Model Leaderboard | Letta

Context Engineering

Context engineering strategies for AI agents: write, select, compress, and isolate context to optimize performance and manage long-running tasks.

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evidence, rather than weight updates. Prior approaches improve usability but often suffer from brevity bias, which drops domain insights for concise summaries, and from context collapse, where iterative rewriting erodes details over time. We introduce ACE (Agentic Context Engineering), a framework that treats contexts as evolving playbooks that accumulate, refine, and organize strategies through a modular process of generation, reflection, and curation. ACE prevents collapse with structured, incremental updates that preserve detailed knowledge and scale with long-context models. Across agent and domain-specific benchmarks, ACE optimizes contexts both offline (e.g., system prompts) and online (e.g., agent memory), consistently outperforming strong baselines: +10.6% on agents and +8.6% on finance, while significantly reducing adaptation latency and rollout cost. Notably, ACE could adapt effectively without labeled supervision and instead by leveraging natural execution feedback. On the AppWorld leaderboard, ACE matches the top-ranked production-level agent on the overall average and surpasses it on the harder test-challenge split, despite using a smaller open-source model. These results show that comprehensive, evolving contexts enable scalable, efficient, and self-improving LLM systems with low overhead.

The Agent Harness

A specification for agent behavior that LLM frameworks leave undefined: error handling, context management, tool execution, and state transitions.

Context Engineering | Meetup

"Context engineering is the delicate art and science of filling the context window with just the right information for the next step" - Andrej KarpathyLearn about tools, frameworks, and code that is in service of context engineering from practitioners in this space.Dive into the set of strategies fo

TBM 406: Seeing Everything, Understanding Nothing (The Context Trap)

AI is supercharging legacy leadership assumptions about context and control.

Luke Wroblewski on Twitter / X

If context engineering is the new hotness for engineering, context management is the new UI hotness/challenge for designers. pic.twitter.com/iXNTYnVRIi— Luke Wroblewski (@LukeW) July 10, 2025

LukeW | Context Management UI in AI Products

They say context is king and that's certainly true in AI products where the content, tools, and instructions applications provide to AI models shape their behav...

Effective context engineering for AI agents

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Context Constitution

Today we are releasing the Context Constitution: a set of principles governing how AI agents manage context to learn from experience.

Supermemory

The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.

Context Engineering for AI Agents: Lessons from Building Manus

This post shares the local optima Manus arrived at through our own "SGD". If you're building your own AI agent, we hope these principles help you converge faster.

Memory in Agents: What, Why and How

LLM memory gives language models persistent context across sessions. Learn how it works, how it differs from RAG and context windows, and how to add LLM memory to your agents with Mem0.

Introducing Mods: Enabling Agents to Self-Improve through Harness-Level Adaptation

We’re introducing Mods to Letta Code — an agent-friendly way to extend and adapt the Letta Code harness.