Buckets:

|
download
raw
4.47 kB
# Echo Environment
A simple test environment that echoes back messages. Perfect for testing the env APIs as well as demonstrating environment usage patterns.
## Quick Start
The simplest way to use the Echo environment is through the `EchoEnv` class. The client is **async by default**:
```python
import asyncio
from echo_env import CallToolAction, EchoEnv
async def main():
# Create environment from Docker image
client = await EchoEnv.from_docker_image("echo-env:latest")
async with client:
await client.reset()
# Send multiple messages
messages = ["Hello, World!", "Testing echo", "Final message"]
for msg in messages:
result = await client.step(
CallToolAction(
tool_name="echo_message",
arguments={"message": msg},
)
)
print(f"Sent: '{msg}'")
print(f" → Echoed: '{result.observation.result}'")
print(f" → Reward: {result.reward}")
asyncio.run(main())
```
For **synchronous usage**, use the `.sync()` wrapper:
```python
from echo_env import CallToolAction, EchoEnv
with EchoEnv(base_url="http://localhost:8000").sync() as client:
client.reset()
result = client.step(
CallToolAction(
tool_name="echo_message",
arguments={"message": "Hello!"},
)
)
print(result.observation.result)
```
The `EchoEnv.from_docker_image()` method handles:
- Starting the Docker container
- Waiting for the server to be ready
- Connecting to the environment
- Container cleanup when the context manager exits
## Building the Docker Image
Before using the environment, you need to build the Docker image:
```bash
# From project root
docker build -t echo-env:latest -f envs/echo_env/server/Dockerfile .
```
## Environment Details
### Tools
- `echo_message(message)` - Echo the provided message
- `echo_with_length(message)` - Echo the message and include its length
### Observation
**CallToolObservation**: Contains the tool result and metadata
- `result` - The tool return value
- `reward` (float) - Reward returned by the environment
- `done` (bool) - Always `False` for the echo environment
- `metadata` (dict) - Additional info like step count
### Reward
The echo environment returns `0.0` reward for tool calls. It is intended as a
minimal MCP integration example rather than a reward-shaping reference.
## Advanced Usage
### Connecting to an Existing Server
If you already have an Echo environment server running, you can connect directly:
```python
from echo_env import CallToolAction, EchoEnv
# Async usage
async with EchoEnv(base_url="http://localhost:8000") as client:
await client.reset()
result = await client.step(
CallToolAction(
tool_name="echo_message",
arguments={"message": "Hello!"},
)
)
# Sync usage
with EchoEnv(base_url="http://localhost:8000").sync() as client:
client.reset()
result = client.step(
CallToolAction(
tool_name="echo_message",
arguments={"message": "Hello!"},
)
)
```
Note: When connecting to an existing server, closing the client will NOT stop the server.
## Development & Testing
### Direct Environment Testing
Test the environment logic directly without starting the HTTP server:
```bash
# From the server directory
python3 envs/echo_env/server/test_echo_env.py
```
This verifies that:
- Environment resets correctly
- Step executes actions properly
- State tracking works
- Rewards are calculated correctly
### Running the Full Example
Run the complete example that demonstrates the full workflow:
```bash
python3 examples/local_echo_env.py
```
This example shows:
- Creating an environment from a Docker image
- Resetting and stepping through the environment
- Automatic cleanup with `close()`
## Project Structure
```
echo_env/
├── __init__.py # Module exports
├── README.md # This file
├── client.py # EchoEnv client implementation
├── models.py # Action and Observation models
└── server/
├── __init__.py # Server module exports
├── echo_environment.py # Core environment logic
├── app.py # FastAPI application
├── test_echo_env.py # Direct environment tests
└── Dockerfile # Container image definition
```

Xet Storage Details

Size:
4.47 kB
·
Xet hash:
77f1ff3678bb9cd005e13117fe57c70f868578efbf6ba35b8a56f4232d36832f

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.