Review AI package for machine learning assistance #8
Conversation
Major changes to the AI package architecture:
1. Development Workflow (OpenHands SDK):
- Created new development process using OpenHands SDK for contained
code modifications (ai/process/development/)
- OpenHandsWorkspace wraps the SDK for git operations and code editing
- DevelopmentProcess coordinates feature analysis, implementation, and review
2. Manifest Workflow:
- Created manifest process for AI-driven experiment management
(ai/process/manifest/)
- ManifestBuilder for constructing valid experiment configurations
- ResultsCollector for gathering and summarizing experiment outputs
- ManifestProcess enables propose → run → analyze research loops
3. Structured Outputs (output_schema):
- Added output_schema to verifier.yml for verdict responses
- Created specialized personas with output_schema:
- ideator_* (architect, catalyst, ml) for structured idea proposals
- file_selector for file selection
- feature_analyst for feature analysis
- code_reviewer for code review
- experiment_proposer for experiment proposals
- results_analyzer for results analysis
- Updated platform_improve/process.py to use Pydantic models
instead of manual JSON parsing
4. Process Registry:
- Updated experiment/runner.py to register development and manifest
processes alongside brainstorm
- Removed platform_improve from registry (replaced by development)
The three main workflows are now:
1. General discussion with persistence (existing brainstorm)
2. Development workflow using OpenHands SDK
3. Manifest workflow for building/running/collecting experiments
- Add openhands-sdk and openhands-tools to pyproject.toml dependencies - Add new packages to setuptools packages list - Add docker-compose services for new personas: - feature-analyst - code-reviewer - experiment-proposer - results-analyzer - Update root agent's sub_agents list to include new personas - Update root agent instructions to document new expert agents The system is now fully self-contained - just start the TUI and talk to the root agent.
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Caution Review failedThe pull request is closed. 📝 WalkthroughSummary by CodeRabbitRelease Notes
✏️ Tip: You can customize this high-level summary in your review settings. WalkthroughAdds two new workflow subsystems: a DevelopmentProcess using OpenHands for agent-driven feature implementation (git/PR automation), and a ManifestProcess for manifest-driven experiment lifecycle (manifest builder, runner integration, results collection). Also refactors platform_improve to use Pydantic schemas and adds related persona configs and infra entries. Changes
Sequence Diagram(s)sequenceDiagram
actor User
participant DP as DevelopmentProcess
participant OHW as OpenHandsWorkspace
participant Agent as OpenHands Agent
participant Git as Git/GitHub
participant Repo as Repository
User->>DP: run(feature_request)
DP->>DP: analyze_feature()
DP->>DP: inspect_codebase()
DP->>OHW: setup()
OHW->>Agent: initialize tools & conversation
OHW->>Git: clone repository / checkout base_branch
DP->>OHW: create_branch(feature_branch)
DP->>OHW: implement_changes(task_description)
OHW->>Agent: execute task (modify files, run commands)
OHW->>Git: git diff -> files_changed
DP->>OHW: run_tests()
OHW->>Agent: execute test_command
alt tests fail and iterations remain
DP->>OHW: implement_changes(fix_task)
end
DP->>DP: review_changes(diff)
OHW->>Git: commit_and_push()
OHW->>Git: gh pr create -> PR URL
DP->>OHW: cleanup()
OHW-->>User: DevelopmentResult (branch, PR URL, files, tests)
sequenceDiagram
actor User
participant MP as ManifestProcess
participant MB as ManifestBuilder
participant Runner as Experiment Runner
participant RC as ResultsCollector
participant Artifacts as Artifacts Dir
User->>MP: run_research_loop(goal)
MP->>MP: propose_experiment(goal)
MP->>MB: build_manifest(proposal)
MB->>MB: build_from_spec() / validate_manifest()
MB->>Runner: run_from_manifest_path(manifest)
Runner->>Artifacts: execute experiment -> write artifacts & metrics
MP->>RC: collect_experiment_results()
RC->>Artifacts: scan & parse experiment dirs -> ExperimentResult[]
MP->>RC: summarize_results()
MP->>MP: analyze_results() -> update goal or stop
loop iterations
end
MP-->>User: analysis + summary markdown
Estimated code review effort🎯 4 (Complex) | ⏱️ ~65 minutes Possibly related PRs
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Comment |
Summary of ChangesHello @TheApeMachine, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly expands the AI capabilities of the Highlights
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Code Review
This pull request introduces significant new functionality for AI-driven development and experiment management through the development and manifest process packages. The changes are well-structured, introducing new agent personas with defined output schemas and refactoring existing processes like platform_improve to use Pydantic models for more robust agent communication.
My review focuses on several critical and high-severity issues. There is a critical bug in the OpenHandsWorkspace where a key method is just a stub, rendering most of the class non-functional. I've also identified command injection vulnerabilities that need to be addressed. Additionally, a change in experiment/runner.py inadvertently removes a process from the process map, which will cause runtime errors. Other comments focus on improving error handling and the robustness of test validation.
| # Get the last assistant message which should contain the output | ||
| # This is a simplified approach - in practice you'd parse the conversation history | ||
| return "" |
There was a problem hiding this comment.
The _run_task method is currently a stub that always returns an empty string. This will cause methods that depend on command output (e.g., get_diff, run_tests) to fail or misbehave. The implementation should be completed to retrieve and return the actual output from the agent's execution from the conversation history.
| # Get the last assistant message which should contain the output | |
| # This is a simplified approach - in practice you'd parse the conversation history | |
| return "" | |
| # Get the last assistant message which should contain the output | |
| # The actual implementation will depend on the `Conversation` object's API. | |
| if self._conversation.history and self._conversation.history[-1].role == 'assistant': | |
| return self._conversation.history[-1].content | |
| return "" |
| from caramba.ai.process.brainstorm import Brainstorm | ||
| from caramba.ai.process.platform_improve import PlatformImprove | ||
| from caramba.ai.process.development import DevelopmentProcess | ||
| from caramba.ai.process.manifest import ManifestProcess | ||
|
|
||
| return { | ||
| "brainstorm": cast(ProcessFactory, Brainstorm), | ||
| "platform_improve": cast(ProcessFactory, PlatformImprove), | ||
| "development": cast(ProcessFactory, DevelopmentProcess), | ||
| "manifest": cast(ProcessFactory, ManifestProcess), | ||
| } |
There was a problem hiding this comment.
The platform_improve process has been removed from the _process_map. However, there is still code and configuration (config/presets/platform_improve.yml) that refers to this process type. This will cause a ValueError at runtime when trying to execute a platform_improve target. This process should be added back to the map.
| from caramba.ai.process.brainstorm import Brainstorm | |
| from caramba.ai.process.platform_improve import PlatformImprove | |
| from caramba.ai.process.development import DevelopmentProcess | |
| from caramba.ai.process.manifest import ManifestProcess | |
| return { | |
| "brainstorm": cast(ProcessFactory, Brainstorm), | |
| "platform_improve": cast(ProcessFactory, PlatformImprove), | |
| "development": cast(ProcessFactory, DevelopmentProcess), | |
| "manifest": cast(ProcessFactory, ManifestProcess), | |
| } | |
| from caramba.ai.process.brainstorm import Brainstorm | |
| from caramba.ai.process.development import DevelopmentProcess | |
| from caramba.ai.process.manifest import ManifestProcess | |
| from caramba.ai.process.platform_improve import PlatformImprove | |
| return { | |
| "brainstorm": cast(ProcessFactory, Brainstorm), | |
| "development": cast(ProcessFactory, DevelopmentProcess), | |
| "manifest": cast(ProcessFactory, ManifestProcess), | |
| "platform_improve": cast(ProcessFactory, PlatformImprove), | |
| } |
|
|
||
| try: | ||
| self._run_task(f"cd {self.repo_dir} && git add -A") | ||
| self._run_task(f"cd {self.repo_dir} && git commit -m '{commit_message}'") |
There was a problem hiding this comment.
The commit_message is used directly in an f-string to construct a shell command. This is vulnerable to command injection if the commit message contains special characters like single quotes. It's safer to escape the message using shlex.quote(). You will need to add import shlex at the top of the file.
| self._run_task(f"cd {self.repo_dir} && git commit -m '{commit_message}'") | |
| self._run_task(f"cd {self.repo_dir} && git commit -m {shlex.quote(commit_message)}") |
| result = self._run_task( | ||
| f"cd {self.repo_dir} && gh pr create --base {base} --head {branch_name} " | ||
| f"--title '{title}' --body '{body}'" | ||
| ) |
There was a problem hiding this comment.
The title and body are used directly in an f-string to construct a shell command. This is vulnerable to command injection if they contain special characters. It's safer to escape these values using shlex.quote(). You will need to add import shlex at the top of the file.
| result = self._run_task( | |
| f"cd {self.repo_dir} && gh pr create --base {base} --head {branch_name} " | |
| f"--title '{title}' --body '{body}'" | |
| ) | |
| result = self._run_task( | |
| f"cd {self.repo_dir} && gh pr create --base {base} --head {branch_name} " | |
| f"--title {shlex.quote(title)} --body {shlex.quote(body)}" | |
| ) |
| try: | ||
| output = self._run_task(f"cd {self.repo_dir} && {test_command}") | ||
| # Check for test failures in output | ||
| success = "FAILED" not in output and "error" not in output.lower() |
There was a problem hiding this comment.
Checking for 'FAILED' or 'error' in the test output is a brittle way to determine success. Test runners like pytest provide exit codes to signal success (0) or failure (non-zero). It would be more robust for _run_task to return the exit code, and then check that here. Since _run_task is being refactored, consider having it return a tuple of (exit_code, output).
| except Exception: | ||
| timestamp = datetime.now() |
There was a problem hiding this comment.
Catching a broad Exception and falling back to datetime.now() can hide underlying issues (e.g., permission errors when stating the directory) and introduces non-determinism in timestamps, which could affect how results are sorted and analyzed. It's better to catch a more specific OSError and use a deterministic fallback, like the Unix epoch, while logging a warning.
| except Exception: | |
| timestamp = datetime.now() | |
| except OSError as e: | |
| logger.warning(f"Failed to get timestamp for {exp_dir}: {e}") | |
| timestamp = datetime.fromtimestamp(0) |
| except Exception as e: | ||
| logger.warning(f"Failed to parse experiment directory {exp_dir}: {e}") | ||
| return None |
There was a problem hiding this comment.
Catching a broad Exception for the entire directory parsing logic can make it difficult to debug specific errors (e.g., a malformed metrics.json vs. a file permission issue). Consider adding more granular error handling for file I/O (OSError) and JSON parsing (json.JSONDecodeError) to provide more specific error messages.
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Actionable comments posted: 25
🤖 Fix all issues with AI agents
In @ai/process/development/openhands_workspace.py:
- Around line 236-246: The _run_task method currently returns an empty string;
update it to retrieve and return the assistant's response after
self._conversation.run() instead of returning ""; locate _run_task and read the
conversation object (e.g., inspect self._conversation.history / messages /
get_last_assistant_message or similar API on the Conversation object) to extract
the last assistant message content, raise a clear RuntimeError if no assistant
response exists, and return the trimmed/raw output so callers like
implement_changes, run_tests, create_pull_request, and setup can rely on actual
command output for parsing.
- Around line 248-254: The cleanup method currently only clears internal state;
add an optional boolean parameter (e.g., remove_workdir: bool = False) to
cleanup and, when true, remove the workspace directory (use the attribute that
stores the path—e.g., self.workdir or self._workdir) with shutil.rmtree guarded
by os.path.exists and wrapped in try/except to log errors via logger.error;
after successful removal set the workdir attribute to None and keep existing
behavior of clearing self._conversation and self._agent and logging "Workspace
cleaned up".
- Around line 227-234: The read_file method allows path traversal because it
concatenates user-supplied path with repo_dir without validation; fix by
resolving the combined path (use full_path = (self.repo_dir / path).resolve())
and verify it is inside repo_dir (use Path.is_relative_to(repo_dir) or compare
Path.resolve() commonpath) and raise an error if not; ensure you resolve
repo_dir once (self.repo_dir.resolve()) to handle symlinks before the comparison
and keep the existing FileNotFoundError if the file does not exist.
- Around line 169-188: The commit_and_push method is vulnerable to shell
injection via commit_message; ensure the message is safely quoted instead of
interpolating raw text into the shell command: either wrap the commit message
with a proper shell-escape (e.g., use shlex.quote(commit_message)) when building
the git commit command used by _run_task, or refactor _run_task to invoke git
via subprocess with an argument list (e.g., ['git','commit','-m',
commit_message]) and call it with cwd=self.repo_dir so no shell interpolation is
needed; update the git commit invocation in commit_and_push accordingly.
- Around line 190-219: The create_pull_request method is vulnerable to shell
injection via title/body (same as commit_and_push); either stop building a shell
string and call the CLI with an argument list (modify _run_task to accept and
run List[str] via subprocess.run([...], shell=False)) or escape inputs using
shlex.quote before interpolating them into the command; update
create_pull_request to use the safer approach (e.g., call _run_task with a
List[str] or wrap title and body with shlex.quote) so user-supplied title/body
cannot inject arbitrary shell commands.
In @ai/process/development/process.py:
- Around line 96-101: The code returns DevelopmentResult(success=True,
error="...") when analysis.is_missing_feature is False which mixes success with
an error string; update the return to either set error=None and rely on
logger.info, or add/use a dedicated field like message or reason on
DevelopmentResult (e.g., DevelopmentResult(success=True, message="Not identified
as a missing feature requiring implementation")) and remove the misleading error
value; modify the call site that constructs DevelopmentResult in this block (the
branch using analysis.is_missing_feature and logger.info) accordingly and ensure
any consumers of DevelopmentResult handle the chosen field consistently.
- Around line 235-241: The generate_branch_name method can produce an empty slug
(and thus an invalid branch like "{self.config.branch_prefix}/") when
feature_description contains only non-alphanumeric characters; update
generate_branch_name to detect an empty slug after sanitization and replace it
with a safe fallback (e.g., "untitled" or a short timestamp/unique suffix) so
the returned branch name always has a non-empty segment after
self.config.branch_prefix; ensure the fallback is sanitized the same way and
retains the existing length constraints.
- Line 11: Remove the unused import by deleting the "import json" statement in
ai/process/development/process.py; ensure no other code in that module
references the json symbol before removing and run tests/lint to confirm the
unused-import warning is resolved.
- Around line 132-141: The loop that calls self.workspace.implement_changes
assigns its result to fix_result but never checks it; modify the loop in the
method containing the current snippet to validate the fix_result (e.g., check
fix_result.success or an error flag) before calling self.workspace.run_tests
again, and log or break appropriately if implement_changes failed; reference the
implement_changes call (assigned to fix_result), the subsequent
run_tests(self.config.test_command) call, and the enclosing loop over
self.config.max_iterations to add the success check and a
logger.info/logger.error message when implement_changes fails.
In @ai/process/manifest/__init__.py:
- Line 11: The __all__ list is not alphabetical; update the module export
ordering to be sorted alphabetically so it follows isort/ruff conventions:
change __all__ to list the symbols in alphabetical order (ManifestBuilder,
ManifestProcess, ResultsCollector) instead of the current order.
In @ai/process/manifest/builder.py:
- Around line 119-121: list_existing_presets currently only matches "*.yml"
while save_manifest accepts ".yml" and ".yaml"; update the list_existing_presets
method to include both extensions by iterating presets_dir.glob("*.yml") and
presets_dir.glob("*.yaml") (or equivalent single-pattern/filter) and return the
combined list of file names (using the same naming/format save_manifest
expects). Ensure you reference the presets_dir attribute and the
list_existing_presets function when making this change.
- Around line 107-117: The validate_manifest function currently returns True,
None immediately after calling Manifest.model_validate inside the try block;
move that success return into an else clause paired with the try/except so the
try only contains the call that may raise, the except returns False with the
error, and the else returns True, None—referencing the validate_manifest
function and the Manifest.model_validate call when making the change.
In @ai/process/manifest/collector.py:
- Around line 214-216: In the loop over summary.best_by_metric, avoid formatting
a non-numeric default "N/A" with :.4f; retrieve the raw value from
result.metrics (use result.metrics.get(metric)), test whether it's a number
(int/float) and only apply f"{value:.4f}" when numeric, otherwise set a safe
display string like "N/A" (or str(value)) and then call lines.append with the
prepared display and result.name; update the block around
summary.best_by_metric/result.metrics/lines.append accordingly.
- Around line 101-104: The timestamp assignment uses naive datetimes; change
both branches to produce UTC-aware datetimes by calling
datetime.fromtimestamp(exp_dir.stat().st_mtime, tz=datetime.timezone.utc) in the
try and datetime.now(datetime.timezone.utc) in the except, and ensure
datetime.timezone is imported or referenced as datetime.timezone.utc so the
variable timestamp is always timezone-aware UTC.
In @ai/process/manifest/process.py:
- Line 17: The import at top only brings in ManifestBuilder and ManifestSpec but
not ExperimentSpec, which causes issues when building ManifestSpec.experiments;
update the import statement that currently references ManifestBuilder and
ManifestSpec to also import ExperimentSpec (so ManifestBuilder, ManifestSpec,
ExperimentSpec) from the same module where ManifestBuilder is defined, ensuring
subsequent code that constructs ManifestSpec.experiments can reference
ExperimentSpec.
- Around line 118-131: The experiments field is being populated with plain dicts
but ManifestSpec.experiments expects list[ExperimentSpec]; construct
ExperimentSpec instances instead of dicts when building spec (e.g., import or
reference ExperimentSpec and replace the dict in the experiments list with
ExperimentSpec(name=proposal.name, description=proposal.hypothesis,
model_type=proposal.model_type, dataset=proposal.dataset,
training_config=proposal.key_parameters, metrics=["loss","perplexity"])); ensure
types align so Pydantic validation succeeds.
In @ai/process/platform_improve/process.py:
- Around line 25-46: Update the IdeaOutput model so files_to_touch is a
list[str] (not a comma-separated str) and adjust its Field description
accordingly; change the type annotation in the IdeaOutput class from
"files_to_touch: str" to "files_to_touch: list[str]" and update the
Field(description=...) to describe a list of relative file paths, and then
search for any usages of IdeaOutput.files_to_touch elsewhere (e.g., code that
splits or joins the string) and update those call sites to treat it as a list.
In @config/personas/code_reviewer.yml:
- Line 17: Replace the invalid model name string "gpt-5.2" in
config/personas/code_reviewer.yml with a supported model; update the value of
the model key (currently model: "gpt-5.2") to one of the valid options such as
"gpt-4.1", "gpt-4o", or "gpt-4o-mini" so the persona uses an existing OpenAI
model.
In @config/personas/verifier.yml:
- Line 43: Remove the extra blank line at the end of the verifier persona YAML
so the file ends with a single newline; open the verifier YAML content and
delete the trailing blank line flagged by YAMLlint to ensure no more than 0
blank lines at EOF.
📜 Review details
Configuration used: Organization UI
Review profile: ASSERTIVE
Plan: Pro
📒 Files selected for processing (23)
ai/process/development/__init__.pyai/process/development/openhands_workspace.pyai/process/development/process.pyai/process/manifest/__init__.pyai/process/manifest/builder.pyai/process/manifest/collector.pyai/process/manifest/process.pyai/process/platform_improve/process.pyconfig/personas/code_reviewer.ymlconfig/personas/experiment_proposer.ymlconfig/personas/feature_analyst.ymlconfig/personas/file_selector.ymlconfig/personas/ideator_architect.ymlconfig/personas/ideator_catalyst.ymlconfig/personas/ideator_ml.ymlconfig/personas/results_analyzer.ymlconfig/personas/root.ymlconfig/personas/verifier.ymlconfig/platform_improve.pyconfig/presets/platform_improve.ymldocker-compose.ymlexperiment/runner.pypyproject.toml
🧰 Additional context used
🧬 Code graph analysis (6)
ai/process/manifest/__init__.py (3)
ai/process/manifest/process.py (1)
ManifestProcess(45-265)ai/process/manifest/builder.py (1)
ManifestBuilder(38-154)ai/process/manifest/collector.py (1)
ResultsCollector(40-249)
experiment/runner.py (2)
ai/process/development/process.py (1)
DevelopmentProcess(55-284)ai/process/manifest/process.py (1)
ManifestProcess(45-265)
ai/process/development/__init__.py (2)
ai/process/development/process.py (1)
DevelopmentProcess(55-284)ai/process/development/openhands_workspace.py (1)
OpenHandsWorkspace(34-254)
ai/process/manifest/builder.py (1)
config/manifest.py (1)
Manifest(25-91)
ai/process/development/openhands_workspace.py (1)
ai/process/development/process.py (1)
run(82-175)
ai/process/platform_improve/process.py (1)
ai/process/platform_improve/docker_workspace.py (2)
ok(36-42)diff(216-225)
🪛 Ruff (0.14.10)
ai/process/manifest/__init__.py
11-11: __all__ is not sorted
Apply an isort-style sorting to __all__
(RUF022)
ai/process/manifest/process.py
77-77: Avoid specifying long messages outside the exception class
(TRY003)
138-138: Avoid specifying long messages outside the exception class
(TRY003)
152-152: Do not catch blind exception: Exception
(BLE001)
173-173: Avoid specifying long messages outside the exception class
(TRY003)
ai/process/manifest/builder.py
115-115: Consider moving this statement to an else block
(TRY300)
116-116: Do not catch blind exception: Exception
(BLE001)
129-129: Avoid specifying long messages outside the exception class
(TRY003)
ai/process/manifest/collector.py
102-102: datetime.datetime.fromtimestamp() called without a tz argument
(DTZ006)
103-103: Do not catch blind exception: Exception
(BLE001)
104-104: datetime.datetime.now() called without a tz argument
(DTZ005)
124-124: Do not catch blind exception: Exception
(BLE001)
ai/process/development/openhands_workspace.py
49-49: Probable insecure usage of temporary file or directory: "/tmp/openhands-workspace"
(S108)
102-102: Avoid specifying long messages outside the exception class
(TRY003)
116-116: Avoid specifying long messages outside the exception class
(TRY003)
145-145: Do not catch blind exception: Exception
(BLE001)
159-159: Avoid specifying long messages outside the exception class
(TRY003)
165-165: Consider moving this statement to an else block
(TRY300)
166-166: Do not catch blind exception: Exception
(BLE001)
178-178: Avoid specifying long messages outside the exception class
(TRY003)
185-185: Consider moving this statement to an else block
(TRY300)
186-186: Do not catch blind exception: Exception
(BLE001)
204-204: Avoid specifying long messages outside the exception class
(TRY003)
217-217: Do not catch blind exception: Exception
(BLE001)
224-224: Avoid specifying long messages outside the exception class
(TRY003)
230-230: Avoid specifying long messages outside the exception class
(TRY003)
233-233: Avoid specifying long messages outside the exception class
(TRY003)
239-239: Avoid specifying long messages outside the exception class
(TRY003)
ai/process/platform_improve/process.py
137-137: Avoid specifying long messages outside the exception class
(TRY003)
150-150: Avoid specifying long messages outside the exception class
(TRY003)
ai/process/development/process.py
136-136: Local variable fix_result is assigned to but never used
Remove assignment to unused variable fix_result
(F841)
167-167: Consider moving this statement to an else block
(TRY300)
169-169: Do not catch blind exception: Exception
(BLE001)
181-181: Avoid specifying long messages outside the exception class
(TRY003)
201-201: Avoid specifying long messages outside the exception class
(TRY003)
217-217: Avoid specifying long messages outside the exception class
(TRY003)
🪛 YAMLlint (1.37.1)
config/personas/verifier.yml
[error] 43-43: too many blank lines (1 > 0)
(empty-lines)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: test
🔇 Additional comments (24)
config/personas/ideator_catalyst.yml (1)
18-18: High temperature appropriate for creative exploration.The temperature of 1.0 is well-suited for the Catalyst persona's goal of "bold ideas" and "thinking outside the box."
config/personas/feature_analyst.yml (2)
18-18: Appropriate temperature for analytical work.The temperature of 0.2 is well-suited for the Feature Analyst's deterministic analysis tasks.
35-39: Correct schema pattern for file lists.Using an array type for
affected_filesis the proper approach. The ideator personas should follow this pattern forfiles_to_touch.experiment/runner.py (1)
40-41: Clean refactoring to new process types.The import changes properly replace
PlatformImprovewith the newDevelopmentProcessandManifestProcessmodules.config/personas/ideator_architect.yml (1)
17-17: No action needed — gpt-5.2 is a valid, available model.GPT-5.2 was publicly launched by OpenAI on December 11, 2025, and is available via the OpenAI API. The model reference in the configuration is correct.
Likely an incorrect or invalid review comment.
config/platform_improve.py (1)
51-51: LGTM!The addition of the
file_selectorfield is consistent with the existing pattern of persona role assignments. The field has an appropriate default value and type annotation.pyproject.toml (2)
107-108: Packages are correctly configured.Both
caramba.ai.process.developmentandcaramba.ai.process.manifestexist as proper Python packages with__init__.pyfiles and supporting modules. The setuptools configuration is valid.
62-64: All dependencies verified as secure and actively maintained.
docker==7.1.0: Official Docker Python SDK, no known CVEs, production-ready.openhands-sdk>=0.1.0: Current version 1.7.4, actively maintained with regular releases, no known CVEs.openhands-tools>=0.1.0: Current version 1.7.4, actively maintained (synchronized with SDK), no known CVEs.All three packages exist on PyPI, are free from known security vulnerabilities, and show healthy maintenance patterns. The openhands packages are more mature than their minimum version constraints suggest.
config/personas/experiment_proposer.yml (1)
19-19: Model identifier is valid.The model
"gpt-5.2"is a valid OpenAI identifier. GPT-5.2 variants (Instant, Thinking, Pro) were rolled out in late 2025 and are available as of January 2026. No changes needed.Likely an incorrect or invalid review comment.
config/personas/code_reviewer.yml (1)
26-51: LGTM!The output schema is well-designed with clear separation between blocking issues, suggestions, and security concerns. The required fields ensure consistent structured output.
docker-compose.yml (2)
680-683: LGTM!The dependency additions ensure proper startup sequencing, where the root-agent waits for all four new agent services to be healthy before starting.
521-639: All referenced persona configuration files exist in the repository:
- ✓
config/personas/feature_analyst.yml- ✓
config/personas/code_reviewer.yml- ✓
config/personas/experiment_proposer.yml- ✓
config/personas/results_analyzer.ymlNo action needed.
config/personas/verifier.yml (1)
31-42: LGTM!The migration from example-based JSON instructions to a structured
output_schemais a solid improvement. The schema clearly defines the expected boolean verdict (ok) and actionable feedback, ensuring consistent, parseable output.ai/process/manifest/__init__.py (1)
1-11: LGTM!The package initializer cleanly exposes the public API for the manifest workflow. The imports and
__all__declaration properly encapsulate the three core components (ManifestProcess, ManifestBuilder, ResultsCollector).ai/process/development/__init__.py (1)
1-10: LGTM!The package initialization correctly re-exports
DevelopmentProcessandOpenHandsWorkspacewith a clear module docstring. The public API surface is well-defined via__all__.config/personas/root.yml (1)
32-35: LGTM!The four new expert personas (
feature_analyst,code_reviewer,experiment_proposer,results_analyzer) are consistently added to both the instructions documentation and thesub_agentslist. These additions align well with the new development and manifest workflow capabilities introduced in this PR.Also applies to: 107-110
ai/process/manifest/builder.py (1)
88-91: Empty metrics list gets replaced with defaults.If
exp.metricsis an empty list[], the expressionexp.metrics or ["loss", "perplexity"]will evaluate to the defaults since empty lists are falsy. If an explicit empty metrics list should be supported, useexp.metrics if exp.metrics is not None else ["loss", "perplexity"].ai/process/manifest/process.py (1)
196-255: Research loop logic is well-structured.The iterative research loop properly handles early exit on hypothesis confirmation, updates goals based on analysis recommendations, and logs progress clearly.
ai/process/platform_improve/process.py (2)
179-201: LGTM!The
developerPatchmethod now usesFileSelectionOutputfor structured parsing of the file selector agent's response, which is cleaner than the previous approach.
134-134: file_selector is properly defined in PlatformImproveProcessConfig — No action needed. Verification confirmsfile_selectoris defined as a string field inPlatformImproveProcessConfig(config/platform_improve.py:51) and is correctly used at line 134 in the required roles list and at line 187 in the team mapping.ai/process/manifest/collector.py (1)
170-199: LGTM!The
_compare_experimentsmethod correctly handles division by zero and uses absolute values for percentage calculations.ai/process/development/process.py (1)
243-284: LGTM!The helper methods
build_implementation_taskandbuild_pr_bodyare well-structured and generate clear, informative content for the OpenHands agent and PR creation.ai/process/development/openhands_workspace.py (2)
22-31: LGTM!The
DevelopmentResultdataclass is well-structured with appropriate optional fields and correct usage offield(default_factory=list)for the mutable default.
106-150: Approve implementation approach, but note dependency on_run_task.The implementation logic correctly delegates to the OpenHands conversation and captures changed files. However, this method depends on
_run_taskreturning valid output for thegit diff --name-onlycall, which currently always returns an empty string (see separate comment).
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| model: "gpt-5.2" |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
OpenAI GPT models available January 2026 latest
💡 Result:
Here are the main OpenAI GPT models (and major families) available as of January 9, 2026:
- GPT‑4.1 family — GPT‑4.1, GPT‑4.1 mini, GPT‑4.1 nano. [1]
- GPT‑4o family — GPT‑4o (multimodal) plus GPT‑4o mini and related transcribe/TTS/realtime variants. [2][3]
- GPT‑4 (legacy API model; replaced in ChatGPT by GPT‑4o/GPT‑4.1). [3][4]
- GPT‑3.5 Turbo (current 3.5 series). [2]
- Open-weight OSS models from OpenAI — gpt-oss-120b and gpt-oss-20b (downloadable/open weights). [5]
- Realtime / realtime-preview variants (note: some realtime/gpt-4o‑realtime-preview models are being deprecated with scheduled shutdowns in Feb 2026—check docs for migration). [6]
If you want, I can fetch the exact API model names/aliases, prices, or deprecation dates for any of the above.
Sources: [1] OpenAI announcement (GPT‑4.1), [2] OpenAI platform models page (GPT‑4o), [3] OpenAI "Hello GPT‑4o" & ChatGPT notes, [4] reporting on GPT‑4 deprecation in ChatGPT, [5] reporting on gpt-oss releases, [6] OpenAI deprecations doc.
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fd "code_reviewer.yml" -type fRepository: TheApeMachine/caramba
Length of output: 235
🏁 Script executed:
find . -name "code_reviewer.yml" -type fRepository: TheApeMachine/caramba
Length of output: 100
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cat -n ./config/personas/code_reviewer.ymlRepository: TheApeMachine/caramba
Length of output: 1701
Replace gpt-5.2 with a valid model name.
The model gpt-5.2 does not exist. Use one of the available OpenAI models: gpt-4.1, gpt-4o, or gpt-4o-mini.
🤖 Prompt for AI Agents
In @config/personas/code_reviewer.yml at line 17, Replace the invalid model name
string "gpt-5.2" in config/personas/code_reviewer.yml with a supported model;
update the value of the model key (currently model: "gpt-5.2") to one of the
valid options such as "gpt-4.1", "gpt-4o", or "gpt-4o-mini" so the persona uses
an existing OpenAI model.
| required: | ||
| - ok | ||
| - feedback | ||
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There was a problem hiding this comment.
🧹 Nitpick | 🔵 Trivial
Remove extra blank line at end of file.
YAMLlint flags too many blank lines (1 > 0) at the end of the file. Remove one blank line for consistency with YAML formatting standards.
♻️ Proposed fix
required:
- ok
- feedback
-📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| required: | |
| - ok | |
| - feedback |
🧰 Tools
🪛 YAMLlint (1.37.1)
[error] 43-43: too many blank lines (1 > 0)
(empty-lines)
🤖 Prompt for AI Agents
In @config/personas/verifier.yml at line 43, Remove the extra blank line at the
end of the verifier persona YAML so the file ends with a single newline; open
the verifier YAML content and delete the trailing blank line flagged by YAMLlint
to ensure no more than 0 blank lines at EOF.
- Fix _run_task stub to extract output from OpenHands conversation history - Add shlex.quote() for shell command safety to prevent injection - Use secure tempfile.TemporaryDirectory instead of hardcoded /tmp - Use exit codes for test success detection instead of string matching - Catch specific RuntimeError exceptions instead of broad Exception - Add platform_improve back to process map in runner.py - Add DevelopmentProcessConfig and ManifestProcessConfig to agents.py - Update DevelopmentProcess and ManifestProcess to use proper config types - Standardize files_to_touch to array type in ideator personas and models - Clean up temporary directory on workspace cleanup
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