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Stanford NLP3.4.1Tested (smoke test)

DSPy: what’s new in 3.4.1 and our verdict

Python framework for programming (rather than prompting) language models, with optimisers for prompts and weights.

Official site

Release Ruler for DSPy: marked at its Tested release date on the timeline of every AI model we track.

At a glance

Evidence
Tested (smoke test)
Released
Oct 12, 2026
Last verified
Oct 25, 2026
Pricing
Partly verified
Best for
agent building

Evidence and ranking

Evidence behind this verdict

  1. Sources read2 cited sources, latest fetched Oct 25, 2026. (done)
  2. Facts re-checkedLast verified Oct 25, 2026. (done)
  3. Run by usSmoke test on Oct 25, 2026 (scope below). (done)

Passed Test log result: Passed. Read the log

Where we rank it (editorial)

Listed here but not yet ranked. Ranks are editorial picks, not benchmark scores.

Pricing: Open source; no pricing page read. (checked 2026-10-25)

Our verdict

Worth a look if you want structured, testable LM pipelines; start with a plain Predict module before optimisers.

Good for

  • Developers who want typed, composable LM programs
  • Teams that want to optimise prompts against a metric

Skip if

  • You only need a quick one-off script calling a model

Tested. Tested 2026-10-25 (America/Chicago). DSPy 3.4.1 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 3.7 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; Optimizers (MIPRO, GEPA, BootstrapFewShot) and evaluation, which are the reason to use DSPy. These are smoke tests, not a benchmark. Full log: /tests/dspy.

Evidence
Tested (smoke test, 2026-10-25, local 3B model (Qwen2.5-3B)). A smoke test is not a benchmark.
What we ran
DSPy 3.4.1 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 3.7 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; Optimizers (MIPRO, GEPA, BootstrapFewShot) and evaluation, which are the reason to use DSPy.
Date
(America/Chicago)

Best for

Key facts

DSPy is a Python framework from Stanford NLP for composing language-model programs as modules with typed signatures. The repository is MIT-licensed; 3.4.1 was tagged on 2026-10-12.

  • 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: stanfordnlp/dspy: 38,790 stars on 2026-10-25, last push 2026-10-25
  • Latest release we saw: 3.4.1, 2026-10-12
  • How to start: uv pip install dspy and read the docs at dspy.ai.
  • 3.4.1 (2026-10-12): the newest release on the repository when we read it on 2026-10-25.

How to set it up: Get started with DSPy

Install steps are exactly what we ran on 2026-10-25; the test log lists what was and was not covered.

  1. Install

    One package; we measured the install on a cold cache.

    uv pip install dspy

  2. Turn off caching while testing

    DSPy caches LM calls by default, which made our first timing look much faster than a real call.

    dspy.configure_cache(enable_disk_cache=False, enable_memory_cache=False)

Pros and cons

Pros

  • MIT license
  • Typed signatures instead of hand-written prompts
  • Worked with a local OpenAI-compatible model in our test

Cons

  • Default caching can hide real latency in tests
  • Our test used one tool call; optimisers were not run

Alternatives and comparisons

  • Pydantic AI: Python agent framework built on Pydantic types, covering agents, realtime voice, image generation and embeddings.
  • LangChain: Open-source framework for building LLM applications and agents in Python and JavaScript, from the team behind LangGraph and LangSmith.
  • Instructor: Python library for getting validated, structured outputs from language models using Pydantic models.
  • Langflow: MIT-licensed visual builder for AI agents and MCP servers with a very large GitHub following.

FAQ

What is DSPy?

A Python framework from Stanford NLP for programming language-model pipelines as modules with signatures. Its repository is MIT-licensed.

Did DSPy work with a local model in our test?

Yes: 3 of 3 trials called a one-function tool correctly with a local 3B model once we disabled DSPy's caches. See /tests/dspy.

How big is the DSPy install?

About 231 MB and 66 packages with uv on our machine in 3.7 s from a cold cache.

What we have not verified

  • DSPy optimisers (MIPRO, GEPA and similar): not run
  • Behaviour with hosted models

Sources

Last verified · Agentic Weekly

  • Langflow: MIT-licensed visual builder for AI agents and MCP servers with a very large GitHub following.
  • Botpress: Customer-support AI agent platform with a free plan and conversation-based paid plans.
  • Gumloop: Agent builder for teams; plans start at $37 per month with 20k included credits.

How to cite this page

Agentic Weekly, "DSPy 3.4.1: what's new, verdict & setup", last updated 2026-10-25, https://theagenticweekly.com/tools/dspy

Keep the Reported or Tested label when you quote a verdict. Plain-text version: https://theagenticweekly.com/tools/dspy.md. More for AI assistants: /ai, /tools.json, /llms.txt.