FareedKhan-dev/contextual-engineering-guide
Implementation of contextual engineering pipeline with LangChain and LangGraph Agents
This guide helps AI developers build more effective AI agents by managing how they access information and tools. It provides strategies for structuring the AI's 'memory' and 'thinking process' to improve performance, reduce errors, and control operational costs. The content is for AI engineers and developers who build applications using LLMs and AI agents.
No commits in the last 6 months.
Use this if you are building AI agents and need to improve their reliability, efficiency, and cost-effectiveness by carefully managing the information they use during complex tasks.
Not ideal if you are an end-user looking for an out-of-the-box AI application rather than a developer building and optimizing AI agent systems.
Stars
85
Forks
18
Language
Jupyter Notebook
License
—
Category
Last pushed
Jul 29, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/agents/FareedKhan-dev/contextual-engineering-guide"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
patterns-ai-core/langchainrb
Build LLM-powered applications in Ruby
3uyuan1ee/Fix_agent
基于 LangChain1.0和DeepAgents的代码优化Agent
FareedKhan-dev/Multi-Agent-AI-System
Building a Multi-Agent AI System with LangGraph and LangSmith
tadata-org/langchain-runner
Zero-configuration way to expose LangChain/LangGraph agents as autonomous services with...
skygazer42/GustoBot
五星大厨:全面Multi-Agent 的客服机器人，基于langraph实现，txt2sql ,txt2cypher, lightrag, 多模态 等