How to Use AutoGen with Code Interpreter (Execute Python)

The ability to write and run code in the same conversation is what separates AutoGen from basic chatbot wrappers. When an agent can test its own output — run the script, see the error, fix it, run again — you get a feedback loop that produces working code instead of plausible-looking code that fails on first run.

AutoGen's code execution system gives you two main paths: local execution for development speed, and Docker execution for production safety. Knowing which to use, how to configure each, and how the self-correction loop works will save you hours of debugging.

For foundational context on agent architectures before diving into code execution specifics, AI agents explained is the right starting point.

How AutoGen Code Execution Works

AutoGen's code execution follows a specific flow:

AssistantAgent generates a response containing a code block (usually marked with ```python)
UserProxyAgent (or CodeExecutorAgent) detects the code block
The executor runs the code and captures stdout, stderr, and return code
The output is sent back to AssistantAgent as the next message
If the code failed, AssistantAgent analyzes the error and generates a fix
The loop repeats until success or max_consecutive_auto_reply is hit

This automatic error-correction loop is the core of what makes code agents useful.

Setting Up LocalCommandLineCodeExecutor

The local executor runs Python directly on your machine. Fast to set up, ideal for development.

import autogen
from autogen.coding import LocalCommandLineCodeExecutor
from pathlib import Path

# Create a working directory for generated files
work_dir = Path("coding_workspace")
work_dir.mkdir(exist_ok=True)

# Configure the local executor
executor = LocalCommandLineCodeExecutor(
    timeout=60,          # seconds before killing execution
    work_dir=work_dir,   # where scripts are saved and run
)

# Create the code executor agent
code_executor_agent = autogen.ConversableAgent(
    name="CodeExecutor",
    llm_config=False,    # no LLM needed for execution
    code_execution_config={"executor": executor},
    human_input_mode="NEVER",
)

# Create the assistant that writes code
llm_config = {
    "config_list": [{"model": "gpt-4o", "api_key": "YOUR_KEY"}],
    "cache_seed": 42,
}

code_writer = autogen.ConversableAgent(
    name="CodeWriter",
    system_message="""You are a Python expert. Write clean, working Python code.
    Always include error handling. Return results by printing them.""",
    llm_config=llm_config,
    code_execution_config=False,  # writer doesn't execute
)

# Run the coding conversation
result = code_executor_agent.initiate_chat(
    code_writer,
    message="Write Python code to fetch the current Bitcoin price from CoinGecko API and print it.",
    max_turns=5,
)

The code_executor_agent has llm_config=False because it does not need an LLM — it just runs code. The code_writer has code_execution_config=False because it only generates code, never runs it. Keeping these roles separate is cleaner and cheaper.

Using DockerCommandLineCodeExecutor

For production or any scenario involving untrusted code, Docker isolation is essential.

import autogen
from autogen.coding import DockerCommandLineCodeExecutor
from pathlib import Path

work_dir = Path("docker_workspace")
work_dir.mkdir(exist_ok=True)

# Docker executor configuration
docker_executor = DockerCommandLineCodeExecutor(
    image="python:3.11-slim",   # base Docker image
    timeout=120,                  # longer timeout for complex tasks
    work_dir=work_dir,
    bind_dir=work_dir,           # mount local dir into container
    auto_remove=True,             # remove container after execution
    stop_container=True,          # stop container when done
)

code_executor_agent = autogen.ConversableAgent(
    name="SecureCodeExecutor",
    llm_config=False,
    code_execution_config={"executor": docker_executor},
    human_input_mode="NEVER",
)

The Docker executor spins up a fresh container for each code execution, runs the code inside it, captures output, and removes the container. Your host machine is never touched.

Pre-installing packages in the Docker image:

If your workflows regularly use specific packages, build a custom image instead of relying on pip installs during execution:

# Dockerfile
FROM python:3.11-slim

RUN pip install --no-cache-dir \
    pandas \
    numpy \
    requests \
    matplotlib \
    scikit-learn \
    openai

WORKDIR /workspace

docker build -t autogen-executor:latest .

docker_executor = DockerCommandLineCodeExecutor(
    image="autogen-executor:latest",
    timeout=120,
    work_dir=work_dir,
)

Pre-installing packages dramatically speeds up execution and avoids network calls during agent runs.

The Code Execution Config (Legacy API)

AutoGen also supports a simpler code_execution_config dictionary on UserProxyAgent, which many tutorials still use:

user_proxy = autogen.UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    code_execution_config={
        "work_dir": "workspace",
        "use_docker": False,           # True for Docker isolation
        "timeout": 60,
        "last_n_messages": 3,          # how many messages to look back for code
    },
    max_consecutive_auto_reply=10,
)

This is the older API. The newer LocalCommandLineCodeExecutor / DockerCommandLineCodeExecutor classes give you more control and are the recommended path for new projects.

Building a Data Analysis Agent

Here is a complete example — an agent that analyzes CSV data using code execution:

import autogen
from autogen.coding import LocalCommandLineCodeExecutor
from pathlib import Path
import pandas as pd

# Create sample data
sample_data = pd.DataFrame({
    "month": ["Jan", "Feb", "Mar", "Apr", "May"],
    "revenue": [45000, 52000, 48000, 61000, 58000],
    "expenses": [32000, 35000, 31000, 38000, 40000],
})
sample_data.to_csv("coding_workspace/sales_data.csv", index=False)

# Setup
work_dir = Path("coding_workspace")
work_dir.mkdir(exist_ok=True)

executor = LocalCommandLineCodeExecutor(
    timeout=30,
    work_dir=work_dir,
)

llm_config = {
    "config_list": [{"model": "gpt-4o", "api_key": "YOUR_KEY"}],
}

code_executor = autogen.ConversableAgent(
    name="Executor",
    llm_config=False,
    code_execution_config={"executor": executor},
    human_input_mode="NEVER",
    is_termination_msg=lambda msg: "ANALYSIS_COMPLETE" in msg.get("content", ""),
)

data_analyst = autogen.ConversableAgent(
    name="DataAnalyst",
    system_message="""You are a Python data analyst. 
    When given data analysis tasks:
    1. Write Python code using pandas
    2. Always read from 'sales_data.csv' in the current directory
    3. Print all results clearly
    4. After the analysis is complete and results are printed, say ANALYSIS_COMPLETE
    """,
    llm_config=llm_config,
    code_execution_config=False,
)

result = code_executor.initiate_chat(
    data_analyst,
    message="""Analyze the sales_data.csv file. Calculate:
    1. Total revenue and expenses for all months
    2. Average monthly profit (revenue - expenses)
    3. The month with highest profit
    Print each result on a separate line.""",
)

When you run this, the DataAnalyst will generate code like:

import pandas as pd

df = pd.read_csv("sales_data.csv")
df["profit"] = df["revenue"] - df["expenses"]

print(f"Total Revenue: ${df['revenue'].sum():,}")
print(f"Total Expenses: ${df['expenses'].sum():,}")
print(f"Average Monthly Profit: ${df['profit'].mean():,.0f}")
print(f"Highest Profit Month: {df.loc[df['profit'].idxmax(), 'month']}")

The executor runs it, captures the output, feeds it back, and the analyst confirms completion.

Handling Multi-File Code Projects

AutoGen can manage multi-file projects within the work directory:

data_analyst = autogen.ConversableAgent(
    name="DataAnalyst",
    system_message="""You are a Python developer. 
    For complex projects, you can create multiple files.
    Always specify filenames explicitly in your code blocks.
    Use: # filename: script.py as the first comment in each code block.
    """,
    llm_config=llm_config,
    code_execution_config=False,
)

With the # filename: comment convention, AutoGen saves each code block as a separate file in the work directory:

```python
# filename: data_loader.py
import pandas as pd

def load_data(filepath):
    return pd.read_csv(filepath)
```

```python
# filename: analysis.py
from data_loader import load_data

df = load_data("sales_data.csv")
print(df.describe())
```

Comparison: Local vs Docker Execution

Feature	LocalCommandLineCodeExecutor	DockerCommandLineCodeExecutor
Setup complexity	Minimal	Requires Docker installed
Execution speed	Fast	Slower (container startup)
Isolation	None	Full container isolation
Network access	Full host access	Configurable
File system access	Work dir only (if restricted)	Container only
Best for	Development, trusted code	Production, untrusted code
Package management	Host Python packages	Container packages
Parallel execution	Limited	Multiple containers

A benchmark using 100 code tasks found that Docker execution adds roughly 3–8 seconds per task for container startup. For quick scripts, that overhead matters. For long-running data processing tasks, it is negligible.

Configuring Timeouts and Error Handling

Code execution can hang. Always configure timeouts:

executor = LocalCommandLineCodeExecutor(
    timeout=30,  # kill after 30 seconds
    work_dir=work_dir,
)

# For the agent, limit how many auto-replies happen
code_executor = autogen.ConversableAgent(
    name="Executor",
    llm_config=False,
    code_execution_config={"executor": executor},
    human_input_mode="NEVER",
    max_consecutive_auto_reply=8,  # max 8 correction attempts
)

When execution times out, the executor returns a timeout error message. The AssistantAgent receives this and typically tries to generate a more efficient version of the code.

Using CodeExecutorAgent (Newer API)

AutoGen 0.4+ introduces a cleaner CodeExecutorAgent class:

from autogen import CodeExecutorAgent, AssistantAgent
from autogen.coding import LocalCommandLineCodeExecutor

executor = LocalCommandLineCodeExecutor(work_dir="workspace", timeout=60)

code_executor = CodeExecutorAgent(
    name="CodeExecutor",
    code_executor=executor,
)

assistant = AssistantAgent(
    name="Assistant",
    llm_config=llm_config,
    system_message="Write and debug Python code. Always test your solutions.",
)

# Two-agent coding workflow
result = assistant.initiate_chat(
    code_executor,
    message="Write a function to find all prime numbers up to 1000 using the Sieve of Eratosthenes. Print the count and the last 10 primes.",
    max_turns=6,
)

CodeExecutorAgent is purpose-built for execution tasks. It handles code block detection, execution, and output formatting automatically.

Streaming Code Output

For longer-running scripts, you may want to stream output rather than wait for completion:

import subprocess
from pathlib import Path

class StreamingLocalExecutor(LocalCommandLineCodeExecutor):
    def execute_code_blocks(self, code_blocks):
        results = []
        for lang, code in code_blocks:
            if lang == "python":
                script_path = self.work_dir / "temp_script.py"
                script_path.write_text(code)
                
                process = subprocess.Popen(
                    ["python", str(script_path)],
                    stdout=subprocess.PIPE,
                    stderr=subprocess.STDOUT,
                    text=True,
                    cwd=self.work_dir,
                )
                
                output_lines = []
                for line in process.stdout:
                    print(f"[Live] {line}", end="")  # stream to terminal
                    output_lines.append(line)
                
                process.wait()
                results.append("\n".join(output_lines))
        
        return results

This modification streams each line of output to your terminal as the code runs, useful for long data processing tasks where you want progress visibility.

Security Considerations for Production

When running code execution in production environments, these practices matter:

# Restrict network access in Docker
docker_executor = DockerCommandLineCodeExecutor(
    image="python:3.11-slim",
    timeout=60,
    work_dir=work_dir,
    # Pass extra Docker run args via environment
    extra_volumes={},  # limit volume mounts
)

# Validate code before execution (basic check)
def safe_code_filter(code_block):
    dangerous_patterns = [
        "import os", "subprocess", "shutil.rmtree",
        "open('/etc", "__import__", "exec(", "eval(",
    ]
    for pattern in dangerous_patterns:
        if pattern in code_block:
            return False, f"Blocked: contains '{pattern}'"
    return True, "OK"

For a deeper look at deploying agents securely, Deploy AI model to production covers the infrastructure side of things.

Combining Code Execution with RAG

The real power emerges when you combine code execution with retrieval. An agent that can search documentation, write code based on what it finds, and verify that code works:

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

# This pattern connects to LangChain-based RAG
# For full RAG pipeline details, see the LangChain guide
# Build AI agent with LangChain: /post/build-ai-agent-langchain

rag_code_writer = autogen.ConversableAgent(
    name="RAGCodeWriter",
    system_message="""You are a Python developer with access to documentation.
    Use the provided documentation context to write accurate code.
    Always test your code and fix any errors.""",
    llm_config=llm_config,
    code_execution_config=False,
)

For the full integration pattern with vector stores, Vector database guide and Build AI agent with LangChain cover the retrieval side.

What Makes Code Execution Agents Actually Useful

The self-correction loop is the key insight. Without it, you get code that looks right but fails on edge cases. With it, you get code that was actually run, errors that were actually fixed, and output that was actually validated — all without human intervention.

The pattern scales from simple scripts to multi-file projects. Start with local execution during development, switch to Docker when you are ready to handle untrusted inputs or run in production. The agent architecture stays the same; only the executor changes.

For broader multi-agent patterns that build on code execution, see AutoGen group chat patterns and Build AI agent with LangChain.

Frequently Asked Questions

Is it safe to run AutoGen code execution without Docker? Running without Docker uses LocalCommandLineCodeExecutor, which executes code directly on your machine. This is fine for development and trusted workloads, but for untrusted or user-supplied code, always use DockerCommandLineCodeExecutor. Docker sandboxes execution so malicious code cannot affect your host system.

What happens when AutoGen-generated code has a bug? AutoGen's code execution loop automatically feeds the error output back to the AssistantAgent, which then generates a corrected version of the code. This self-correction loop continues until the code runs successfully or max_consecutive_auto_reply is reached.

Can AutoGen install Python packages during code execution? Yes. If the agent generates code that imports a missing package, AutoGen can detect the ImportError and generate a pip install command. You can also pre-configure allowed packages or use a virtual environment to control what gets installed.

For foundational context on agent architectures before diving into code execution specifics, AI agents explained is the right starting point.

How AutoGen Code Execution Works

AutoGen's code execution follows a specific flow:

AssistantAgent generates a response containing a code block (usually marked with ```python)
UserProxyAgent (or CodeExecutorAgent) detects the code block
The executor runs the code and captures stdout, stderr, and return code
The output is sent back to AssistantAgent as the next message
If the code failed, AssistantAgent analyzes the error and generates a fix
The loop repeats until success or max_consecutive_auto_reply is hit

This automatic error-correction loop is the core of what makes code agents useful.

Setting Up LocalCommandLineCodeExecutor

The local executor runs Python directly on your machine. Fast to set up, ideal for development.

import autogen
from autogen.coding import LocalCommandLineCodeExecutor
from pathlib import Path

# Create a working directory for generated files
work_dir = Path("coding_workspace")
work_dir.mkdir(exist_ok=True)

# Configure the local executor
executor = LocalCommandLineCodeExecutor(
    timeout=60,          # seconds before killing execution
    work_dir=work_dir,   # where scripts are saved and run
)

# Create the code executor agent
code_executor_agent = autogen.ConversableAgent(
    name="CodeExecutor",
    llm_config=False,    # no LLM needed for execution
    code_execution_config={"executor": executor},
    human_input_mode="NEVER",
)

# Create the assistant that writes code
llm_config = {
    "config_list": [{"model": "gpt-4o", "api_key": "YOUR_KEY"}],
    "cache_seed": 42,
}

code_writer = autogen.ConversableAgent(
    name="CodeWriter",
    system_message="""You are a Python expert. Write clean, working Python code.
    Always include error handling. Return results by printing them.""",
    llm_config=llm_config,
    code_execution_config=False,  # writer doesn't execute
)

# Run the coding conversation
result = code_executor_agent.initiate_chat(
    code_writer,
    message="Write Python code to fetch the current Bitcoin price from CoinGecko API and print it.",
    max_turns=5,
)

Using DockerCommandLineCodeExecutor

For production or any scenario involving untrusted code, Docker isolation is essential.

import autogen
from autogen.coding import DockerCommandLineCodeExecutor
from pathlib import Path

work_dir = Path("docker_workspace")
work_dir.mkdir(exist_ok=True)

# Docker executor configuration
docker_executor = DockerCommandLineCodeExecutor(
    image="python:3.11-slim",   # base Docker image
    timeout=120,                  # longer timeout for complex tasks
    work_dir=work_dir,
    bind_dir=work_dir,           # mount local dir into container
    auto_remove=True,             # remove container after execution
    stop_container=True,          # stop container when done
)

code_executor_agent = autogen.ConversableAgent(
    name="SecureCodeExecutor",
    llm_config=False,
    code_execution_config={"executor": docker_executor},
    human_input_mode="NEVER",
)

The Docker executor spins up a fresh container for each code execution, runs the code inside it, captures output, and removes the container. Your host machine is never touched.

Pre-installing packages in the Docker image:

If your workflows regularly use specific packages, build a custom image instead of relying on pip installs during execution:

# Dockerfile
FROM python:3.11-slim

RUN pip install --no-cache-dir \
    pandas \
    numpy \
    requests \
    matplotlib \
    scikit-learn \
    openai

WORKDIR /workspace

docker build -t autogen-executor:latest .

docker_executor = DockerCommandLineCodeExecutor(
    image="autogen-executor:latest",
    timeout=120,
    work_dir=work_dir,
)

Pre-installing packages dramatically speeds up execution and avoids network calls during agent runs.

The Code Execution Config (Legacy API)

AutoGen also supports a simpler code_execution_config dictionary on UserProxyAgent, which many tutorials still use:

user_proxy = autogen.UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    code_execution_config={
        "work_dir": "workspace",
        "use_docker": False,           # True for Docker isolation
        "timeout": 60,
        "last_n_messages": 3,          # how many messages to look back for code
    },
    max_consecutive_auto_reply=10,
)

This is the older API. The newer LocalCommandLineCodeExecutor / DockerCommandLineCodeExecutor classes give you more control and are the recommended path for new projects.

Building a Data Analysis Agent

Here is a complete example — an agent that analyzes CSV data using code execution:

import autogen
from autogen.coding import LocalCommandLineCodeExecutor
from pathlib import Path
import pandas as pd

# Create sample data
sample_data = pd.DataFrame({
    "month": ["Jan", "Feb", "Mar", "Apr", "May"],
    "revenue": [45000, 52000, 48000, 61000, 58000],
    "expenses": [32000, 35000, 31000, 38000, 40000],
})
sample_data.to_csv("coding_workspace/sales_data.csv", index=False)

# Setup
work_dir = Path("coding_workspace")
work_dir.mkdir(exist_ok=True)

executor = LocalCommandLineCodeExecutor(
    timeout=30,
    work_dir=work_dir,
)

llm_config = {
    "config_list": [{"model": "gpt-4o", "api_key": "YOUR_KEY"}],
}

code_executor = autogen.ConversableAgent(
    name="Executor",
    llm_config=False,
    code_execution_config={"executor": executor},
    human_input_mode="NEVER",
    is_termination_msg=lambda msg: "ANALYSIS_COMPLETE" in msg.get("content", ""),
)

data_analyst = autogen.ConversableAgent(
    name="DataAnalyst",
    system_message="""You are a Python data analyst. 
    When given data analysis tasks:
    1. Write Python code using pandas
    2. Always read from 'sales_data.csv' in the current directory
    3. Print all results clearly
    4. After the analysis is complete and results are printed, say ANALYSIS_COMPLETE
    """,
    llm_config=llm_config,
    code_execution_config=False,
)

result = code_executor.initiate_chat(
    data_analyst,
    message="""Analyze the sales_data.csv file. Calculate:
    1. Total revenue and expenses for all months
    2. Average monthly profit (revenue - expenses)
    3. The month with highest profit
    Print each result on a separate line.""",
)

When you run this, the DataAnalyst will generate code like:

import pandas as pd

df = pd.read_csv("sales_data.csv")
df["profit"] = df["revenue"] - df["expenses"]

print(f"Total Revenue: ${df['revenue'].sum():,}")
print(f"Total Expenses: ${df['expenses'].sum():,}")
print(f"Average Monthly Profit: ${df['profit'].mean():,.0f}")
print(f"Highest Profit Month: {df.loc[df['profit'].idxmax(), 'month']}")

The executor runs it, captures the output, feeds it back, and the analyst confirms completion.

Handling Multi-File Code Projects

AutoGen can manage multi-file projects within the work directory:

data_analyst = autogen.ConversableAgent(
    name="DataAnalyst",
    system_message="""You are a Python developer. 
    For complex projects, you can create multiple files.
    Always specify filenames explicitly in your code blocks.
    Use: # filename: script.py as the first comment in each code block.
    """,
    llm_config=llm_config,
    code_execution_config=False,
)

With the # filename: comment convention, AutoGen saves each code block as a separate file in the work directory:

```python
# filename: data_loader.py
import pandas as pd

def load_data(filepath):
    return pd.read_csv(filepath)
```

```python
# filename: analysis.py
from data_loader import load_data

df = load_data("sales_data.csv")
print(df.describe())
```

Comparison: Local vs Docker Execution

Feature	LocalCommandLineCodeExecutor	DockerCommandLineCodeExecutor
Setup complexity	Minimal	Requires Docker installed
Execution speed	Fast	Slower (container startup)
Isolation	None	Full container isolation
Network access	Full host access	Configurable
File system access	Work dir only (if restricted)	Container only
Best for	Development, trusted code	Production, untrusted code
Package management	Host Python packages	Container packages
Parallel execution	Limited	Multiple containers

Configuring Timeouts and Error Handling

Code execution can hang. Always configure timeouts:

executor = LocalCommandLineCodeExecutor(
    timeout=30,  # kill after 30 seconds
    work_dir=work_dir,
)

# For the agent, limit how many auto-replies happen
code_executor = autogen.ConversableAgent(
    name="Executor",
    llm_config=False,
    code_execution_config={"executor": executor},
    human_input_mode="NEVER",
    max_consecutive_auto_reply=8,  # max 8 correction attempts
)

When execution times out, the executor returns a timeout error message. The AssistantAgent receives this and typically tries to generate a more efficient version of the code.

Using CodeExecutorAgent (Newer API)

AutoGen 0.4+ introduces a cleaner CodeExecutorAgent class:

from autogen import CodeExecutorAgent, AssistantAgent
from autogen.coding import LocalCommandLineCodeExecutor

executor = LocalCommandLineCodeExecutor(work_dir="workspace", timeout=60)

code_executor = CodeExecutorAgent(
    name="CodeExecutor",
    code_executor=executor,
)

assistant = AssistantAgent(
    name="Assistant",
    llm_config=llm_config,
    system_message="Write and debug Python code. Always test your solutions.",
)

# Two-agent coding workflow
result = assistant.initiate_chat(
    code_executor,
    message="Write a function to find all prime numbers up to 1000 using the Sieve of Eratosthenes. Print the count and the last 10 primes.",
    max_turns=6,
)

CodeExecutorAgent is purpose-built for execution tasks. It handles code block detection, execution, and output formatting automatically.

Streaming Code Output

For longer-running scripts, you may want to stream output rather than wait for completion:

import subprocess
from pathlib import Path

class StreamingLocalExecutor(LocalCommandLineCodeExecutor):
    def execute_code_blocks(self, code_blocks):
        results = []
        for lang, code in code_blocks:
            if lang == "python":
                script_path = self.work_dir / "temp_script.py"
                script_path.write_text(code)
                
                process = subprocess.Popen(
                    ["python", str(script_path)],
                    stdout=subprocess.PIPE,
                    stderr=subprocess.STDOUT,
                    text=True,
                    cwd=self.work_dir,
                )
                
                output_lines = []
                for line in process.stdout:
                    print(f"[Live] {line}", end="")  # stream to terminal
                    output_lines.append(line)
                
                process.wait()
                results.append("\n".join(output_lines))
        
        return results

This modification streams each line of output to your terminal as the code runs, useful for long data processing tasks where you want progress visibility.

Security Considerations for Production

When running code execution in production environments, these practices matter:

# Restrict network access in Docker
docker_executor = DockerCommandLineCodeExecutor(
    image="python:3.11-slim",
    timeout=60,
    work_dir=work_dir,
    # Pass extra Docker run args via environment
    extra_volumes={},  # limit volume mounts
)

# Validate code before execution (basic check)
def safe_code_filter(code_block):
    dangerous_patterns = [
        "import os", "subprocess", "shutil.rmtree",
        "open('/etc", "__import__", "exec(", "eval(",
    ]
    for pattern in dangerous_patterns:
        if pattern in code_block:
            return False, f"Blocked: contains '{pattern}'"
    return True, "OK"

For a deeper look at deploying agents securely, Deploy AI model to production covers the infrastructure side of things.

Combining Code Execution with RAG

The real power emerges when you combine code execution with retrieval. An agent that can search documentation, write code based on what it finds, and verify that code works:

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

# This pattern connects to LangChain-based RAG
# For full RAG pipeline details, see the LangChain guide
# Build AI agent with LangChain: /post/build-ai-agent-langchain

rag_code_writer = autogen.ConversableAgent(
    name="RAGCodeWriter",
    system_message="""You are a Python developer with access to documentation.
    Use the provided documentation context to write accurate code.
    Always test your code and fix any errors.""",
    llm_config=llm_config,
    code_execution_config=False,
)

For the full integration pattern with vector stores, Vector database guide and Build AI agent with LangChain cover the retrieval side.

What Makes Code Execution Agents Actually Useful

For broader multi-agent patterns that build on code execution, see AutoGen group chat patterns and Build AI agent with LangChain.

How to Use AutoGen with Code Interpreter (Execute Python)

How AutoGen Code Execution Works

Setting Up LocalCommandLineCodeExecutor

Using DockerCommandLineCodeExecutor

The Code Execution Config (Legacy API)

Building a Data Analysis Agent

Handling Multi-File Code Projects

Comparison: Local vs Docker Execution

Configuring Timeouts and Error Handling

Using CodeExecutorAgent (Newer API)

Streaming Code Output

Security Considerations for Production

Combining Code Execution with RAG

What Makes Code Execution Agents Actually Useful

Frequently Asked Questions

Frequently Asked Questions

AiTechWorlds Team

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Combining Code Execution with RAG

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Frequently Asked Questions

Frequently Asked Questions

AiTechWorlds Team

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Get Free AI Notes Daily