Code Mode

CodeMode replaces individual tool calls with a single sandboxed Python execution environment. Instead of the model issuing one tool call per action, it writes a Python program that calls your tools as functions — with loops, conditionals, variables, and asyncio.gather — all inside a sandboxed Monty runtime.

Source

The problem

Standard tool calling often needs another model turn for each dependent batch of tool calls. An agent that needs to fetch 10 items and then process their results can require many model turns, increasing latency, cost, and context use. Intermediate results also grow the conversation history.

The solution

CodeMode wraps eligible tools into a single run_code tool. The model writes sandboxed orchestration code that fans calls out with asyncio.gather, filters and transforms results, and returns only what matters. Calls from that code are dispatched through Pydantic AI to the host tools.

Standard tool calling	Code mode
Dependent tool batches across model turns	Many dependent calls in one `run_code`
Parallel only when the model emits a batch	Parallelism expressed in Python
No local computation	Filter, transform, aggregate in code
Large conversation history	Compact — fewer messages

Durable execution integrations can record nested calls for deterministic replay.

Installation

Code mode requires the Monty sandbox, available via the codemode extra (the code-mode extra is an equivalent alias):

Terminal

uv add "pydantic-ai-harness[codemode]"

Usage

Construct an Agent with CodeMode() in its capabilities, then register tools as usual. Every eligible regular tool becomes callable from inside run_code:

from pydantic_ai import Agent
from pydantic_ai_harness import CodeMode

agent = Agent('anthropic:claude-sonnet-4-6', capabilities=[CodeMode()])


@agent.tool_plain
def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    return {'city': city, 'temp_f': 72, 'condition': 'sunny'}


result = agent.run_sync("What's the weather in Paris and Tokyo, in Celsius?")
print(result.output)

Inside a single run_code call, the model writes code like the following (illustrative — the exact code the model emits will vary):

import asyncio

paris, tokyo = await asyncio.gather(
    get_weather(city='Paris'),
    get_weather(city='Tokyo'),
)
paris_c = round((paris['temp_f'] - 32) * 5 / 9, 1)
tokyo_c = round((tokyo['temp_f'] - 32) * 5 / 9, 1)
{'paris': paris_c, 'tokyo': tokyo_c}

Both weather lookups run in parallel and the conversions run inside Monty, all within one run_code call.

Selective tool sandboxing

By default, CodeMode(tools='all') sandboxes every eligible regular tool. Framework control tools, undiscovered deferred tools, native fallbacks, and other code-execution tools remain native. The tools field is a Pydantic AI ToolSelector, so you can control which eligible tools go through the sandbox. Tools that match the selector become callables inside run_code; non-matching tools stay visible to the model as regular tool calls.

from pydantic_ai_harness import CodeMode

# By name -- only these tools are available inside run_code
CodeMode(tools=['search', 'fetch'])

# By predicate -- (ctx, tool_def) -> bool | Awaitable[bool]
CodeMode(tools=lambda ctx, td: td.name != 'dangerous_tool')

# By metadata -- combine with SetToolMetadata or a toolset's .with_metadata()
CodeMode(tools={'code_mode': True})

Metadata-based selection

Use metadata when the decision should travel with a tool or toolset, rather than with one CodeMode instance. This suits shared toolsets: the toolset author tags the tools that are safe and useful to call from generated code, and each agent opts into that tag with CodeMode(tools={...}).

CodeMode(tools={'code_mode': True}) uses the standard Pydantic AI ToolSelector metadata form. A tool is sandboxed when its ToolDefinition.metadata contains all of the selector’s key-value pairs. Extra metadata on the tool is fine, and nested dictionaries are matched by deep inclusion.

The common pattern is to tag an entire toolset with .with_metadata(...):

from pydantic_ai import Agent
from pydantic_ai.toolsets import FunctionToolset
from pydantic_ai_harness import CodeMode


def search(query: str) -> str:
    """Search the web."""
    return f'results for {query}'


def fetch(url: str) -> str:
    """Fetch a URL."""
    return f'contents of {url}'


search_tools = FunctionToolset(tools=[search, fetch]).with_metadata(code_mode=True)

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    toolsets=[search_tools],
    capabilities=[CodeMode(tools={'code_mode': True})],
)

Here search and fetch are removed from the model-facing tool list and become callable functions inside run_code. Tools without metadata['code_mode'] == True stay visible as regular tool calls.

Tool Search interaction

When you mark tools or whole toolsets defer_loading=True (Tool Search), CodeMode keeps them out of run_code while they’re undiscovered — they pass straight through, so Tool Search drives them as usual (sent on the wire with defer_loading on providers with native tool search; otherwise dropped until discovered, with a search_tools tool alongside run_code). CodeMode uses RunContext.is_tool_available to follow that reveal state. Once the model discovers a tool — or loads the deferred capability that owns it — CodeMode folds it into run_code like any other tool from then on, so it’s callable from generated code. (The tool keeps defer_loading=True, which records what its author asked for; what changes is its availability for the run.)

That fold-in grows run_code’s description, which invalidates the prompt-cache prefix once at the moment of discovery (turns with no discovery stay cache-warm). Two ways to avoid the bust:

Pass dynamic_catalog=True to keep run_code’s description static across discoveries. The catalog of sandboxed-tool signatures moves into the agent instructions (as a dynamic InstructionPart) and newly-discovered tools are announced via ctx.enqueue instead of by rebuilding the description:
```
from pydantic_ai_harness import CodeMode

CodeMode(dynamic_catalog=True)
```
This pays off when paired with Tool Search: the tool-definitions block stays byte-stable so the prefix cache survives discoveries, at the cost of a larger (but cache-friendly) system prompt. With a fixed toolset and no Tool Search, the default keeps the system prompt shorter and is the better choice.
To instead keep a Tool Search corpus fully native — never folded into run_code, but not callable from inside it — exclude it with a tools selector; corpus members carry with_native set to the managing native tool:
```
from pydantic_ai_harness import CodeMode

CodeMode(tools=lambda ctx, td: td.with_native is None)
```

Return values

The last expression in the snippet is automatically captured as the return value — the model does not need to print(). An assignment stores a value in the REPL but does not return it. A final expression that evaluates to None is also treated as no result. Without a non-None final expression or print output, run_code returns {}. Put the assigned name on the final line:

result = await get_weather(city='Paris')
result

Reserve print() for supplementary logging: printed text is surfaced separately, wrapped alongside the last-expression result.

Scenario	Return
Non-`None` final expression with no print output	Last expression value
Final assignment or `None` result with no print output	`{}`
Print output with no final expression or a `None` result	`{'output': '<printed text>'}`
Print output with a plain, non-`None` final expression	`{'output': '<printed text>', 'result': <last expression>}`
Multimodal final expression with no print output	Returned natively for model processing
Print output with a multimodal final expression	List with printed text followed by native multimodal content

Printed output is limited to 10 MiB. Exceeding the limit makes run_code return a model retry.

REPL state

State persists between run_code calls within the same agent run — variables, imports, and function definitions carry over. Pass restart: true in the tool call to reset state. If a worker crash or host-side execution failure invalidates the session, run_code returns a model retry that reports the reset; the next snippet must recreate any required state.

Temporal durability

Install both integrations:

Terminal

uv add "pydantic-ai-harness[codemode,temporal]"

Construct the named agent and its stable-ID toolsets outside the workflow, then attach TemporalDurability alongside CodeMode:

from pydantic_ai import Agent
from pydantic_ai.durable_exec.temporal import TemporalDurability
from pydantic_ai_harness import CodeMode

agent = Agent(
    'openai:gpt-5.6-sol',
    name='coding-agent',
    capabilities=[CodeMode(), TemporalDurability()],
)

Follow the Pydantic AI Temporal guide to call the plain agent from a workflow and register its activities with PydanticAIPlugin and either __pydantic_ai_agents__ or AgentPlugin.

PydanticAIPlugin passes pydantic_monty through Temporal’s workflow sandbox. This makes Monty runnable there, but run_code still executes in workflow code and is re-executed during replay. Model requests and, by default, nested tool calls cross Temporal activity boundaries; asyncio.gather can schedule nested tool activities concurrently. The REPL is process-local state for one agent run, not durable storage. Replay reconstructs it by running the recorded snippets again against recorded activity results.

Keep workflow-side code deterministic. mount reads and writes, os_access callbacks, and host-clock calls happen again during replay; changing their results can change which activities the workflow schedules and cause a NondeterminismError. Put external reads, writes, clock access, and other side effects in wrapped tools so Temporal records them as activities. Replay may not flag changed arguments when the same activity remains at the same history position, so replay validation is not a substitute for this boundary. Temporal activity timeouts apply to nested tools, not pure computation inside run_code, so keep sandbox loops bounded.

Observability

Nested tool calls inside run_code produce their own spans when instrumented with Logfire or any OpenTelemetry backend — the easiest way to understand what code mode actually did, since each run_code span fans out into the tool calls the model issued from inside the sandbox. See the Pydantic AI Logfire docs for setup.

The run_code tool return also carries metadata with every nested call, keyed by call id:

from pydantic_ai import Agent
from pydantic_ai.messages import ToolReturnPart
from pydantic_ai_harness import CodeMode

agent = Agent('anthropic:claude-sonnet-4-6', capabilities=[CodeMode()])


@agent.tool_plain
def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    return {'city': city, 'temp_f': 72}


result = agent.run_sync("What's the weather in Paris?")

for msg in result.all_messages():
    for part in msg.parts:
        if isinstance(part, ToolReturnPart) and part.tool_name == 'run_code':
            metadata = part.metadata or {}
            tool_calls = metadata['tool_calls']    # dict[str, ToolCallPart]
            tool_returns = metadata['tool_returns']  # dict[str, ToolReturnPart]

In practice

A representative run wires CodeMode up against an MCP server and a web search and asks it to find the most-discussed Hacker News story across three feeds, pull the comment thread and the submitter’s profile, and search the web for follow-up coverage. CodeMode collapses that into two run_code calls: the first fetches all three feeds in parallel via asyncio.gather, dedupes by id, filters by score, and ranks by comment count — in plain Python; the second batches the three follow-up calls (hn_get_thread, hn_get_user, duckduckgo_search) together.

See the full Logfire trace -> Each run_code span fans out into the tool calls the model issued from inside the sandbox.

Filesystem and OS access

Sandboxed code starts with no access to the host’s files, environment, or clock. Two parameters add controlled filesystem, environment, or clock behavior.

Both parameters are fixed when the capability is built, so construct CodeMode per request to scope the configured access to that request.

`mount` — share host directories

Reach for mount when the agent works with real files: analyzing a dataset you’ve dropped in a folder and writing a report back, editing a checkout, or processing a batch of documents. Sandboxed pathlib code reads and writes under the mounted path. (For environment variables or the clock, use os_access instead.)

from pydantic_ai import Agent
from pydantic_monty import MountDir
from pydantic_ai_harness import CodeMode

# The agent can read /work/data.csv and write /work/summary.md back to the host:
agent = Agent(
    'anthropic:claude-sonnet-4-6',
    capabilities=[CodeMode(mount=MountDir(virtual_path='/work', host_path='/tmp/agent-workspace', mode='read-write'))],
)

A MountDir defaults to copy-on-write mode='overlay': the sandbox reads host files and sees writes made during the current run_code call, but Monty discards those writes before the next call and they do not reach the host. Pass mode='read-write' when later calls need to read the writes, or mode='read-only' to forbid writes. mount also accepts a list of MountDir for multiple mount points.

`os_access` — answer the sandbox’s OS calls yourself

Reach for os_access when the agent needs environment variables, the current date and time, or filesystem behavior you control. Hand it a ready-made OS implementation (AbstractOS), or a callback that decides each call — so you can inject just the secrets it needs, pin “now” for reproducible runs, or route file access to your own store.

from pydantic_ai import Agent
from pydantic_monty import OSAccess
from pydantic_ai_harness import CodeMode

# Give the agent a fixed set of environment values:
agent = Agent(
    'anthropic:claude-sonnet-4-6',
    capabilities=[CodeMode(os_access=OSAccess(environ={'API_BASE': 'https://api.example.com'}))],
)

A callback receives each OS call and decides its fate:

from pydantic_ai import Agent
from pydantic_monty import NOT_HANDLED
from pydantic_ai_harness import CodeMode

allowed_env = {'API_KEY': 'sk-...'}


def my_os(fn, args, kwargs):
    if fn == 'os.getenv':
        # Answer the call: allow-listed keys resolve, every other key reads back
        # as None -- absent, exactly like a real unset variable.
        return allowed_env.get(args[0])
    # Refuse everything else: NOT_HANDLED makes the call fail in the sandbox.
    return NOT_HANDLED


agent = Agent('anthropic:claude-sonnet-4-6', capabilities=[CodeMode(os_access=my_os)])

Your callback’s return value decides the call’s fate, and the two outcomes are easy to confuse:

Return any value — including None, '', or 0 — and that becomes the result the sandbox sees. os.getenv returning None looks exactly like a normal unset variable, so the agent’s code keeps running. This is how you hide something: answer with an empty value.
Return NOT_HANDLED and the call is treated as unsupported: it raises inside the sandbox and the model gets a retry. This refuses a capability outright — use it to block, not to say “no value”. Returning NOT_HANDLED for a key the agent reasonably expects will burn retries.

Sandbox restrictions

Code runs inside Monty, a sandboxed Python subset. Key restrictions:

No third-party imports. Allowed stdlib modules: sys, typing, asyncio, math, json, re, unicodedata, datetime, os, pathlib (each must be imported before use).
asyncio.gather(...) accepts positional awaitables but no keyword arguments. Other task creation and wait APIs are unavailable.
No wall-clock or timing primitives by default: asyncio.sleep, datetime.datetime.now(), datetime.date.today(), and the time module. datetime.datetime.now() / datetime.date.today() become available when an os_access handler implements them (the built-in OSAccess does); asyncio.sleep and time never do.
No import *.
Filesystem I/O needs an os_access handler or a mount; os.getenv / os.environ need an os_access handler.
Tools requiring approval or with deferred (CallDeferred) execution are sandboxed like any other tool; without a HandleDeferredToolCalls (or equivalent) capability on the agent to resolve them inline, calling one from run_code raises an error that surfaces to the model as a retry.

Agent spec (YAML/JSON)

CodeMode works with Pydantic AI’s agent spec feature for defining agents in YAML or JSON:

# agent.yaml
model: anthropic:claude-sonnet-4-6
capabilities:
  - CodeMode: {}

from pydantic_ai import Agent
from pydantic_ai_harness import CodeMode

agent = Agent.from_file('agent.yaml', custom_capability_types=[CodeMode])
result = agent.run_sync('...')
print(result.output)

Pass custom_capability_types so the spec loader knows how to instantiate CodeMode. Arguments can be passed in the YAML too:

capabilities:
  - CodeMode:
      tools: ['search', 'fetch']
      max_retries: 5

API reference

CodeMode

Bases: AbstractCapability[AgentDepsT]

Capability that exposes selected tools as callables inside a run_code sandbox.

By default (tools='all') every eligible regular tool the agent has is wrapped behind a single run_code tool — the model writes Python that calls them as functions instead of issuing tool calls directly. Framework control tools, undiscovered deferred tools, native fallbacks, and other code-execution tools remain native.

Pass a list of tool names or a callable predicate to tools to split the toolset: matching tools become callables inside the sandbox, and the rest stay visible to the model as normal tool calls.

from pydantic_ai import Agent
from pydantic_ai_harness import CodeMode

# Sandbox all tools
agent = Agent('openai:gpt-5', capabilities=[CodeMode()])

# Sandbox only specific tools
agent = Agent('openai:gpt-5', capabilities=[CodeMode(tools=['search', 'fetch'])])

By default, sandboxed code cannot touch the host — no filesystem, environment variables, or clock. Two parameters open it up:

mount shares specific host directories: reach for it when the agent reads or writes real files.
os_access routes the sandbox’s OS calls to a handler you provide: reach for it when the agent needs environment variables, the clock, or filesystem behavior you control.

mount exposes selected host directories. The built-in OSAccess has an isolated filesystem and environment but uses the host clock by default; custom OS handlers can expose other host resources.

from pydantic_monty import MountDir

agent = Agent('openai:gpt-5', capabilities=[CodeMode(mount=MountDir(virtual_path='/work', host_path='/tmp/agent-work'))])

Attributes

tools

Which wrapped tools should be sandboxed inside run_code.

'all' (default): every eligible regular tool the agent has is sandboxed.
Sequence[str]: only tools whose names are listed are sandboxed.
Callable (ctx, tool_def) -> bool | Awaitable[bool]: tools where the callable returns True are sandboxed; the rest stay as native tool calls.

Type: ToolSelector[AgentDepsT] Default: field(default='all')

max_retries

Maximum number of retries for the run_code tool (syntax errors count as retries).

Type: int Default: 3

os_access

Give sandboxed code environment variables, the clock, and file I/O through a handler you provide; unset, they are unavailable.

Type: CodeModeOS | None Default: None

mount

Host directories to expose to sandboxed pathlib code; each mount’s mode controls whether writes reach the host.

Type: CodeModeMount | None Default: None

dynamic_catalog

Keep the run_code tool definition cache-stable as the sandboxed toolset grows.

By default the signatures of all sandboxed tools are rendered into run_code’s description, which lives in the prompt-cache-keyed tool-definitions block. When the toolset changes mid-run — e.g. ToolSearch reveals a new tool that then gets folded into run_code — the description changes and busts the prefix cache from that point on.

Set dynamic_catalog=True to instead:

keep only the static base prose (sandbox restrictions, return-value contract) in run_code.description, so the tool-definitions block stays byte-stable across discoveries;
move the “available functions” catalog (TypedDict definitions + signatures) into agent instructions as a dynamic InstructionPart, which providers with static/dynamic instruction splitting (Anthropic, Bedrock) place after the cache breakpoint;
announce newly-discovered tools via a short SystemPromptPart enqueued through RunContext.enqueue, so the model knows the new functions are callable without rewriting the cached description.

This pays off when paired with ToolSearch: the tool-definitions cache survives discoveries at the cost of a larger (but cache-friendly) system prompt. With a fixed toolset and no ToolSearch, the default keeps the system prompt shorter and is the better choice.

Type: bool Default: False

Methods

get_ordering

def get_ordering() -> CapabilityOrdering

CodeMode wraps around ToolSearch so that search_tools stays native.

Returns

CapabilityOrdering

for_run

@async

def for_run(ctx: RunContext[AgentDepsT]) -> CodeMode[AgentDepsT]

Return a fresh instance so concurrent runs don’t share _announced_tools.

Returns

CodeMode[AgentDepsT]

get_wrapper_toolset

def get_wrapper_toolset(
    toolset: AbstractToolset[AgentDepsT],
) -> AbstractToolset[AgentDepsT] | None

Wrap the agent’s assembled toolset, splitting it into native + sandboxed subsets if needed.

Returns

AbstractToolset[AgentDepsT] | None

after_tool_execute

@async

def after_tool_execute(
    ctx: RunContext[AgentDepsT],
    *,
    call: ToolCallPart,
    tool_def: ToolDefinition,
    args: ValidatedToolArgs,
    result: Any,
) -> Any

Announce newly-discovered tools from a local search_tools return.

Only active with dynamic_catalog=True. The native-search path is handled by after_model_request instead (server-side search emits a NativeToolSearchReturnPart rather than a regular tool execute result).

Returns

Any

after_model_request

@async

def after_model_request(
    ctx: RunContext[AgentDepsT],
    *,
    request_context: ModelRequestContext,
    response: ModelResponse,
) -> ModelResponse

Announce newly-discovered tools from a native (server-side) tool-search return.

Only active with dynamic_catalog=True.

Returns

ModelResponse

Code Mode

The problem

The solution

Installation

Usage

Selective tool sandboxing

Metadata-based selection

Tool Search interaction

Return values

REPL state

Temporal durability

Observability

In practice

Filesystem and OS access

mount — share host directories

os_access — answer the sandbox’s OS calls yourself

Sandbox restrictions

Agent spec (YAML/JSON)

Further reading

API reference

CodeMode

Attributes

tools

max_retries

os_access

mount

dynamic_catalog

Methods

get_ordering

Returns

for_run

Returns

get_wrapper_toolset

Returns

after_tool_execute

Returns

after_model_request

Returns

`mount` — share host directories

`os_access` — answer the sandbox’s OS calls yourself