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pydantic/pydantic-ai-harness

pydantic

pydantic-ai-harness

Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness -- Code Mode (collapse many tool calls into one sandboxed Python execution), a filesystem and shell, sub-agents, planning, context compaction, and more. Use when the user mentions pydantic-ai-harness, CodeMode, Monty, code mode, or tool sandboxing, when they want first-party filesystem/shell/sub-agent/planning/compaction capabilities for a Pydantic AI agent, when they want an agent to run agent-written Python, or when a Pydantic AI agent would benefit from orchestrating multiple tool calls in a single sandboxed script.

v1.0LATEST
NewUpdated Oct 13, 2026

Building with Pydantic AI Harness

Pydantic AI Harness is the official capability library for Pydantic AI. Capabilities that need model or framework support -- and those fundamental to every agent -- live in core pydantic-ai; optional, batteries-included capabilities live here. Both are composed onto an agent through the same capabilities=[...] API.

This skill covers the capabilities shipped by pydantic-ai-harness. For the core framework -- agents, tools, structured output, hooks, and testing -- use the building-pydantic-ai-agents skill instead.

When to Use This Skill

Invoke this skill when:

  • The user mentions pydantic-ai-harness, CodeMode, code mode, or the Monty sandbox
  • An agent makes many sequential tool calls that could collapse into one sandboxed Python execution
  • The user wants the model to write Python that loops, branches, aggregates, or parallelizes tool calls with asyncio.gather
  • The user asks to sandbox or constrain the code an agent runs

Do not use this skill for:

  • Core Pydantic AI usage -- building agents, adding tools, structured output, streaming, or testing (use building-pydantic-ai-agents)
  • Capabilities that ship in core pydantic-ai, such as web search, tool search, and thinking
  • The Pydantic validation library on its own (pydantic/BaseModel without agents)

Supported Capabilities

CodeMode has a full reference below; it is the flagship capability and the one this skill goes deep on. The rest ship today and each has its own README with API and examples.

Each capability lives in its own submodule and is imported from there (from pydantic_ai_harness.<module> import ...). Capabilities are not importable from the top-level pydantic_ai_harness package by design, so each one keeps its own optional dependencies isolated. CodeMode, FileSystem, Shell, and ManagedPrompt also have top-level re-exports (importable directly from pydantic_ai_harness).

APIs are subject to change between releases; breaking changes ship deprecation warnings where practical.

Capability Module Description
CodeMode pydantic_ai_harness.code_mode (also top-level) Wraps eligible tools into a single sandboxed run_code tool so the model orchestrates them in Python -- see Code Mode
FileSystem pydantic_ai_harness.filesystem (also top-level) Read, write, edit, and search files under a root directory, with traversal prevention
Shell pydantic_ai_harness.shell (also top-level) Run commands in a subprocess with allowlists, a default denylist, timeouts, and env masking
ManagedPrompt pydantic_ai_harness.logfire (also top-level) Back an agent's instructions with a Logfire-managed prompt
SubAgents pydantic_ai_harness.subagents Delegate subtasks to specialized child agents
DynamicWorkflow pydantic_ai_harness.dynamic_workflow Orchestrate sub-agents from a model-written Python script
Planning pydantic_ai_harness.planning Break complex tasks into structured plans before execution
compaction family (SlidingWindowCompaction, SummarizingCompaction, ...) pydantic_ai_harness.compaction Trim or summarize conversation history to stay within token limits
ToolOutputLimits pydantic_ai_harness.tool_output_limits Truncate, summarize, or spill large tool outputs
RepoContext pydantic_ai_harness.repo_context Auto-load CLAUDE.md/AGENTS.md and repo structure
StepPersistence pydantic_ai_harness.step_persistence Save, restore, resume, and fork run state
PydanticAIDocs pydantic_ai_harness.pydantic_ai_docs On-demand read_pyai_docs tool for Pydantic AI docs
CapabilityCreation pydantic_ai_harness.capability_creation Let an agent author, validate, and load real capabilities at runtime
media externalization pydantic_ai_harness.media Offload large BinaryContent to content-addressed stores

Still experimental: an ACP server adapter, imported from pydantic_ai_harness.experimental.acp. Importing it emits a HarnessExperimentalWarning.

The full, current list with links and status is in the capability matrix.

Install

uv add pydantic-ai-harness

Each capability declares its own extra. Code Mode needs the Monty sandbox:

uv add "pydantic-ai-harness[codemode]"   # `code-mode` is also accepted as an alias

Requires Python 3.10+ and pydantic-ai-slim>=2.18.0.

Quick Start

A harness capability is added to the agent like any other. Here CodeMode wraps locally registered tools into a single run_code tool that the model drives with Python.

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_temperature_f(city: str) -> float:
    return {'Paris': 68.0, 'Tokyo': 77.0}[city]


@agent.tool_plain
def convert_temp(fahrenheit: float) -> float:
    return round((fahrenheit - 32) * 5 / 9, 1)

result = agent.run_sync(
    'Compare the weather in Paris and Tokyo, and report both temperatures in Celsius.'
)
print(result.output)
#> Paris is 20.0 C and Tokyo is 25.0 C.

The model writes a single Python script that fetches both temperatures with asyncio.gather and then converts them -- performing four tool calls across two dependent stages in one run_code invocation.

Key Practices

  • Confirm a harness capability is actually needed. If core Pydantic AI tools and capabilities are enough, use the building-pydantic-ai-agents skill instead -- don't reach for the harness by default.
  • Read the reference before writing code. Each capability has its own configuration, constraints, and gotchas -- load the linked reference (e.g. Code Mode) first.
  • Install the capability's extra. Importing CodeMode without pydantic-ai-harness[codemode] raises an ImportError; the Monty sandbox is an optional dependency.

Common Gotchas

  • native=True tools bypass CodeMode. Provider-native MCP servers and web search execute server-side, so run_code never sees them. Use native=False for client-side dispatch that CodeMode can wrap, but do not treat a remote server as trusted or sandboxed; see the Code Mode trust boundary.
  • The Monty sandbox is a Python subset. It has no third-party imports and only a small stdlib allowlist -- read Code Mode before debugging generated code that fails to run.
  • CodeMode needs its extra. Install pydantic-ai-harness[codemode], not the bare package.
Files2
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Overall Score

88/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

87

Quality

90

Clarity

89

Completeness

85

Summary

Pydantic AI Harness is a batteries-included capability library for Pydantic AI that extends agents with advanced features. The skill guides developers on using CodeMode (which sandboxes agent-written Python to orchestrate multiple tool calls), plus FileSystem, Shell, Planning, SubAgents, and other optional capabilities. It emphasizes when to use the harness vs. core Pydantic AI, clarifies module imports, and documents sandbox restrictions.

Detected Capabilities

dependency-managementcode-execution-guidanceframework-specific-patternssecurity-boundary-documentationapi-referencesandboxing-guidance

Trigger Keywords

Phrases that agents use to match this skill to user intent.

code mode executionagent tool orchestrationsandbox python codepydantic ai harnessmonty sandboxasync gather toolsagent sub-agentsstructured planning

Risk Signals

INFO

Sandbox intentionally restricts code execution to a Python subset with no third-party imports and allowlisted stdlib only (asyncio, math, json, pathlib, etc.)

references/CODE-MODE.md#Sandbox Restrictions
INFO

os_access handler grants host access to clock and environment variables; guidance provided to grant only resources the task needs

references/CODE-MODE.md#Sandbox Restrictions
INFO

mount parameter allows sharing host directories; defaults to copy-on-write overlay; explicit guidance to use read-only mode unless writes must persist

references/CODE-MODE.md#Sandbox Restrictions
WARNING

Tools retain normal host and network access; skill explicitly documents the trust boundary: sandbox constrains model-generated Python, not tool implementations

references/CODE-MODE.md#Sandbox Restrictions

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.com

Use Cases

  • Orchestrate multiple tool calls with Python logic inside a sandbox (loops, branches, async.gather)
  • Run agent-written Python with controlled access to filesystem, shell, and environment
  • Delegate tasks to specialized sub-agents while maintaining a top-level coordination layer
  • Add planning, conversation compaction, and tool output limiting to an existing Pydantic AI agent
  • Load and manage agent instructions dynamically via Logfire
  • Persist and resume agent runs across sessions

Quality Notes

  • Well-structured skill with clear boundaries between core Pydantic AI and harness capabilities — 'Do not use this skill for' section prevents scope creep
  • Excellent trust boundary documentation — explicitly states that Monty sandboxes generated code, not tool implementations, so developers understand what they're responsible for
  • Comprehensive coverage of all 12+ capabilities with import patterns and a reference matrix; each capability has its own README
  • Realistic quick-start example showing a multi-step task (fetch two temperatures, convert both) solved in a single run_code call with asyncio.gather
  • Common gotchas section identifies frequent developer errors: native tools bypass CodeMode, Monty has a limited stdlib, and the codemode extra is required
  • API reference with all CodeMode parameters documented and explained
  • Sandbox restrictions are thorough and actionable — developers can debug generated code failures by checking the allowlist
  • Supporting file (CODE-MODE.md) is present and extends the skill with detailed reference material
  • Clear guidance on metadata-based tool selection and toolset composition for fine-grained control
  • Installation instructions are simple and include the dependency extra needed for CodeMode
Model: claude-haiku-4-5-20251001Analyzed: Oct 13, 2026

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