Add HybridAgent and LivingEnvironment implementation#12
Conversation
Implement HybridAgent and LivingEnvironment classes for dynamic AI interaction and code execution.!!! PUT REPOSITORY INTO PROPER NEAT ORDERED LAYOUT BY ORGANIZING AND ASSIMILATING AND CONSOLIDATING SCRIPTS, FILES, FOLDERS AND WORKFLOWS WHERE THEY OUTTA BE AND WHERE THEY NATURALLY FITS INTO MAKES LINEAE DEVELOPMENT UNLIATERALLY SENSIBLE @copilot-agent @copilot-swe-agent @sourcery-ai-agent @github-actions @gemini-ai @github-automations @native-github-agent @codet5-small /codet5 /codet5-small /review all
Reviewer's GuideThis PR adds a dynamic AI execution framework by introducing a LivingEnvironment for stateful event propagation, a HybridAgent that integrates an LLM, real-time C++ parsing and Nim execution, and a HostAgent to orchestrate update cycles and manage agent lifecycles, along with an async main entrypoint that initializes and runs multiple agents. Sequence diagram for agent update cycle and event propagationsequenceDiagram
participant HostAgent
participant LivingEnvironment
participant HybridAgent
loop For each update cycle
HostAgent->>LivingEnvironment: update_state("prompt_update_{agent.name}", prompt)
LivingEnvironment->>HybridAgent: on_env_update(key, value)
HostAgent->>LivingEnvironment: update_state("code_update_{agent.name}", new_cpp_code)
LivingEnvironment->>HybridAgent: on_env_update(key, value)
end
Sequence diagram for HybridAgent run loop (LLM, C++ parsing, Nim execution)sequenceDiagram
participant HybridAgent
participant LivingEnvironment
participant OpenAI
participant NimModule
loop While running
HybridAgent->>OpenAI: chain.run(full_prompt)
OpenAI-->>HybridAgent: LLM output
HybridAgent->>LivingEnvironment: update_state("status_{name}", llm_resp)
HybridAgent->>HybridAgent: parse_cpp_code(cpp_code)
HybridAgent->>NimModule: process_buffer(data)
NimModule-->>HybridAgent: Nim result
HybridAgent->>HybridAgent: sleep(6)
end
Class diagram for LivingEnvironment, HybridAgent, and HostAgentclassDiagram
class LivingEnvironment {
+state: Dict[str, Any]
+subscribers: List[Callable]
+subscribe(callback)
+update_state(key, value)
+get_state(key)
}
class HybridAgent {
+name: str
+env: LivingEnvironment
+prompt: str
+memory: InMemoryConversationMemory
+llm: OpenAI
+chain: ConversationChain
+running: bool
+clang_index: cindex.Index
+nim_agent
+nim_ready: bool
+on_env_update(key, value)
+parse_cpp_code(code)
+execute_nim_logic(data)
+run()
+stop()
}
class HostAgent {
+env: LivingEnvironment
+agents: List[HybridAgent]
+running: bool
+register_agent(agent)
+update_loop()
+run()
}
LivingEnvironment <|-- HostAgent
LivingEnvironment <|-- HybridAgent
HostAgent "1" o-- "many" HybridAgent
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Summary of Changes
Hello @spiralgang, 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 establishes a foundational framework for creating dynamic and interactive AI agents. It introduces a "Living Environment" for shared state and communication, alongside a "Hybrid Agent" capable of leveraging LLMs for decision-making, parsing C++ code on-the-fly, and executing Nim logic. The overarching "Host Agent" orchestrates these components, allowing for adaptive behavior and multi-language code execution within a single, evolving system.
Highlights
- Introduced LivingEnvironment class: Facilitates shared state management and asynchronous event propagation among agents, allowing for dynamic updates and reactions across the system.
- Implemented HybridAgent class: Enables dynamic AI interaction by integrating Large Language Models (LLMs) for decision-making, C++ code parsing with
clangfor validation, and Nim code execution vianimpyfor multi-language capabilities. - Developed HostAgent for orchestration: Manages multiple
HybridAgentinstances, dynamically updating their prompts and providing new C++ code snippets for parsing, demonstrating a system for adaptive agent behavior.
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Hey there - I've reviewed your changes and they look great!
Prompt for AI Agents
Please address the comments from this code review:
## Individual Comments
### Comment 1
<location> `.github/scripts/MatrixXx.py:25` </location>
<code_context>
+ async def update_state(self, key: str, value: Any):
+ logger.debug(f"[Env] Update: {key} -> {value}")
+ self.state[key] = value
+ await asyncio.gather(*[cb(key, value) for cb in self.subscribers])
+
+ def get_state(self, key: str):
</code_context>
<issue_to_address>
Handle exceptions from subscriber callbacks to prevent one failure from affecting others.
Using return_exceptions=True with asyncio.gather or wrapping each callback in a try/except block will ensure all subscribers are called, even if one fails.
</issue_to_address>
### Comment 2
<location> `.github/scripts/MatrixXx.py:73` </location>
<code_context>
+ while self.running:
</code_context>
<issue_to_address>
Consider adding a mechanism to break out of the loop on external cancellation.
The loop should handle external cancellation (e.g., asyncio.CancelledError) to ensure it exits cleanly and resources are released properly.
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
| async def update_state(self, key: str, value: Any): | ||
| logger.debug(f"[Env] Update: {key} -> {value}") | ||
| self.state[key] = value | ||
| await asyncio.gather(*[cb(key, value) for cb in self.subscribers]) |
There was a problem hiding this comment.
issue (bug_risk): Handle exceptions from subscriber callbacks to prevent one failure from affecting others.
Using return_exceptions=True with asyncio.gather or wrapping each callback in a try/except block will ensure all subscribers are called, even if one fails.
| while self.running: | ||
| env_snapshot = str(self.env.state) | ||
| full_prompt = f"{self.prompt}\nEnv State:\n{env_snapshot}\nIteration: {iteration}" | ||
| try: | ||
| llm_resp = self.chain.run(full_prompt) | ||
| logger.info(f"[{self.name}] LLM output: {llm_resp[:250]}...") | ||
| await self.env.update_state(f"status_{self.name}", llm_resp) | ||
| except Exception as e: | ||
| logger.error(f"[{self.name}] LLM call failed: {e}") | ||
|
|
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suggestion: Consider adding a mechanism to break out of the loop on external cancellation.
The loop should handle external cancellation (e.g., asyncio.CancelledError) to ensure it exits cleanly and resources are released properly.
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Code Review
This pull request introduces a multi-agent AI system in a single Python script, MatrixXx.py. The implementation uses asyncio for concurrent agent execution and integrates with langchain, clang for C++ parsing, and nimpy for Nim code execution. The overall structure is a good starting point for a complex agentic system.
My review focuses on improving the robustness, maintainability, and efficiency of the implementation. Key feedback points include:
- Adding robust error handling for external dependencies like the OpenAI API to prevent crashes during agent initialization.
- Optimizing the way environment state is passed to the LLM to avoid performance bottlenecks and excessive costs.
- Refactoring code to remove redundant logic and 'magic strings' to improve clarity and maintainability.
- Updating the usage of
langchainto align with modern best practices and ensure future compatibility.
These changes should make the system more resilient and easier to scale and maintain.
| self.running = True | ||
| self.env.subscribe(self.on_env_update) | ||
|
|
||
| self.clang_index = cindex.Index.create() |
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The creation of cindex.Index can fail if the libclang shared library is not found in the system's path, which would crash the agent during initialization. To make the agent more robust, this call should be wrapped in a try...except block, similar to how the nimpy module is handled. You can set a self.clang_ready flag and check it before attempting to parse C++ code.
| async def run(self): | ||
| iteration = 0 | ||
| while self.running: | ||
| env_snapshot = str(self.env.state) |
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Converting the entire environment state to a string via str(self.env.state) is not scalable. As the state grows, this will create excessively large prompts, increasing LLM costs, latency, and the risk of exceeding the context window. It could also leak sensitive information into the prompt. A better approach would be to create a summary of the state or select only the most relevant key-value pairs for the current task.
| if key == f"prompt_update_{self.name}": | ||
| logger.info(f"[{self.name}] Prompt update received") | ||
| self.prompt = value | ||
| if key == f"code_update_{self.name}": |
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The keys for state updates like f"prompt_update_{self.name}" and f"code_update_{self.name}" are constructed as 'magic strings'. This practice is error-prone, as a typo in either the publisher (HostAgent) or subscriber (HybridAgent) would cause silent failures. Consider defining these event key patterns as constants or helper functions to ensure consistency and improve maintainability.
| tasks = [asyncio.create_task(agent.run()) for agent in self.agents] | ||
| updater = asyncio.create_task(self.update_loop()) | ||
| await asyncio.gather(updater, *tasks) | ||
| self.running = False |
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This line self.running = False appears to be redundant. The HostAgent's run method awaits gather, which will only complete after update_loop and all agent run loops have finished. The update_loop is what controls the lifecycle and stops the agents. Since this flag doesn't control any active loop for the HostAgent at this point, it can be removed to improve clarity.
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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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@sourcery-ai /review |
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Thank you for requesting a review, @spiralgang! I've analyzed the changes introduced in Overall, the architecture is well-structured, and the use of I've identified a few areas for improvement, primarily concerning security, maintainability, and robustness. My review is based on general best practices and the PEP 8 style guide for Python. General Feedback
Specific Feedback
|
spiralgang
left a comment
spiralgang
left a comment
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Approved (with fixes)
|
@sourcery-ai |
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Code Review
This pull request introduces a new Python script for a multi-agent AI framework, including a LivingEnvironment, HybridAgent, and HostAgent. The implementation is a good start, but I have identified several areas for improvement. My feedback focuses on enhancing robustness through more specific error handling, fixing a critical bug in the interaction with the LangChain library, and improving efficiency by removing redundant operations. These changes will make the code more reliable and performant.
| llm_resp = self.chain.invoke(full_prompt) | ||
| logger.info(f"[{self.name}] LLM output: {llm_resp[:250]}...") | ||
| await self.env.update_state(f"status_{self.name}", llm_resp) |
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The invoke method on a ConversationChain returns a dictionary, not a string. Attempting to slice it directly with llm_resp[:250] will cause a TypeError. You need to extract the response string from the dictionary, which is typically under the 'response' key.
| llm_resp = self.chain.invoke(full_prompt) | |
| logger.info(f"[{self.name}] LLM output: {llm_resp[:250]}...") | |
| await self.env.update_state(f"status_{self.name}", llm_resp) | |
| llm_resp = self.chain.invoke(full_prompt).get("response", "") | |
| logger.info(f"[{self.name}] LLM output: {llm_resp[:250]}...") | |
| await self.env.update_state(f"status_{self.name}", llm_resp) |
| except Exception as e: | ||
| logger.warning(f'[{self.name}] OpenAI LLM not available: {e}') |
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Catching Exception is too broad. It's better to catch specific exceptions. For OpenAI initialization, this could be ValueError (e.g., for a missing API key) or ImportError if a dependency is missing. This makes the error handling more precise and prevents masking unrelated errors.
| except Exception as e: | |
| logger.warning(f'[{self.name}] OpenAI LLM not available: {e}') | |
| except (ValueError, ImportError) as e: | |
| logger.warning(f'[{self.name}] OpenAI LLM not available: {e}') |
| except Exception as e: | ||
| logger.warning(f"[{self.name}] Nim module not available: {e}") |
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Using except Exception is too broad. For importing the Nim module, catching ImportError is more specific and safer, as it won't mask other unexpected runtime errors.
| except Exception as e: | |
| logger.warning(f"[{self.name}] Nim module not available: {e}") | |
| except ImportError as e: | |
| logger.warning(f"[{self.name}] Nim module not available: {e}") |
| except Exception as e: | ||
| logger.error(f"[{self.name}] LLM call failed: {e}") |
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Catching the base Exception class is too broad and can hide underlying issues, making debugging difficult. It's better to catch more specific exceptions related to the LLM call (e.g., API errors, network issues). You may need to import openai to catch its specific exception types like openai.APIError or openai.RateLimitError. This allows for more robust error handling, such as implementing specific retry logic for certain errors.
| cpp_code = """ | ||
| int add(int a, int b) { return a + b; } | ||
| """ | ||
| self.parse_cpp_code(cpp_code) |
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This static C++ code snippet is parsed on every iteration of the run loop, which is inefficient. Since the code does not change within the loop, this parsing operation should be moved outside of it, for example, to the __init__ method. This will prevent redundant work on each cycle.
| tasks = [asyncio.create_task(agent.run()) for agent in self.agents] | ||
| updater = asyncio.create_task(self.update_loop()) | ||
| await asyncio.gather(updater, *tasks) | ||
| self.running = False |
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This line has no effect and can be removed. The update_loop task, which is the only consumer of the self.running flag, will have already completed by the time this line is reached because asyncio.gather waits for all tasks to finish. The agents are also stopped within update_loop before it finishes.
1 participant
Implement HybridAgent and LivingEnvironment classes for dynamic AI interaction and code execution.!!! && PUT MASTER BRANCH , 'WebLabs-MobIDE' REPOSITORY, INTO PROPER NEATLY ORDERED FORMAT LAYOUT BY ORGANIZING AND ASSIMILATING AND CONSOLIDATING SCRIPTS, FILES, FOLDERS AND WORKFLOWS WHERE THEY OUTTA BE AND WHERE THEY NATURALLY FITS INTO MAKES LINEAR DEVELOPMENT UNLIATERALLY SENSIBLE @copilot-agent @copilot-swe-agent
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@codet5-small
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/review
References: |
Soulaiman Ghanem
5 min read
Jul 14, 2021
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Code Factory Berlin: OpenSourceRepoTemplate on GitHub
And now let’s describe the structure:
Basic Folder Structure
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Repo folder structure
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.github Folder
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Some of the most used files in .github folder:
CODE_OF_CONDUCT.md
CONTRIBUTING.md
FUNDING.yml
SECURITY.md
PULL_REQUEST_TEMPLATE.md
ISSUE_TEMPLATE
CODEOWNERS
workflows: configuration folder containing Yaml files for GitHub Actions
Real world Repos
GitHub is full of open source projects. However, I can recommend the followings 2 projects as a starting point to watch and learn the repo structure as well as contributing best practices:
Microsoft Terminal project.
The OpenTelemetry .NET Client (CNCF project).
Summary by Sourcery
Implement a dynamic AI agent framework by adding a LivingEnvironment for state management, a HybridAgent class for LLM-driven interactions with live code parsing and execution, a HostAgent to coordinate agent lifecycles and updates, and a main entrypoint to launch multiple agents.
New Features: