"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Create the agent\n",
- "agent = create_deep_agent(\n",
- " model=model,\n",
- " tools=tools, \n",
- " system_prompt=INSTRUCTIONS,\n",
- " subagents=[research_sub_agent],\n",
- " )\n",
- " \n",
- "# Show the agent\n",
- "display(Image(agent.get_graph().draw_mermaid_png()))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "613634c2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "╭─────────────────────────────────────────────────── 🧑 Human ────────────────────────────────────────────────────╮\n",
- "│ research context engineering approaches used to build AI agents │\n",
- "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
- "
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- "╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮\n",
- "│ Context engineering is the systematic practice of designing, managing, and optimizing the information provided │\n",
- "│ to AI models to ensure accurate, consistent, and context-aware behavior. It moves beyond simple prompt │\n",
- "│ engineering by treating context as a dynamic resource that must be architected, managed, and validated. │\n",
- "│ │\n",
- "│ ### 1. Definition and Core Concepts │\n",
- "│ **Context Engineering** is the \"process of designing, testing, and iterating on the contextual information │\n",
- "│ provided to AI agents to shape their behavior and improve task performance\" [1]. Unlike a static prompt, │\n",
- "│ context in an agentic system is a living state that changes with every user interaction, tool output, and │\n",
- "│ internal reasoning step. │\n",
- "│ │\n",
- "│ * **Core Concepts**: │\n",
- "│ * **Context Window**: The working memory limit of the LLM (e.g., 128k tokens). Managing this finite │\n",
- "│ resource is the central challenge. │\n",
- "│ * **State Management**: Tracking the \"world state\" (user intent, task progress, tool results) across a │\n",
- "│ multi-turn conversation. │\n",
- "│ * **Context Pollution**: The degradation of model performance caused by irrelevant or contradictory │\n",
- "│ information in the context [2]. │\n",
- "│ * **Context Continuity**: Ensuring the agent retains critical information (like user constraints) │\n",
- "│ throughout a long interaction without \"forgetting\" them due to truncation. │\n",
- "│ │\n",
- "│ ### 2. Key Techniques and Strategies │\n",
- "│ Effective context engineering employs a mix of retrieval, summarization, and structural strategies: │\n",
- "│ │\n",
- "│ * **Dynamic Context Injection**: Instead of loading all data at once, agents \"pull\" information only when │\n",
- "│ needed. For example, an agent might use a `search_tool` to inject real-time web results into the context only │\n",
- "│ after identifying a knowledge gap [2]. │\n",
- "│ * **Retrieval-Augmented Generation (RAG)**: A standard pattern for handling large knowledge bases. The agent │\n",
- "│ retrieves strictly relevant chunks (e.g., top-5 matches) from a vector database to populate the context window, │\n",
- "│ rather than stuffing the entire document [2, 3]. │\n",
- "│ * **Memory Management**: │\n",
- "│ * **Short-term Memory**: Stores immediate conversation history (e.g., last 10 turns). │\n",
- "│ * **Long-term Memory**: Persists key facts (e.g., \"User is a vegetarian\") in an external database │\n",
- "│ (Postgres, Vector Store) and retrieves them only when relevant [2]. │\n",
- "│ * **Pruning/Trimming**: Algorithms that remove older, less relevant tokens to free up space while │\n",
- "│ preserving recent interactions [2]. │\n",
- "│ * **Summarization**: Compressing verbose tool outputs. Instead of feeding raw HTML from a scraped website, an │\n",
- "│ intermediate \"summarizer agent\" distills it into key points before passing it to the main agent [2]. │\n",
- "│ * **Prompt Structuring**: Using structured formats (like JSON schemas or XML tags) within the context to │\n",
- "│ clearly separate \"System Instructions,\" \"User Input,\" and \"Tool Outputs,\" preventing the model from confusing │\n",
- "│ data with instructions. │\n",
- "│ │\n",
- "│ ### 3. Architectures and Frameworks │\n",
- "│ Two primary frameworks dominate the landscape, each with distinct approaches to context handling: │\n",
- "│ │\n",
- "│ | Feature | **LangChain** [3, 4] | **LlamaIndex** [3, 4] | │\n",
- "│ | :--- | :--- | :--- | │\n",
- "│ | **Primary Focus** | Flexible workflow & agent orchestration | Data indexing & advanced retrieval (RAG) | │\n",
- "│ | **Memory Classes** | `ConversationBufferMemory` (raw history)<br>`ConversationSummaryMemory` (LLM-summarized │\n",
- "│ history)<br>`ChatMessageHistory` (storage backend) | `ChatEngine` with modes:<br>`context` (standard │\n",
- "│ RAG)<br>`condense_question` (rewrites queries based on history) | │\n",
- "│ | **Context Strategy** | **Chains**: Sequential passing of context between steps.<br>**Agents**: Dynamic tool │\n",
- "│ selection to populate context. | **Indices**: `VectorStoreIndex` (semantic search), `ListIndex` (sequential), │\n",
- "│ `TreeIndex` (hierarchical summaries). | │\n",
- "│ | **Optimization** | `Context Compression`: Removing irrelevant tokens from retrieved docs. | `Response │\n",
- "│ Synthesizers`: <br>`compact` (stuffs max chunks into prompt)<br>`tree_summarize` (hierarchical reduction) | │\n",
- "│ │\n",
- "│ ### 4. Handling Context Limits vs. Long-Context Models │\n",
- "│ A critical architectural decision is whether to use RAG or rely on massive context windows (e.g., Gemini 1.5 │\n",
- "│ Pro's 2M tokens). │\n",
- "│ │\n",
- "│ * **RAG + Small Context**: │\n",
- "│ * **Pros**: Significantly cheaper (fewer tokens processed), lower latency, and often higher accuracy for │\n",
- "│ specific fact retrieval. │\n",
- "│ * **Cons**: Can miss \"global\" insights that require reading the entire corpus at once. │\n",
- "│ * **Long-Context Models (LCW)**: │\n",
- "│ * **Pros**: Can ingest entire books or codebases, enabling \"whole-document\" reasoning. │\n",
- "│ * **Cons**: \"Lost in the Middle\" phenomenon where models forget information in the middle of a massive │\n",
- "│ context. │\n",
- "│ * **Trade-off**: Benchmarks show that RAG systems often outperform LCW models in \"Needle in a Haystack\" │\n",
- "│ tests for large corpora (2M+ tokens) because they filter noise before the model sees it [3]. │\n",
- "│ │\n",
- "│ ### 5. Specific Patterns │\n",
- "│ * **Needle in a Haystack Optimization**: │\n",
- "│ * **The Problem**: LLMs struggle to find a specific fact (\"needle\") buried in a large amount of │\n",
- "│ irrelevant text (\"haystack\"). │\n",
- "│ * **Optimization**: Place critical instructions or facts at the very **beginning** or very **end** of the │\n",
- "│ context window, as models pay most attention to these areas (Serial Position Effect). │\n",
- "│ * **Context Stuffing vs. Context Selection**: │\n",
- "│ * **Context Stuffing**: Naively filling the window with as much data as possible. This often leads to │\n",
- "│ *context distraction* and higher costs [2]. │\n",
- "│ * **Context Selection**: Intentionally curating what enters the window. │\n",
- "│ * *Techniques*: Using **Node Postprocessors** (LlamaIndex) to filter retrieved nodes by keywords or │\n",
- "│ similarity score before they reach the LLM [4]. │\n",
- "│ * *Query Transformations*: Rewriting a user's vague query into a precise search term to select better │\n",
- "│ context (e.g., LangChain's `HyDE` or LlamaIndex's `Multi-step query decomposition`) [4]. │\n",
- "│ │\n",
- "│ ### Sources │\n",
- "│ [1] Context Engineering Guide: https://www.promptingguide.ai/agents/context-engineering │\n",
- "│ [2] Context Engineering Strategies (n8n): │\n",
- "│ https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/ │\n",
- "│ [3] RAG vs Long Context Window Study: │\n",
- "│ https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs │\n",
- "│ [4] LangChain & LlamaIndex Comparison: │\n",
- "│ https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperability- │\n",
- "│ in-large-2b5f3fabc360 │\n",
- "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
- "
\n"
- ],
- "text/plain": [
- "\u001b[33m╭─\u001b[0m\u001b[33m───────────────────────────────────────────────\u001b[0m\u001b[33m 🔧 Tool Output \u001b[0m\u001b[33m────────────────────────────────────────────────\u001b[0m\u001b[33m─╮\u001b[0m\n",
- "\u001b[33m│\u001b[0m Context engineering is the systematic practice of designing, managing, and optimizing the information provided \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m to AI models to ensure accurate, consistent, and context-aware behavior. It moves beyond simple prompt \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m engineering by treating context as a dynamic resource that must be architected, managed, and validated. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m ### 1. Definition and Core Concepts \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m **Context Engineering** is the \"process of designing, testing, and iterating on the contextual information \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m provided to AI agents to shape their behavior and improve task performance\" [1]. Unlike a static prompt, \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m context in an agentic system is a living state that changes with every user interaction, tool output, and \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m internal reasoning step. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Core Concepts**: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Context Window**: The working memory limit of the LLM (e.g., 128k tokens). Managing this finite \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m resource is the central challenge. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **State Management**: Tracking the \"world state\" (user intent, task progress, tool results) across a \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m multi-turn conversation. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Context Pollution**: The degradation of model performance caused by irrelevant or contradictory \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m information in the context [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Context Continuity**: Ensuring the agent retains critical information (like user constraints) \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m throughout a long interaction without \"forgetting\" them due to truncation. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m ### 2. Key Techniques and Strategies \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m Effective context engineering employs a mix of retrieval, summarization, and structural strategies: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Dynamic Context Injection**: Instead of loading all data at once, agents \"pull\" information only when \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m needed. For example, an agent might use a `search_tool` to inject real-time web results into the context only \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m after identifying a knowledge gap [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Retrieval-Augmented Generation (RAG)**: A standard pattern for handling large knowledge bases. The agent \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m retrieves strictly relevant chunks (e.g., top-5 matches) from a vector database to populate the context window, \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m rather than stuffing the entire document [2, 3]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Memory Management**: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Short-term Memory**: Stores immediate conversation history (e.g., last 10 turns). \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Long-term Memory**: Persists key facts (e.g., \"User is a vegetarian\") in an external database \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m (Postgres, Vector Store) and retrieves them only when relevant [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Pruning/Trimming**: Algorithms that remove older, less relevant tokens to free up space while \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m preserving recent interactions [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Summarization**: Compressing verbose tool outputs. Instead of feeding raw HTML from a scraped website, an \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m intermediate \"summarizer agent\" distills it into key points before passing it to the main agent [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Prompt Structuring**: Using structured formats (like JSON schemas or XML tags) within the context to \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m clearly separate \"System Instructions,\" \"User Input,\" and \"Tool Outputs,\" preventing the model from confusing \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m data with instructions. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m ### 3. Architectures and Frameworks \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m Two primary frameworks dominate the landscape, each with distinct approaches to context handling: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m | Feature | **LangChain** [3, 4] | **LlamaIndex** [3, 4] | \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m | :--- | :--- | :--- | \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m | **Primary Focus** | Flexible workflow & agent orchestration | Data indexing & advanced retrieval (RAG) | \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m | **Memory Classes** | `ConversationBufferMemory` (raw history)
`ConversationSummaryMemory` (LLM-summarized \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m history)
`ChatMessageHistory` (storage backend) | `ChatEngine` with modes:
`context` (standard \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m RAG)
`condense_question` (rewrites queries based on history) | \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m | **Context Strategy** | **Chains**: Sequential passing of context between steps.
**Agents**: Dynamic tool \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m selection to populate context. | **Indices**: `VectorStoreIndex` (semantic search), `ListIndex` (sequential), \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m `TreeIndex` (hierarchical summaries). | \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m | **Optimization** | `Context Compression`: Removing irrelevant tokens from retrieved docs. | `Response \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m Synthesizers`:
`compact` (stuffs max chunks into prompt)
`tree_summarize` (hierarchical reduction) | \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m ### 4. Handling Context Limits vs. Long-Context Models \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m A critical architectural decision is whether to use RAG or rely on massive context windows (e.g., Gemini 1.5 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m Pro's 2M tokens). \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **RAG + Small Context**: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Pros**: Significantly cheaper (fewer tokens processed), lower latency, and often higher accuracy for \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m specific fact retrieval. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Cons**: Can miss \"global\" insights that require reading the entire corpus at once. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Long-Context Models (LCW)**: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Pros**: Can ingest entire books or codebases, enabling \"whole-document\" reasoning. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Cons**: \"Lost in the Middle\" phenomenon where models forget information in the middle of a massive \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m context. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Trade-off**: Benchmarks show that RAG systems often outperform LCW models in \"Needle in a Haystack\" \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m tests for large corpora (2M+ tokens) because they filter noise before the model sees it [3]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m ### 5. Specific Patterns \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Needle in a Haystack Optimization**: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **The Problem**: LLMs struggle to find a specific fact (\"needle\") buried in a large amount of \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m irrelevant text (\"haystack\"). \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Optimization**: Place critical instructions or facts at the very **beginning** or very **end** of the \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m context window, as models pay most attention to these areas (Serial Position Effect). \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Context Stuffing vs. Context Selection**: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Context Stuffing**: Naively filling the window with as much data as possible. This often leads to \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m *context distraction* and higher costs [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * **Context Selection**: Intentionally curating what enters the window. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * *Techniques*: Using **Node Postprocessors** (LlamaIndex) to filter retrieved nodes by keywords or \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m similarity score before they reach the LLM [4]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m * *Query Transformations*: Rewriting a user's vague query into a precise search term to select better \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m context (e.g., LangChain's `HyDE` or LlamaIndex's `Multi-step query decomposition`) [4]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m ### Sources \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m [1] Context Engineering Guide: https://www.promptingguide.ai/agents/context-engineering \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m [2] Context Engineering Strategies (n8n): \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/ \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m [3] RAG vs Long Context Window Study: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m [4] LangChain & LlamaIndex Comparison: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperability- \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m in-large-2b5f3fabc360 \u001b[33m│\u001b[0m\n",
- "\u001b[33m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮\n",
- "│ │\n",
- "│ 🔧 Tool Call: write_todos │\n",
- "│ Args: { │\n",
- "│ \"todos\": [ │\n",
- "│ { │\n",
- "│ \"status\": \"completed\", │\n",
- "│ \"content\": \"Save research request to /research_request.md\" │\n",
- "│ }, │\n",
- "│ { │\n",
- "│ \"status\": \"completed\", │\n",
- "│ \"content\": \"Research context engineering approaches for AI agents using a sub-agent\" │\n",
- "│ }, │\n",
- "│ { │\n",
- "│ \"status\": \"in_progress\", │\n",
- "│ \"content\": \"Synthesize findings and write final report to /final_report.md\" │\n",
- "│ }, │\n",
- "│ { │\n",
- "│ \"status\": \"pending\", │\n",
- "│ \"content\": \"Verify report against original request\" │\n",
- "│ } │\n",
- "│ ] │\n",
- "│ } │\n",
- "│ ID: ea8aeaae-7e6a-4f8b-beba-1f252efabd24 │\n",
- "│ │\n",
- "│ 🔧 Tool Call: write_file │\n",
- "│ Args: { │\n",
- "│ \"file_path\": \"/final_report.md\", │\n",
- "│ \"content\": \"# Context Engineering Approaches for AI Agents\\n\\n## 1. Introduction\\nContext Engineering is the │\n",
- "│ systematic practice of designing, managing, and optimizing the information provided to AI models to ensure │\n",
- "│ accurate, consistent, and context-aware behavior. While often confused with prompt engineering (which focuses │\n",
- "│ on static instructions), context engineering treats the AI's context window as a dynamic, finite resource that │\n",
- "│ must be architected and managed throughout the lifecycle of an interaction. It is a critical discipline for │\n",
- "│ building robust AI agents that can maintain state and relevance over long, complex tasks.\\n\\n## 2. Core │\n",
- "│ Concepts\\nTo build effective agents, developers must manage several fundamental aspects of the Large Language │\n",
- "│ Model (LLM) environment:\\n\\n* **Context Window**: The hard limit on the amount of text (tokens) an LLM can │\n",
- "│ process at once (e.g., 8k, 128k, or 1M tokens). Effective engineering ensures this space is used │\n",
- "│ efficiently.\\n* **State Management**: Tracking the \\\"world state\\\" across multi-turn conversations. This │\n",
- "│ includes user intent, task progress, and tool outputs.\\n* **Context Pollution**: The phenomenon where │\n",
- "│ irrelevant or contradictory information in the context degrades model performance, leading to hallucinations or │\n",
- "│ confusion [2].\\n* **Serial Position Effect**: The tendency of LLMs to pay more attention to information at │\n",
- "│ the very beginning and very end of the context window, often ignoring details buried in the middle (\\\"Lost in │\n",
- "│ the Middle\\\") [3].\\n\\n## 3. Key Approaches and Techniques\\n\\n### Dynamic Context Injection & Retrieval │\n",
- "│ (RAG)\\nRather than loading all available data at once, agents \\\"pull\\\" information only when necessary.\\n* │\n",
- "│ **Dynamic Injection**: Agents use tools (like search APIs or database queries) to fetch real-time data only │\n",
- "│ after identifying a specific knowledge gap [2].\\n* **RAG (Retrieval-Augmented Generation)**: The standard for │\n",
- "│ large knowledge bases. Instead of \\\"stuffing\\\" a document, the system retrieves only the top-k most relevant │\n",
- "│ chunks from a vector store to populate the context [2][3].\\n\\n### Memory Management\\nAgents require │\n",
- "│ sophisticated memory systems to maintain continuity without overflowing context limits:\\n* **Short-term │\n",
- "│ Memory**: A sliding window of the most recent interaction turns (e.g., last 10 messages).\\n* **Long-term │\n",
- "│ Memory**: Key facts (e.g., user preferences) are persisted in external databases (Postgres, Vector Stores) and │\n",
- "│ retrieved only when relevant to the current query [2].\\n* **Pruning and Trimming**: Algorithms that │\n",
- "│ selectively remove older, less relevant tokens while preserving the semantic core of the conversation │\n",
- "│ history.\\n\\n### Summarization and Compression\\n* **Tool Output Summarization**: Raw outputs from tools (like │\n",
- "│ HTML from a web scraper) are often too verbose. An intermediate step or \\\"summarizer agent\\\" distills this data │\n",
- "│ into key points before passing it to the main reasoning agent [2].\\n* **Context Compression**: Removing stop │\n",
- "│ words or irrelevant sections from retrieved documents to maximize information density per token.\\n\\n### Prompt │\n",
- "│ Structuring\\nUsing strict formats within the context window helps the model parse information correctly:\\n* │\n",
- "│ **Separation of Concerns**: Clearly formatting \\\"System Instructions,\\\" \\\"User Input,\\\" and \\\"Tool Outputs\\\" │\n",
- "│ (often using XML tags or JSON schemas) prevents the model from confusing data with instructions.\\n\\n## 4. │\n",
- "│ Architectural Frameworks\\nTwo primary frameworks dominate the implementation of these │\n",
- "│ strategies:\\n\\n**LangChain**\\n* **Focus**: Flexible orchestration and agent workflows.\\n* **Approach**: │\n",
- "│ Uses \\\"Chains\\\" to pass context sequentially between steps and \\\"Memory\\\" classes (e.g., │\n",
- "│ `ConversationSummaryMemory`) to automatically manage history compression and persistence [4].\\n* │\n",
- "│ **Strategy**: Emphasizes dynamic tool selection where the agent decides what information to add to its own │\n",
- "│ context.\\n\\n**LlamaIndex**\\n* **Focus**: Data indexing and advanced retrieval.\\n* **Approach**: specialized │\n",
- "│ in structuring data for LLMs using indices (`VectorStoreIndex`, `TreeIndex`).\\n* **Strategy**: Uses │\n",
- "│ \\\"Response Synthesizers\\\" to optimize how retrieved context is presented to the LLM (e.g., `compact` mode to │\n",
- "│ stuff chunks or `tree_summarize` for hierarchical reduction) [4].\\n\\n## 5. Strategic Patterns\\n\\n### RAG vs. │\n",
- "│ Long-Context Windows (LCW)\\n* **RAG**: Preferred for cost efficiency and low latency. It filters noise before │\n",
- "│ the model sees it, often resulting in higher accuracy for \\\"Needle in a Haystack\\\" retrieval tasks [3].\\n* │\n",
- "│ **Long-Context Models (e.g., Gemini 1.5)**: Best for \\\"whole-document\\\" reasoning where global context is │\n",
- "│ required. However, they are more expensive and prone to the \\\"Lost in the Middle\\\" effect.\\n\\n### Context │\n",
- "│ Selection vs. Stuffing\\n* **Context Stuffing**: Naively filling the window with all available data. This is │\n",
- "│ generally an anti-pattern as it increases costs and distracts the model.\\n* **Context Selection**: The │\n",
- "│ proactive curation of inputs. Techniques include \\\"Node Postprocessors\\\" (filtering results by similarity │\n",
- "│ score) and \\\"Query Transformations\\\" (rewriting vague user queries to fetch better context) [4].\\n\\n## 6. │\n",
- "│ Conclusion\\nContext engineering is the bridge between raw data and intelligent agent behavior. By moving from │\n",
- "│ naive context stuffing to sophisticated dynamic injection, memory management, and structured retrieval, │\n",
- "│ developers can build agents that are both powerful and cost-effective. The choice between RAG architectures and │\n",
- "│ long-context models remains a key trade-off, with hybrid approaches often providing the best balance of │\n",
- "│ accuracy and performance.\\n\\n### Sources\\n[1] Context Engineering Guide: │\n",
- "│ https://www.promptingguide.ai/agents/context-engineering\\n[2] Context Engineering Strategies (n8n): │\n",
- "│ https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/\\n[3] RAG vs Long │\n",
- "│ Context Window Study: https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs\\n[4] │\n",
- "│ LangChain & LlamaIndex Comparison: │\n",
- "│ https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperability- │\n",
- "│ in-large-2b5f3fabc366\" │\n",
- "│ } │\n",
- "│ ID: 589357a0-fbce-4941-9090-8f91d12880cb │\n",
- "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
- "\n"
- ],
- "text/plain": [
- "\u001b[37m╭─\u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m 📝 AI \u001b[0m\u001b[37m────────────────────────────────────────────────────\u001b[0m\u001b[37m─╮\u001b[0m\n",
- "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m 🔧 Tool Call: write_todos \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Args: { \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"todos\": [ \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"status\": \"completed\", \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"content\": \"Save research request to /research_request.md\" \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m }, \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"status\": \"completed\", \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"content\": \"Research context engineering approaches for AI agents using a sub-agent\" \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m }, \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"status\": \"in_progress\", \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"content\": \"Synthesize findings and write final report to /final_report.md\" \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m }, \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m { \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"status\": \"pending\", \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"content\": \"Verify report against original request\" \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m } \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m ] \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m } \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m ID: ea8aeaae-7e6a-4f8b-beba-1f252efabd24 \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m 🔧 Tool Call: write_file \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Args: { \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"file_path\": \"/final_report.md\", \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \"content\": \"# Context Engineering Approaches for AI Agents\\n\\n## 1. Introduction\\nContext Engineering is the \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m systematic practice of designing, managing, and optimizing the information provided to AI models to ensure \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m accurate, consistent, and context-aware behavior. While often confused with prompt engineering (which focuses \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m on static instructions), context engineering treats the AI's context window as a dynamic, finite resource that \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m must be architected and managed throughout the lifecycle of an interaction. It is a critical discipline for \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m building robust AI agents that can maintain state and relevance over long, complex tasks.\\n\\n## 2. Core \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Concepts\\nTo build effective agents, developers must manage several fundamental aspects of the Large Language \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Model (LLM) environment:\\n\\n* **Context Window**: The hard limit on the amount of text (tokens) an LLM can \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m process at once (e.g., 8k, 128k, or 1M tokens). Effective engineering ensures this space is used \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m efficiently.\\n* **State Management**: Tracking the \\\"world state\\\" across multi-turn conversations. This \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m includes user intent, task progress, and tool outputs.\\n* **Context Pollution**: The phenomenon where \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m irrelevant or contradictory information in the context degrades model performance, leading to hallucinations or \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m confusion [2].\\n* **Serial Position Effect**: The tendency of LLMs to pay more attention to information at \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m the very beginning and very end of the context window, often ignoring details buried in the middle (\\\"Lost in \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m the Middle\\\") [3].\\n\\n## 3. Key Approaches and Techniques\\n\\n### Dynamic Context Injection & Retrieval \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m (RAG)\\nRather than loading all available data at once, agents \\\"pull\\\" information only when necessary.\\n* \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m **Dynamic Injection**: Agents use tools (like search APIs or database queries) to fetch real-time data only \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m after identifying a specific knowledge gap [2].\\n* **RAG (Retrieval-Augmented Generation)**: The standard for \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m large knowledge bases. Instead of \\\"stuffing\\\" a document, the system retrieves only the top-k most relevant \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m chunks from a vector store to populate the context [2][3].\\n\\n### Memory Management\\nAgents require \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m sophisticated memory systems to maintain continuity without overflowing context limits:\\n* **Short-term \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Memory**: A sliding window of the most recent interaction turns (e.g., last 10 messages).\\n* **Long-term \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Memory**: Key facts (e.g., user preferences) are persisted in external databases (Postgres, Vector Stores) and \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m retrieved only when relevant to the current query [2].\\n* **Pruning and Trimming**: Algorithms that \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m selectively remove older, less relevant tokens while preserving the semantic core of the conversation \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m history.\\n\\n### Summarization and Compression\\n* **Tool Output Summarization**: Raw outputs from tools (like \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m HTML from a web scraper) are often too verbose. An intermediate step or \\\"summarizer agent\\\" distills this data \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m into key points before passing it to the main reasoning agent [2].\\n* **Context Compression**: Removing stop \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m words or irrelevant sections from retrieved documents to maximize information density per token.\\n\\n### Prompt \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Structuring\\nUsing strict formats within the context window helps the model parse information correctly:\\n* \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m **Separation of Concerns**: Clearly formatting \\\"System Instructions,\\\" \\\"User Input,\\\" and \\\"Tool Outputs\\\" \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m (often using XML tags or JSON schemas) prevents the model from confusing data with instructions.\\n\\n## 4. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Architectural Frameworks\\nTwo primary frameworks dominate the implementation of these \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m strategies:\\n\\n**LangChain**\\n* **Focus**: Flexible orchestration and agent workflows.\\n* **Approach**: \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Uses \\\"Chains\\\" to pass context sequentially between steps and \\\"Memory\\\" classes (e.g., \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m `ConversationSummaryMemory`) to automatically manage history compression and persistence [4].\\n* \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m **Strategy**: Emphasizes dynamic tool selection where the agent decides what information to add to its own \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m context.\\n\\n**LlamaIndex**\\n* **Focus**: Data indexing and advanced retrieval.\\n* **Approach**: specialized \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m in structuring data for LLMs using indices (`VectorStoreIndex`, `TreeIndex`).\\n* **Strategy**: Uses \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \\\"Response Synthesizers\\\" to optimize how retrieved context is presented to the LLM (e.g., `compact` mode to \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m stuff chunks or `tree_summarize` for hierarchical reduction) [4].\\n\\n## 5. Strategic Patterns\\n\\n### RAG vs. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Long-Context Windows (LCW)\\n* **RAG**: Preferred for cost efficiency and low latency. It filters noise before \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m the model sees it, often resulting in higher accuracy for \\\"Needle in a Haystack\\\" retrieval tasks [3].\\n* \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m **Long-Context Models (e.g., Gemini 1.5)**: Best for \\\"whole-document\\\" reasoning where global context is \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m required. However, they are more expensive and prone to the \\\"Lost in the Middle\\\" effect.\\n\\n### Context \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Selection vs. Stuffing\\n* **Context Stuffing**: Naively filling the window with all available data. This is \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m generally an anti-pattern as it increases costs and distracts the model.\\n* **Context Selection**: The \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m proactive curation of inputs. Techniques include \\\"Node Postprocessors\\\" (filtering results by similarity \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m score) and \\\"Query Transformations\\\" (rewriting vague user queries to fetch better context) [4].\\n\\n## 6. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Conclusion\\nContext engineering is the bridge between raw data and intelligent agent behavior. By moving from \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m naive context stuffing to sophisticated dynamic injection, memory management, and structured retrieval, \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m developers can build agents that are both powerful and cost-effective. The choice between RAG architectures and \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m long-context models remains a key trade-off, with hybrid approaches often providing the best balance of \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m accuracy and performance.\\n\\n### Sources\\n[1] Context Engineering Guide: \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m https://www.promptingguide.ai/agents/context-engineering\\n[2] Context Engineering Strategies (n8n): \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/\\n[3] RAG vs Long \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Context Window Study: https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs\\n[4] \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m LangChain & LlamaIndex Comparison: \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperability- \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m in-large-2b5f3fabc366\" \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m } \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m ID: 589357a0-fbce-4941-9090-8f91d12880cb \u001b[37m│\u001b[0m\n",
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- "╭──────────────────────────────────────────────── 🔧 Tool Output ─────────────────────────────────────────────────╮\n",
- "│ 1 # Context Engineering Approaches for AI Agents │\n",
- "│ 2 │\n",
- "│ 3 ## 1. Introduction │\n",
- "│ 4 Context Engineering is the systematic practice of designing, managing, and optimizing the information │\n",
- "│ provided to AI models to ensure accurate, consistent, and context-aware behavior. While often confused with │\n",
- "│ prompt engineering (which focuses on static instructions), context engineering treats the AI's context window │\n",
- "│ as a dynamic, finite resource that must be architected and managed throughout the lifecycle of an interaction. │\n",
- "│ It is a critical discipline for building robust AI agents that can maintain state and relevance over long, │\n",
- "│ complex tasks. │\n",
- "│ 5 │\n",
- "│ 6 ## 2. Core Concepts │\n",
- "│ 7 To build effective agents, developers must manage several fundamental aspects of the Large Language │\n",
- "│ Model (LLM) environment: │\n",
- "│ 8 │\n",
- "│ 9 * **Context Window**: The hard limit on the amount of text (tokens) an LLM can process at once (e.g., │\n",
- "│ 8k, 128k, or 1M tokens). Effective engineering ensures this space is used efficiently. │\n",
- "│ 10 * **State Management**: Tracking the \"world state\" across multi-turn conversations. This includes │\n",
- "│ user intent, task progress, and tool outputs. │\n",
- "│ 11 * **Context Pollution**: The phenomenon where irrelevant or contradictory information in the context │\n",
- "│ degrades model performance, leading to hallucinations or confusion [2]. │\n",
- "│ 12 * **Serial Position Effect**: The tendency of LLMs to pay more attention to information at the very │\n",
- "│ beginning and very end of the context window, often ignoring details buried in the middle (\"Lost in the │\n",
- "│ Middle\") [3]. │\n",
- "│ 13 │\n",
- "│ 14 ## 3. Key Approaches and Techniques │\n",
- "│ 15 │\n",
- "│ 16 ### Dynamic Context Injection & Retrieval (RAG) │\n",
- "│ 17 Rather than loading all available data at once, agents \"pull\" information only when necessary. │\n",
- "│ 18 * **Dynamic Injection**: Agents use tools (like search APIs or database queries) to fetch real-time │\n",
- "│ data only after identifying a specific knowledge gap [2]. │\n",
- "│ 19 * **RAG (Retrieval-Augmented Generation)**: The standard for large knowledge bases. Instead of │\n",
- "│ \"stuffing\" a document, the system retrieves only the top-k most relevant chunks from a vector store to populate │\n",
- "│ the context [2][3]. │\n",
- "│ 20 │\n",
- "│ 21 ### Memory Management │\n",
- "│ 22 Agents require sophisticated memory systems to maintain continuity without overflowing context limits: │\n",
- "│ 23 * **Short-term Memory**: A sliding window of the most recent interaction turns (e.g., last 10 │\n",
- "│ messages). │\n",
- "│ 24 * **Long-term Memory**: Key facts (e.g., user preferences) are persisted in external databases │\n",
- "│ (Postgres, Vector Stores) and retrieved only when relevant to the current query [2]. │\n",
- "│ 25 * **Pruning and Trimming**: Algorithms that selectively remove older, less relevant tokens while │\n",
- "│ preserving the semantic core of the conversation history. │\n",
- "│ 26 │\n",
- "│ 27 ### Summarization and Compression │\n",
- "│ 28 * **Tool Output Summarization**: Raw outputs from tools (like HTML from a web scraper) are often too │\n",
- "│ verbose. An intermediate step or \"summarizer agent\" distills this data into key points before passing it to the │\n",
- "│ main reasoning agent [2]. │\n",
- "│ 29 * **Context Compression**: Removing stop words or irrelevant sections from retrieved documents to │\n",
- "│ maximize information density per token. │\n",
- "│ 30 │\n",
- "│ 31 ### Prompt Structuring │\n",
- "│ 32 Using strict formats within the context window helps the model parse information correctly: │\n",
- "│ 33 * **Separation of Concerns**: Clearly formatting \"System Instructions,\" \"User Input,\" and \"Tool │\n",
- "│ Outputs\" (often using XML tags or JSON schemas) prevents the model from confusing data with instructions. │\n",
- "│ 34 │\n",
- "│ 35 ## 4. Architectural Frameworks │\n",
- "│ 36 Two primary frameworks dominate the implementation of these strategies: │\n",
- "│ 37 │\n",
- "│ 38 **LangChain** │\n",
- "│ 39 * **Focus**: Flexible orchestration and agent workflows. │\n",
- "│ 40 * **Approach**: Uses \"Chains\" to pass context sequentially between steps and \"Memory\" classes (e.g., │\n",
- "│ `ConversationSummaryMemory`) to automatically manage history compression and persistence [4]. │\n",
- "│ 41 * **Strategy**: Emphasizes dynamic tool selection where the agent decides what information to add to │\n",
- "│ its own context. │\n",
- "│ 42 │\n",
- "│ 43 **LlamaIndex** │\n",
- "│ 44 * **Focus**: Data indexing and advanced retrieval. │\n",
- "│ 45 * **Approach**: specialized in structuring data for LLMs using indices (`VectorStoreIndex`, │\n",
- "│ `TreeIndex`). │\n",
- "│ 46 * **Strategy**: Uses \"Response Synthesizers\" to optimize how retrieved context is presented to the │\n",
- "│ LLM (e.g., `compact` mode to stuff chunks or `tree_summarize` for hierarchical reduction) [4]. │\n",
- "│ 47 │\n",
- "│ 48 ## 5. Strategic Patterns │\n",
- "│ 49 │\n",
- "│ 50 ### RAG vs. Long-Context Windows (LCW) │\n",
- "│ 51 * **RAG**: Preferred for cost efficiency and low latency. It filters noise before the model sees it, │\n",
- "│ often resulting in higher accuracy for \"Needle in a Haystack\" retrieval tasks [3]. │\n",
- "│ 52 * **Long-Context Models (e.g., Gemini 1.5)**: Best for \"whole-document\" reasoning where global │\n",
- "│ context is required. However, they are more expensive and prone to the \"Lost in the Middle\" effect. │\n",
- "│ 53 │\n",
- "│ 54 ### Context Selection vs. Stuffing │\n",
- "│ 55 * **Context Stuffing**: Naively filling the window with all available data. This is generally an │\n",
- "│ anti-pattern as it increases costs and distracts the model. │\n",
- "│ 56 * **Context Selection**: The proactive curation of inputs. Techniques include \"Node Postprocessors\" │\n",
- "│ (filtering results by similarity score) and \"Query Transformations\" (rewriting vague user queries to fetch │\n",
- "│ better context) [4]. │\n",
- "│ 57 │\n",
- "│ 58 ## 6. Conclusion │\n",
- "│ 59 Context engineering is the bridge between raw data and intelligent agent behavior. By moving from naive │\n",
- "│ context stuffing to sophisticated dynamic injection, memory management, and structured retrieval, developers │\n",
- "│ can build agents that are both powerful and cost-effective. The choice between RAG architectures and │\n",
- "│ long-context models remains a key trade-off, with hybrid approaches often providing the best balance of │\n",
- "│ accuracy and performance. │\n",
- "│ 60 │\n",
- "│ 61 ### Sources │\n",
- "│ 62 [1] Context Engineering Guide: https://www.promptingguide.ai/agents/context-engineering │\n",
- "│ 63 [2] Context Engineering Strategies (n8n): │\n",
- "│ https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/ │\n",
- "│ 64 [3] RAG vs Long Context Window Study: │\n",
- "│ https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs │\n",
- "│ 65 [4] LangChain & LlamaIndex Comparison: │\n",
- "│ https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperability- │\n",
- "│ in-large-2b5f3fabc366 │\n",
- "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
- "
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- "\u001b[33m│\u001b[0m 1 # Context Engineering Approaches for AI Agents \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 2 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 3 ## 1. Introduction \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 4 Context Engineering is the systematic practice of designing, managing, and optimizing the information \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m provided to AI models to ensure accurate, consistent, and context-aware behavior. While often confused with \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m prompt engineering (which focuses on static instructions), context engineering treats the AI's context window \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m as a dynamic, finite resource that must be architected and managed throughout the lifecycle of an interaction. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m It is a critical discipline for building robust AI agents that can maintain state and relevance over long, \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m complex tasks. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 5 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 6 ## 2. Core Concepts \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 7 To build effective agents, developers must manage several fundamental aspects of the Large Language \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m Model (LLM) environment: \u001b[33m│\u001b[0m\n",
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- "\u001b[33m│\u001b[0m 9 * **Context Window**: The hard limit on the amount of text (tokens) an LLM can process at once (e.g., \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 8k, 128k, or 1M tokens). Effective engineering ensures this space is used efficiently. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 10 * **State Management**: Tracking the \"world state\" across multi-turn conversations. This includes \u001b[33m│\u001b[0m\n",
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- "\u001b[33m│\u001b[0m 11 * **Context Pollution**: The phenomenon where irrelevant or contradictory information in the context \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m degrades model performance, leading to hallucinations or confusion [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 12 * **Serial Position Effect**: The tendency of LLMs to pay more attention to information at the very \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m beginning and very end of the context window, often ignoring details buried in the middle (\"Lost in the \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m Middle\") [3]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 13 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 14 ## 3. Key Approaches and Techniques \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 15 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 16 ### Dynamic Context Injection & Retrieval (RAG) \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 17 Rather than loading all available data at once, agents \"pull\" information only when necessary. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 18 * **Dynamic Injection**: Agents use tools (like search APIs or database queries) to fetch real-time \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m data only after identifying a specific knowledge gap [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 19 * **RAG (Retrieval-Augmented Generation)**: The standard for large knowledge bases. Instead of \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m \"stuffing\" a document, the system retrieves only the top-k most relevant chunks from a vector store to populate \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m the context [2][3]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 20 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 21 ### Memory Management \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 22 Agents require sophisticated memory systems to maintain continuity without overflowing context limits: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 23 * **Short-term Memory**: A sliding window of the most recent interaction turns (e.g., last 10 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m messages). \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 24 * **Long-term Memory**: Key facts (e.g., user preferences) are persisted in external databases \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m (Postgres, Vector Stores) and retrieved only when relevant to the current query [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 25 * **Pruning and Trimming**: Algorithms that selectively remove older, less relevant tokens while \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m preserving the semantic core of the conversation history. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 26 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 27 ### Summarization and Compression \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 28 * **Tool Output Summarization**: Raw outputs from tools (like HTML from a web scraper) are often too \u001b[33m│\u001b[0m\n",
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- "\u001b[33m│\u001b[0m main reasoning agent [2]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 29 * **Context Compression**: Removing stop words or irrelevant sections from retrieved documents to \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m maximize information density per token. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 30 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 31 ### Prompt Structuring \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 32 Using strict formats within the context window helps the model parse information correctly: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 33 * **Separation of Concerns**: Clearly formatting \"System Instructions,\" \"User Input,\" and \"Tool \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m Outputs\" (often using XML tags or JSON schemas) prevents the model from confusing data with instructions. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 34 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 35 ## 4. Architectural Frameworks \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 36 Two primary frameworks dominate the implementation of these strategies: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 37 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 38 **LangChain** \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 39 * **Focus**: Flexible orchestration and agent workflows. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 40 * **Approach**: Uses \"Chains\" to pass context sequentially between steps and \"Memory\" classes (e.g., \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m `ConversationSummaryMemory`) to automatically manage history compression and persistence [4]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 41 * **Strategy**: Emphasizes dynamic tool selection where the agent decides what information to add to \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m its own context. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 42 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 43 **LlamaIndex** \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 44 * **Focus**: Data indexing and advanced retrieval. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 45 * **Approach**: specialized in structuring data for LLMs using indices (`VectorStoreIndex`, \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m `TreeIndex`). \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 46 * **Strategy**: Uses \"Response Synthesizers\" to optimize how retrieved context is presented to the \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m LLM (e.g., `compact` mode to stuff chunks or `tree_summarize` for hierarchical reduction) [4]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 47 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 48 ## 5. Strategic Patterns \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 49 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 50 ### RAG vs. Long-Context Windows (LCW) \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 51 * **RAG**: Preferred for cost efficiency and low latency. It filters noise before the model sees it, \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m often resulting in higher accuracy for \"Needle in a Haystack\" retrieval tasks [3]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 52 * **Long-Context Models (e.g., Gemini 1.5)**: Best for \"whole-document\" reasoning where global \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m context is required. However, they are more expensive and prone to the \"Lost in the Middle\" effect. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 53 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 54 ### Context Selection vs. Stuffing \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 55 * **Context Stuffing**: Naively filling the window with all available data. This is generally an \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m anti-pattern as it increases costs and distracts the model. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 56 * **Context Selection**: The proactive curation of inputs. Techniques include \"Node Postprocessors\" \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m (filtering results by similarity score) and \"Query Transformations\" (rewriting vague user queries to fetch \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m better context) [4]. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 57 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 58 ## 6. Conclusion \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 59 Context engineering is the bridge between raw data and intelligent agent behavior. By moving from naive \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m context stuffing to sophisticated dynamic injection, memory management, and structured retrieval, developers \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m can build agents that are both powerful and cost-effective. The choice between RAG architectures and \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m long-context models remains a key trade-off, with hybrid approaches often providing the best balance of \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m accuracy and performance. \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 60 \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 61 ### Sources \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 62 [1] Context Engineering Guide: https://www.promptingguide.ai/agents/context-engineering \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 63 [2] Context Engineering Strategies (n8n): \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/ \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 64 [3] RAG vs Long Context Window Study: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m 65 [4] LangChain & LlamaIndex Comparison: \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperability- \u001b[33m│\u001b[0m\n",
- "\u001b[33m│\u001b[0m in-large-2b5f3fabc366 \u001b[33m│\u001b[0m\n",
- "\u001b[33m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
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- "╭───────────────────────────────────────────────────── 📝 AI ─────────────────────────────────────────────────────╮\n",
- "│ I have completed the research on context engineering approaches for AI agents. The final report details │\n",
- "│ definitions, core concepts, key techniques (RAG, Memory, Summarization), and architectural frameworks │\n",
- "│ (LangChain vs LlamaIndex). │\n",
- "│ │\n",
- "│ ### **Research Findings Summary** │\n",
- "│ Context engineering is the discipline of managing the finite \"context window\" of an AI model to ensure it has │\n",
- "│ the right information at the right time. It is distinct from prompt engineering and focuses on dynamic state │\n",
- "│ management. │\n",
- "│ │\n",
- "│ **Key Approaches:** │\n",
- "│ 1. **Dynamic Context Injection (RAG)**: Instead of loading all data, agents \"pull\" specific information from │\n",
- "│ vector databases or APIs only when needed to answer a query. │\n",
- "│ 2. **Memory Management**: Using sliding windows (short-term memory) and external databases (long-term memory) │\n",
- "│ to maintain conversation history without exceeding token limits. │\n",
- "│ 3. **Summarization**: Compressing verbose tool outputs (like web scrape results) before passing them to the │\n",
- "│ reasoning agent to save tokens. │\n",
- "│ 4. **Architectural Frameworks**: │\n",
- "│ * **LangChain**: Focuses on \"Chains\" and dynamic tool selection. │\n",
- "│ * **LlamaIndex**: Focuses on data indexing and advanced retrieval strategies. │\n",
- "│ │\n",
- "│ **Strategic Trade-offs:** │\n",
- "│ * **RAG vs. Long-Context**: RAG is often more cost-effective and accurate for finding specific facts │\n",
- "│ (\"needles\") in large datasets, while massive context windows (e.g., 1M+ tokens) are better for holistic │\n",
- "│ analysis but prone to the \"Lost in the Middle\" phenomenon. │\n",
- "│ * **Selection vs. Stuffing**: \"Context Stuffing\" (filling the window blindly) is an anti-pattern. \"Context │\n",
- "│ Selection\" (curating inputs via filtering and query transformation) leads to better performance. │\n",
- "│ │\n",
- "│ The full detailed report can be found in `/final_report.md`. │\n",
- "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
- "
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- "\u001b[37m│\u001b[0m I have completed the research on context engineering approaches for AI agents. The final report details \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m definitions, core concepts, key techniques (RAG, Memory, Summarization), and architectural frameworks \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m (LangChain vs LlamaIndex). \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m ### **Research Findings Summary** \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Context engineering is the discipline of managing the finite \"context window\" of an AI model to ensure it has \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m the right information at the right time. It is distinct from prompt engineering and focuses on dynamic state \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m management. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m **Key Approaches:** \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m 1. **Dynamic Context Injection (RAG)**: Instead of loading all data, agents \"pull\" specific information from \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m vector databases or APIs only when needed to answer a query. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m 2. **Memory Management**: Using sliding windows (short-term memory) and external databases (long-term memory) \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m to maintain conversation history without exceeding token limits. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m 3. **Summarization**: Compressing verbose tool outputs (like web scrape results) before passing them to the \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m reasoning agent to save tokens. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m 4. **Architectural Frameworks**: \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m * **LangChain**: Focuses on \"Chains\" and dynamic tool selection. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m * **LlamaIndex**: Focuses on data indexing and advanced retrieval strategies. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m **Strategic Trade-offs:** \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m * **RAG vs. Long-Context**: RAG is often more cost-effective and accurate for finding specific facts \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m (\"needles\") in large datasets, while massive context windows (e.g., 1M+ tokens) are better for holistic \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m analysis but prone to the \"Lost in the Middle\" phenomenon. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m * **Selection vs. Stuffing**: \"Context Stuffing\" (filling the window blindly) is an anti-pattern. \"Context \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m Selection\" (curating inputs via filtering and query transformation) leads to better performance. \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
- "\u001b[37m│\u001b[0m The full detailed report can be found in `/final_report.md`. \u001b[37m│\u001b[0m\n",
- "\u001b[37m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
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- "╭──────────────────────────────────────────────────── Prompt ─────────────────────────────────────────────────────╮\n",
- "│ │\n",
- "│ # Context Engineering Approaches for AI Agents │\n",
- "│ │\n",
- "│ ## 1. Introduction │\n",
- "│ Context Engineering is the systematic practice of designing, managing, and optimizing the information │\n",
- "│ provided to AI models to ensure accurate, consistent, and context-aware behavior. While often confused with │\n",
- "│ prompt engineering (which focuses on static instructions), context engineering treats the AI's context window │\n",
- "│ as a dynamic, finite resource that must be architected and managed throughout the lifecycle of an │\n",
- "│ interaction. It is a critical discipline for building robust AI agents that can maintain state and relevance │\n",
- "│ over long, complex tasks. │\n",
- "│ │\n",
- "│ ## 2. Core Concepts │\n",
- "│ To build effective agents, developers must manage several fundamental aspects of the Large Language Model │\n",
- "│ (LLM) environment: │\n",
- "│ │\n",
- "│ * **Context Window**: The hard limit on the amount of text (tokens) an LLM can process at once (e.g., 8k, │\n",
- "│ 128k, or 1M tokens). Effective engineering ensures this space is used efficiently. │\n",
- "│ * **State Management**: Tracking the \"world state\" across multi-turn conversations. This includes user │\n",
- "│ intent, task progress, and tool outputs. │\n",
- "│ * **Context Pollution**: The phenomenon where irrelevant or contradictory information in the context │\n",
- "│ degrades model performance, leading to hallucinations or confusion [2]. │\n",
- "│ * **Serial Position Effect**: The tendency of LLMs to pay more attention to information at the very │\n",
- "│ beginning and very end of the context window, often ignoring details buried in the middle (\"Lost in the │\n",
- "│ Middle\") [3]. │\n",
- "│ │\n",
- "│ ## 3. Key Approaches and Techniques │\n",
- "│ │\n",
- "│ ### Dynamic Context Injection & Retrieval (RAG) │\n",
- "│ Rather than loading all available data at once, agents \"pull\" information only when necessary. │\n",
- "│ * **Dynamic Injection**: Agents use tools (like search APIs or database queries) to fetch real-time data │\n",
- "│ only after identifying a specific knowledge gap [2]. │\n",
- "│ * **RAG (Retrieval-Augmented Generation)**: The standard for large knowledge bases. Instead of \"stuffing\" a │\n",
- "│ document, the system retrieves only the top-k most relevant chunks from a vector store to populate the │\n",
- "│ context [2][3]. │\n",
- "│ │\n",
- "│ ### Memory Management │\n",
- "│ Agents require sophisticated memory systems to maintain continuity without overflowing context limits: │\n",
- "│ * **Short-term Memory**: A sliding window of the most recent interaction turns (e.g., last 10 messages). │\n",
- "│ * **Long-term Memory**: Key facts (e.g., user preferences) are persisted in external databases (Postgres, │\n",
- "│ Vector Stores) and retrieved only when relevant to the current query [2]. │\n",
- "│ * **Pruning and Trimming**: Algorithms that selectively remove older, less relevant tokens while preserving │\n",
- "│ the semantic core of the conversation history. │\n",
- "│ │\n",
- "│ ### Summarization and Compression │\n",
- "│ * **Tool Output Summarization**: Raw outputs from tools (like HTML from a web scraper) are often too │\n",
- "│ verbose. An intermediate step or \"summarizer agent\" distills this data into key points before passing it to │\n",
- "│ the main reasoning agent [2]. │\n",
- "│ * **Context Compression**: Removing stop words or irrelevant sections from retrieved documents to maximize │\n",
- "│ information density per token. │\n",
- "│ │\n",
- "│ ### Prompt Structuring │\n",
- "│ Using strict formats within the context window helps the model parse information correctly: │\n",
- "│ * **Separation of Concerns**: Clearly formatting \"System Instructions,\" \"User Input,\" and \"Tool Outputs\" │\n",
- "│ (often using XML tags or JSON schemas) prevents the model from confusing data with instructions. │\n",
- "│ │\n",
- "│ ## 4. Architectural Frameworks │\n",
- "│ Two primary frameworks dominate the implementation of these strategies: │\n",
- "│ │\n",
- "│ **LangChain** │\n",
- "│ * **Focus**: Flexible orchestration and agent workflows. │\n",
- "│ * **Approach**: Uses \"Chains\" to pass context sequentially between steps and \"Memory\" classes (e.g., │\n",
- "│ `ConversationSummaryMemory`) to automatically manage history compression and persistence [4]. │\n",
- "│ * **Strategy**: Emphasizes dynamic tool selection where the agent decides what information to add to its │\n",
- "│ own context. │\n",
- "│ │\n",
- "│ **LlamaIndex** │\n",
- "│ * **Focus**: Data indexing and advanced retrieval. │\n",
- "│ * **Approach**: specialized in structuring data for LLMs using indices (`VectorStoreIndex`, `TreeIndex`). │\n",
- "│ * **Strategy**: Uses \"Response Synthesizers\" to optimize how retrieved context is presented to the LLM │\n",
- "│ (e.g., `compact` mode to stuff chunks or `tree_summarize` for hierarchical reduction) [4]. │\n",
- "│ │\n",
- "│ ## 5. Strategic Patterns │\n",
- "│ │\n",
- "│ ### RAG vs. Long-Context Windows (LCW) │\n",
- "│ * **RAG**: Preferred for cost efficiency and low latency. It filters noise before the model sees it, often │\n",
- "│ resulting in higher accuracy for \"Needle in a Haystack\" retrieval tasks [3]. │\n",
- "│ * **Long-Context Models (e.g., Gemini 1.5)**: Best for \"whole-document\" reasoning where global context is │\n",
- "│ required. However, they are more expensive and prone to the \"Lost in the Middle\" effect. │\n",
- "│ │\n",
- "│ ### Context Selection vs. Stuffing │\n",
- "│ * **Context Stuffing**: Naively filling the window with all available data. This is generally an │\n",
- "│ anti-pattern as it increases costs and distracts the model. │\n",
- "│ * **Context Selection**: The proactive curation of inputs. Techniques include \"Node Postprocessors\" │\n",
- "│ (filtering results by similarity score) and \"Query Transformations\" (rewriting vague user queries to fetch │\n",
- "│ better context) [4]. │\n",
- "│ │\n",
- "│ ## 6. Conclusion │\n",
- "│ Context engineering is the bridge between raw data and intelligent agent behavior. By moving from naive │\n",
- "│ context stuffing to sophisticated dynamic injection, memory management, and structured retrieval, developers │\n",
- "│ can build agents that are both powerful and cost-effective. The choice between RAG architectures and │\n",
- "│ long-context models remains a key trade-off, with hybrid approaches often providing the best balance of │\n",
- "│ accuracy and performance. │\n",
- "│ │\n",
- "│ ### Sources │\n",
- "│ [1] Context Engineering Guide: https://www.promptingguide.ai/agents/context-engineering │\n",
- "│ [2] Context Engineering Strategies (n8n): │\n",
- "│ https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/ │\n",
- "│ [3] RAG vs Long Context Window Study: │\n",
- "│ https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs │\n",
- "│ [4] LangChain & LlamaIndex Comparison: │\n",
- "│ https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperabilit │\n",
- "│ y-in-large-2b5f3fabc366 │\n",
- "│ │\n",
- "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
- "
\n"
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- "\u001b[34m╭─\u001b[0m\u001b[34m───────────────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mPrompt\u001b[0m\u001b[34m \u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m # Context Engineering Approaches for AI Agents \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;35m## 1. Introduction\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Context Engineering is the systematic practice of designing, managing, and optimizing the information \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m provided to AI models to ensure accurate, consistent, and context-aware behavior. While often confused with \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m prompt engineering (which focuses on static instructions), context engineering treats the AI's context window \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m as a dynamic, finite resource that must be architected and managed throughout the lifecycle of an \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m interaction. It is a critical discipline for building robust AI agents that can maintain state and relevance \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m over long, complex tasks. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;35m## 2. Core Concepts\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m To build effective agents, developers must manage several fundamental aspects of the Large Language Model \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m (LLM) environment: \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Context Window**: The hard limit on the amount of text (tokens) an LLM can process at once (e.g., 8k, \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m 128k, or 1M tokens). Effective engineering ensures this space is used efficiently. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **State Management**: Tracking the \"world state\" across multi-turn conversations. This includes user \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m intent, task progress, and tool outputs. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Context Pollution**: The phenomenon where irrelevant or contradictory information in the context \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m degrades model performance, leading to hallucinations or confusion [2]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Serial Position Effect**: The tendency of LLMs to pay more attention to information at the very \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m beginning and very end of the context window, often ignoring details buried in the middle (\"Lost in the \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Middle\") [3]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;35m## 3. Key Approaches and Techniques\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## Dynamic Context Injection & Retrieval (RAG)\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Rather than loading all available data at once, agents \"pull\" information only when necessary. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Dynamic Injection**: Agents use tools (like search APIs or database queries) to fetch real-time data \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m only after identifying a specific knowledge gap [2]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **RAG (Retrieval-Augmented Generation)**: The standard for large knowledge bases. Instead of \"stuffing\" a \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m document, the system retrieves only the top-k most relevant chunks from a vector store to populate the \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m context [2][3]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## Memory Management\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Agents require sophisticated memory systems to maintain continuity without overflowing context limits: \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Short-term Memory**: A sliding window of the most recent interaction turns (e.g., last 10 messages). \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Long-term Memory**: Key facts (e.g., user preferences) are persisted in external databases (Postgres, \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Vector Stores) and retrieved only when relevant to the current query [2]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Pruning and Trimming**: Algorithms that selectively remove older, less relevant tokens while preserving \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m the semantic core of the conversation history. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## Summarization and Compression\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Tool Output Summarization**: Raw outputs from tools (like HTML from a web scraper) are often too \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m verbose. An intermediate step or \"summarizer agent\" distills this data into key points before passing it to \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m the main reasoning agent [2]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Context Compression**: Removing stop words or irrelevant sections from retrieved documents to maximize \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m information density per token. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## Prompt Structuring\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Using strict formats within the context window helps the model parse information correctly: \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Separation of Concerns**: Clearly formatting \"System Instructions,\" \"User Input,\" and \"Tool Outputs\" \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m (often using XML tags or JSON schemas) prevents the model from confusing data with instructions. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;35m## 4. Architectural Frameworks\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Two primary frameworks dominate the implementation of these strategies: \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m **LangChain** \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Focus**: Flexible orchestration and agent workflows. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Approach**: Uses \"Chains\" to pass context sequentially between steps and \"Memory\" classes (e.g., \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m `ConversationSummaryMemory`) to automatically manage history compression and persistence [4]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Strategy**: Emphasizes dynamic tool selection where the agent decides what information to add to its \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m own context. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m **LlamaIndex** \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Focus**: Data indexing and advanced retrieval. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Approach**: specialized in structuring data for LLMs using indices (`VectorStoreIndex`, `TreeIndex`). \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Strategy**: Uses \"Response Synthesizers\" to optimize how retrieved context is presented to the LLM \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m (e.g., `compact` mode to stuff chunks or `tree_summarize` for hierarchical reduction) [4]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;35m## 5. Strategic Patterns\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## RAG vs. Long-Context Windows (LCW)\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **RAG**: Preferred for cost efficiency and low latency. It filters noise before the model sees it, often \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m resulting in higher accuracy for \"Needle in a Haystack\" retrieval tasks [3]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Long-Context Models (e.g., Gemini 1.5)**: Best for \"whole-document\" reasoning where global context is \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m required. However, they are more expensive and prone to the \"Lost in the Middle\" effect. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## Context Selection vs. Stuffing\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Context Stuffing**: Naively filling the window with all available data. This is generally an \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m anti-pattern as it increases costs and distracts the model. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m * **Context Selection**: The proactive curation of inputs. Techniques include \"Node Postprocessors\" \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m (filtering results by similarity score) and \"Query Transformations\" (rewriting vague user queries to fetch \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m better context) [4]. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;35m## 6. Conclusion\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m Context engineering is the bridge between raw data and intelligent agent behavior. By moving from naive \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m context stuffing to sophisticated dynamic injection, memory management, and structured retrieval, developers \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m can build agents that are both powerful and cost-effective. The choice between RAG architectures and \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m long-context models remains a key trade-off, with hybrid approaches often providing the best balance of \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m accuracy and performance. \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[1;36m#\u001b[0m\u001b[1;36m## Sources\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m [1] Context Engineering Guide: https://www.promptingguide.ai/agents/context-engineering \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m [2] Context Engineering Strategies (n8n): \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m https://www.theaiautomators.com/context-engineering-strategies-to-build-better-ai-agents/ \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m [3] RAG vs Long Context Window Study: \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m https://www.legionintel.com/blog/rag-systems-vs-lcw-performance-and-cost-trade-offs \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m [4] LangChain & LlamaIndex Comparison: \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m https://medium.com/badal-io/exploring-langchain-and-llamaindex-to-achieve-standardization-and-interoperabilit \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m y-in-large-2b5f3fabc366 \u001b[34m│\u001b[0m\n",
- "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n",
- "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from deepagents.backends.utils import file_data_to_string\n",
- "\n",
- "# Convert a specific file to string\n",
- "file_content = file_data_to_string(result[\"files\"]['/final_report.md'])\n",
- "show_prompt(file_content) "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fdcc6784",
- "metadata": {},
- "source": [
- "Trace: \n",
- "\n",
- "https://smith.langchain.com/public/72d23852-4616-4bcc-8d8a-b0d1905c945b/r"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2d73925c",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.13.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/deep_research/research_agent/__init__.py b/deep_research/research_agent/__init__.py
deleted file mode 100644
index be9540d..0000000
--- a/deep_research/research_agent/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""Deep Research Agent Example.
-
-This module demonstrates building a research agent using the deepagents package
-with custom tools for web search and strategic thinking.
-"""
-
-from research_agent.prompts import (
- RESEARCHER_INSTRUCTIONS,
- RESEARCH_WORKFLOW_INSTRUCTIONS,
- SUBAGENT_DELEGATION_INSTRUCTIONS,
-)
-from research_agent.tools import tavily_search, think_tool
-
-__all__ = [
- "tavily_search",
- "think_tool",
- "RESEARCHER_INSTRUCTIONS",
- "RESEARCH_WORKFLOW_INSTRUCTIONS",
- "SUBAGENT_DELEGATION_INSTRUCTIONS",
-]
diff --git a/deep_research/research_agent/prompts.py b/deep_research/research_agent/prompts.py
deleted file mode 100644
index 588bec0..0000000
--- a/deep_research/research_agent/prompts.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""Prompt templates and tool descriptions for the research deepagent."""
-
-RESEARCH_WORKFLOW_INSTRUCTIONS = """# Research Workflow
-
-Follow this workflow for all research requests:
-
-1. **Plan**: Create a todo list with write_todos to break down the research into focused tasks
-2. **Save the request**: Use write_file() to save the user's research question to `/research_request.md`
-3. **Research**: Delegate research tasks to sub-agents using the task() tool - ALWAYS use sub-agents for research, never conduct research yourself
-4. **Synthesize**: Review all sub-agent findings and consolidate citations (each unique URL gets one number across all findings)
-5. **Write Report**: Write a comprehensive final report to `/final_report.md` (see Report Writing Guidelines below)
-6. **Verify**: Read `/research_request.md` and confirm you've addressed all aspects with proper citations and structure
-
-## Research Planning Guidelines
-- Batch similar research tasks into a single TODO to minimize overhead
-- For simple fact-finding questions, use 1 sub-agent
-- For comparisons or multi-faceted topics, delegate to multiple parallel sub-agents
-- Each sub-agent should research one specific aspect and return findings
-
-## Report Writing Guidelines
-
-When writing the final report to `/final_report.md`, follow these structure patterns:
-
-**For comparisons:**
-1. Introduction
-2. Overview of topic A
-3. Overview of topic B
-4. Detailed comparison
-5. Conclusion
-
-**For lists/rankings:**
-Simply list items with details - no introduction needed:
-1. Item 1 with explanation
-2. Item 2 with explanation
-3. Item 3 with explanation
-
-**For summaries/overviews:**
-1. Overview of topic
-2. Key concept 1
-3. Key concept 2
-4. Key concept 3
-5. Conclusion
-
-**General guidelines:**
-- Use clear section headings (## for sections, ### for subsections)
-- Write in paragraph form by default - be text-heavy, not just bullet points
-- Do NOT use self-referential language ("I found...", "I researched...")
-- Write as a professional report without meta-commentary
-- Each section should be comprehensive and detailed
-- Use bullet points only when listing is more appropriate than prose
-
-**Citation format:**
-- Cite sources inline using [1], [2], [3] format
-- Assign each unique URL a single citation number across ALL sub-agent findings
-- End report with ### Sources section listing each numbered source
-- Number sources sequentially without gaps (1,2,3,4...)
-- Format: [1] Source Title: URL (each on separate line for proper list rendering)
-- Example:
-
- Some important finding [1]. Another key insight [2].
-
- ### Sources
- [1] AI Research Paper: https://example.com/paper
- [2] Industry Analysis: https://example.com/analysis
-"""
-
-RESEARCHER_INSTRUCTIONS = """You are a research assistant conducting research on the user's input topic. For context, today's date is {date}.
-
-
-Your job is to use tools to gather information about the user's input topic.
-You can use any of the research tools provided to you to find resources that can help answer the research question.
-You can call these tools in series or in parallel, your research is conducted in a tool-calling loop.
-
-
-
-You have access to two specific research tools:
-1. **tavily_search**: For conducting web searches to gather information
-2. **think_tool**: For reflection and strategic planning during research
-**CRITICAL: Use think_tool after each search to reflect on results and plan next steps**
-
-
-
-Think like a human researcher with limited time. Follow these steps:
-
-1. **Read the question carefully** - What specific information does the user need?
-2. **Start with broader searches** - Use broad, comprehensive queries first
-3. **After each search, pause and assess** - Do I have enough to answer? What's still missing?
-4. **Execute narrower searches as you gather information** - Fill in the gaps
-5. **Stop when you can answer confidently** - Don't keep searching for perfection
-
-
-
-**Tool Call Budgets** (Prevent excessive searching):
-- **Simple queries**: Use 2-3 search tool calls maximum
-- **Complex queries**: Use up to 5 search tool calls maximum
-- **Always stop**: After 5 search tool calls if you cannot find the right sources
-
-**Stop Immediately When**:
-- You can answer the user's question comprehensively
-- You have 3+ relevant examples/sources for the question
-- Your last 2 searches returned similar information
-
-
-
-After each search tool call, use think_tool to analyze the results:
-- What key information did I find?
-- What's missing?
-- Do I have enough to answer the question comprehensively?
-- Should I search more or provide my answer?
-
-
-
-When providing your findings back to the orchestrator:
-
-1. **Structure your response**: Organize findings with clear headings and detailed explanations
-2. **Cite sources inline**: Use [1], [2], [3] format when referencing information from your searches
-3. **Include Sources section**: End with ### Sources listing each numbered source with title and URL
-
-Example:
-```
-## Key Findings
-
-Context engineering is a critical technique for AI agents [1]. Studies show that proper context management can improve performance by 40% [2].
-
-### Sources
-[1] Context Engineering Guide: https://example.com/context-guide
-[2] AI Performance Study: https://example.com/study
-```
-
-The orchestrator will consolidate citations from all sub-agents into the final report.
-
-"""
-
-TASK_DESCRIPTION_PREFIX = """Delegate a task to a specialized sub-agent with isolated context. Available agents for delegation are:
-{other_agents}
-"""
-
-SUBAGENT_DELEGATION_INSTRUCTIONS = """# Sub-Agent Research Coordination
-
-Your role is to coordinate research by delegating tasks from your TODO list to specialized research sub-agents.
-
-## Delegation Strategy
-
-**DEFAULT: Start with 1 sub-agent** for most queries:
-- "What is quantum computing?" → 1 sub-agent (general overview)
-- "List the top 10 coffee shops in San Francisco" → 1 sub-agent
-- "Summarize the history of the internet" → 1 sub-agent
-- "Research context engineering for AI agents" → 1 sub-agent (covers all aspects)
-
-**ONLY parallelize when the query EXPLICITLY requires comparison or has clearly independent aspects:**
-
-**Explicit comparisons** → 1 sub-agent per element:
-- "Compare OpenAI vs Anthropic vs DeepMind AI safety approaches" → 3 parallel sub-agents
-- "Compare Python vs JavaScript for web development" → 2 parallel sub-agents
-
-**Clearly separated aspects** → 1 sub-agent per aspect (use sparingly):
-- "Research renewable energy adoption in Europe, Asia, and North America" → 3 parallel sub-agents (geographic separation)
-- Only use this pattern when aspects cannot be covered efficiently by a single comprehensive search
-
-## Key Principles
-- **Bias towards single sub-agent**: One comprehensive research task is more token-efficient than multiple narrow ones
-- **Avoid premature decomposition**: Don't break "research X" into "research X overview", "research X techniques", "research X applications" - just use 1 sub-agent for all of X
-- **Parallelize only for clear comparisons**: Use multiple sub-agents when comparing distinct entities or geographically separated data
-
-## Parallel Execution Limits
-- Use at most {max_concurrent_research_units} parallel sub-agents per iteration
-- Make multiple task() calls in a single response to enable parallel execution
-- Each sub-agent returns findings independently
-
-## Research Limits
-- Stop after {max_researcher_iterations} delegation rounds if you haven't found adequate sources
-- Stop when you have sufficient information to answer comprehensively
-- Bias towards focused research over exhaustive exploration"""
\ No newline at end of file
diff --git a/deep_research/research_agent/tools.py b/deep_research/research_agent/tools.py
deleted file mode 100644
index 5a97cdc..0000000
--- a/deep_research/research_agent/tools.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""Research Tools.
-
-This module provides search and content processing utilities for the research agent,
-using Tavily for URL discovery and fetching full webpage content.
-"""
-
-import httpx
-from langchain_core.tools import InjectedToolArg, tool
-from markdownify import markdownify
-from tavily import TavilyClient
-from typing_extensions import Annotated, Literal
-
-tavily_client = TavilyClient()
-
-
-def fetch_webpage_content(url: str, timeout: float = 10.0) -> str:
- """Fetch and convert webpage content to markdown.
-
- Args:
- url: URL to fetch
- timeout: Request timeout in seconds
-
- Returns:
- Webpage content as markdown
- """
- headers = {
- "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
- }
-
- try:
- response = httpx.get(url, headers=headers, timeout=timeout)
- response.raise_for_status()
- return markdownify(response.text)
- except Exception as e:
- return f"Error fetching content from {url}: {str(e)}"
-
-
-@tool(parse_docstring=True)
-def tavily_search(
- query: str,
- max_results: Annotated[int, InjectedToolArg] = 1,
- topic: Annotated[
- Literal["general", "news", "finance"], InjectedToolArg
- ] = "general",
-) -> str:
- """Search the web for information on a given query.
-
- Uses Tavily to discover relevant URLs, then fetches and returns full webpage content as markdown.
-
- Args:
- query: Search query to execute
- max_results: Maximum number of results to return (default: 1)
- topic: Topic filter - 'general', 'news', or 'finance' (default: 'general')
-
- Returns:
- Formatted search results with full webpage content
- """
- # Use Tavily to discover URLs
- search_results = tavily_client.search(
- query,
- max_results=max_results,
- topic=topic,
- )
-
- # Fetch full content for each URL
- result_texts = []
- for result in search_results.get("results", []):
- url = result["url"]
- title = result["title"]
-
- # Fetch webpage content
- content = fetch_webpage_content(url)
-
- result_text = f"""## {title}
-**URL:** {url}
-
-{content}
-
----
-"""
- result_texts.append(result_text)
-
- # Format final response
- response = f"""🔍 Found {len(result_texts)} result(s) for '{query}':
-
-{chr(10).join(result_texts)}"""
-
- return response
-
-
-@tool(parse_docstring=True)
-def think_tool(reflection: str) -> str:
- """Tool for strategic reflection on research progress and decision-making.
-
- Use this tool after each search to analyze results and plan next steps systematically.
- This creates a deliberate pause in the research workflow for quality decision-making.
-
- When to use:
- - After receiving search results: What key information did I find?
- - Before deciding next steps: Do I have enough to answer comprehensively?
- - When assessing research gaps: What specific information am I still missing?
- - Before concluding research: Can I provide a complete answer now?
-
- Reflection should address:
- 1. Analysis of current findings - What concrete information have I gathered?
- 2. Gap assessment - What crucial information is still missing?
- 3. Quality evaluation - Do I have sufficient evidence/examples for a good answer?
- 4. Strategic decision - Should I continue searching or provide my answer?
-
- Args:
- reflection: Your detailed reflection on research progress, findings, gaps, and next steps
-
- Returns:
- Confirmation that reflection was recorded for decision-making
- """
- return f"Reflection recorded: {reflection}"
diff --git a/deep_research/utils.py b/deep_research/utils.py
deleted file mode 100644
index 48aecfb..0000000
--- a/deep_research/utils.py
+++ /dev/null
@@ -1,94 +0,0 @@
-"""Utility functions for displaying messages and prompts in Jupyter notebooks."""
-
-import json
-
-from rich.console import Console
-from rich.panel import Panel
-from rich.text import Text
-
-console = Console()
-
-
-def format_message_content(message):
- """Convert message content to displayable string."""
- parts = []
- tool_calls_processed = False
-
- # Handle main content
- if isinstance(message.content, str):
- parts.append(message.content)
- elif isinstance(message.content, list):
- # Handle complex content like tool calls (Anthropic format)
- for item in message.content:
- if item.get("type") == "text":
- parts.append(item["text"])
- elif item.get("type") == "tool_use":
- parts.append(f"\n🔧 Tool Call: {item['name']}")
- parts.append(f" Args: {json.dumps(item['input'], indent=2)}")
- parts.append(f" ID: {item.get('id', 'N/A')}")
- tool_calls_processed = True
- else:
- parts.append(str(message.content))
-
- # Handle tool calls attached to the message (OpenAI format) - only if not already processed
- if (
- not tool_calls_processed
- and hasattr(message, "tool_calls")
- and message.tool_calls
- ):
- for tool_call in message.tool_calls:
- parts.append(f"\n🔧 Tool Call: {tool_call['name']}")
- parts.append(f" Args: {json.dumps(tool_call['args'], indent=2)}")
- parts.append(f" ID: {tool_call['id']}")
-
- return "\n".join(parts)
-
-
-def format_messages(messages):
- """Format and display a list of messages with Rich formatting."""
- for m in messages:
- msg_type = m.__class__.__name__.replace("Message", "")
- content = format_message_content(m)
-
- if msg_type == "Human":
- console.print(Panel(content, title="🧑 Human", border_style="blue"))
- elif msg_type == "Ai":
- console.print(Panel(content, title="🤖 Assistant", border_style="green"))
- elif msg_type == "Tool":
- console.print(Panel(content, title="🔧 Tool Output", border_style="yellow"))
- else:
- console.print(Panel(content, title=f"📝 {msg_type}", border_style="white"))
-
-
-def format_message(messages):
- """Alias for format_messages for backward compatibility."""
- return format_messages(messages)
-
-
-def show_prompt(prompt_text: str, title: str = "Prompt", border_style: str = "blue"):
- """Display a prompt with rich formatting and XML tag highlighting.
-
- Args:
- prompt_text: The prompt string to display
- title: Title for the panel (default: "Prompt")
- border_style: Border color style (default: "blue")
- """
- # Create a formatted display of the prompt
- formatted_text = Text(prompt_text)
- formatted_text.highlight_regex(r"<[^>]+>", style="bold blue") # Highlight XML tags
- formatted_text.highlight_regex(
- r"##[^#\n]+", style="bold magenta"
- ) # Highlight headers
- formatted_text.highlight_regex(
- r"###[^#\n]+", style="bold cyan"
- ) # Highlight sub-headers
-
- # Display in a panel for better presentation
- console.print(
- Panel(
- formatted_text,
- title=f"[bold green]{title}[/bold green]",
- border_style=border_style,
- padding=(1, 2),
- )
- )
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deleted file mode 100644
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