promptmap-extra-model-kinds

Extra model kinds for promptmap:

And RAG kinds, called from any template:

The three LLM searches return [answer, "title\nurl", ...]. Override their models with GEMINI_SEARCH_MODEL (default gemini-3.6-flash), OPENAI_SEARCH_MODEL (gpt-5.4-mini) and ANTHROPIC_SEARCH_MODEL (claude-opus-5-5, effort ANTHROPIC_SEARCH_EFFORT=low).

{% set hits = rag_brave_search(ref('topic'), count=5) %}
Use these sources:
{{ hits | join("\n\n") }}

Installing the package is enough: promptmap finds the kinds through the promptmap.model_kinds and promptmap.rag_kinds (promptmap >= 0.5.16) entry point groups and loads them on first use. Nothing has to import it.

Configuration is read from the environment — see each module's docstring.

coding_agent

A coding agent as a pure function: a Dir in, the patched Dir out. The agent works on a scratch copy of its input, free to build and run tests; the copy only exists to find out which files to create, edit or delete, and where it lives is irrelevant. The input always comes from upstream through promptfiles, so getting a real repo into a Dir is a static node's job:

{# map/repo.prompt — a static node decides what the input tree is #}
{{ config(model_type="execute_python", cache=False) }}
output = Dir("../app")

{# map/api.prompt #}
{{ config(model_type="coding_agent", promptfiles="repo") }}
Add a /health endpoint to the API.

{# map/tests.prompt — takes the previous coding_agent's patched tree #}
{{ config(model_type="coding_agent", promptfiles="api") }}
Add tests for every endpoint.

Output: output (the agent's answer), result (the patched Dir), changes ({path: text} of only the files it created or modified), deleted, sum_of_changes (ast_summary lines plus a unified_diff of result vs input) and cache. result is the only file output, so promptmap run writes the complete patched tree to outputs/<model>/result/ and nothing else; the rest is plain data. Binary files (images, video, PDFs, …) are kept only as {"sha256": ...}: a changed binary's bytes are read back from the previous result, and a binary both this model and upstream changed is never merged — the agent re-runs. Build and cache folders the agent creates while testing (node_modules, .venv, __pycache__, …) are not counted as changes.

The node keeps no state of its own: its output also carries base (the input tree it worked from, as {path: sha256} hashes plus the original text of only the files it changed — the rest is rebuilt from the previous result), and promptmap hands the whole output back next run as the model's previous result (call.previous, promptmap ≥ 0.5.17) — the same row templates see as {{ previous }}. Editing the .prompt file, or deleting the run database, starts it afresh.

With the same prompt it reuses that change instead of running the agent:

"Meaning" comes from tree-sitter-language-pack: files are compared with comments (except directives like # type: ignore) and formatting stripped, and changes are summarised per symbol — ADDED_SYMBOL, REMOVED_SYMBOL, SIGNATURE_CHANGED, BODY_CHANGED. Grammars download on first use (into ~/.cache/tree-sitter-language-pack); files with no grammar, or that don't parse, are compared byte for byte.

The verifier sees the upstream change (upstream_input_ast_diff), everything this model changed last run (previous_run_sum_of_changes) and what reuse would apply now (replayed_changes), each as symbol summary plus unified diff. Verification uses classify_call in client.py (or hosted Jev with TYPESAFE_API_KEY), else a YES/NO LLM call. On a miss the agent is told its previous change and answer, what changed upstream and why it is re-running. Extra config keys: jev_cache_threshold (0.85), merge_max_files (5), context_max_chars (20000), force_refresh.