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🌟 What is PISA?

PISA (Prismer Intelligence Server Agents) is a next-generation AI agent framework built on top of the OpenAI Agent SDK. It enables developers to create sophisticated, production-ready AI agents using markdown-based configuration and modular architecture.

Why PISA?

• 🎯 Markdown-First: Define agents, tools, and workflows entirely in markdown files
• 🔧 Modular Design: Compose agents from reusable modules (Planning, Execution, Observation, Reflection)
• 🛠️ Rich Tooling: Built-in support for Functions, MCP Servers, and Subagents
• 📊 Observable: Real-time execution tracking with beautiful CLI output powered by Rich
• 🔄 Event-Driven: Production-ready deployment with Temporal integration
• 🎨 Developer-Friendly: Intuitive CLI, comprehensive logging, and extensive documentation
• Context Engineering: 1st version 'Pyramid Context Engineering' solution

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🚀 Quick Start

Installation

From PyPI (Recommended):

pip install pisa-python

From Source:

# Clone the repository
git clone https://github.com/Prismer-AI/pisa.git
cd pisa

# Install dependencies (using uv for faster installation)
uv pip install -e .

# Or using pip
pip install -e .

Configure Environment

Create a .env file in the project root:

# OpenAI Configuration
OPENAI_API_KEY=your_api_key_here
OPENAI_BASE_URL=https://api.openai.com/v1

# Default Model
AGENT_DEFAULT_MODEL=gpt-4o-mini

# Optional: Temporal Configuration
TEMPORAL_ADDRESS=localhost:7233

Create Your First Agent

1. Initialize a new agent project:

pisa init my-agent
cd my-agent

2. Define your agent in .prismer/agent.md:

---
name: my-first-agent
version: 1.0.0
description: A simple agent that performs calculations
loop_type: plan_execute

capabilities:
  - calculator
  - text_to_table

planning:
  max_iterations: 10
  
# ... more configuration
---

## Planning Instructions

You are a helpful mathematical assistant. Break down complex calculations into steps.

## Reflection Guidelines

After each calculation, verify the result makes sense.

3. Define capabilities in .prismer/capability/function/:

from pisa.capability import capability
from agents.extensions.function_tool import function_tool

@capability(
    name="calculator",
    description="Perform basic mathematical operations",
    capability_type="function",
    tags=["math", "calculation"]
)
def calculator(operation: str, a: float, b: float) -> float:
    """
    Perform a mathematical operation.
    
    Args:
        operation: The operation (add, subtract, multiply, divide)
        a: First number
        b: Second number
    """
    ops = {
        "add": lambda x, y: x + y,
        "subtract": lambda x, y: x - y,
        "multiply": lambda x, y: x * y,
        "divide": lambda x, y: x / y
    }
    return ops[operation](a, b)

4. Run your agent:

# Validate configuration
pisa validate .prismer/agent.md

# List available capabilities
pisa list-capabilities

# Run the agent
pisa run .prismer/agent.md -i "Calculate 123 * 456 and show the result"

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✨ Features

🎯 Markdown-Based Agent Definition

Define your entire agent in a single markdown file:

---
name: data-processor
loop_type: plan_execute
capabilities:
  - data_loader
  - data_cleaner
  - data_analyzer
---

## Planning Instructions
Break down data processing tasks into logical steps...

## Execution Guidelines
Ensure data quality at each step...

🔄 Multiple Loop Templates

Choose from battle-tested agent loop patterns:

• Plan-Execute: Plan first, then execute tasks sequentially
• ReAct (coming soon): Reason and Act in interleaved fashion
• Custom: Define your own loop template

🛠️ Three Types of Capabilities

1. Functions: Simple Python functions with @function_tool decorator
2. MCP Servers: Connect to Model Context Protocol servers
3. Subagents: Delegate to specialized sub-agents via handoff

# Function Tool
@capability(capability_type="function")
@function_tool
def my_function(param: str) -> str:
    return f"Processed: {param}"

# Subagent
@capability(capability_type="agent")
def my_subagent() -> Agent:
    return Agent(
        name="specialist",
        instructions="You are a specialist in..."
    )

📊 Beautiful CLI Output

Real-time execution visualization powered by Rich:

╭─────────────────────────────── 👤 User Query ────────────────────────────────╮
│ Calculate the matrix multiplication of [[1,2],[3,4]] × [[5,6],[7,8]]        │
╰──────────────────────────────────────────────────────────────────────────────╯

╭───────────────────────── 📋 Planning (Iteration 0) ──────────────────────────╮
│ Goal: Matrix multiplication and analysis                                     │
│ Total Tasks: 3                                                               │
╰──────────────────────────────────────────────────────────────────────────────╯

╭───────────────────────── 🔧 Execution (Iteration 1) ─────────────────────────╮
│ Task: task_01                                                                │
│ Capability: matrix_multiply                                                  │
│ Status: ✅                                                                   │
│ Result: [[19, 22], [43, 50]]                                                 │
╰──────────────────────────────────────────────────────────────────────────────╯

🔍 Comprehensive Observability

• Structured Logging: Context-aware logs with structlog
• Execution Tracking: Monitor planning, execution, observation, and reflection phases
• Debug Mode: Deep inspection of LLM interactions and tool calls
• Metrics Collection: Performance tracking for production deployments

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📚 Documentation

Document	Description
README	This file - Quick start and overview
Contributing Guide	How to contribute to PISA
CHANGELOG	Version history and release notes
Example Agent	Full example with math & data processing
Agent Template	Agent definition reference
Capability README	Capability system documentation

📖 Full documentation coming soon! We're working on comprehensive guides for:

• Quick Start Tutorial
• Architecture Deep Dive
• Capability Development Guide
• Loop Template Creation
• API Reference

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🎨 Examples

Example: Math & Data Processing Agent

A sophisticated agent that performs matrix operations, calculates softmax, and generates structured tables.

Location: example/agent_example/

Run the example:

cd example/agent_example
pisa run .prismer/agent.md -i "Calculate [[1,2],[3,4]] × [[5,6],[7,8]], compute softmax, and show results as a table"

Features demonstrated:

• ✅ Matrix operations (Function capability)
• ✅ Softmax calculations (MCP capability)
• ✅ Text-to-table conversion (Subagent capability)
• ✅ Plan-Execute loop
• ✅ Context management and persistence
• ✅ Rich CLI output

Example capabilities:

• matrix_operations - Matrix math (add, multiply, transpose, etc.)
• compute_softmax - Temperature-scaled softmax
• softmax_with_attention - Attention weights calculation
• text_to_table - Convert text to structured tables

More Examples Coming Soon!

We're working on additional examples:

• 🔜 Research Assistant - Web search and paper summarization
• 🔜 Code Review Agent - Automated code analysis and suggestions
• 🔜 Data Analysis Agent - CSV/JSON processing and visualization
• 🔜 Customer Support Bot - Multi-turn conversation with knowledge base

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🏗️ Architecture

PISA follows a clean, modular architecture designed for both development and production deployment:

Development Mode (Local)

┌─────────────────────────────────────────────────┐
│            Agent Definition Layer               │
│         (agent.md - Markdown Config)            │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│         Agent Loop Engine (Core)                │
│                                                 │
│  ┌───────────────────┐   ┌──────────────────┐  │
│  │  Loop Templates   │   │ Capability System│  │
│  │  - Plan-Execute   │   │  - Functions     │  │
│  │  - ReAct (soon)   │   │  - MCP Servers   │  │
│  │  - Custom         │   │  - Subagents     │  │
│  └─────────┬─────────┘   └────────┬─────────┘  │
│            │                      │             │
│  ┌─────────▼──────────────────────▼─────────┐  │
│  │         Core Modules                     │  │
│  │  - Planning    - Observation             │  │
│  │  - Execution   - Reflection              │  │
│  │  - Validation  - Context Management      │  │
│  └──────────────────────────────────────────┘  │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│          OpenAI Agent SDK Runtime              │
│   (Messages, Tools, Handoffs, Streaming)       │
└────────────────────────────────────────────────┘

Production Mode (Temporal Workflow)

┌─────────────────────────────────────────────────┐
│              Temporal Cluster                   │
│         (Orchestration & Durability)            │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│         PISA Temporal Workflow                  │
│                                                 │
│  ┌──────────────────────────────────────────┐  │
│  │  Workflow Orchestration Layer            │  │
│  │  - State Management & Persistence        │  │
│  │  - Checkpointing & Recovery              │  │
│  │  - Human-in-the-Loop Support             │  │
│  │  - Long-running Task Execution           │  │
│  └─────────────────┬────────────────────────┘  │
│                    │                            │
│  ┌─────────────────▼────────────────────────┐  │
│  │         Temporal Activities              │  │
│  │  - Agent Loop Execution                  │  │
│  │  - State Checkpoint Storage              │  │
│  │  - User Notification                     │  │
│  │  - External API Calls                    │  │
│  └──────────────────────────────────────────┘  │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│         Agent Loop Engine (Same as Dev)         │
│   (Referenced from Development Mode above)      │
└─────────────────────────────────────────────────┘

Key Components

Agent Definition Layer

• Markdown-based configuration (agent.md)
• Declarative instructions and settings
• Version-controlled agent specifications

Agent Loop Engine

• Loop Templates: Reusable behavior patterns (Plan-Execute, ReAct, etc.)
• Core Modules: Planning, Execution, Observation, Reflection, Validation
• Capability System: Unified interface for Functions, MCP Servers, and Subagents
• Context Management: Pyramid Context Engineering with compression

OpenAI Agent SDK

• LLM interaction primitives
• Tool/function calling
• Agent handoffs
• Message streaming

Temporal Workflow Layer (Production Only)

• Durable execution with automatic state persistence
• Failure recovery and retry mechanisms
• Long-running task support (hours/days)
• Human-in-the-loop workflows
• Built on Temporal's OpenAI Agents integration

Execution Modes

Feature	Development Mode	Production Mode (Temporal)
Use Case	Local testing & iteration	Production deployment
Execution	Direct Python process	Temporal Workflow
State	In-memory	Durable (persisted)
Recovery	Manual restart	Automatic retry & resume
Monitoring	CLI logs	Temporal UI + metrics
Scalability	Single instance	Distributed workers
Cost	Free (local)	Infrastructure cost

State Management

PISA uses a sophisticated state management system:

• LoopState: Centralized state for agent execution
• Context Compression: Pyramid Context Engineering to manage token limits
• Checkpointing: Periodic state snapshots for recovery
• State Serialization: JSON-based state persistence

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🛣️ Roadmap

Current (v0.1 - Alpha)

• ✅ Core framework with Plan-Execute loop
• ✅ Function, MCP, and Subagent capabilities
• ✅ CLI tools and rich observability
• ✅ Markdown-based agent definition
• ✅ Context management with Pyramid Context Engineering
• 🚧 Temporal workflow integration (experimental)

Coming Soon (v0.2 - Beta)

• 🔲 Complete Temporal production deployment guide
• 🔲 Additional loop templates (ReAct, ReWOO)
• 🔲 Enhanced context compression with LOD (Level of Detail)
• 🔲 Multi-agent collaboration patterns
• 🔲 Streaming response support
• 🔲 Comprehensive test coverage (target: 80%+)

Future (v1.0 - Stable)

• 🔲 Production-grade Temporal workflow orchestration
• 🔲 High-performance server mode for concurrent agents
• 🔲 Agent marketplace and community templates
• 🔲 Auto-optimization with feedback loops
• 🔲 Multi-modal support (images, audio, video)
• 🔲 Advanced monitoring and observability
• 🔲 Enterprise features (RBAC, audit logs, etc.)

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone and install in development mode
git clone https://github.com/Prismer-AI/pisa.git
cd pisa
uv pip install -e ".[dev]"

# Run tests
uv run pytest tests/ -v

# Run linter
uv run ruff check src/

# Format code (using ruff format)
uv run ruff format src/

Areas We Need Help

• 📝 Documentation improvements
• 🐛 Bug reports and fixes
• ✨ New capability implementations
• 🎨 Loop template designs
• 🌍 Internationalization

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📄 License

PISA is released under the MIT License.

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🙏 Acknowledgments

PISA is built on the shoulders of giants:

• OpenAI Agent SDK - Core agent primitives and LLM interactions
• Temporal - Durable workflow orchestration for production deployments
• Temporal OpenAI Agents Integration - Production-ready agent workflow patterns
• Rich - Beautiful terminal output and progress tracking
• Python Ecosystem - The amazing tools and libraries that make this possible

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📬 Contact & Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@prismer.ai
• Twitter: @PrismerAI

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