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Promptise Foundry

The production framework for agentic AI systems. Build agents that discover tools, reason with custom patterns, remember context, enforce security, and run autonomously.

pip install promptise

Quick Start

import asyncio
from promptise import build_agent

async def main():
    agent = await build_agent(
        model="openai:gpt-4o-mini",
        instructions="You are a helpful assistant.",
    )
    result = await agent.ainvoke(
        {"messages": [{"role": "user", "content": "Hello!"}]}
    )
    print(result["messages"][-1].content)
    await agent.shutdown()

asyncio.run(main())

You need an API key first

Put it in a .env file in your project (OPENAI_API_KEY=sk-...) — Promptise loads it automatically — or pass it in code with Model(...) — explained in Models & Providers → In code: Model: Model("gpt-4o-mini", provider="openai", api_key="..."). The exact variable name for every provider — Azure AI Foundry, Bedrock, Gemini, Ollama, … — is in Configuration & Secrets → Every provider's variables; self-hosted and inference endpoints are in Models & Providers → Custom, self-hosted and inference endpoints. promptise models env <provider> prints the lines a provider needs.

See the full Quick Start for tools, reasoning patterns, and production features.

Already have an API? MCPcast it

Don't hand-write a server for an API that already exists — generate one from its OpenAPI spec:

promptise mcpcast openapi.yaml --name myapi --no-curate --auth env-token  # → myapi-mcp/
export MCPCAST_UPSTREAM_TOKEN="Bearer <your API token>"
claude mcp add myapi -- python myapi-mcp/server.py

Read-only by default; writes, deletes and money-moving calls are only generated when you opt in, and every one of them is approval-gated server-side — so any MCP client can drive your product safely. Drop --no-curate to have a model design a smaller intent-tool surface. Start with MCPcast, end to end — how MCP works, a real API turned into a real server, and the review checklist — then the step-by-step guide and the reference.

Five Subsystems

Subsystem What it does Start here
Agent Turn any LLM into a production agent. MCP tool discovery, memory, guardrails, semantic cache, streaming, approval workflows. Building Agents
Reasoning Engine Design how your agent thinks. 20 composable nodes, 7 prebuilt patterns, 18 typed flags, 0.02ms overhead. Custom reasoning patterns for any task. Custom Reasoning
MCP Server Build tool APIs that agents call. JWT auth, guards, middleware, rate limiting, audit logs, TestClient. The FastAPI of MCP. Building Servers
Agent Runtime Run agents autonomously. Cron triggers, crash recovery, budget enforcement, health monitoring, mission tracking, distributed coordination. Runtime Systems
Prompting Prompts as software. Typed blocks with token budgeting, conversation flows, composable strategies, guards, version control, testing. Prompt Engineering

Hands-On Labs

Complete, runnable tutorials for real-world use cases:

Install

Python 3.10+ required.

pip install promptise

Put your LLM key in a .env file in your project — scripts and the CLI load it automatically (or pass it in code with Model(...); see Configuration & Secrets):

.env
OPENAI_API_KEY=sk-...

Any other provider — Azure AI Foundry, Bedrock, Gemini, Ollama, … — is one string or one Model(...) away; promptise models env <provider> prints the lines it needs. Verify:

promptise models check openai:gpt-5-mini --ping

Next Steps