Skip to content

Conversation

@Zochory
Copy link
Member

@Zochory Zochory commented Feb 10, 2026

Summary

  • Refresh README.md and docs/ sections referenced in the plan so CLI commands, helper signatures, and scaffold inventory reflect the current implementation.
  • Clarify sub-LLM explanations and runtime behavior to match the actual llm_query/llm_query_batched usage while keeping doc tone.
  • Remove stale quantitative claims and ensure helper defaults match driver.py and chunking.py.

Testing

  • Not run (not requested)

Copilot AI review requested due to automatic review settings February 10, 2026 08:58
@gemini-code-assist
Copy link
Contributor

Summary of Changes

Hello @Zochory, 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 updates the fleet-rlm project by aligning its core functionalities with the latest DSPy RLM patterns and enhancing its integration with Claude Code. The changes focus on improving the flexibility and efficiency of recursive language model workflows within Modal sandboxes, particularly through direct sub-LLM calls and refined output handling. It also introduces a robust set of new scaffold assets, including agent teams and hooks, to provide a more structured and guided experience for users leveraging Claude Code for complex tasks.

Highlights

  • Enhanced RLM Sandbox Capabilities: Introduced built-in llm_query() and llm_query_batched() functions within the Modal sandbox, enabling recursive and parallel sub-LLM calls directly from sandboxed code. This aligns fleet-rlm more closely with the DSPy RLM pattern.
  • New Output Conventions and Controls: Implemented the 'Final Variable Convention' for structured output, allowing sandbox code to signal completion by setting a Final variable. Additionally, added 'Metadata-Only History' for stdout, summarizing long outputs to prevent context window pollution, with configurable thresholds.
  • Expanded Scaffold Assets for Claude Code: Integrated new Claude Code agent team templates, individual agent definitions (e.g., agent-designer, architect-explorer), and prompt hooks. These assets streamline multi-agent workflows and provide contextual guidance for common RLM tasks and troubleshooting.
  • Updated Documentation and CLI: Refreshed all relevant documentation (README, AGENTS.md, docs/concepts.md, docs/getting-started.md, CLI reference, skills guides) to reflect the new features, updated CLI commands (e.g., fleet-rlm init with new options), and clarified runtime details.
  • Comprehensive Testing: Added new mock tests for llm_query functionality, ensuring call counting, limit enforcement, and sub-LM usage are correct. New integration tests were also added to verify end-to-end functionality with real API calls and Modal V2 volumes.
Changelog
  • .claude/agents/modal-interpreter-agent.md
    • Updated API quick reference to include max_llm_calls, sub_lm, summarize_stdout, and stdout_summary_threshold parameters.
    • Added llm_query and llm_query_batched to the API quick reference.
    • Introduced sections for 'Built-in Sandbox Tools', 'Configuration Options', and 'Output Conventions'.
    • Expanded common issues table with 'LLM call limit exceeded' and 'llm_query not defined' entries.
  • .claude/agents/rlm-orchestrator.md
    • Added a 'Built-in RLM tools for semantic analysis' section.
    • Included a 'Phase 3 Alternative: Parallel Analysis with llm_query_batched' section with a code example.
    • Documented 'Output Conventions' and 'Metadata-Only History' sections.
  • .claude/agents/rlm-specialist.md
    • Added 'Built-in RLM Tools' and 'Final Variable Convention' sections.
    • Updated the performance optimization checklist with new strategies.
    • Introduced 'Debugging llm_query Issues' and 'Cost Optimization with sub_lm' sections.
  • .claude/agents/teams/agent-designer.md
    • Added a new agent definition for agent-designer.
  • .claude/agents/teams/architect-explorer.md
    • Added a new agent definition for architect-explorer.
  • .claude/agents/teams/fleet-rlm-explorer-team.md
    • Added a new team definition for fleet-rlm-explorer-team.
  • .claude/agents/teams/testing-analyst.md
    • Added a new agent definition for testing-analyst.
  • .claude/agents/teams/ux-reviewer.md
    • Added a new agent definition for ux-reviewer.
  • .claude/hooks/README.md
    • Added a new README file for Claude Code hooks.
  • .claude/hooks/hookify.fleet-rlm-document-process.local.md
    • Added a new prompt hook for document processing workflows.
  • .claude/hooks/hookify.fleet-rlm-large-file.local.md
    • Added a new prompt hook for large file mentions.
  • .claude/hooks/hookify.fleet-rlm-llm-query-error.local.md
    • Added a new prompt hook for llm_query troubleshooting guidance.
  • .claude/hooks/hookify.fleet-rlm-modal-error.local.md
    • Added a new prompt hook for Modal/sandbox troubleshooting guidance.
  • .claude/skills/rlm/SKILL.md
    • Updated 'Delegation Guidance' to include quick agent spawning examples and a detailed delegation matrix.
    • Added 'New Features (DSPy RLM Aligned)' section, detailing built-in sandbox tools, configuration options, and output conventions.
  • .claude/teams/fleet-rlm/config.json
    • Added configuration for the fleet-rlm agent team, including member prompts and removing transient fields.
  • .claude/teams/fleet-rlm/inboxes/modal-interpreter-agent.json
    • Added initial inbox messages for modal-interpreter-agent.
  • .claude/teams/fleet-rlm/inboxes/rlm-orchestrator.json
    • Added initial inbox messages for rlm-orchestrator.
  • .claude/teams/fleet-rlm/inboxes/rlm-specialist.json
    • Added initial inbox messages for rlm-specialist.
  • .claude/teams/fleet-rlm/inboxes/rlm-subcall.json
    • Added initial inbox messages for rlm-subcall.
  • .claude/teams/fleet-rlm/inboxes/team-lead.json
    • Added an empty inbox for the team-lead.
  • AGENTS.md
    • Added uv run fleet-rlm init commands and a note on enabling experimental agent teams.
  • CLAUDE.md
    • Added a new comprehensive CLAUDE.md file for Claude Code guidance.
  • README.md
    • Updated the description of fleet-rlm integration with Claude Code to include team templates and hooks.
    • Revised the 'Complex multi-step analysis' RLM solution to mention llm_query() / llm_query_batched().
    • Updated 'Quick Start with Claude Code' to refer to 'scaffold assets' and clarify installation details.
    • Changed 'Sub-Agent Patterns' to 'Sub-LLM Patterns' with updated code examples.
    • Expanded 'Scaffold Installation' section with new init command options and notes on experimental Agent Teams.
    • Added 'Available Team Templates' and 'Available Hooks' sections.
    • Included run-long-context command example and description.
  • docs/concepts.md
    • Updated default values for peek, chunk_by_size, and chunk_by_headers helper functions.
    • Clarified 'Sub-LLM Calls' to indicate llm_query() and llm_query_batched() are built-in tools.
    • Updated the architecture diagram and comparison table to reflect direct llm_query() usage.
  • docs/getting-started.md
    • Renamed 'Skills and Agents Installation' to 'Scaffold Assets Installation'.
    • Updated Modal setup commands to use uv run.
    • Detailed the new types of assets (agent definitions, team templates, hook templates) installed by init.
  • docs/guides/cli-reference.md
    • Updated init command description and options to include teams and hooks.
    • Clarified run-api-endpoints description.
    • Added the run-long-context command.
  • docs/guides/skills-and-agents.md
    • Refactored the guide to consistently use 'Scaffold Assets' terminology.
    • Updated Modal CLI commands to use uv run.
    • Expanded the overview and file structure to include agent definitions, team templates, and hook templates.
    • Updated sections on updating and cross-project usage to reflect scaffold assets.
  • docs/guides/skills-usage.md
    • Updated rlm-test-suite description.
    • Revised installation instructions to use 'scaffold assets' and detail new asset types.
  • docs/index.md
    • Updated the description for the 'Skills and Agents' guide to include team templates and hooks.
  • docs/tutorials/doc-analysis.md
    • Updated 'Scenario 2: Batch Processing (API Endpoints)' to emphasize signature-driven extraction.
    • Clarified run-long-context 'Analyze Mode' and 'Summarize Mode' descriptions.
    • Updated sandbox-side helper descriptions with default parameters and included llm_query_batched in the Planner LLM's role.
  • pyproject.toml
    • Updated project version to 0.3.2.
    • Included **/*.json in fleet_rlm._scaffold package data.
  • rlm_content/claude-code-teams/claude-code-teams-guide.md
    • Added new documentation for orchestrating Claude Code agent teams.
  • src/fleet_rlm/_scaffold/skills/rlm-debug/scripts/diagnose.py
    • Modified secret checking output to list individual secret statuses.
    • Updated failed checks summary to show count instead of listing names.
  • src/fleet_rlm/_scaffold/skills/rlm-long-context/scripts/orchestrate.py
    • Refactored module imports for flexibility.
    • Integrated rank_chunks and cache_manager modules directly.
    • Updated chunk processing logic to use start index from ranked chunks.
  • src/fleet_rlm/_scaffold/skills/rlm-long-context/scripts/rank_chunks.py
    • Moved pickle import into load_context function and updated its docstring.
  • src/fleet_rlm/_scaffold/skills/rlm-long-context/scripts/semantic_chunk.py
    • Added _find_json_span helper function.
    • Enhanced chunk_json for lists and dictionaries with more robust range recovery and fallback.
  • src/fleet_rlm/cli.py
    • Extended the init command with options for installing specific scaffold asset types (--teams-only, --hooks-only, --no-teams, --no-hooks).
    • Updated init command description and installation summary to include teams and hooks.
  • src/fleet_rlm/driver.py
    • Implemented the 'Final Variable Convention' for structured output.
    • Integrated llm_query and llm_query_batched as built-in tools within the sandbox.
    • Updated docstrings to reflect new capabilities and conventions.
  • src/fleet_rlm/interpreter.py
    • Integrated built-in llm_query and llm_query_batched tools.
    • Implemented max_llm_calls limiting and metadata-only stdout summarization.
    • Added configurable sub_lm parameter for sub-queries.
    • Updated docstrings and _tool_names to reflect new features.
  • src/fleet_rlm/scaffold.py
    • Expanded to support new asset types (teams and hooks) with dedicated listing and installation functions.
    • Updated agent handling for nested structures.
    • Modified install_all to include options for teams and hooks.
  • test_full_integration.py
    • Added a new full integration test for llm_query and llm_query_batched with real API calls.
  • test_llm_query_features.py
    • Added a new test for llm_query and llm_query_batched features, including PDF loading, chunking, parallel analysis, and synthesis.
  • test_v2_volume.py
    • Added a new integration test to verify fleet-rlm functionality with Modal V2 volumes.
  • tests/test_cli_smoke.py
    • Expanded tests for the fleet-rlm init command, covering various installation options and their combinations.
  • tests/test_driver_protocol.py
    • Added new tests to verify the behavior of the Final variable convention, including handling runtime errors and state leakage.
  • tests/test_llm_query_mock.py
    • Added new mock tests for llm_query and llm_query_batched functionality.
  • tests/test_scaffold.py
    • Expanded tests to thoroughly cover the listing and installation of new scaffold asset types (teams and hooks), and updated agent handling.
  • tests/test_scaffold_scripts.py
    • Added new regression tests for scaffolded long-context scripts, verifying direct execution and JSON chunking ranges.
  • uv.lock
    • Updated various package versions and their corresponding hashes.
Activity
  • The modal-interpreter-agent claimed Task Bump the dependencies group with 2 updates #3 'Diagnose Modal sandbox issues' and reported successful diagnostics, confirming Modal credentials, LITELLM secrets, sandbox creation, and ModalInterpreter execution are fully operational.
  • The rlm-orchestrator completed Task Initialize JetBrains Junie 🚀 #1 'Orchestrate long-context RLM processing', successfully extracting module names from dspy-doc.txt using parallel rlm-subcall agents.
  • The rlm-specialist successfully created the /data directory structure in the Modal volume and downloaded an arXiv paper PDF to it.
  • The rlm-orchestrator completed a comprehensive analysis of the RLM paper, providing insights on decoupling strategies, aggregation mechanisms, retrieval innovations, and implementation ideas for fleet-rlm.
  • The rlm-specialist implemented the 'Final Variable Convention' in src/fleet_rlm/driver.py and the 'Metadata-Only History' enhancement in src/fleet_rlm/interpreter.py.
  • The rlm-specialist provided a detailed 'DSPy RLM Alignment Analysis Report', identifying patterns that needed adjustment in fleet-rlm's implementation.
  • The rlm-specialist implemented llm_query, llm_query_batched tools, and max_llm_calls limiting, and created comprehensive mock tests for llm_query functionality, all passing.
  • The rlm-orchestrator reported that the llm_query integration test was blocked due to missing PDF and unconfigured Modal credentials, then later identified a bug in driver.py preventing llm_query injection.
  • The rlm-specialist fixed the llm_query injection bug in src/fleet_rlm/driver.py.
  • The rlm-orchestrator successfully ran the integration test with real API calls after the driver fix and .env loading, confirming llm_query and llm_query_batched are fully functional.
  • The rlm-specialist updated all agent definition files in .claude/agents/ to reflect the new improvements and synced them to the scaffold.
  • The modal-interpreter-agent successfully recreated rlm-volume-dspy as a Modal V2 volume, preserving and restoring data.
  • The rlm-orchestrator successfully ran a final integration test with the new V2 volume, verifying accessibility, read/write operations, and llm_query functionality.
Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature Command Description
Code Review /gemini review Performs a code review for the current pull request in its current state.
Pull Request Summary /gemini summary Provides a summary of the current pull request in its current state.
Comment @gemini-code-assist Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help /gemini help Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

  1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution.

Copy link
Contributor

Copilot AI left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Pull request overview

This PR updates fleet-rlm’s documentation and bundled Claude Code scaffold assets to reflect new runtime capabilities (built-in llm_query / llm_query_batched, Final convention, stdout summarization) and expanded scaffold inventory (teams + hooks), while also extending the CLI and test suite to cover these additions.

Changes:

  • Add built-in sandbox tools (llm_query, llm_query_batched), Final variable convention, and metadata-only stdout summarization support in the Modal interpreter/driver path.
  • Expand scaffold support beyond skills/agents to also include team templates and hook templates; update fleet-rlm init CLI accordingly.
  • Refresh docs and add/adjust tests for scaffold listing/installation and RLM runtime behavior.

Reviewed changes

Copilot reviewed 68 out of 69 changed files in this pull request and generated 1 comment.

Show a summary per file
File Description
uv.lock Updates locked dependency versions and editable package version metadata.
pyproject.toml Bumps package version and includes *.json scaffold assets in package data.
src/fleet_rlm/interpreter.py Adds sub-LLM querying tools, call limiting, and stdout summarization behavior.
src/fleet_rlm/driver.py Adds built-in sandbox tool injection and Final variable convention handling.
src/fleet_rlm/scaffold.py Expands scaffold listing/installation to include nested agents, teams, and hooks.
src/fleet_rlm/cli.py Extends init command with teams/hooks options and listing output.
tests/test_llm_query_mock.py Adds unit tests for llm_query/llm_query_batched behavior with mocks.
tests/test_driver_protocol.py Adds regression tests for Final variable behavior in the driver protocol.
tests/test_scaffold.py Updates scaffold tests for new teams/hooks inventory and nested agent paths.
tests/test_scaffold_scripts.py Adds regression coverage for scaffolded scripts and semantic chunking ranges.
tests/test_cli_smoke.py Adds CLI smoke tests for new init modes (teams/hooks + include/exclude flags).
test_full_integration.py Adds a manual integration script for live API testing of llm_query features.
test_llm_query_features.py Adds a manual workflow script testing chunking + batched querying + synthesis.
test_v2_volume.py Adds a manual integration script for verifying V2 volume behavior with RLM.
docs/index.md Updates docs index wording to reflect broader scaffold assets (teams/hooks).
docs/getting-started.md Updates onboarding text from “skills/agents” to “scaffold assets” and inventory.
docs/concepts.md Updates conceptual docs to reflect built-in sub-LLM tools and helper defaults.
docs/tutorials/doc-analysis.md Updates tutorial descriptions to match actual sandbox/signature behaviors.
docs/guides/cli-reference.md Updates init command docs and helper defaults to match runtime.
docs/guides/skills-and-agents.md Updates guide to include teams/hooks and revised install tree.
docs/guides/skills-usage.md Updates references from skills/agents to scaffold assets + updated inventory.
README.md Refreshes README to reflect new runtime + scaffold categories and CLI flags.
CLAUDE.md Adds repo guidance for commands/architecture/testing aligned with latest behavior.
AGENTS.md Updates quick commands to include scaffold install/list and teams env flag note.
src/fleet_rlm/_scaffold/skills/rlm/SKILL.md Documents new tools/config/output conventions in the bundled RLM skill.
src/fleet_rlm/_scaffold/skills/rlm-long-context/scripts/orchestrate.py Improves script importability when executed directly vs as a module.
src/fleet_rlm/_scaffold/skills/rlm-long-context/scripts/rank_chunks.py Moves pickle import into function and clarifies pickle format in docstring.
src/fleet_rlm/_scaffold/skills/rlm-long-context/scripts/semantic_chunk.py Improves JSON chunk span recovery with fallback to size-based chunking.
src/fleet_rlm/_scaffold/skills/rlm-debug/scripts/diagnose.py Tweaks secret-check output formatting and failure reporting.
src/fleet_rlm/_scaffold/hooks/README.md Adds documentation for hook templates and naming conventions.
src/fleet_rlm/_scaffold/hooks/hookify.fleet-rlm-document-process.local.md Adds prompt hook guidance for document-processing prompts.
src/fleet_rlm/_scaffold/hooks/hookify.fleet-rlm-large-file.local.md Adds prompt hook guidance for large-file prompts.
src/fleet_rlm/_scaffold/hooks/hookify.fleet-rlm-llm-query-error.local.md Adds prompt hook guidance for llm_query-related errors.
src/fleet_rlm/_scaffold/hooks/hookify.fleet-rlm-modal-error.local.md Adds prompt hook guidance for Modal/sandbox-related errors.
src/fleet_rlm/_scaffold/agents/rlm-orchestrator.md Updates agent docs for new tools/output conventions/summarization behavior.
src/fleet_rlm/_scaffold/agents/rlm-specialist.md Updates agent docs for debugging/cost/limits related to new tools.
src/fleet_rlm/_scaffold/agents/modal-interpreter-agent.md Updates agent docs for new tools/config/output conventions.
src/fleet_rlm/_scaffold/agents/teams/agent-designer.md Adds team-support agent definition for scaffolded teams.
src/fleet_rlm/_scaffold/agents/teams/architect-explorer.md Adds team-support agent definition for scaffolded teams.
src/fleet_rlm/_scaffold/agents/teams/fleet-rlm-explorer-team.md Adds multi-agent “explorer team” definition.
src/fleet_rlm/_scaffold/agents/teams/testing-analyst.md Adds team-support agent definition for scaffolded teams.
src/fleet_rlm/_scaffold/agents/teams/ux-reviewer.md Adds team-support agent definition for scaffolded teams.
src/fleet_rlm/_scaffold/teams/fleet-rlm/config.json Adds scaffolded team template config for Claude Code agent teams.
src/fleet_rlm/_scaffold/teams/fleet-rlm/inboxes/modal-interpreter-agent.json Adds scaffolded initial inbox seed for modal-interpreter-agent.
src/fleet_rlm/_scaffold/teams/fleet-rlm/inboxes/rlm-orchestrator.json Adds scaffolded initial inbox seed for rlm-orchestrator.
src/fleet_rlm/_scaffold/teams/fleet-rlm/inboxes/rlm-specialist.json Adds scaffolded initial inbox seed for rlm-specialist.
src/fleet_rlm/_scaffold/teams/fleet-rlm/inboxes/rlm-subcall.json Adds scaffolded initial inbox seed for rlm-subcall.
src/fleet_rlm/_scaffold/teams/fleet-rlm/inboxes/team-lead.json Adds scaffolded initial inbox seed for team-lead.
.claude/skills/rlm/SKILL.md Mirrors updated RLM skill content in repo-local Claude scaffolding.
.claude/hooks/README.md Mirrors hook README in repo-local Claude scaffolding.
.claude/hooks/hookify.fleet-rlm-document-process.local.md Mirrors hook template in repo-local Claude scaffolding.
.claude/hooks/hookify.fleet-rlm-large-file.local.md Mirrors hook template in repo-local Claude scaffolding.
.claude/hooks/hookify.fleet-rlm-llm-query-error.local.md Mirrors hook template in repo-local Claude scaffolding.
.claude/hooks/hookify.fleet-rlm-modal-error.local.md Mirrors hook template in repo-local Claude scaffolding.
.claude/agents/rlm-orchestrator.md Mirrors updated agent doc in repo-local Claude scaffolding.
.claude/agents/rlm-specialist.md Mirrors updated agent doc in repo-local Claude scaffolding.
.claude/agents/modal-interpreter-agent.md Mirrors updated agent doc in repo-local Claude scaffolding.
.claude/agents/teams/agent-designer.md Mirrors team-support agent definition in repo-local Claude scaffolding.
.claude/agents/teams/architect-explorer.md Mirrors team-support agent definition in repo-local Claude scaffolding.
.claude/agents/teams/fleet-rlm-explorer-team.md Mirrors explorer team definition in repo-local Claude scaffolding.
.claude/agents/teams/testing-analyst.md Mirrors team-support agent definition in repo-local Claude scaffolding.
.claude/agents/teams/ux-reviewer.md Mirrors team-support agent definition in repo-local Claude scaffolding.
.claude/teams/fleet-rlm/config.json Adds a repo-local Claude teams config (appears to include machine/session-specific state).
.claude/teams/fleet-rlm/inboxes/modal-interpreter-agent.json Adds repo-local team inbox state.
.claude/teams/fleet-rlm/inboxes/rlm-orchestrator.json Adds repo-local team inbox state (contains sensitive credential-like text).
.claude/teams/fleet-rlm/inboxes/rlm-specialist.json Adds repo-local team inbox state.
.claude/teams/fleet-rlm/inboxes/rlm-subcall.json Adds repo-local team inbox state.

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

},
{
"from": "team-lead",
"text": "The API credentials are in the `.env` file at the project root. Re-run the integration test with the environment properly loaded.\n\nThe `.env` file contains:\n- DSPY_LM_MODEL=openai/gemini-3-flash-preview\n- DSPY_LM_API_BASE=https://litellm-proxy-gojcb5mtua-uc.a.run.app \n- DSPY_LLM_API_KEY=sk-bd2c13c7...\n\nMake sure the test loads the `.env` file before running, or run with:\n`uv run --env-file .env python test_llm_query_features.py`\n\nRe-run the integration test now.",
Copy link

Copilot AI Feb 10, 2026

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This inbox log includes what appears to be an API key value (DSPY_LLM_API_KEY=sk-...). Secrets (even partially redacted) and credential hints should not be committed; please remove this content from the repo and rotate/revoke the exposed key if it was real.

Suggested change
"text": "The API credentials are in the `.env` file at the project root. Re-run the integration test with the environment properly loaded.\n\nThe `.env` file contains:\n- DSPY_LM_MODEL=openai/gemini-3-flash-preview\n- DSPY_LM_API_BASE=https://litellm-proxy-gojcb5mtua-uc.a.run.app \n- DSPY_LLM_API_KEY=sk-bd2c13c7...\n\nMake sure the test loads the `.env` file before running, or run with:\n`uv run --env-file .env python test_llm_query_features.py`\n\nRe-run the integration test now.",
"text": "The API credentials are in the `.env` file at the project root. Re-run the integration test with the environment properly loaded.\n\nThe `.env` file contains:\n- DSPY_LM_MODEL=openai/gemini-3-flash-preview\n- DSPY_LM_API_BASE=https://litellm-proxy-gojcb5mtua-uc.a.run.app \n- DSPY_LLM_API_KEY=<redacted>\n\nMake sure the test loads the `.env` file before running, or run with:\n`uv run --env-file .env python test_llm_query_features.py`\n\nRe-run the integration test now.",

Copilot uses AI. Check for mistakes.
Copy link
Contributor

@gemini-code-assist gemini-code-assist bot left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Code Review

This pull request updates the documentation to align with new CLI commands and runtime features, particularly around sub-LLM calls (llm_query, llm_query_batched) and agent teams. While the changes significantly improve clarity and provide excellent examples, with robust implementation in interpreter.py and driver.py, a medium-severity security vulnerability was identified related to insecure deserialization using the pickle module in the rank_chunks.py script. Additionally, there are minor documentation formatting issues and the inclusion of user-specific absolute paths in some configuration and log files, which impacts portability.

"model": "kimi-for-coding",
"joinedAt": 1770672919989,
"tmuxPaneId": "",
"cwd": "/Volumes/Samsung-SSD-T7/Workspaces/Github/qredence/agent-framework/v0.5/_WORLD/_RLM/fleet-rlm-dspy",
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

high

The cwd property is set to a user-specific absolute path ("/Volumes/Samsung-SSD-T7/..."). This is also present for other members in this file. Configuration files in version control should not contain absolute paths to ensure portability across different developer machines. This should be replaced with a relative path, an environment variable, or a placeholder that can be resolved at runtime.

Comment on lines 17 to 18
with open(state_path, "rb") as f:
state = pickle.load(f)
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

security-medium medium

The load_context function uses the pickle module to deserialize data from a file specified by state_path. The pickle module is known to be insecure and can lead to arbitrary code execution if the input data is malicious. Since this script is part of the scaffolded skills and can be executed with user-supplied paths, an attacker could potentially achieve remote code execution by providing a crafted pickle file. It is highly recommended to replace pickle with a secure serialization format such as json.

Comment on lines +53 to +60
```markdown
## 1. Design Philosophy
[Core principles observed]

## 2. Coordination Patterns
```
[Diagram or description of delegation flows]
```
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

medium

There's an inconsistency in how placeholders are documented in the Output Format section. [Core principles observed] is inside the main markdown code block, while [Diagram or description of delegation flows] is wrapped in its own separate code block. For consistency, placeholders should either all be inside or all be outside the example code blocks. Placing them outside as descriptive text is generally clearer for documentation.

},
{
"from": "team-lead",
"text": "{\"type\":\"permission_response\",\"request_id\":\"perm-1770699937657-v2y1cew\",\"subtype\":\"success\",\"response\":{\"updated_input\":{\"command\":\"for dir in dspy-knowledge rlm-knowledge output memory cache knowledge; do\\n echo \\\"=== Backing up $dir ===\\\"\\n mkdir -p /tmp/rlm-volume-backup/$dir\\n modal volume get rlm-volume-dspy $dir /tmp/rlm-volume-backup/ 2>&1 || echo \\\"Failed to backup $dir\\\"\\ndone\",\"timeout\":120000,\"description\":\"Back up all directories from the volume\"},\"permission_updates\":[]}}",
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

medium

This file appears to be a log or message history from a specific run, and it contains user-specific absolute paths (e.g., in the command field of permission responses). Checking in runtime-specific files with absolute paths is not recommended as they are not portable and can expose environment details. These files should either be sanitized to act as generic templates or be excluded from version control via .gitignore.

@Zochory Zochory merged commit 2e30103 into main Feb 10, 2026
5 checks passed
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

None yet

Projects

None yet

Development

Successfully merging this pull request may close these issues.

2 participants