@@ -138,6 +138,8 @@ This will display:
 | `other_backend_kwargs` | `list` | `None` | Configs for additional backends |
 | `logger` | `RLMLogger` | `None` | Logger for trajectory tracking |
 | `verbose` | `bool` | `False` | Enable console output |
+| `custom_tools` | `dict` | `None` | Custom functions/data available in REPL |
+| `custom_sub_tools` | `dict` | `None` | Tools for sub-agents (inherits from `custom_tools` if None) |
 
 ### The `completion()` Method
 
@@ -285,6 +287,112 @@ rlm = RLM(
 
 ---
 
+## Custom Tools
+
+You can provide custom functions and data that the RLM can use in its REPL environment. This allows you to give the model access to domain-specific tools, APIs, or helper functions.
+
+### Basic Usage
+
+```python
+def fetch_weather(city: str) -> str:
+    """Fetch weather data for a city."""
+    # Your API call here
+    return f"Weather in {city}: Sunny, 72°F"
+
+def calculate_shipping(weight: float, distance: float) -> float:
+    """Calculate shipping cost."""
+    return weight * 0.5 + distance * 0.1
+
+rlm = RLM(
+    backend="openai",
+    backend_kwargs={"model_name": "gpt-4o"},
+    custom_tools={
+        "fetch_weather": fetch_weather,
+        "calculate_shipping": calculate_shipping,
+        "API_KEY": "your-api-key",  # Non-callable values become variables
+    },
+)
+
+result = rlm.completion("What's the weather in Tokyo and calculate shipping for 10kg over 500km?")
+```
+
+Inside the REPL, the model can now call:
+```python
+weather = fetch_weather("Tokyo")
+cost = calculate_shipping(10, 500)
+```
+
+### Tool Descriptions
+
+You can provide descriptions for your tools that will be included in the system prompt, helping the model understand what each tool does:
+
+```python
+rlm = RLM(
+    backend="openai",
+    backend_kwargs={"model_name": "gpt-4o"},
+    custom_tools={
+        # Dict format: {"tool": callable_or_value, "description": "..."}
+        "fetch_weather": {"tool": fetch_weather, "description": "Fetch current weather data for a city name"},
+        "calculate_shipping": {"tool": calculate_shipping, "description": "Calculate shipping cost given weight (kg) and distance (km)"},
+        "API_KEY": {"tool": "your-api-key", "description": "API key for the weather service"},
+    },
+)
+```
+
+The descriptions are automatically added to the system prompt:
+```
+6. Custom tools and data available in the REPL:
+- `fetch_weather`: Fetch current weather data for a city name
+- `calculate_shipping`: Calculate shipping cost given weight (kg) and distance (km)
+- `API_KEY`: API key for the weather service
+```
+
+### Custom Tools for Sub-Agents
+
+By default, sub-agents (code using `llm_query()`) inherit the same custom tools. You can customize this:
+
+```python
+# Sub-agents get different tools
+rlm = RLM(
+    backend="openai",
+    backend_kwargs={"model_name": "gpt-4o"},
+    custom_tools={"main_tool": my_main_function},
+    custom_sub_tools={"sub_tool": my_sub_function},  # Different tools for sub-agents
+)
+
+# Sub-agents get no tools
+rlm = RLM(
+    backend="openai",
+    backend_kwargs={"model_name": "gpt-4o"},
+    custom_tools={"main_tool": my_main_function},
+    custom_sub_tools={},  # Empty dict = no tools for sub-agents
+)
+```
+
+### Isolated Environments (Modal, Daytona)
+
+For isolated environments, custom tools must be serializable. You can provide:
+
+1. **Code strings** - Python code that defines the function:
+```python
+custom_tools = {
+    "helper": '''
+def helper(x):
+    return x * 2
+''',
+}
+```
+
+2. **Serializable data** - JSON-compatible values (strings, numbers, dicts, lists):
+```python
+custom_tools = {
+    "CONFIG": {"api_url": "https://api.example.com", "timeout": 30},
+    "ALLOWED_CITIES": ["Tokyo", "London", "New York"],
+}
+```
+
+---
+
 ## Logging and Debugging
 
 ### Enable Logging
 
@@ -109,6 +109,10 @@ export default function EnvironmentsPage() {
                 <code key="4" className="text-sm font-semibold">FINAL_VAR(var_name)</code>, 
                 "Mark a variable as the final answer to return from the RLM"
               ],
+              [
+                <code key="5" className="text-sm font-semibold">custom_tools</code>, 
+                "Any custom functions or data you provide via the custom_tools parameter"
+              ],
             ]}
           />
         </div>
@@ -119,13 +123,60 @@ context = "Your input here"
 # Query a sub-LM
 result = llm_query("Summarize the context", model="gpt-5-mini")
 
+# Use a custom tool (if provided)
+data = fetch_data(context["url"])  # Custom function
+
 # Process the result
 summary = process(result)
 
 # Return final answer
 FINAL_VAR(summary)`} />
       </div>
 
+      <div className="my-12">
+        <h2 className="text-2xl font-bold mb-4">Custom Tools</h2>
+        
+        <p className="text-muted-foreground mb-6 leading-relaxed">
+          You can provide custom functions and data that the RLM can use in its REPL environment 
+          via the <code className="px-1.5 py-0.5 rounded bg-muted text-foreground text-sm font-semibold">custom_tools</code> parameter:
+        </p>
+
+        <CodeBlock code={`from rlm import RLM
+
+def fetch_weather(city: str) -> str:
+    """Fetch weather data for a city."""
+    return f"Weather in {city}: Sunny, 72°F"
+
+rlm = RLM(
+    backend="openai",
+    backend_kwargs={"model_name": "gpt-4o"},
+    custom_tools={
+        # Plain format (no description)
+        "fetch_weather": fetch_weather,
+        
+        # Dict format with description (recommended)
+        "calculate_tip": {"tool": lambda x: x * 0.2, "description": "Calculate 20% tip for a bill amount"},
+        "API_KEY": {"tool": "your-key", "description": "API key for external services"},
+    },
+)
+
+# The model can now call fetch_weather() in its REPL code`} />
+
+        <p className="text-muted-foreground mt-6 leading-relaxed">
+          <strong className="text-foreground">Tool descriptions:</strong> Use the dict format 
+          <code className="px-1.5 py-0.5 rounded bg-muted text-foreground text-sm font-semibold">{`{"tool": value, "description": "..."}`}</code> to 
+          provide descriptions that help the model understand what each tool does. Descriptions are automatically 
+          included in the system prompt.
+        </p>
+
+        <p className="text-muted-foreground mt-4 leading-relaxed">
+          <strong className="text-foreground">Sub-agent tools:</strong> By default, sub-agents (code using 
+          <code className="px-1.5 py-0.5 rounded bg-muted text-foreground text-sm font-semibold">llm_query()</code>) 
+          inherit the same custom tools. Use <code className="px-1.5 py-0.5 rounded bg-muted text-foreground text-sm font-semibold">custom_sub_tools</code> to 
+          provide different tools for sub-agents, or pass an empty dict to disable tools for sub-agents.
+        </p>
+      </div>
+
       <div className="my-12">
         <h2 className="text-2xl font-bold mb-4">Architecture</h2>