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Deepset vs DSPy

DSPy scores higher on the AgentReady, 47/100 against 44/100. They differ on 4 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Deepset. Deepset is a company that provides Haystack, an open platform to build, run, and govern AI agents and applications.

DSPy. DSPy is a Python framework for building AI systems.

Where Deepset is ahead

Deepset passes observable execution, and DSPy does not. That is operate, whether an agent can run against it in production and recover when a call fails.

Where DSPy is ahead

DSPy passes clear product positioning, and Deepset does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds adopt: fast time to first request and copyable quickstart. Deepset misses those.

What neither does

Both fail public docs discoverable, llms-full.txt / full agent docs, structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, official typescript sdk, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified. If your agent needs any of those, you will be building it yourself either way.

Score, pillar by pillar

The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.

Discover is whether an agent can find the product at all without being told it exists. DSPy leads 80 to 67. Deepset misses clear product positioning, public docs discoverable, llms-full.txt / full agent docs; DSPy misses public docs discoverable, llms-full.txt / full agent docs.

Understand. Both sit at 23/100 here. Deepset misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; DSPy misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt. DSPy leads 60 to 50. Deepset misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, fast time to first request, copyable quickstart, official typescript sdk; DSPy misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, official typescript sdk.

Operate. Deepset leads 35 to 24. Deepset misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified; DSPy misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution, agent compatibility verified.

Pricing

Deepset starts at $0/mo and has a free tier. DSPy does not publish one and has a free tier.

Signal by signal

Which to pick

DSPy clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: Deepset and DSPy. Alternatives to each: Deepset, DSPy.

An agent can fetch this as data: POST /v1/compare {"slugs": ["deepset", "dspy"]}