Develop #598
Develop #598
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…ved coverage reporting and adding Codecov upload steps for comprehensive, core, and quick validation tests.
… and knowledge dependencies from praisonai-agents
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Caution Review failedThe pull request is closed. WalkthroughThis update adds a new GitHub Actions workflow for running tests and uploading coverage reports, enhances existing workflows to use Changes
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Hello @MervinPraison, 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!
Summary of Changes
Hello team, gemini-code-assist here to provide a summary of this pull request. Based on the changes in the src/praisonai-agents/praisonaiagents/agent/agent.py file, this PR introduces support for multi-turn tool execution within a single _chat_completion call. Previously, the agent would call tools once and then potentially generate a final response. With this change, the agent can now enter a loop, executing tools and potentially receiving new instructions or tool calls from the language model based on the results of the previous tool executions. This enables more complex, sequential reasoning processes facilitated by tools, particularly driven by a check for a specific condition from a 'sequentialthinking' tool.
Highlights
- Multi-Turn Tool Execution: The core change enables the agent to perform multiple rounds of tool calls and subsequent LLM interactions within a single
_chat_completionrequest, moving beyond a single tool use phase. - Sequential Thinking Support: A mechanism is added to check the output of a tool (specifically looking for a 'sequentialthinking' tool call with
nextThoughtNeeded: True) to determine if the multi-turn loop should continue. - Loop Safeguard: A
max_iterationslimit (set to 10) is introduced to prevent potential infinite loops during the multi-turn tool execution process.
Changelog
- src/praisonai-agents/praisonaiagents/agent/agent.py
- Wrapped the tool execution logic within a
whileloop to allow for multiple iterations. - Added a
max_iterationsvariable (defaulting to 10) to limit the number of loop cycles. - Implemented logic to check the results of tool calls (specifically the 'sequentialthinking' tool) to decide whether to continue the multi-turn loop.
- Ensured that the message history is updated correctly with assistant messages and tool results in each iteration.
- Wrapped the tool execution logic within a
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A loop for the tool,
Thinking step by step, cool,
Ten tries, then it's through.
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Code Review
This pull request significantly refactors the agent's tool execution to support sequential tool calls, which is a valuable enhancement. The introduction of an iterative loop with max_iterations and conditional continuation logic is well-implemented for the most part. The updates to CI workflows to use pytest and Codecov are also great improvements for testing and code quality monitoring.
I've found one high-severity issue related to how responses are handled when max_iterations is reached, and a few medium-severity suggestions for improving maintainability and correctness. Please see the detailed comments below.
Summary of Findings
- Stale Response on Max Iterations: If the tool execution loop reaches
max_iterations, the agent might return a response that doesn't incorporate the results from the very last set of tool calls. A final LLM call is needed in this scenario. (Severity: high) - Hardcoded Configuration Values: The
max_iterationslimit (10) is hardcoded. Making this configurable or a class constant would improve maintainability. (Severity: medium) - Magic Strings for Tool Identification: The tool name
"sequentialthinking"is used as a magic string. Defining it as a constant would enhance readability and robustness. (Severity: medium) - Tool Usage in Final Response (Streaming): When a tool sequence concludes (not due to max_iterations) and streaming is enabled, new tools could still be invoked during the final response generation. This might be unintended. (Severity: medium)
- Redundant JSON Parsing (Not Commented): Tool call arguments (
tool_call.function.arguments) are parsed into JSON twice within the same loop iteration for each tool call. This is a minor inefficiency. (Severity: low - not commented due to review settings)
Merge Readiness
The pull request introduces valuable features for sequential tool execution. However, there is a high-severity issue concerning potentially stale responses when max_iterations is reached, which should be addressed before merging. The medium-severity suggestions would also improve the code's robustness and maintainability.
As an AI, I am not authorized to approve pull requests. Please ensure thorough testing and further review by team members before merging these changes.
| # No tool calls, we're done | ||
| break | ||
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| return final_response |
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If the while loop terminates because iteration_count reaches max_iterations (and should_continue was true in the last iteration), the final_response returned will be from the LLM call before the results of the last set of tool calls were processed by the LLM. The messages list will contain these latest tool results, but the LLM hasn't generated a response based on them.
This could lead to the agent returning a stale response that doesn't reflect the outcome of the final tool operations.
Consider adding logic before this return statement to make a final LLM call if the loop exited due to max_iterations and tools were processed in that last iteration. This final call should likely not allow further tool usage (i.e., tools=None).
# If the loop terminated due to max_iterations and the last LLM call requested tools
# (implying those tools were run and their results are in 'messages' but not yet processed by LLM for a final response).
if iteration_count == max_iterations and getattr(final_response.choices[0].message, 'tool_calls', None):
logging.warning(f"Agent {self.name} reached max_iterations ({max_iterations}). Generating final response based on accumulated tool results.")
if stream:
final_response = self._process_stream_response(
messages,
temperature,
start_time,
formatted_tools=None, # No further tools when max_iterations is hit
reasoning_steps=reasoning_steps
)
else:
final_response = client.chat.completions.create(
model=self.llm,
messages=messages,
temperature=temperature,
tools=None, # No further tools when max_iterations is hit
stream=False
)
return final_response| "tool_calls": tool_calls | ||
| }) | ||
| # Continue tool execution loop until no more tool calls are needed | ||
| max_iterations = 10 # Prevent infinite loops |
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The max_iterations value is currently hardcoded as 10. For better maintainability and configurability, would it be beneficial to define this as a class attribute (e.g., self.MAX_TOOL_ITERATIONS) or make it configurable, perhaps through the agent's initialization parameters? This would make it easier to adjust this limit without modifying the method's core logic.
| max_iterations = 10 # Prevent infinite loops | |
| max_iterations = self.config.get('max_tool_iterations', 10) # Or use a class constant |
| arguments = json.loads(tool_call.function.arguments) | ||
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| # For sequential thinking tool, check if nextThoughtNeeded is True | ||
| if function_name == "sequentialthinking" and arguments.get("nextThoughtNeeded", False): |
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The string "sequentialthinking" is used directly here to identify the tool. To improve code clarity, reduce the risk of typos, and make future refactoring easier, could this be defined as a named constant at the class or module level (e.g., SEQUENTIAL_THINKING_TOOL_NAME = "sequentialthinking")?
| if function_name == "sequentialthinking" and arguments.get("nextThoughtNeeded", False): | |
| if function_name == SEQUENTIAL_THINKING_TOOL_NAME and arguments.get("nextThoughtNeeded", False): |
| final_response = self._process_stream_response( | ||
| messages, | ||
| temperature, | ||
| start_time, | ||
| formatted_tools=formatted_tools if formatted_tools else None, | ||
| reasoning_steps=reasoning_steps | ||
| ) |
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When should_continue is False, it implies the tool execution sequence (e.g., sequential thinking) has decided to conclude. However, in the streaming case, formatted_tools are still passed to _process_stream_response. Could this potentially lead to the LLM initiating new, unexpected tool calls even though the sequence was intended to stop? The non-streaming counterpart (lines 865-870) correctly omits tools for what appears to be the final response generation. For consistency and to ensure the sequence truly concludes, perhaps formatted_tools should be None here as well?
| final_response = self._process_stream_response( | |
| messages, | |
| temperature, | |
| start_time, | |
| formatted_tools=formatted_tools if formatted_tools else None, | |
| reasoning_steps=reasoning_steps | |
| ) | |
| final_response = self._process_stream_response( | |
| messages, | |
| temperature, | |
| start_time, | |
| formatted_tools=None, # Explicitly no tools for final response generation | |
| reasoning_steps=reasoning_steps | |
| ) |
PR Type
Enhancement, Tests
Description
Refactor agent tool execution for sequential thinking support
Migrate test workflows to use pytest and coverage reporting
Add dedicated coverage workflow for unit tests
Changes walkthrough 📝
agent.py
Refactor agent tool execution for sequential/sequentialthinking toolssrc/praisonai-agents/praisonaiagents/agent/agent.py
coverage.yml
Add dedicated coverage workflow with Codecov upload.github/workflows/coverage.yml
test-comprehensive.yml
Use pytest and upload coverage for comprehensive tests.github/workflows/test-comprehensive.yml
reporting
test-core.yml
Add Codecov upload and improve coverage for core tests.github/workflows/test-core.yml
unittest.yml
Use pytest and upload coverage for quick validation tests.github/workflows/unittest.yml
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