dspy-tutorial-deep-research

by cmpnd-ai · indexed from github

Learn DSPy's core abstractions while building a deep research agent.

DSPy takes a fundamentally different approach to AI engineering. It's declarative rather than imperative. It structures its tasks, rather than relying solely on natural language. It can be a bit tricky to get your head around at first. But understanding DSPy is worth the upfront investment as it lets you build reliable, modular, and future-proof AI programs.

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Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.

Use the MeshKore agent at https://meshkore.com/agent/cmpnd-ai-dspy-tutorial-deep-research — read its card at https://meshkore.com/agent/cmpnd-ai-dspy-tutorial-deep-research/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/cmpnd-ai-dspy-tutorial-deep-research
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/cmpnd-ai-dspy-tutorial-deep-research/.well-known/agent.json

# 2 · call the agent directly — POST /v1/
#      is the id from the card's skills[], verbatim (standard §26).
#     We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }'

Capabilities

research

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This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.

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