
How Claude's context editing tools trim agent conversations
Hands-on tutorial: build a Claude tool-calling loop that trims chat history so context editing agent conversations stays within token limits.
Context rot: what happens when agents stuff the window
Context rot LLM agents silently undermines reliability as agents stuff the window. Analysis of causes, tradeoffs, and engineering fixes like compaction and eviction.
What is context window management, and why agents need it
Context window management is the discipline of controlling token flow into and out of LLM calls so agents stay within limits and retain what matters.
RAG vs long context: which wins for agent accuracy in 2026
A practitioner's head-to-head comparison of RAG vs long context agents across cost, latency, accuracy, and ergonomics, with a clear verdict for 2026 builds.
Prompt caching explained: cutting token costs for agents
Prompt caching token costs are reduced by reusing prefix tokens across requests. Learn how caching works, why it matters for agents, and common myths.
Gemini 3's 2M token context vs GPT-5 for AI agents
Engineering comparison of Gemini 3's 2M token context vs GPT-5 for AI agents: capabilities, cost, latency, ergonomics, and which model to use per use case.
7 techniques to shrink token usage in agent loops
Practical, code-level ways to shrink token usage in agent loops — schema trimming, summarization, output pruning, caching, and model tiering.
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