Improve an existing NeMo agent's source or harness from an Insight or explicit Harbor-compatible evaluation datasets. Run the Experimentalist to propose and validate candidate code changes, then optionally publish a changed winner as a draft PR or MR.
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name
nemo-experimentalist
description
Improve an existing NeMo agent's source or harness from an Insight or explicit Harbor-compatible evaluation datasets. Run the Experimentalist to propose and validate candidate code changes, then optionally publish a changed winner as a draft PR or MR.
triggers
["run experimentalist","experimentalist plugin","optimize from an insight","improve an agent with evaluation data","improve an agent on a benchmark dataset","create an experimentalist candidate","validate a candidate code change","improve an agent harness","optimize an agent from train and validation splits","draft PR for an agent improvement"]
not-for
["nemo-explore (use to design a new agent before it exists)","nemo-experiments-upload (use to upload traces or evaluation results)","nemo-evaluator (use to author a new evaluation or metric)","nemo-insights / agents analyst (use to generate the Insight before optimizing from it)","nemo-eval-author (use to author an evaluation outside an Experimentalist run)","agents-optimize (use to tune routing, cost, or latency for a deployed agent)"]
compatibility
requires the enabled `nemo-experimentalist-plugin`, Docker for Harbor evaluation, a running local platform, and the repository-root `.venv` for LLM-authored code.
maturity
beta
license
Apache-2.0
user-invocable
true
allowed-tools
["Bash","Read","Write"]
NeMo Experimentalist
Use the Experimentalist to improve an existing agent. It evaluates a baseline,
proposes code changes, and keeps only candidates that improve validation.
Choose the optimizer
Use the Experimentalist when the improvement belongs in the agent harness:
the source that owns its workflow, tool use, state, and runtime behavior. It
changes that source in candidate branches, evaluates each candidate against the
agent's own harness, and can publish a validated winner for review.
Hand off to agents-optimize only when the request is explicitly limited to a
deployed agent's routing, skills, prompts, or cost/latency, with no runtime
implementation change. That workflow owns the deployment-tuning commands; do
not substitute it for an Experimentalist source-change run.
Prepare the agent
The Experimentalist improves an existing, evaluable agent; it does not design
or scaffold one. Before running it, make sure you have:
agent source code and locked dependencies. For PR/MR publication, use a Git
repository with a clean working tree and a pushed baseline revision;
an ETHOS.md covering the agent's Goal, Scope, Tools, and
Evaluation contract, including access configuration and constraints but
never secret values;
versioned, Harbor-compatible train and validation datasets. Keep a separate
test split for a final manual baseline-versus-winner comparison. The
Experimentalist profile and CLI accept only train and validation datasets, so
run this third split manually with the agent's evaluator; never use it for
candidate selection;
a baseline smoke test that proves the agent runs, scores, and emits its
expected artifacts and traces; and
an optimizer.yaml profile, or the explicit inputs described below.
Record the agent revision, dataset versions, configuration, and result path
with each run so another operator can reproduce it. If the agent or its Ethos
does not exist yet, use nemo-explore to design it and nemo-ethos to create
the Ethos before returning here.
Configure the environment
The repository-root .venv is the only supported Experimentalist environment.
From the repository root, synchronize it and use its CLI:
uv syncexport NEMO="$PWD/.venv/bin/nemo"
Before running $NEMO or relying on a local platform, complete the
repository-root . Then set the Platform URL and verify readiness:
Stop if setup is incomplete or the readiness check fails.
The Experimentalist needs a running NeMo Platform, an LLM endpoint for its
optimizer agents, and a model for each tier. For the NVIDIA Inference Gateway,
set the inference key and model tiers:
Choose a strong frontier model for SMART: it performs the highest-stakes
reasoning and code changes. Use a capable, lower-cost model for MID, where
good judgment still matters but the work is less demanding. Use a fast,
low-latency model for FAST; it serves high-volume supporting work and does
not need the same reasoning depth.
The CLI uses the NVIDIA Inference Gateway by default and reuses
INFERENCE_API_KEY as the Experimentalist key. For another OpenAI-compatible
endpoint, set these instead of INFERENCE_API_KEY:
Keep these values in your shell environment or an ignored environment file,
never in optimizer.yaml, the run configuration, or Git. When a profile is
loaded, the CLI automatically loads <profile-dir>/.env; existing shell values
take precedence. Configure any credentials required by the agent under test
according to its own setup. To publish a winning Git candidate, authenticate
the relevant CLI in the same execution environment: gh auth login for GitHub
or glab auth login for GitLab.
Isolate the run
The optimizer asks an LLM to edit the agent source and execute commands; Harbor
also runs task containers. Do not run an unreviewed optimization with broad host
access or production credentials. Prefer a disposable Docker Sandbox in clone
mode, use dedicated, revocable, spending-limited keys, and retain only the
network access the agent, Harbor, and model endpoint need. Clone mode protects
the host checkout from writes but does not make ignored files secret: they
remain readable in the sandbox. Follow the plugin's recommended laptop
isolation
when running locally.
From the repository root, create a clone-mode sandbox and run the
Experimentalist inside it. Append the run-mode options from this skill to the
last line:
The sandbox has its own writable clone and virtual environment; do not point it
at the host .venv. It is not a secret boundary: ignored files remain readable
inside the sandbox. Forward only dedicated, revocable credentials and ensure
the sandbox can reach the Platform, model endpoint, registry, and Harbor
datasets it needs.
Pre-flight
Complete the SETUP.md and readiness check above before continuing. Run the
built-in checks from the agent directory that contains optimizer.yaml,
or pass the profile explicitly. Without a loaded profile, doctor cannot check
the dataset or task-template artifacts:
$NEMO agents experimentalist doctor --profile path/to/optimizer.yaml
Stop and resolve any missing platform, credential, Docker, or dataset check
before starting a run.
Choose a run mode
Insight-driven: use an Insight ID or the default Insight written by
nemo agents analyst run in the agent profile. An Insight is a systematic
symptom inferred from a set of traces. Use this mode when the agent has no
reliable, verifiable reward for the behavior you need to improve.
Explicit evaluation: pass --no-insight, an agent, and separate train
and validation datasets. Use this mode when a benchmark or other evaluator
provides a trustworthy, verifiable reward that is a good measure of agent
quality.
Keep validation independent from training. Never change an evaluation dataset
just to make a candidate win.
Inputs
Choose either a reusable optimizer.yaml profile or pass the required inputs
directly as CLI flags. A profile is optional; when used, flags override its
values. With no --profile, the CLI discovers optimizer.yaml by walking up
from the current directory.
Input
Purpose
Required
--profile
The optimizer.yaml profile containing the agent, datasets, workspace, and optional config.
No; discovered when present.
--insight / --insight-id
The problem to improve. --insight accepts a local Insight file or a platform Insight ID; --insight-id selects an entry in a local multi-Insight file.
Insight-driven mode.
--no-insight
Disables profile Insight discovery for an explicit evaluation run.
Explicit mode.
--agent
Local agent directory or Git URL. A Git URL enables candidate branch and PR/MR publication.
Explicit mode; optional when an Insight supplies the agent.
--ethos
Markdown description of the agent.
Optional; use the profile or conventional ETHOS.md when available.
--train-dataset / --validation-dataset
Separate local Harbor datasets or registry references used to measure improvement.
Yes, unless the profile supplies both.
--task-template
A directory containing one Harbor task template (task.toml, with placeholder values). In Insight-driven mode, Eval Author copies and fills it for representative failing traces to create the targeted evaluation suite.
Required in Insight-driven mode unless the profile supplies it.
--config
YAML or JSON mapping that validates as the Experimentalist run configuration: top-level run limits plus optional source, storage, goal_config, coder, analyzer, proposer, evaluator, and eval_author sections. It does not configure model endpoints or model tiers.
No; profile or defaults apply.
--workspace / --base-url
NeMo workspace and platform URL.
Workspace defaults to the profile or default; base URL uses NMP_BASE_URL or localhost.
Experiment directory that receives eval-and-optimize/: the resolved source agent, generated candidates, per-trial results, analysis, run.json state, and OPTIMIZATION.md summary.
No; default is <profile-dir>/.nemo-optimizer/experiments/<timestamp>-<uuid> with a profile, otherwise ./tmp/<timestamp>-<uuid>.
--framework-skills
Additional framework-skill directories for the optimization agents.
No; may be repeated.
An optimizer.yaml profile must identify the agent and its source, one task
template, and independent train and validation datasets. It can also set the
workspace, Ethos, run configuration, and framework skills. The
Analyst and Experimentalist share this profile, allowing the latter to use the
default Insight created by the former.
Explore configuration
Start with the deliberately small configuration below. The top-level options control rounds and
candidate counts; source controls checkout behavior; storage controls
candidate branches and PR/MR publication; outcome_evaluator_config controls trial
execution; and eval_author controls Insight-driven evaluation authoring. See
the example-agent walkthrough
for a complete worked optimizer configuration and run.
To inspect the complete run schema—including nested options and their defaults—run
this in the environment where the Experimentalist plugin is installed:
outcome_evaluator_config deliberately renders as an open mapping in that schema. For its
Harbor-specific settings, use the smoke configuration below and the plugin's
Harbor evaluator documentation; for example, outcome_evaluator_config.n_attempts defaults
to 1.
Keep model endpoint and model-tier settings out of this file; configure those
as Experimentalist deployment settings instead.
Tune the Evolutionary Optimizer
Use a smoke configuration to prove that source checkout, Harbor, credentials,
and artifact collection work together. It is intentionally too small to judge
whether an agent is better. For a real run, give the optimizer enough
candidates and train tasks to learn, then rely on the separate validation split
to select the winner.
Setting
Smoke run
Real run starting point
Effect
max_rounds
1
10–15
Maximum optimization rounds.
max_candidates / max_survivors
1 / 1
3 / 3
Exploration per round and candidates retained as parents.
max_train_batch_tasks
Small fixed batch, e.g. 4
null for all train tasks, or a representative fixed batch
Cost and signal available while proposing changes.
max_trajectory_tasks
2
8
Tasks used for trajectory/goal-tree scoring.
trajectory_scorer / terminator
null / null
Omit them (they default to goal-tree and convergence)
null means no implementation of that role, skipping costly diagnostic work for smoke runs; omitting restores quality and early stopping for real runs.
builder_config.max_fix_attempts
1
2 (default)
Maximum repair iterations when a candidate fails its integration check.
outcome_evaluator_config.n_attempts
1
1; increase only when task results are noisy
Repeats each evaluation trial.
eval_author.max_traces
3
10
Representative Insight traces deeply analyzed in Insight-driven mode.
A small explicit smoke configuration looks like this:
max_rounds:1min_rounds_before_stopping:1max_survivors:1max_candidates:1max_trajectory_tasks:2max_train_batch_tasks:4trajectory_scorer:nullterminator:nullstorage:# The real default is true. Keep this false until you intend to create a PR/MR.publish_winner:falsebuilder_config:max_fix_attempts:1outcome_evaluator_config:n_attempts:1eval_author:max_traces:3
builder_config.max_architecture_doc_iterations is how many iterations the
builder can use to write architecture.md, and it defaults to 100. It is
separate from the evaluation budget above, but each additional iteration adds
model usage and run time to that step. Increase it when a run stops with
Generation failed after 100 iterations (max_iterations=100). An agent that has
many source files needs more iterations.
Create a low-cost smoke dataset
When the full dataset is expensive, create small copied train and
validation subsets for the smoke run. Select a few representative task
directories from each original split—for example, different task types or
known failure modes—and copy each complete Harbor task directory, including
its task.toml and verifier files:
mkdir -p smoke/train smoke/validation
for task in task-a task-b task-c; docp -R "full/train/$task" smoke/train/
donefor task in task-d task-e; docp -R "full/validation/$task" smoke/validation/
done
Point the smoke command at smoke/train and smoke/validation. Record the
selected task IDs with the smoke configuration. Do not edit or replace the
canonical splits, and do not use a smoke subset to decide whether a candidate
is an improvement; run that decision against the full validation split.
For a real run, begin with the default values, raise only the settings that
match the available evaluation budget, and preserve the same validation split
throughout. Use storage.archive_candidates: true only when you need every
candidate branch for review; it increases remote repository noise.
1. Run a smoke check
Use copied, low-cost dataset subsets and the smoke configuration above. Keep
storage.publish_winner: false: its default is true, so a Git source can
otherwise create a draft PR/MR when a candidate wins.
Run this command in a persistent terminal session such as tmux, because even
the shortened walkthrough can take up to an hour. Redirect output to a log if
you need to disconnect, then follow it with tail -f.
2. Run from an Insight
Before this mode, install the Experimentalist plugin, record agent traces in
Intake, and run nemo agents analyst run to create the Insight. Use explicit
evaluation instead if you already have a reliable reward and do not need an
Insight.
Pass the inputs explicitly when running a one-off optimization:
--agent is optional when the Insight already identifies the agent source.
For a reusable setup, the equivalent information can live in an
optimizer.yaml profile. When the profile is complete and the Analyst has
created a default Insight for the agent, this shorter command is enough:
$NEMO agents experimentalist run --profile path/to/optimizer.yaml
Pass --insight <platform-id-or-local-file> to override the profile's default
Insight.
For a Git source, append a ref such as
git@github.com:owner/repository.git@main.
The .git@ marker is required to select a ref; a URL such as
https://github.com/owner/repository@main is treated as a repository URL with
no ref and uses its default branch.
For a local source, candidates remain in the experiment artifacts and no PR or
MR can be opened.
4. Verify and review
Inspect <experiment-dir>/eval-and-optimize/run.json for the selected winner
and compare candidate directories under <experiment-dir>/eval-and-optimize/agents/.
Review the validation result and generated PR or MR before merging. Do not
overwrite the baseline agent directly.
If verification fails
Fix every required doctor failure before retrying; run it with the same
--profile and credentials as the optimization.
If a run is interrupted, rerun with the same--experiment-dir. The
optimizer detects completed rounds and resumes from its saved
eval-and-optimize/run.json and artifacts.
Do not reuse a partial experiment directory for a materially different
source revision, dataset, or configuration; start a new directory instead.
Gotchas
A smoke result proves the workflow, not an improvement. Select candidates on
the full validation split and perform any third test-split comparison manually.
Real runs are expensive and can run for hours. Bound rounds and task counts
before starting, and inspect the generated winner before merging its PR/MR.
