majiayu000 Context Engine - AI Agent Context Management
Context Engine provides production-grade patterns for managing what AI agents know, remember, and retrieve. It covers the full lifecycle: ingestion of project knowledge, optimal packing of context windows, persistent memory across sessions, and retrieval-augmented generation for large codebases. The difference between a useful agent and a hallucinating one is context management.
Core Capabilities
- Context window architecture — token budget allocation plus greedy, tiered, and adaptive-compression packing strategies.
- Memory architecture — three-layer model (working / session / knowledge base), promotion protocol, and staleness detection.
- Code retrieval — file-level, chunk-level (RAG), and dependency-aware retrieval with code chunking and embedding guidance.
- Knowledge graph construction — codebase graph schema (nodes + edges) and graph queries that resolve agent questions.
- Window optimization patterns — sliding window with anchors, progressive summarization, selective tool-result caching.
- Memory tool & context editing — file-backed persistent memory across sessions, plus context compaction (evict stale tool outputs, summarize-and-replace history) to keep a long loop from exhausting the window.
- Long-context strategies — when to use a 1M-token window vs. RAG vs. a hybrid agent loop, budget allocation across a big window, and position/attention effects.
- Multi-agent context sharing — shared context bus and a five-element handoff protocol.
When to Use
- Bootstrapping agent context for a new codebase (index → graph → summary → tiers).
- Optimizing context for a specific task (bug fix, feature, refactor, review).
- Capturing, promoting, and pruning session memory across sessions.
- Designing a RAG pipeline for code retrieval.
- Coordinating context across multiple collaborating agents.
Clarify First
Before designing or analyzing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
- Which task — bootstrap context for a codebase, optimize for a specific task, design persistent memory, or build a code RAG (selects the analyzer/pruner/indexer and the playbook)
- Token budget — the context-window ceiling (sets
--budgetand which packing strategy applies) - Source content — the files/codebase or knowledge base to index (the input the scripts process)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
| Tool | Purpose | Command |
|---|---|---|
context_analyzer.py |
Analyze files/prompts for token usage, relevance, and optimization suggestions | python scripts/context_analyzer.py src/ --budget 128000 --json |
context_pruner.py |
Prune low-relevance content, redundancy, and verbose patterns from context | python scripts/context_pruner.py src/main.py --aggressive --json |
memory_indexer.py |
Index and search a memory/knowledge base with TF-IDF relevance scoring | python scripts/memory_indexer.py docs/ --query 'auth middleware' --top 5 |
context_budget_planner.py |
Allocate a window across components, flag overflow, and suggest what to compact/evict first | python scripts/context_budget_planner.py --window-size 200000 --system 4000 --history 60000 --tools 90000 --rag 40000 --reserve-output 8000 |
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- references/context-window-strategies.md — budget allocation, packing strategies, and window-optimization patterns. Read when planning budgets or optimizing a long conversation.
- references/memory-architecture-guide.md — three-layer memory model, promotion protocol, staleness detection, shared context bus + handoff protocol. Read when designing persistent memory or coordinating agents.
- references/code-retrieval-patterns.md — file/chunk/dependency-aware retrieval, chunking/embedding guidance, knowledge-graph schema and queries. Read when building RAG for code.
- references/memory-and-context-editing.md — the memory-tool pattern (file-backed memory, what to store vs. recompute, retention, security) and context editing/compaction (eviction priority, summarize-and-replace, token-savings payoff) and how both weave into the agent loop. Read when persisting state across sessions or keeping a long loop from exhausting the window.
- references/long-context-strategies.md — long-context vs. RAG vs. hybrid decision-making, budget allocation across a 1M-token window, position/attention effects, and when a bigger window hurts (cost, latency, distraction). Read when choosing a window-vs-retrieval strategy.
- references/workflows-and-quality.md — the three workflows, anti-patterns, evaluation metrics, troubleshooting, and success criteria. Read before running a workflow and before shipping.
Scope & Limitations
This skill covers:
- Context window token budget planning, allocation strategies, and packing algorithms for AI coding agents.
- Multi-layer memory architecture design (working memory, session memory, knowledge base) with promotion and staleness protocols.
- Code-specific retrieval strategies including file-level, chunk-level, and dependency-aware retrieval for RAG pipelines.
- Knowledge graph construction from codebases and graph-based context queries for agent workflows.
This skill does NOT cover:
- Vector store infrastructure setup, embedding model selection, or database deployment — see rag-architect for vector store design and embedding strategies.
- Agent role definition, personality design, or multi-agent orchestration logic — see agent-designer for agent architecture and agent-workflow-designer for orchestration patterns.
- Runtime observability, metrics dashboards, or alerting for agent systems — see observability-designer for monitoring and instrumentation.
- Prompt engineering techniques, chain-of-thought design, or instruction tuning — see prompt-engineer-toolkit for prompt construction patterns.
Integration Points
| Skill | Integration | Data Flow |
|---|---|---|
| rag-architect | Context Engine defines retrieval strategies; RAG Architect implements the vector store and embedding pipeline | Retrieval queries flow from Context Engine to RAG Architect's indexed store; ranked results flow back as context chunks |
| agent-designer | Agent Designer defines agent roles and capabilities; Context Engine manages per-agent context budgets and memory layers | Agent specifications define context requirements; Context Engine returns tailored context windows per agent role |
| self-improving-agent | Self-Improving Agent identifies recurring patterns and corrections; Context Engine decides when to promote learnings to persistent memory | Candidate learnings flow from Self-Improving Agent; promotion decisions and memory updates flow back through Context Engine's staleness and promotion protocols |
| observability-designer | Observability Designer instruments context utilization metrics (relevance, staleness, cache hits); Context Engine exposes metric endpoints | Raw metric events flow from Context Engine; Observability Designer aggregates into dashboards and alerts |
| agent-workflow-designer | Agent Workflow Designer defines multi-agent handoff sequences; Context Engine implements the shared context bus and handoff protocol | Workflow definitions specify which agents share context; Context Engine manages the context bus, serialization, and handoff payloads |
| codebase-onboarding | Codebase Onboarding generates project summaries and architecture maps; Context Engine consumes these as Tier 0 bootstrap context | Onboarding artifacts (project summary, directory map, entry points) feed into Context Engine's initial knowledge graph and context tiers |