Promptulate

Promptulate's strongest asset is restraint. Where most agent frameworks ask you to learn chains, runnables and a dozen abstractions, Promptulate asks you to call pne.chat() and, when you need more, to compose agents, tools and memory around it. The lifecycle and hook system is the part that punches above the framework's size: you can inject custom code at defined stages of agent, tool and LLM execution, which is exactly what teams need when they want observability or guardrails without forking the library. Model breadth is genuinely broad because Promptulate integrates litellm rather than hand-rolling each provider. The docs list OpenAI, Azure, Sagemaker, Bedrock, Vertex AI, Google PaLM and Gemini, Mistral, Cloudflare AI Workers, Cohere, Anthropic, HuggingFace, Replicate, Together AI, OpenRouter, AI21, Baseten, vLLM, NLP Cloud, Aleph Alpha, Petals, Ollama, DeepInfra, Perplexity, Groq, Anyscale, Voyage AI and Xorbits Inference, with a capability matrix covering completion, streaming, async and embeddings. Switching from a hosted model to a local Ollama model is a string change, not a rewrite. Tooling is pragmatic. Any Python function can become a tool, and LangChain tools can be imported rather than rebuilt, which lowers migration cost if you already have LangChain code. The built-in terminal client for prompt debugging and the prompt caching layer are small features that save real time in day-to-day development. Streamlit and Gradio component examples mean you can go from script to demo quickly. The honest weaknesses: the project is small, community support is community-scale, and the public material is mostly the docs site itself. It is a framework, so uptime, SLAs, cost and data handling are your responsibility and your model provider's. There is no visual builder and no no-code path — this is for people who write Python. If you need vendor-backed support contracts or a large hiring pool that already knows the framework, that is a different class of product.