aid: context-engineering url: >- https://raw.githubusercontent.com/api-evangelist/context-engineering/refs/heads/main/apis.yml name: Context Engineering x-type: topic description: >- Context engineering is the practice of curating the information that large language models receive at inference time so that the model can perform a task reliably and cost-effectively. It treats the context window as a finite attention budget and looks for the smallest set of high-signal tokens that maximize the likelihood of the desired outcome. Context engineering subsumes and extends prompt engineering, system prompts, tool design, retrieval, agent loops, structured note taking, compaction, and multi-agent decomposition. It is a foundational discipline for building production AI agents and assistants. image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg tags: - Agents - AI - Anthropic - Compaction - Context Window - LLM - Memory - Prompt Engineering - RAG - Tools created: '2025-01-01' modified: '2026-04-28' specificationVersion: '0.19' apis: - aid: context-engineering:anthropic-guide name: Effective Context Engineering for AI Agents description: >- Anthropic's engineering guide to context engineering, framing context as a finite attention budget and walking through system prompts, tool design, few-shot examples, just-in-time retrieval, compaction, structured note taking, and multi-agent architectures. humanURL: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents baseURL: https://www.anthropic.com tags: - Anthropic - Best Practices - Engineering properties: - type: Documentation url: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents - type: Reference url: https://docs.anthropic.com/en/docs/agents-and-tools/agent-best-practices x-features: - Frames context as a finite attention budget - Distinguishes context engineering from prompt engineering - Covers system prompts, tools, few-shot, and retrieval - Long-horizon strategies (compaction, notes, sub-agents) x-useCases: - Building production AI agents and assistants - Tuning system prompts and toolsets for reliability - Designing memory and compaction for long-running agents - aid: context-engineering:retrieval-augmented-generation name: Retrieval-Augmented Generation (RAG) description: >- RAG is a context engineering pattern that augments LLM prompts with passages retrieved at inference time from a vector store, search index, or knowledge base. RAG keeps facts outside the model and is one of the most widely used context engineering techniques. humanURL: https://arxiv.org/abs/2005.11401 baseURL: https://arxiv.org tags: - Embeddings - Knowledge Base - RAG - Retrieval properties: - type: Specification url: https://arxiv.org/abs/2005.11401 - type: Reference url: https://docs.llamaindex.ai/ - type: Reference url: https://python.langchain.com/docs/concepts/rag/ x-features: - Pluggable retrievers over vector and keyword indexes - Pre-retrieval rewriting and post-retrieval re-ranking - Hybrid retrieval combining BM25 and dense vectors - Citations and grounding for answer auditing x-useCases: - Domain-specific question answering over private documents - Customer support agents with up-to-date knowledge - Long-tail factual recall outside model training - aid: context-engineering:prompt-engineering name: Prompt Engineering description: >- Prompt engineering is the discipline of crafting model instructions and examples to guide model behavior. Prompt engineering remains a sub-discipline of context engineering and includes techniques like role prompting, chain-of-thought, few-shot examples, and structured output formats. humanURL: https://www.promptingguide.ai/ baseURL: https://www.promptingguide.ai tags: - Few-Shot - Instructions - Prompting properties: - type: Documentation url: https://www.promptingguide.ai/ - type: Reference url: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview - type: Reference url: https://platform.openai.com/docs/guides/prompt-engineering x-features: - Chain-of-thought and reasoning prompts - Few-shot, zero-shot, and self-consistency prompts - Structured output formatting (JSON, XML) - Role and persona instructions x-useCases: - Steering model behavior in zero-shot tasks - Eliciting structured responses suitable for downstream tools - Mitigating undesired outputs through guardrails - aid: context-engineering:agent-loops name: Agentic Loops and Tool Use description: >- Agentic loops are iterative reasoning patterns in which an LLM plans, calls tools, observes results, and refines its plan. Tool design is a central context engineering concern: tools must be token-efficient, have minimal overlap, and include clear, motivating descriptions. humanURL: https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview baseURL: https://docs.anthropic.com tags: - Agents - Function Calling - ReAct - Tool Use properties: - type: Documentation url: https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview - type: Reference url: https://platform.openai.com/docs/guides/function-calling - type: Reference url: https://arxiv.org/abs/2210.03629 x-features: - Tool definitions with JSON Schema arguments - Iterative plan-act-observe loops - Parallel tool invocation - Server- and client-side tool execution x-useCases: - Building task-completing AI agents - Wiring LLMs to internal APIs and databases - Decomposing complex problems with sub-tools - aid: context-engineering:long-horizon-strategies name: Long-Horizon Context Strategies description: >- Long-horizon strategies handle conversations and tasks that exceed the context window. Techniques include compaction (summarizing history into a smaller representation), structured note taking (persistent external memory), and multi-agent decomposition where sub-agents handle bounded subtasks and return condensed summaries. humanURL: https://www.anthropic.com/news/contextual-retrieval baseURL: https://www.anthropic.com tags: - Compaction - Long Context - Memory - Multi-Agent properties: - type: Documentation url: https://www.anthropic.com/news/contextual-retrieval - type: Reference url: https://www.anthropic.com/research/swe-bench-sonnet - type: Reference url: https://github.com/microsoft/autogen x-features: - Conversation summarization for compaction - Persistent memory files for cross-session knowledge - Multi-agent decomposition with bounded sub-agents - Hierarchical planning over long-running tasks x-useCases: - Long-running coding agents and SWE assistants - Multi-day customer engagements requiring memory - Complex research tasks decomposed across sub-agents common: - type: Reference url: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents - type: Reference url: https://www.promptingguide.ai/ - type: Reference url: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview - type: Reference url: https://docs.llamaindex.ai/ - type: Reference url: https://python.langchain.com/docs/concepts/rag/ maintainers: - FN: Kin Lane email: kin@apievangelist.com