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📝 WalkthroughWalkthroughThe pull request introduces a mandatory planning workflow using a persistent Changes
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Actionable comments posted: 4
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@lambda_agent/agent.py`:
- Around line 172-178: The loop that drives tool/model iterations (the while
True loop around send_message and finish_task) lacks a hard iteration cap, so
add a small max-iteration guard (e.g., MAX_TOOL_ITERS = 10) and increment an
iteration counter each pass; if the counter exceeds the cap return a clean
failure tuple (message and current turn_usage) instead of continuing. Update the
block that calls self.chat_session.send_message (and accumulates via
self._accumulate) to check the counter before each iteration and to abort with a
clear error when hit; apply the same MAX_TOOL_ITERS guard and abort behavior to
the other similar loop referenced around finish_task (the block at the other
location).
In `@lambda_agent/cli_setup.py`:
- Around line 20-23: The default model value is set to a paid tier; update the
default to a free-tier model in both the CLI setup and the config so new users
don't hit quota/auth failures: change the default_model variable in
lambda_agent/cli_setup.py (currently "gemini-3.1-pro-preview" referenced where
model_name is read) to "gemini-3.1-flash-lite-preview" (or
"gemini-3-flash-preview"), and make the matching change in
lambda_agent/config.py to ensure any CONFIG_DEFAULT_MODEL (or equivalent
default_model/defaults dict) uses the same free-tier model string.
In `@lambda_agent/config.py`:
- Around line 23-31: The Gemma model entries in AVAILABLE_MODELS are
incompatible with the Agent codepath that sends a separate system_instruction
and structured function schemas; either remove the "gemma-*" entries from
AVAILABLE_MODELS or gate them behind a capability check so the Agent uses an
alternate prompt-based adapter. Locate AVAILABLE_MODELS and remove
"gemma-4-26b-a4b-it" and "gemma-4-31b-it" or add a capability predicate (e.g.,
model_supports_system_instruction/model_supports_structured_tools) and update
Agent usage where it sends system_instruction and structured tool schemas to
skip or route Gemma models into a prompt-formatting adapter that embeds system
instructions and function call prompts into the user prompt instead.
In `@lambda_agent/todo.py`:
- Around line 37-47: The helpers call _ensure_todo() outside their try blocks,
so failures creating/seeding .agent/ bubble out; move the call to _ensure_todo()
inside the try for each public tool (read_todo, write_todo, update_todo,
clear_todo) so filesystem/setup errors are caught and you return the
error-string as intended; update the functions read_todo(), write_todo(),
update_todo(), and clear_todo() to call path = _ensure_todo() within their
existing try blocks and keep the existing exception handling that returns the
"Error ..." messages.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
⚙️ Run configuration
Configuration used: defaults
Review profile: CHILL
Plan: Pro
Run ID: fe3112df-bb8c-4204-9ca9-45f87e75251a
📒 Files selected for processing (8)
README.mdlambda_agent/agent.pylambda_agent/cli_setup.pylambda_agent/config.pylambda_agent/main.pylambda_agent/todo.pylambda_agent/tools.pypyproject.toml
| try: | ||
| # Send the initial user message | ||
| with Spinner(): | ||
| response = self.chat_session.send_message(payload) | ||
| turn_usage = turn_usage + self._accumulate(response) | ||
| except Exception as e: | ||
| return f"An error occurred while contacting the API: {str(e)}", turn_usage |
There was a problem hiding this comment.
Keep a hard cap on tool iterations.
finish_task helps when the model behaves, but it's not a safety boundary. With no iteration limit around the while True loop, any bad tool/result cycle can keep spending tokens until the user manually interrupts the session. Please restore a small max-iteration guard and fail the turn cleanly when it's hit.
Suggested fix
# Track tokens for this turn
turn_usage = TokenUsage()
+ max_tool_iterations = 20
+ tool_iterations = 0
@@
# The loop will continue as long as Gemini decides to call tools
while True:
try:
+ tool_iterations += 1
+ if tool_iterations > max_tool_iterations:
+ return (
+ "The agent exceeded the maximum number of tool iterations for one turn.",
+ turn_usage,
+ )
+
# 1. Check if the model returned a function_call
tool_calls = response.function_calls if response.function_calls else []Also applies to: 257-259
🧰 Tools
🪛 Ruff (0.15.9)
[warning] 177-177: Do not catch blind exception: Exception
(BLE001)
[warning] 178-178: Use explicit conversion flag
Replace with conversion flag
(RUF010)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@lambda_agent/agent.py` around lines 172 - 178, The loop that drives
tool/model iterations (the while True loop around send_message and finish_task)
lacks a hard iteration cap, so add a small max-iteration guard (e.g.,
MAX_TOOL_ITERS = 10) and increment an iteration counter each pass; if the
counter exceeds the cap return a clean failure tuple (message and current
turn_usage) instead of continuing. Update the block that calls
self.chat_session.send_message (and accumulates via self._accumulate) to check
the counter before each iteration and to abort with a clear error when hit;
apply the same MAX_TOOL_ITERS guard and abort behavior to the other similar loop
referenced around finish_task (the block at the other location).
| default_model = "gemini-3.1-pro-preview" | ||
| model_name = input(f"Enter model name (default: {default_model}): ").strip() | ||
| if not model_name: | ||
| model_name = default_model |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
Does gemini-3.1-pro-preview have a free tier in the Gemini API, and which Gemini 3 text models do have a free tier?
💡 Result:
No, gemini-3.1-pro-preview does not have a free tier in the Gemini API; it is paid-only with input prices starting at $2.00 per 1M tokens. Gemini 3 text models with a free tier are: gemini-3.1-flash-lite-preview (free input/output tokens) and gemini-3-flash-preview (free tier access confirmed in multiple sources).
Citations:
- 1: https://ai.google.dev/gemini-api/docs/pricing
- 2: https://blog.laozhang.ai/en/posts/gemini-api-free-tier
- 3: https://www.metacto.com/blogs/the-true-cost-of-google-gemini-a-guide-to-api-pricing-and-integration
🏁 Script executed:
cat -n lambda_agent/cli_setup.py | head -30Repository: ayusrjn/lambda
Length of output: 1263
🏁 Script executed:
# Check for README or setup documentation references to free tier
rg -i "free\|api key" --max-count 5 -A 2Repository: ayusrjn/lambda
Length of output: 40
🏁 Script executed:
# Check if there are other model defaults in the codebase
rg "gemini-3" lambda_agent/Repository: ayusrjn/lambda
Length of output: 380
Change the default model to a free-tier option in both setup and config.
The setup flow explicitly tells users they can get an API key "for free" (line 12), but then defaults to gemini-3.1-pro-preview, which has no free tier and costs $2.00 per 1M input tokens. New users will hit auth/quota failures before changing anything. Update the default in lambda_agent/cli_setup.py (line 20) and lambda_agent/config.py to use gemini-3.1-flash-lite-preview or gemini-3-flash-preview, which both have free tier access.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@lambda_agent/cli_setup.py` around lines 20 - 23, The default model value is
set to a paid tier; update the default to a free-tier model in both the CLI
setup and the config so new users don't hit quota/auth failures: change the
default_model variable in lambda_agent/cli_setup.py (currently
"gemini-3.1-pro-preview" referenced where model_name is read) to
"gemini-3.1-flash-lite-preview" (or "gemini-3-flash-preview"), and make the
matching change in lambda_agent/config.py to ensure any CONFIG_DEFAULT_MODEL (or
equivalent default_model/defaults dict) uses the same free-tier model string.
| AVAILABLE_MODELS = [ | ||
| "gemini-3.1-pro-preview", | ||
| "gemini-3-flash-preview", | ||
| "gemini-3.1-flash-lite-preview", | ||
| "gemini-2.5-pro", | ||
| "gemini-2.5-flash", | ||
| "gemini-3.1-pro-preview", | ||
| "gemini-2.5-pro-preview-05-06", | ||
| "gemini-2.0-flash", | ||
| "gemini-2.0-flash-lite", | ||
| "gemini-2.5-flash-lite", | ||
| "gemma-4-26b-a4b-it", | ||
| "gemma-4-31b-it", |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
Do Gemma models in the Gemini API support a separate system_instruction role and structured Gemini function-calling tools, or do they require prompt-based formatting instead?
💡 Result:
Gemma models in the Gemini API do not support a separate system_instruction role; they require prompt-based formatting for system-level instructions. They also do not support structured Gemini function-calling tools (e.g., via tools parameter with automatic parsing); instead, function calling requires manual prompt-based formatting where tools are described in text and outputs are parsed from the model's free-form response.
Citations:
- 1: https://ai.google.dev/gemma/docs/core/prompt-structure
- 2: https://ai.google.dev/gemma/docs/core/gemma_on_gemini_api
- 3: https://ai.google.dev/gemma/docs/capabilities/function-calling
- 4: https://www.philschmid.de/gemma-4-gemini-api
Remove Gemma models from AVAILABLE_MODELS or gate them behind capability checks.
The current Agent architecture passes system_instruction and structured Gemini tool schemas to all models in this list. Gemma models in the Gemini API do not support a separate system_instruction role and do not support structured function-calling tools—they require prompt-based formatting for both system instructions and function calls. This will cause runtime failures or silent capability loss for users selecting these models. Either remove the Gemma entries or ensure they are only accessible through a separate code path with appropriate adapter logic.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@lambda_agent/config.py` around lines 23 - 31, The Gemma model entries in
AVAILABLE_MODELS are incompatible with the Agent codepath that sends a separate
system_instruction and structured function schemas; either remove the "gemma-*"
entries from AVAILABLE_MODELS or gate them behind a capability check so the
Agent uses an alternate prompt-based adapter. Locate AVAILABLE_MODELS and remove
"gemma-4-26b-a4b-it" and "gemma-4-31b-it" or add a capability predicate (e.g.,
model_supports_system_instruction/model_supports_structured_tools) and update
Agent usage where it sends system_instruction and structured tool schemas to
skip or route Gemma models into a prompt-formatting adapter that embeds system
instructions and function call prompts into the user prompt instead.
| def read_todo() -> str: | ||
| """Reads the full contents of the Lambda todo file (.agent/todo.md). | ||
|
|
||
| Use this to recall your current task list and implementation plan. | ||
| """ | ||
| path = _ensure_todo() | ||
| try: | ||
| with open(path, "r", encoding="utf-8") as f: | ||
| return f.read() | ||
| except Exception as e: | ||
| return f"Error reading todo list: {e}" |
There was a problem hiding this comment.
Move _ensure_todo() inside the public tools' error handling.
All four public todo tools call _ensure_todo() before entering their try blocks. If creating .agent/ or seeding todo.md fails, the tool raises out of the agent instead of returning the "Error ..." string promised by the API, which can abort the whole turn.
Suggested fix
def read_todo() -> str:
"""Reads the full contents of the Lambda todo file (.agent/todo.md).
@@
- path = _ensure_todo()
try:
+ path = _ensure_todo()
with open(path, "r", encoding="utf-8") as f:
return f.read()
except Exception as e:
return f"Error reading todo list: {e}"Apply the same pattern to write_todo(), update_todo(), and clear_todo().
Also applies to: 50-65, 68-107, 110-121
🧰 Tools
🪛 Ruff (0.15.9)
[warning] 46-46: Do not catch blind exception: Exception
(BLE001)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@lambda_agent/todo.py` around lines 37 - 47, The helpers call _ensure_todo()
outside their try blocks, so failures creating/seeding .agent/ bubble out; move
the call to _ensure_todo() inside the try for each public tool (read_todo,
write_todo, update_todo, clear_todo) so filesystem/setup errors are caught and
you return the error-string as intended; update the functions read_todo(),
write_todo(), update_todo(), and clear_todo() to call path = _ensure_todo()
within their existing try blocks and keep the existing exception handling that
returns the "Error ..." messages.
1 participant
Summary by CodeRabbit
Release Notes
New Features
finish_taskcapabilityImprovements
Documentation
Chores