Agentic Development¶
PAIS provides the AI backbone for agentic research software — systems where an LLM plans, reasons, and takes actions rather than just answering a single question.
Because PAIS exposes an OpenAI-compatible API, every major agentic framework works with it immediately.
What is an agentic harness?¶
An agentic harness is a framework that connects an LLM to:
- Tools — functions the model can call (search, run code, read files, query databases)
- Memory — conversation history, vector stores, or structured state
- Planning — chains of reasoning steps, reflection, or multi-agent coordination
flowchart LR
U[Researcher\nor script] --> A[Agent loop]
A --> LLM[PAIS LLM\nLlama 3.1 8B]
A --> T1[Search arXiv]
A --> T2[Run Python code]
A --> T3[Query vector store\nover VAST data]
LLM --> A
T1 & T2 & T3 --> A
Supported frameworks¶
| Framework | Best for | Guide |
|---|---|---|
| OpenAI SDK | Direct API calls, simple chains, structured output | OpenAI SDK |
| LangChain | RAG, agents with tools, complex chains | LangChain |
| LlamaIndex | Document indexing, RAG, knowledge graphs | LlamaIndex |
| Claude Code | Writing and testing research code with an AI assistant | Claude Code |
| MCP Servers | Connecting PAIS to external tools and data sources | MCP Servers |
Choosing a framework¶
flowchart TD
Q1{Do you need\nto index documents?}
Q1 -->|Yes| Q2{Complex retrieval\nor multi-index?}
Q1 -->|No| Q3{Need tool use\nor agents?}
Q2 -->|Yes| LI[LlamaIndex]
Q2 -->|No| LC[LangChain]
Q3 -->|Yes| LC
Q3 -->|No| OAI[OpenAI SDK]
Q0{Writing/debugging\nresearch code?} --> CC[Claude Code]
Start with the OpenAI SDK if you're writing a simple pipeline or script — it has the least overhead and the most direct control. Move to LangChain or LlamaIndex when you need document retrieval, complex chains, or multi-step agents.
Environment setup (all frameworks)¶
All frameworks need the same two environment variables:
Add these to your shell profile or a .env file at the root of your project. Never commit the key.