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feat(python_repl): add configurable security modes with file path restrictions #193

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feat(python_repl): add configurable security modes with file path res…
gonzalo123 Aug 3, 2025
3a33604
fix: resolve remaining conflicts and updates
gonzalo123 Aug 30, 2025
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140 changes: 1 addition & 139 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,6 @@ Strands Agents Tools is a community-driven project that provides a powerful set
- 📁 **File Operations** - Read, write, and edit files with syntax highlighting and intelligent modifications
- 🖥️ **Shell Integration** - Execute and interact with shell commands securely
- 🧠 **Memory** - Store user and agent memories across agent runs to provide personalized experiences with both Mem0 and Amazon Bedrock Knowledge Bases
- 🕸️ **Web Infrastructure** - Perform web searches, extract page content, and crawl websites with Tavily and Exa-powered tools
- 🌐 **HTTP Client** - Make API requests with comprehensive authentication support
- 💬 **Slack Client** - Real-time Slack events, message processing, and Slack API access
- 🐍 **Python Execution** - Run Python code snippets with state persistence, user confirmation for code execution, and safety features
Expand Down Expand Up @@ -104,12 +103,6 @@ Below is a comprehensive table of all available tools, how to use them with an a
| editor | `agent.tool.editor(command="view", path="path/to/file.py")` | Advanced file operations like syntax highlighting, pattern replacement, and multi-file edits |
| shell* | `agent.tool.shell(command="ls -la")` | Executing shell commands, interacting with the operating system, running scripts |
| http_request | `agent.tool.http_request(method="GET", url="https://api.example.com/data")` | Making API calls, fetching web data, sending data to external services |
| tavily_search | `agent.tool.tavily_search(query="What is artificial intelligence?", search_depth="advanced")` | Real-time web search optimized for AI agents with a variety of custom parameters |
| tavily_extract | `agent.tool.tavily_extract(urls=["www.tavily.com"], extract_depth="advanced")` | Extract clean, structured content from web pages with advanced processing and noise removal |
| tavily_crawl | `agent.tool.tavily_crawl(url="www.tavily.com", max_depth=2, instructions="Find API docs")` | Crawl websites intelligently starting from a base URL with filtering and extraction |
| tavily_map | `agent.tool.tavily_map(url="www.tavily.com", max_depth=2, instructions="Find all pages")` | Map website structure and discover URLs starting from a base URL without content extraction |
| exa_search | `agent.tool.exa_search(query="Best project management tools", text=True)` | Intelligent web search with auto mode (default) that combines neural and keyword search for optimal results |
| exa_get_contents | `agent.tool.exa_get_contents(urls=["https://example.com/article"], text=True, summary={"query": "key points"})` | Extract full content and summaries from specific URLs with live crawling fallback |
| python_repl* | `agent.tool.python_repl(code="import pandas as pd\ndf = pd.read_csv('data.csv')\nprint(df.head())")` | Running Python code snippets, data analysis, executing complex logic with user confirmation for security |
| calculator | `agent.tool.calculator(expression="2 * sin(pi/4) + log(e**2)")` | Performing mathematical operations, symbolic math, equation solving |
| code_interpreter | `code_interpreter = AgentCoreCodeInterpreter(region="us-west-2"); agent = Agent(tools=[code_interpreter.code_interpreter])` | Execute code in isolated sandbox environments with multi-language support (Python, JavaScript, TypeScript), persistent sessions, and file operations |
Expand Down Expand Up @@ -275,114 +268,6 @@ response = agent.tool.http_request(
)
```

### Tavily Search, Extract, Crawl, and Map

```python
from strands import Agent
from strands_tools.tavily import (
tavily_search, tavily_extract, tavily_crawl, tavily_map
)

# For async usage, call the corresponding *_async function with await.
# Synchronous usage
agent = Agent(tools=[tavily_search, tavily_extract, tavily_crawl, tavily_map])

# Real-time web search
result = agent.tool.tavily_search(
query="Latest developments in renewable energy",
search_depth="advanced",
topic="news",
max_results=10,
include_raw_content=True
)

# Extract content from multiple URLs
result = agent.tool.tavily_extract(
urls=["www.tavily.com", "www.apple.com"],
extract_depth="advanced",
format="markdown"
)

# Advanced crawl with instructions and filtering
result = agent.tool.tavily_crawl(
url="www.tavily.com",
max_depth=2,
limit=50,
instructions="Find all API documentation and developer guides",
extract_depth="advanced",
include_images=True
)

# Basic website mapping
result = agent.tool.tavily_map(url="www.tavily.com")

```

### Exa Search and Contents

```python
from strands import Agent
from strands_tools.exa import exa_search, exa_get_contents

agent = Agent(tools=[exa_search, exa_get_contents])

# Basic search (auto mode is default and recommended)
result = agent.tool.exa_search(
query="Best project management software",
text=True
)

# Company-specific search when needed
result = agent.tool.exa_search(
query="Anthropic AI safety research",
category="company",
include_domains=["anthropic.com"],
num_results=5,
summary={"query": "key research areas and findings"}
)

# News search with date filtering
result = agent.tool.exa_search(
query="AI regulation policy updates",
category="news",
start_published_date="2024-01-01T00:00:00.000Z",
text=True
)

# Get detailed content from specific URLs
result = agent.tool.exa_get_contents(
urls=[
"https://example.com/blog-post",
"https://github.com/microsoft/semantic-kernel"
],
text={"maxCharacters": 5000, "includeHtmlTags": False},
summary={
"query": "main points and practical applications"
},
subpages=2,
extras={"links": 5, "imageLinks": 2}
)

# Structured summary with JSON schema
result = agent.tool.exa_get_contents(
urls=["https://example.com/article"],
summary={
"query": "main findings and recommendations",
"schema": {
"type": "object",
"properties": {
"main_points": {"type": "string", "description": "Key points from the article"},
"recommendations": {"type": "string", "description": "Suggested actions or advice"},
"conclusion": {"type": "string", "description": "Overall conclusion"},
"relevance": {"type": "string", "description": "Why this matters"}
},
"required": ["main_points", "conclusion"]
}
}
)

```

### Python Code Execution

*Note: `python_repl` does not work on Windows.*
Expand Down Expand Up @@ -797,20 +682,6 @@ These variables affect multiple tools:
|----------------------|-------------|---------|
| MAX_SLEEP_SECONDS | Maximum allowed sleep duration in seconds | 300 |

#### Tavily Search, Extract, Crawl, and Map Tools

| Environment Variable | Description | Default |
|----------------------|-------------|---------|
| TAVILY_API_KEY | Tavily API key (required for all Tavily functionality) | None |
- Visit https://www.tavily.com/ to create a free account and API key.

#### Exa Search and Contents Tools

| Environment Variable | Description | Default |
|----------------------|-------------|---------|
| EXA_API_KEY | Exa API key (required for all Exa functionality) | None |
- Visit https://dashboard.exa.ai/api-keys to create a free account and API key.

#### Mem0 Memory Tool

The Mem0 Memory Tool supports three different backend configurations:
Expand All @@ -833,19 +704,12 @@ The Mem0 Memory Tool supports three different backend configurations:
| OPENSEARCH_HOST | OpenSearch Host URL | None | OpenSearch |
| AWS_REGION | AWS Region for OpenSearch | us-west-2 | OpenSearch |
| DEV | Enable development mode (bypasses confirmations) | false | All modes |
| MEM0_LLM_PROVIDER | LLM provider for memory processing | aws_bedrock | All modes |
| MEM0_LLM_MODEL | LLM model for memory processing | anthropic.claude-3-5-haiku-20241022-v1:0 | All modes |
| MEM0_LLM_TEMPERATURE | LLM temperature (0.0-2.0) | 0.1 | All modes |
| MEM0_LLM_MAX_TOKENS | LLM maximum tokens | 2000 | All modes |
| MEM0_EMBEDDER_PROVIDER | Embedder provider for vector embeddings | aws_bedrock | All modes |
| MEM0_EMBEDDER_MODEL | Embedder model for vector embeddings | amazon.titan-embed-text-v2:0 | All modes |


**Note**:
- If `MEM0_API_KEY` is set, the tool will use the Mem0 Platform
- If `OPENSEARCH_HOST` is set, the tool will use OpenSearch
- If neither is set, the tool will default to FAISS (requires `faiss-cpu` package)
- LLM configuration applies to all backend modes and allows customization of the language model used for memory processing

#### Memory Tool

| Environment Variable | Description | Default |
Expand All @@ -866,8 +730,6 @@ The Mem0 Memory Tool supports three different backend configurations:
| Environment Variable | Description | Default |
|----------------------|-------------|---------|
| PYTHON_REPL_BINARY_MAX_LEN | Maximum length for binary content before truncation | 100 |
| PYTHON_REPL_INTERACTIVE | Whether to enable interactive PTY mode | None |
| PYTHON_REPL_RESET_STATE | Whether to reset the REPL state before execution | None |

#### Shell Tool

Expand Down
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