Module 11 References: AI Engineering Ecosystem
Primary Documentation
LangChain
- Docs: https://python.langchain.com/docs/introduction
- LCEL conceptual guide: https://python.langchain.com/docs/concepts/lcel
- Integrations directory: https://python.langchain.com/docs/integrations/providers
- LangSmith (observability): https://docs.smith.langchain.com
- GitHub: https://github.com/langchain-ai/langchain
LangGraph
- Docs: https://langchain-ai.github.io/langgraph
- Conceptual guides: https://langchain-ai.github.io/langgraph/concepts/
- How-to guides: https://langchain-ai.github.io/langgraph/how-tos/
- Checkpointers: https://langchain-ai.github.io/langgraph/concepts/persistence/
- Human-in-the-loop: https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/
- GitHub: https://github.com/langchain-ai/langgraph
LlamaIndex
- Docs: https://docs.llamaindex.ai
- RAG guide: https://docs.llamaindex.ai/en/stable/understanding/rag/
- Node parsers: https://docs.llamaindex.ai/en/stable/module_guides/loading/node_parsers/
- Query engines: https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/
- GitHub: https://github.com/run-llama/llama_index
AutoGen (Microsoft)
- Docs: https://microsoft.github.io/autogen
- Multi-agent conversation guide: https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat
- GitHub: https://github.com/microsoft/autogen
CrewAI
- Docs: https://docs.crewai.com
- Agents guide: https://docs.crewai.com/concepts/agents
- Tasks guide: https://docs.crewai.com/concepts/tasks
- Processes: https://docs.crewai.com/concepts/processes
- GitHub: https://github.com/crewAIInc/crewAI
OpenAI Assistants API
- Docs: https://platform.openai.com/docs/assistants/overview
- Threads and runs: https://platform.openai.com/docs/assistants/how-it-works
- File search: https://platform.openai.com/docs/assistants/tools/file-search
- Code interpreter: https://platform.openai.com/docs/assistants/tools/code-interpreter
Semantic Kernel (Microsoft)
- Docs: https://learn.microsoft.com/en-us/semantic-kernel/overview/
- Python SDK: https://learn.microsoft.com/en-us/semantic-kernel/get-started/quick-start-guide?pivots=programming-language-python
- GitHub: https://github.com/microsoft/semantic-kernel
IDE AI Tools
Cursor
- Docs: https://docs.cursor.com
- Rules system: https://docs.cursor.com/context/rules-for-ai
- Composer (multi-file edits): https://docs.cursor.com/composer
- Website: https://cursor.com
Windsurf (Codeium)
- Docs: https://docs.windsurf.com
- Cascade (agentic AI): https://docs.windsurf.com/windsurf/cascade
- Website: https://windsurf.com
GitHub Copilot
- Docs: https://docs.github.com/en/copilot
- Copilot Chat: https://docs.github.com/en/copilot/using-github-copilot/asking-github-copilot-questions-in-your-ide
- Repository custom instructions: https://docs.github.com/en/copilot/customizing-copilot/adding-repository-custom-instructions-for-github-copilot
Claude Code
- Docs: https://docs.anthropic.com/en/docs/claude-code
- GitHub: https://github.com/anthropics/claude-code
Vector Databases
Chroma
- Docs: https://docs.trychroma.com
- Python quickstart: https://docs.trychroma.com/getting-started
Pinecone
- Docs: https://docs.pinecone.io
- Best for: managed, serverless vector search at scale
Weaviate
- Docs: https://weaviate.io/developers/weaviate
- Best for: hybrid search (vector + keyword BM25) and multi-modal
pgvector
- Docs: https://github.com/pgvector/pgvector
- Best for: teams already on PostgreSQL who want vector search without a new service
Qdrant
- Docs: https://qdrant.tech/documentation/
- Best for: high-performance Rust-native vector search with filtering
Observability
LangSmith
- Docs: https://docs.smith.langchain.com
- Best for: LangChain/LangGraph applications — tight integration, trace visualization
LangFuse
- Docs: https://langfuse.com/docs
- Best for: framework-agnostic observability, self-hostable, good cost tracking
Arize Phoenix
- Docs: https://docs.arize.com/phoenix
- Best for: evaluation-focused teams, evals and fine-tuning workflows
Helicone
- Docs: https://docs.helicone.ai
- Best for: proxy-based observability with zero code changes, cost dashboards
Key Reads
Papers and Technical Reports
- “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (Lewis et al., 2020) — the original RAG paper: https://arxiv.org/abs/2005.11401
- “ReAct: Synergizing Reasoning and Acting in Language Models” (Yao et al., 2022) — the foundation for tool-using agents: https://arxiv.org/abs/2210.03629
- “Toolformer: Language Models Can Teach Themselves to Use Tools” (Schick et al., 2023): https://arxiv.org/abs/2302.04761
Blog Posts Worth Reading
- “Don’t Build AI Products on Top of Sand” — on LangChain abstraction risks (search for it; multiple versions exist)
- Anthropic’s “Building effective agents” guide: https://www.anthropic.com/research/building-effective-agents
- LangChain’s “LangGraph vs LangChain” explanation: https://blog.langchain.dev/langgraph
Community
- LangChain Discord: https://discord.gg/langchain
- LlamaIndex Discord: https://discord.gg/dGcwcsnxhU
- r/LocalLLaMA (for open-source model ecosystem discussion): https://reddit.com/r/LocalLLaMA
Notes on Reference Quality
Stable references (unlikely to go stale):
- The original RAG and ReAct papers
- Anthropic’s API documentation (versioned)
- OpenAI’s API documentation (versioned)
Moderate churn (check for updates every 3-6 months):
- LangChain docs — the API surface changes frequently; always check the version
- LlamaIndex docs — more stable than LangChain but still evolving
Fast-moving (verify before relying on):
- LangGraph docs — released 1.0 in 2024, active development
- AutoGen docs — Microsoft continues to make significant changes
- CrewAI docs — rapidly expanding feature set
When in doubt, refer to the GitHub source and recent release notes rather than tutorial blog posts, which may document outdated APIs.