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- Incremented version number to 2.1.1 in `pyproject.toml`, `praisonai.rb`, and `uv.lock` to reflect recent changes. - Updated dependency version for `praisonaiagents` to 0.0.71 in `pyproject.toml` and `uv.lock`. - Modified Dockerfile and deployment scripts to install the updated version of `praisonai`. - Added new documentation files for various MCP integrations, including Anthropic, Gemini, Groq, Mistral, Ollama, OpenAI, and XAI. - Introduced example scripts for each MCP integration to facilitate user understanding and implementation. - Enhanced the LLM class to process tool results based on the original user query for improved response accuracy.
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Caution Review failedThe pull request is closed. WalkthroughThis pull request primarily upgrades the PraisonAI package version from 2.1.0 to 2.1.1 across various files. In addition to updating Dockerfile and deployment scripts, the change introduces several new MCP integration documentation files covering multiple AI models (Anthropic, Gemini, Groq, Mistral, Ollama, OpenAI, OpenRouter, and xAI). New example scripts and a stock price assistant agent implementation are also added. Minor adjustments in internal prompt handling and project configuration updates are included throughout the codebase. Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant A as Agent
participant L as LLM
participant T as MCP Tool
U->>A: Submit query (e.g. "What is the Stock Price of Apple?")
A->>L: Send initial prompt with query
L-->>A: Return response with tool call info
A->>T: Execute tool command based on response
T-->>A: Return execution result
A->>L: Request detailed answer with result context
L-->>A: Return final answer
A->>U: Deliver answer
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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
This pull request updates the PraisonAI framework to version 2.1.1. The changes include incrementing the version number in multiple files, updating the praisonaiagents dependency, modifying the Dockerfile and deployment scripts, adding new documentation files and example scripts for various MCP integrations (Anthropic, Gemini, Groq, Mistral, Ollama, OpenAI, and XAI), and enhancing the LLM class to process tool results based on the original user query. The goal is to reflect recent changes, improve user understanding, and enhance response accuracy.
Highlights
- Version Update: The core PraisonAI library and related files are updated to version 2.1.1.
- Dependency Update: The
praisonaiagentsdependency is updated to version 0.0.71. - MCP Integrations: New documentation and example scripts are added for Anthropic, Gemini, Groq, Mistral, Ollama, OpenAI, and XAI MCP integrations, providing users with quick start guides and feature overviews.
- LLM Enhancement: The LLM class is enhanced to process tool results based on the original user query, improving response accuracy.
Changelog
Click here to see the changelog
- docker/Dockerfile
- Updated
praisonaiversion to 2.1.1 in the Dockerfile.
- Updated
- docs/api/praisonai/deploy.html
- Updated
praisonaiversion to 2.1.1 in the deployment documentation.
- Updated
- docs/mcp/anthropic.mdx
- Added documentation for Anthropic MCP integration, including a quick start guide and features overview.
- docs/mcp/gemini.mdx
- Added documentation for Gemini MCP integration, including a quick start guide and features overview.
- docs/mcp/groq.mdx
- Added documentation for Groq MCP integration, including a quick start guide and features overview.
- docs/mcp/mistral.mdx
- Added documentation for Mistral MCP integration, including a quick start guide and features overview.
- docs/mcp/ollama-python.mdx
- Added documentation for Ollama Python MCP integration, including a quick start guide and features overview.
- docs/mcp/ollama.mdx
- Added documentation for Ollama MCP integration, including a quick start guide and features overview.
- docs/mcp/openai.mdx
- Added documentation for OpenAI MCP integration, including a quick start guide and features overview.
- docs/mcp/openrouter.mdx
- Added documentation for OpenRouter MCP integration, including a quick start guide and features overview.
- docs/mcp/xai.mdx
- Added documentation for xAI MCP integration, including a quick start guide and features overview.
- docs/mint.json
- Added new MCP documentation files to the documentation index.
- examples/mcp/ollama-python.py
- Updated example script for Ollama Python MCP integration to use stock price assistant.
- examples/mcp/openrouter-mcp.py
- Added example script for OpenRouter MCP integration.
- examples/mcp/xai-mcp.py
- Added example script for xAI MCP integration.
- praisonai.rb
- Updated
Praisonaiformula version to 2.1.1.
- Updated
- praisonai/deploy.py
- Updated
praisonaiversion to 2.1.1 in the deployment script.
- Updated
- pyproject.toml
- Updated
PraisonAIversion to 2.1.1. - Updated
praisonaiagentsdependency to version 0.0.71.
- Updated
- src/praisonai-agents/mcp-ollama-python.py
- Added example script for Ollama Python MCP integration to use stock price assistant.
- src/praisonai-agents/praisonaiagents/llm/llm.py
- Enhanced LLM class to process tool results based on the original user query for improved response accuracy.
- src/praisonai-agents/pyproject.toml
- Updated
praisonaiagentsversion to 0.0.71.
- Updated
- src/praisonai-agents/uv.lock
- Updated
praisonaiagentsversion to 0.0.71.
- Updated
- uv.lock
- Updated
praisonaiversion to 2.1.1. - Updated
praisonaiagentsversion to 0.0.71.
- Updated
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A version ascends,
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Code Review
This pull request updates the project to version 2.1.1, updates a dependency, modifies Dockerfile and deployment scripts, adds new documentation files for various MCP integrations, introduces example scripts, and enhances the LLM class. Overall, the changes seem well-organized and contribute to the project's functionality and usability.
Summary of Findings
- Missing API Key Environment Variables in Groq and XAI Integrations: The Groq and XAI MCP integration guides lack explicit instructions on setting the API key as an environment variable, unlike other integrations. This inconsistency could lead to confusion for users trying to set up these integrations.
- Inconsistent Dependency Installation Instructions: The OpenAI and OpenRouter MCP integration guides use
pip install praisonaiagents, while other guides usepip install "praisonaiagents[llm]". This inconsistency could lead to confusion for users installing dependencies. - Hardcoded Paths in Ollama Python Example: The Ollama Python example script contains hardcoded file paths, which need to be replaced by the user. While there is a comment noting this, it's a potential point of failure if users miss the comment or don't understand how to modify the paths correctly.
Merge Readiness
The pull request includes several enhancements and updates, including documentation and dependency updates. However, there are a few inconsistencies and potential issues that should be addressed before merging to ensure a smooth user experience. Specifically, the missing API key instructions for Groq and XAI, the inconsistent dependency installation instructions, and the hardcoded paths in the Ollama Python example should be reviewed and corrected. I am unable to directly approve this pull request, and other reviewers should review and approve this code before merging.
| instructions="""You help book apartments on Airbnb.""", | ||
| llm="groq/llama-3.2-90b-vision-preview", | ||
| tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt") | ||
| ) |
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The Groq API key is not being passed to the MCP server. This is inconsistent with other integrations. You should pass the Groq API key as an environment variable to the MCP server.
tools=MCP(
command="npx",
args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
env={"GROQ_API_KEY": os.environ.get("GROQ_API_KEY")}
)
| tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt") | ||
| ) |
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The XAI API key is not being passed to the MCP server. This is inconsistent with other integrations. You should pass the XAI API key as an environment variable to the MCP server.
tools=MCP(
command="npx",
args=["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"],
env={"XAI_API_KEY": os.environ.get("XAI_API_KEY")}
)
| # Create a prompt that asks the model to process the tool results based on original context | ||
| # Extract the original user query from messages |
There was a problem hiding this comment.
Consider extracting the prompt construction logic into a separate function for better readability and maintainability. This would also allow for easier testing and modification of the prompt.
| # Create a prompt that asks the model to process the tool results based on original context | |
| # Extract the original user query from messages | |
| # Extract the original user query from messages | |
| original_query = "" | |
| for msg in messages: | |
| if msg.get("role") == "user": | |
| original_query = msg.get("content", "") | |
| break |
| # Create a shorter follow-up prompt | ||
| follow_up_prompt = f"Results:\n{json.dumps(tool_result, indent=2)}\nProvide Answer to this Original Question based on the above results: '{original_query}'" |
There was a problem hiding this comment.
The follow-up prompt could be improved by including more context about the original task. This might help the model generate a more relevant and accurate response. Consider adding a brief summary of the user's goal or instructions to the prompt.
follow_up_prompt = f"Original Question: {original_query}\nResults:\n{json.dumps(tool_result, indent=2)}\nProvide Answer to this Original Question based on the above results: '{original_query}'"| # Create a prompt that asks the model to process the tool results based on original context | ||
| # Extract the original user query from messages |
There was a problem hiding this comment.
Consider extracting the prompt construction logic into a separate function for better readability and maintainability. This would also allow for easier testing and modification of the prompt.
# Extract the original user query from messages
original_query = ""
for msg in messages:
if msg.get("role") == "user":
original_query = msg.get("content", "")
break| # Create a shorter follow-up prompt | ||
| follow_up_prompt = f"Results:\n{json.dumps(tool_result, indent=2)}\nProvide Answer to this Original Question based on the above results: '{original_query}'" |
There was a problem hiding this comment.
The follow-up prompt could be improved by including more context about the original task. This might help the model generate a more relevant and accurate response. Consider adding a brief summary of the user's goal or instructions to the prompt.
follow_up_prompt = f"Original Question: {original_query}\nResults:\n{json.dumps(tool_result, indent=2)}\nProvide Answer to this Original Question based on the above results: '{original_query}'"| # NOTE: Python Path replace with yours: /Users/praison/miniconda3/envs/mcp/bin/python | ||
| # NOTE: app.py file path, replace it with yours: /Users/praison/stockprice/app.py |
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While the note is helpful, consider using os.path.expanduser('~') to make the path more portable across different user environments. Also, consider using os.path.join for constructing the path to ensure cross-platform compatibility.
| # NOTE: Python Path replace with yours: /Users/praison/miniconda3/envs/mcp/bin/python | |
| # NOTE: app.py file path, replace it with yours: /Users/praison/stockprice/app.py | |
| import os | |
| # NOTE: Python Path replace with yours: os.path.join(os.path.expanduser('~'), 'miniconda3', 'envs', 'mcp', 'bin', 'python') | |
| # NOTE: app.py file path, replace it with yours: os.path.join(os.path.expanduser('~'), 'stockprice', 'app.py') |
| tools=MCP("/Users/praison/miniconda3/envs/mcp/bin/python /Users/praison/stockprice/app.py") | ||
| ) |
There was a problem hiding this comment.
Consider using os.path.expanduser('~') to make the path more portable across different user environments. Also, consider using os.path.join for constructing the path to ensure cross-platform compatibility.
import os
tools=MCP(os.path.join(os.path.expanduser('~'), 'miniconda3', 'envs', 'mcp', 'bin', 'python'), os.path.join(os.path.expanduser('~'), 'stockprice', 'app.py'))| ```zsh | ||
| pip install praisonaiagents |
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The installation instructions are inconsistent with other MCP integration guides. Most guides use pip install "praisonaiagents[llm]", while this one uses pip install praisonaiagents. Consider standardizing the installation instructions across all MCP guides.
pip install "praisonaiagents[llm]"
| pip install praisonaiagents | ||
| ``` |
There was a problem hiding this comment.
The installation instructions are inconsistent with other MCP integration guides. Most guides use pip install "praisonaiagents[llm]", while this one uses pip install praisonaiagents. Consider standardizing the installation instructions across all MCP guides.
pip install "praisonaiagents[llm]"
Update to version 2.1.1
1 participant
pyproject.toml,praisonai.rb, anduv.lockto reflect recent changes.praisonaiagentsto 0.0.71 inpyproject.tomlanduv.lock.praisonai.Summary by CodeRabbit
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