Pydantic Harness provides a library of capabilities -- self-contained bundles of system prompts, tools, and lifecycle hooks -- that you can attach to any Pydantic AI agent to give it new powers without writing boilerplate.

Each capability is an AbstractCapability subclass that plugs into the agent loop via Pydantic AI's capabilities API.

Installation

pip install pydantic-harness

Requires Python 3.10+ and pydantic-ai-slim>=1.76.0.

Quick start

from pydantic_ai import Agent
from pydantic_harness import Memory, Skills, Compaction

agent = Agent(
    'openai:gpt-4o',
    capabilities=[Memory(), Skills(), Compaction()],
)

result = agent.run_sync('Remember that my favourite colour is blue.')

Available capabilities

Capability	Description
AdaptiveReasoning	Dynamically adjust reasoning effort based on task complexity
Approval	Require human approval before executing sensitive operations
Compaction	Compress conversation history to stay within context limits
FileSystem	Read, write, and navigate the local filesystem
Guardrails	Validate inputs/outputs and enforce cost and tool constraints
KnowsCurrentTime	Inject the current date and time into the system prompt
Memory	Persistent key-value memory across agent sessions
Planning	Break complex tasks into plans before execution
RepoContextInjection	Inject repository structure and context into the system prompt
SecretMasking	Detect and redact secrets in agent inputs and outputs
SessionPersistence	Save and restore full conversation sessions
Shell	Execute shell commands with safety controls
Skills	Progressive tool loading via search and activate
SlidingWindow	Keep conversation history within a sliding token window
StuckLoopDetection	Detect and break out of repetitive agent loops
SubAgent	Delegate subtasks to specialised child agents
SystemReminders	Inject periodic reminders into the conversation
ToolErrorRecovery	Automatically retry or recover from tool execution errors
ToolOrphanRepair	Repair orphaned tool calls in conversation history
ToolOutputManagement	Control and format tool output for the model

Documentation

Development

make install   # install dependencies
make lint      # ruff format check + lint
make typecheck # pyright strict
make test      # pytest
make testcov   # pytest with coverage

License

MIT