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Kneo Agent Platform examples

Runnable spec files and supporting Python helpers for kneo-serv. Use them to validate a local install, exercise the CLI, or as starting points for your own specs.

For the annotated walkthrough — exact kneo spec / kneo run commands, expected output, caveats — see docs/user/examples.md.

Reading order

If you're new, work through these specs in order. Each one builds on concepts the previous one introduces.

  1. research_agent.yaml — single-agent research pipeline with a plan-act strategy, two tools, and a sequential workflow. The simplest end-to-end example. Three environment overlays (research_agent.dev.yaml, research_agent.staging.yaml, research_agent.prod.yaml) demonstrate the overlay system.
  2. graph_review_workflow.yaml — graph workflow with conditional edges (retrieve → analyze → review → revise/finalize). Adds graph shape and conditional routing on top of the basic sequential pattern.
  3. concurrent_review_workflow.yaml — concurrent workflow where three reviewers (security, accessibility, performance) run against the same input in parallel and the platform collects their responses. Adds fan-out / fan-in to your mental model.
  4. group_chat_workflow.yaml — group-chat workflow where a proponent, skeptic, and pragmatist debate a proposal over two rounds. Adds multi-agent conversation with rounds: N and ordered participant declaration.
  5. human_approval_workflow.yaml — sequential workflow with a human-in-the-loop step between draft and publish. Adds the pause/resume continuation API.
  6. smoke_human_workflow.yaml — minimal human-in-the-loop spec that uses the dummy provider. Useful for deployment smoke tests that exercise the pause/resume path without a real LLM.

Reference (not a spec to run)

  • project_config.yaml — example .kneo/config.yaml showing per-environment overlays and policy enforcement. Project-level config that kneo reads when pointed at a project directory.

Supporting Python helpers

  • app_functions.py — function references for research_agent.yaml (compress_history, web_search, webpage_reader, summarize). Stub implementations meant to be replaced with real ones in your own spec.
  • human_functions.py — function references for human_approval_workflow.yaml (draft_report, publish_report). Also stubs.

Caveats

  • These specs are non-production placeholders. The provider/model fields point at common defaults (openai/gpt-4o, openai/gpt-4o-mini) and should be retargeted before any real use.
  • The Python helpers return placeholder strings — replace with real implementations before running against production tooling.

Files