Coding Agents

Coding agents handle repository work that starts with reading the project and ends with verified changes. Agno gives engineering teams scoped workspaces, command boundaries, approval gates, background runs, and a production API for these agents.

coding_agent.py

import tempfile
from pathlib import Path

from agno.agent import Agent
from agno.tools.coding import CodingTools

project = Path(tempfile.mkdtemp(prefix="coding-agent-"))

(project / "math_utils.py").write_text(
    "def factorial(n: int) -> int:\n"
    "    return 1 if n <= 1 else n * factorial(n - 1)\n"
)

(project / "test_math_utils.py").write_text(
    "import unittest\n\n"
    "from math_utils import factorial\n\n\n"
    "class TestFactorial(unittest.TestCase):\n"
    "    def test_factorial(self):\n"
    "        self.assertEqual(factorial(5), 120)\n\n\n"
    "if __name__ == '__main__':\n"
    "    unittest.main()\n"
)

coding_agent = Agent(
    name="Coding Agent",
    model="openai:gpt-5.5",
    tools=[
        CodingTools(
            base_dir=project,
            restrict_to_base_dir=True,
            all=True,
            allowed_commands=["python3"],
        )
    ],
    instructions=[
        "Read existing files before changing them.",
        "Make focused edits.",
        "Run the test suite after every change.",
        "Report the files changed and the final test result.",
    ],
)

coding_agent.print_response(
    "Update factorial() to reject negative inputs, add a regression test, "
    "then run python3 -m unittest -v.",
    stream=True,
)

Create a virtual environment, install the OpenAI integration, and set OPENAI_API_KEY before running the agent:

uv venv --python 3.12
uv pip install -U "agno[openai]"
export OPENAI_API_KEY="your_openai_api_key"
uv run python coding_agent.py

The example creates a fresh fixture on every run. base_dir scopes file operations to that directory. allowed_commands limits shell entry points to python3.

base_dir limits file paths. The process still inherits the host environment, network access, and operating-system permissions. Run untrusted code inside a dedicated container, virtual machine, or remote sandbox.

To give Codex, Claude Code, Cursor, or another client access to Agno documentation, see Connect your coding agent.

Common patterns

Pattern	Agent task
Repository onboarding	Map the project and answer questions with file references.
Bug fixing	Reproduce a failure, make a focused change, and rerun the failing tests.
Code review	Inspect a diff, run targeted checks, and return prioritized findings.
Dependency upgrades	Update a dependency, resolve compatibility failures, and run the project gates.
CI remediation	Read failure logs, reproduce the failure, patch the cause, and verify the fix.
Migrations	Apply repeatable changes across files and report unresolved cases.

Choose the execution boundary

Requirement	Use
Compact tools for a trusted local project	`CodingTools` with a scoped `base_dir` and narrow `allowed_commands`.
Read-only repository analysis	`Workspace` with `allowed=["read", "list", "search"]`.
Approval before edits or shell commands	`Workspace` with its default confirmation policy and `require_read_before_write=True`.
Execute untrusted code remotely	Daytona or E2B.
Execute untrusted code on your infrastructure	Run the agent inside a dedicated container or virtual machine.

Define the engineering loop

Stage	Boundary
Understand	Read and search the relevant files before proposing a change.
Change	Keep edits focused and require confirmation when the workspace is not disposable.
Verify	Run the repository’s tests, lint, type checks, and build inside the execution environment.
Review	Return changed files, command results, and unresolved risks.
Publish	Keep commits, pushes, pull requests, and deployments outside the default loop or expose them as separately gated tools.

Production path

Need	Agno capability
Serve coding tasks through an API	Register the agent with AgentOS.
Continue after the client disconnects	Use background execution.
Pause before edits or shell commands	Use `Workspace` confirmation with AgentOS HITL.
Authenticate CI and machine callers	Mint a scoped service account.
Inspect model calls, tool arguments, and failures	Configure a database and enable observability.
Catch behavioral regressions	Run coding tasks against fresh fixture repositories with an eval suite.

Evaluate coding agents

Quality layer	Check
Project gates	Compile the project and run its unit, integration, lint, and type checks inside the execution environment.
Reliability	Verify required tool calls such as reading the target file and running the test command.
Agent as judge	Score whether the change follows the request, stays in scope, and explains its verification.
Performance	Track end-to-end runtime and memory use.

Restore the fixture before every eval case so one run cannot affect the next.

Next steps

Task	Guide
Configure the compact coding toolkit	CodingTools
Add confirmation-gated workspace access	Workspace
Isolate remote execution	Daytona or E2B
Add behavioral regression cases	Agent Evaluation
Serve the agent in production	Agent API

Welcome

Get Started

Features

Use Cases

Coding Agents

Common patterns

Choose the execution boundary

Define the engineering loop

Production path

Evaluate coding agents

Next steps

​Common patterns

​Choose the execution boundary

​Define the engineering loop

​Production path

​Evaluate coding agents

​Next steps

Common patterns

Choose the execution boundary

Define the engineering loop

Production path

Evaluate coding agents

Next steps