# Instructor v1.17.0: what's new, verdict & setup Canonical: https://theagenticweekly.com/tools/instructor · Released: 2026-09-09 · Last verified: 2026-10-25 · Evidence: Tested (smoke test, 2026-10-25, local 3B model (Qwen2.5-3B)) · Vendor: 567 Labs · Official site: https://python.useinstructor.com/ > Python library for getting validated, structured outputs from language models using Pydantic models. ## Verdict **A small, light way to get typed output from a model; good first step before reaching for a full framework.** Good for: - Developers who need typed, validated model output - Teams already using Pydantic Skip if: - You need an agent runtime with tools and memory Tested. Tested 2026-10-25 (America/Chicago). Instructor 1.17.0 returned a validated Pydantic object from a local 3B model in 3 of 3 trials (structured extraction, not a tool call); cold uv install 1.4 s. Test environment: Linux x86_64 virtual machine (Debian 13), 8 vCPU, 15 GiB RAM, no GPU; no vendor account, login or API key. Models were local (Qwen2.5-3B-Instruct (Q4_K_M) through Ollama 0.40.11); we tested the tool, not any vendor's hosted models. Not tested: Any vendor-hosted model: everything ran on a local 3B model, so tool-call reliability with frontier models is not measured here; Multi-agent flows, memory, long conversations, streaming, tracing and cloud deployment; Anything beyond one tool and one question; three trials is a smoke test, not a benchmark. These are smoke tests, not a benchmark. Full log: /tests/instructor. Test scope: - Kind and date: smoke test, 2026-10-25 (America/Chicago). Not a benchmark. - What we ran: Instructor 1.17.0 returned a validated Pydantic object from a local 3B model in 3 of 3 trials (structured extraction, not a tool call); cold uv install 1.4 s. - Model used: Qwen2.5-3B-Instruct (Q4_K_M) through Ollama 0.40.11 - Environment: Linux x86_64 virtual machine (Debian 13), 8 vCPU, 15 GiB RAM, no GPU; no vendor account, login or API key - Not tested: Any vendor-hosted model: everything ran on a local 3B model, so tool-call reliability with frontier models is not measured here; Multi-agent flows, memory, long conversations, streaming, tracing and cloud deployment; Anything beyond one tool and one question; three trials is a smoke test, not a benchmark. Pricing: Partly verified. Open source; no pricing page read. (checked 2026-10-25) ## Best for - [Best AI tool for agent building](https://theagenticweekly.com/best/agent-building) (#-): MIT-licensed structured-output library built on Pydantic. ## Key facts Instructor is a Python library that turns model responses into validated Pydantic objects. It is MIT-licensed; v1.17.0 was tagged on 2026-09-09. - License: MIT (per GitHub) - Platforms: Python - Pricing model: Open source - Free tier: Open source, free to use - Paid plans: Hosted or enterprise offerings not read - GitHub: 567-labs/instructor: 14,063 stars on 2026-10-25, last push 2026-10-20 - Latest release we saw: v1.17.0, 2026-09-09 - How to start: uv pip install instructor and follow the docs. - v1.17.0 (2026-09-09): the newest release on the repository when we read it on 2026-10-25. ## How to set it up: Get started with Instructor Install steps are exactly what we ran on 2026-10-25; the test log lists what was and was not covered. 1. **Install.** A light install in our measurement. ``` uv pip install instructor ``` 2. **Wrap your client.** Patch an OpenAI-compatible client and pass a Pydantic response_model. ``` client = instructor.from_openai(OpenAI(base_url="http://127.0.0.1:11434/v1", api_key="x")) ``` ## Pros and cons Pros: - MIT license - Small install - Worked against a local OpenAI-compatible server Cons: - Structured output only: not an agent framework - We tested one extraction, not retries or streaming ## Alternatives - [Pydantic AI](https://theagenticweekly.com/tools/pydantic-ai): Python agent framework built on Pydantic types, covering agents, realtime voice, image generation and embeddings. - [DSPy](https://theagenticweekly.com/tools/dspy): Python framework for programming (rather than prompting) language models, with optimisers for prompts and weights. - [LiteLLM](https://theagenticweekly.com/tools/litellm): Python SDK and proxy that gives one OpenAI-style interface to many model providers. - [Langflow](https://theagenticweekly.com/tools/langflow): MIT-licensed visual builder for AI agents and MCP servers with a very large GitHub following. - [Botpress](https://theagenticweekly.com/tools/botpress): Customer-support AI agent platform with a free plan and conversation-based paid plans. - [Gumloop](https://theagenticweekly.com/tools/gumloop): Agent builder for teams; plans start at $37 per month with 20k included credits. - [Lindy](https://theagenticweekly.com/tools/lindy): AI 'employee' builder that connects to your tools; Free plan with $50 in credits that last 7 days, then credit-based plans. - [Microsoft Copilot Studio](https://theagenticweekly.com/tools/copilot-studio): Microsoft's platform for creating and managing custom agents, workflows and apps with no code. - [Relevance AI](https://theagenticweekly.com/tools/relevance-ai): Platform for specialist AI agents for sales, customer success, marketing and HR, with an embedded deployment team. - [Voiceflow](https://theagenticweekly.com/tools/voiceflow): Platform for chat and voice AI agents for customer experience teams. - [LangGraph](https://theagenticweekly.com/tools/langgraph): Low-level framework for building long-running, stateful agents as graphs. - [Vercel AI SDK](https://theagenticweekly.com/tools/vercel-ai-sdk): Vercel's TypeScript SDK that gives one API across many model providers, with streaming and agent helpers. ## FAQ ### What is Instructor? A Python library that makes language models return validated Pydantic objects. ### Did Instructor work with a local model in our test? Yes: 3 of 3 trials returned a valid structured result from a local 3B model. See /tests/instructor. ### How big is the Instructor install? About 49 MB and 41 packages with uv on our machine in 1.4 s from a cold cache. ## What we have not verified - Retries, validation failures and streaming: not run - Behaviour with hosted models ## Sources - [Instructor documentation](https://python.useinstructor.com/) (fetched 2026-10-25) - [Instructor repository (GitHub)](https://github.com/567-labs/instructor) (fetched 2026-10-25) ## How to cite Agentic Weekly, "Instructor v1.17.0: what's new, verdict & setup", last updated 2026-10-25, https://theagenticweekly.com/tools/instructor Keep the Reported or Tested label when you quote the verdict.