Imported from Leeroo-AI/kapso (skills/opencode/kapso/SKILL.md). Install upstream with npx skills add Leeroo-AI/kapso --skill kapso. Copyright stays with the author.

Kapso

Kapso runs experiment campaigns: propose a candidate, implement it on its own git branch, score it with the judge, keep the best, repeat. Everything below is 0.4.x. Prefer the facts here and the docs links at the end over reading the package source; the source is large and a session that greps it runs out of turns before answering.

Facts that prevent the common mistakes

• Install with pip install leeroo-kapso. The PyPI package named kapso is an unrelated WhatsApp tool that shadows the kapso command. Python 3.10+.
• Every model call runs through a coding-agent CLI that must be logged in: claude (ideation, implementation, judging) and codex (research, utilities). There is no API-key fallback. OPENAI_API_KEY is used only for embeddings.
• Secrets come from .env in the directory you run from (find_dotenv(usecwd=True)); a shell export works only if it is actually exported. Config never holds secrets.
• All knobs live in one YAML. To change anything, copy the packaged file, edit, and pass it with --config / config_path=. Never set Kapso behaviour through environment variables.
• Run kapso doctor (or kapso doctor evolve|learn|research|learn_knowledge|deploy) before any run. Every required row must read [OK ]; [-- ] rows are optional. Add --models to live-probe every configured model with one token each; a model the login cannot serve fails here in seconds instead of hours in. A usage cap on a model it can serve is not visible to the probe.
• A campaign takes tens of minutes to hours. Launch it in the background with its output in a log file, check once that the process is alive, and hand off in your reply right away: the goal you passed, the metric and target you assumed and that the user's own can replace them, the kapso watch <campaign> --follow command, and that WAITING ON YOU is a pause answered through kapso inbox. The status file appears only after the seed copy and repo-memory bootstrap, usually a few minutes in, so do not wait for it, poll the log, or arm monitors unless asked. Never run kapso evolve in a foreground tool call.
• A run that ends with WAITING ON YOU has paused, not failed (exit code 0). Do not restart it; reply through the inbox (below).

Evolve: run a campaign

The goal text

The judge stops the campaign only when the goal reads as fully achieved, and it scores every experiment from what the evaluation prints. So the goal has to name a success metric and a number, and it helps to name the judge: "accuracy above 0.85 as measured by eval/evaluate.py". Both are the user's to give and optional; whatever the user gives goes into the goal verbatim.

First look for an evaluation the repo already has: a scoring script, a test suite, a benchmark command. Then:

• The repo has one but the request names no metric or target: run it once for the baseline, write the goal with that metric and a target you propose, and tell the user in one line what you assumed and that their own success metric goes straight into the goal. Leave headroom below any ceiling you can see; a target the data cannot reach ends in the inbox, not in a result.
• The repo has none and the request names none: your whole reply is one question, one line, asking whether they have a script or command that scores this and what "good" means; nothing is launched, measured or written before the answer. A script they have goes in with --eval-dir, protected. If they answer that they have none or do not know, launch with the best metric you can state in the goal and no --eval-dir: the campaign builds its own evaluation in kapso_evaluation/ from the goal, and the judge checks that it is fair. Never write an evaluator on the user's behalf, and never ask twice.
• Put rules in the goal, not methods. The judge enforces data rules and prohibitions ("do not modify eval/evaluate.py", "do not fit on data/test.csv") as invariants for the whole campaign, and would freeze a method preference the same way.

Launch

kapso doctor evolve
nohup kapso evolve \
  --goal "Get the test accuracy of the churn model in train.py above 0.85 as measured by eval/evaluate.py. The evaluator must not be modified." \
  --initial-repo . \
  --eval-dir eval \
  --data-dir data \
  --output ../churn-campaign \
  --time-budget-minutes 60 --iterations 10 \
  > ../churn-campaign.log 2>&1 &
kapso watch ../churn-campaign --follow

• The nohup … & line is a tool call of its own: nothing chained in front of it. test … && nohup … & backgrounds the whole chain instead of Kapso, the call then hangs on it until the harness kills it, campaign included.
• --initial-repo <path|github url> seeds the campaign; a non-git directory is copied and committed as the baseline. The original is never modified. Every experiment lands on branch generic_exp_N inside --output, an empty directory outside the repo being seeded (a sibling), so the seed copy never contains the campaign.
• --eval-dir is copied to kapso_evaluation/ and integrity-protected: any candidate that edits it is rejected and unscored. Use it whenever the user has a judge. --data-dir is copied to kapso_datasets/. The seed copy includes everything in the directory, .env included.
• Bound the run: --iterations (default 10), --time-budget-minutes (durable across resumes), --cost-budget (best effort; codex sessions report no cost).
• -m GENERIC (default, knowledge search on) or -m MINIMAL; -a picks the coding agent (claude_code default, codex, gemini, openhands, oss_claude_code).
• The summary block ends COMPLETED with score and stop reason, or WAITING ON YOU.

Python, same thing:

from kapso import Kapso
solution = Kapso().evolve(
    goal="...", initial_repo=".", eval_dir="eval", data_dir="data",
    output_path="./campaign", time_budget_minutes=60,
)
print(solution.explain())   # .final_score .succeeded .code_path .requests

evolve() blocks for the whole campaign, so run scripts with nohup as well.

Inbox: the campaign is waiting on you

A session that needs something only a person can provide (a credential, access, a file) records a request and the campaign pauses.

kapso inbox ./campaign                 # the open requests: key, hit, tried, fix, next
kapso inbox reply ./campaign 1 "added the key to .env"

• The id may be omitted when one request is open; an empty note means done.
• The reply resumes the very session that asked, in the foreground of that command.
• A credential-shaped reply is refused: put the value where fix says (usually .env) and reply with a note.

Resume an interrupted campaign

kapso watch ../churn-campaign                  # DEAD (pid gone) or STALLED means it died
nohup kapso evolve --output ../churn-campaign --resume > ../churn-campaign.log 2>&1 &

A resume is a launch: run it in the background the same way, check once that the process is alive, and hand off with kapso watch … --follow. Do it in the same turn you diagnose the death in; a reply that only announces a resume leaves the campaign dead. Other kapso evolve processes on the machine belong to other campaigns; never kill one. The checkpoint in .kapso/run_state.json carries the goal and the search state; the launch record .kapso/launch.json carries every flag of the launch, and a resume reads both, so pass only what you mean to change (a bigger --time-budget-minutes, say). A resume that changes the mode, coding agent, eval-dir, config or knowledge index is refused by name; start a new campaign in a new output path instead. A campaign paused by the inbox resumes through kapso inbox reply, not --resume.

Learn: bank what a campaign taught

learn() mines one finished campaign into the lesson bank, a local git repo of evidence-priced cards. It runs crews for an hour or more and needs both CLIs.

from kapso import Kapso
k = Kapso()
lesson = k.learn("./campaign")      # or learn(solution); a trajectory id also works
print(lesson.explain())             # cards created/updated, admitted, report paths
print(k.memory.explain())           # bank head, active cards, serving flag

• The bank is created on first use at learning.bank.local_path (~/.kapso/bank.git). Share it with kapso bank connect <git-url> or kapso bank create org/name; after that every learn() pushes.
• Serving is off by default. For the next campaign to read the bank, set learning.serving.enabled: true in your config and pass it. Without that, banked cards have no effect on evolve().
• Follow a learn with kapso watch learning/status.
• The kapso learn ... CLI subcommands (import, mine, grade, update, develop, codify, gauntlet, behave) are the learner-development regime, not the everyday path. From a shell the everyday path is the two Python lines above.

learn_knowledge: import outside knowledge

Repos and research become wiki pages in a knowledge graph. This is a different memory from the lesson bank.

from kapso import Kapso, Source
k = Kapso()
findings = k.research("gradient boosting for churn", mode=["idea", "implementation"], depth="deep")
k.learn_knowledge(Source.Repo("https://github.com/org/repo"), findings, wiki_dir="data/wikis")
index = k.index_kg(wiki_dir="data/wikis", save_to="data/indexes/churn.index")
Kapso(kg_index=index).evolve(goal="...")

Needs Weaviate on 8080 and Neo4j on 7687 (from a source checkout: bash scripts/start_infra.sh). Ingest time scales with the material, not with depth, and can run for hours.

Research

kapso research --objective "..." --mode idea --mode implementation --depth deep -o findings.json

mode is idea, implementation, or study (repeat the flag); depth is light or deep. In Python research(objective, mode=[...], depth=...) with keyword-only mode and depth; pass results into a campaign with evolve(context=[findings.to_string()]). Runs on codex with web search.

Deploy

Deploy runs one claude session that adapts a copy of the solution (<path>_adapted_<strategy>) for the target and returns when it is ready. It takes a few minutes, not hours: run it in the foreground with a long tool timeout and use the result, rather than backgrounding it and polling.

kapso doctor deploy
kapso deploy --solution-path ../churn-campaign --strategy local   # auto|local|docker|modal|bentoml|langgraph

To call what was deployed, use the Python API, which hands back the running software:

from kapso import Kapso, DeployStrategy, SolutionResult
solution = SolutionResult(goal="churn model", code_path="../churn-campaign")  # or the object evolve() returned
software = Kapso().deploy(solution, strategy=DeployStrategy.LOCAL)
print(software.run({"tenure_months": 3, "monthly_spend": 92.0, "support_tickets": 4, "logins_per_week": 1.0, "plan": "basic"}))
software.stop()

The original solution is untouched. AUTO lets a selector pick the target.

Models and config

python -c "from kapso.kapso import DEFAULT_CONFIG_PATH; print(DEFAULT_CONFIG_PATH)"
cp "$(python -c 'from kapso.kapso import DEFAULT_CONFIG_PATH; print(DEFAULT_CONFIG_PATH)')" kapso-config.yaml
# edit, then:
kapso doctor learn --models --config kapso-config.yaml

Shipped models: campaigns on claude-opus-5, learning crews on claude-fable-5, codex roles on gpt-5.6-sol. Swap a model by editing every occurrence in the block you care about (learning.* for the crews, modes.<MODE>.* for campaigns). The timeout_minutes caps were calibrated on the shipped models; tell the user a slower model may need them raised, but change only what was asked. Kapso(config_path="kapso-config.yaml") and --config on every CLI verb select your file.

When Kapso is the wrong tool

A one-file change with an obvious fix is faster by hand. Reach for a campaign when there is a judge to beat, many candidates worth trying, and the user accepts an unattended run of an hour or more on their coding-agent subscription. Say which you are doing and why; never launch a campaign the user did not ask for.

Docs, served as Markdown

• CLI reference: https://docs.leeroo.com/docs/reference/cli.md
• Python API: https://docs.leeroo.com/docs/reference/kapso-api.md
• Configuration: https://docs.leeroo.com/docs/reference/configuration.md
• Inbox: https://docs.leeroo.com/docs/evolve/inbox.md
• Resuming runs: https://docs.leeroo.com/docs/evolve/resuming-runs.md
• Evaluation integrity: https://docs.leeroo.com/docs/evolve/evaluation-integrity.md
• Trajectory learning: https://docs.leeroo.com/docs/learning/overview.md
• Knowledge graph: https://docs.leeroo.com/docs/knowledge/overview.md
• Deployment: https://docs.leeroo.com/docs/deployment/strategies.md
• Index of every page: https://docs.leeroo.com/llms.txt

The whole site is also an MCP server: claude mcp add --transport http kapso-docs https://docs.leeroo.com/mcp.