pydantic-deep 0.3.14

Batteries-included agent harness for Python — tool-calling, sandboxed execution, multi-agent teams, and unlimited context on Pydantic AI

Pydantic Deep Agents

The batteries-included deep agent harness for Python.
Terminal AI assistant out of the box — or build production agents with one function call.

Docs · PyPI · CLI · Framework · DeepResearch · Examples

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What's New

• 2026-04-12  v0.3.8 — Stuck loop detection, context limit warnings for the model, expanded context file discovery (CLAUDE.md, .cursorrules, etc.), eviction & orphan repair migrated to capabilities hooks.
• 2026-04-11  v0.3.6 — One-command installer + self-update: curl -fsSL .../install.sh | bash installs everything automatically. New pydantic-deep update command. Startup update notifications with 24-hour PyPI cache.
• 2026-04-10  v0.3.5 — Headless runner (pydantic-deep run), Docker sandbox with named workspaces, browser automation via Playwright, Harbor adapter for Terminal Bench evaluation.

Full history: CHANGELOG.md

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The Agent Harness

Pydantic Deep Agents is an agent harness — the complete infrastructure that wraps an LLM and makes it a functional autonomous agent. The model provides intelligence; the harness provides planning, tools, memory, sandboxed execution, and unlimited context.

🔧 Tool-calling	File read/write/edit, shell execution, glob, grep, web search, web fetch, browser automation — wired up and ready.
🧠 Persistent memory	MEMORY.md persists across sessions. Auto-injected into the system prompt. Each agent has isolated memory by default.
♾️ Unlimited context	Auto-summarization when approaching the token budget. LLM-based or zero-cost sliding window. Never hits a context wall.
🤝 Multi-agent / swarm	Spawn subagents for parallel workstreams. Shared TODO lists with claiming. Peer-to-peer message bus. Full team coordination.
🐳 Sandboxed execution	Docker sandbox with named workspaces. Installed packages persist between sessions. Project dir mounted at /workspace.
🗂️ Plan Mode	Dedicated planner subagent asks clarifying questions and structures the work before execution begins. Headless-compatible.
🔖 Checkpoints	Save conversation state at any point. Rewind to any checkpoint. Fork sessions to explore alternative approaches.
📚 Skills system	Domain-specific knowledge loaded on demand from SKILL.md files. Built-in skills: code-review, refactor, test-writer, git-workflow, and more.
🔌 MCP	Connect any Model Context Protocol server via pydantic-ai's native MCP capability.
⚡ Lifecycle hooks	Claude Code-style PRE_TOOL_USE / POST_TOOL_USE hooks. Shell commands or Python handlers. Audit logging, safety gates.
📐 Structured output	Type-safe Pydantic model responses via output_type. No JSON parsing. No dict["key"]. Full IDE autocomplete.
🔄 Stuck loop detection	Detects repeated identical tool calls, A-B-A-B alternating patterns, and no-op calls. Warns the model or stops the run.
⚠️ Context limit warnings	Model receives URGENT/CRITICAL warnings when approaching context limits (70%), well before auto-compression (90%).
💰 Cost tracking	Real-time token and USD cost tracking per run and cumulative. Hard budget limits with BudgetExceededError.
✨ Self-improving	/improve analyzes past sessions and proposes updates to MEMORY.md, SOUL.md, and AGENTS.md.
🏷️ 100% type-safe	Pyright strict + MyPy strict. 100% test coverage. Every public API is fully typed — safe to use in production.

Built natively on pydantic-ai — uses the Capabilities API directly, inherits all pydantic-ai streaming, multi-model support, and Pydantic validation automatically.

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🖥️ CLI — Terminal AI Assistant

A Claude Code-style terminal AI assistant that works with any model and any provider.

Install (macOS & Linux)

curl -fsSL https://raw.githubusercontent.com/vstorm-co/pydantic-deep/main/install.sh | bash

No Python setup required — the script installs uv and the CLI automatically. Then:

export ANTHROPIC_API_KEY=sk-ant-...
pydantic-deep

Windows / manual: pip install "pydantic-deep[cli]"  ·  Update: pydantic-deep update

Model & Provider Support

Works with any model that supports tool-calling:

Provider	Example models
Anthropic	anthropic:claude-opus-4-6, claude-sonnet-4-6
OpenAI	openai:gpt-5.4, gpt-4.1
OpenRouter	openrouter:anthropic/claude-opus-4-6 (200+ models)
Google Gemini	google-gla:gemini-2.5-pro
Ollama (local)	ollama:qwen3, ollama:llama3.3
Any OpenAI-compatible	Custom base URL via env

Switch model anytime: pydantic-deep config set model openai:gpt-5.4 or /model in the TUI.

What you get in the TUI

	Feature
💬	Streaming chat with tool call visualization
📁	File read / write / edit, shell execution, glob, grep
🧠	Persistent memory and self-improvement across sessions
🗂️	Task planning, plan mode, and subagent delegation
♾️	Context compression for unlimited conversations
🔖	Checkpoints — save, rewind, and fork any session
🌐	Web search & fetch built-in
🖥️	Browser automation via Playwright (--browser)
🐳	Docker sandbox — sandboxed execution with named workspaces
💭	Extended thinking — minimal / low / medium / high / xhigh
💰	Real-time cost and token tracking per session
🛡️	Tool approval dialogs — approve, auto-approve, or deny per tool call
@	@filename file references · !command shell passthrough
✨	/improve, /skills, /diff, /model, /theme, /compact, and more

Usage

# Interactive TUI (default)
pydantic-deep
pydantic-deep tui --model openrouter:anthropic/claude-opus-4-6

# Headless deep agent — benchmarks, CI/CD, scripted automation
pydantic-deep run "Fix the failing test in test_auth.py"
pydantic-deep run --task-file task.md --json
pydantic-deep run "Refactor utils.py" --no-web-search --thinking false

# Docker sandbox — sandboxed execution, project dir mounted at /workspace
pydantic-deep tui --sandbox docker
pydantic-deep tui --workspace ml-env     # named workspace, packages persist

# Browser automation (requires pydantic-deep[browser])
pydantic-deep tui --browser
pydantic-deep run "Go to example.com and summarize the content" --browser

# Config & skills
pydantic-deep config set model anthropic:claude-sonnet-4-6
pydantic-deep skills list
pydantic-deep update                     # update to latest version

See CLI docs for the full reference.

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🐍 Framework — Build Your Own Agent

pip install pydantic-deep

One function call gives you a production deep agent with planning, tool-calling, multi-agent delegation, persistent memory, unlimited context, and cost tracking. Everything is a toggle:

from pydantic_ai_backends import StateBackend
from pydantic_deep import create_deep_agent, create_default_deps

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    include_todo=True,          # Task planning with subtasks and dependencies
    include_subagents=True,     # Multi-agent swarm — delegate to subagents
    include_skills=True,        # Domain-specific skills from SKILL.md files
    include_memory=True,        # Persistent memory across sessions
    include_plan=True,          # Structured planning before execution
    include_teams=True,         # Agent teams with shared TODO lists + message bus
    web_search=True,            # Tool-calling: web search
    web_fetch=True,             # Tool-calling: web fetch
    thinking="high",            # Extended thinking / reasoning effort
    context_manager=True,       # Unlimited context via auto-summarization
    cost_tracking=True,         # Token/USD budget enforcement
    include_checkpoints=True,   # Save, rewind, and fork conversations
)

deps = create_default_deps(StateBackend())
result = await agent.run("Build a REST API for user auth", deps=deps)

Structured Output

Type-safe responses with Pydantic models — no JSON parsing, no dict["key"]:

from pydantic import BaseModel

class CodeReview(BaseModel):
    summary: str
    issues: list[str]
    score: int

agent = create_deep_agent(output_type=CodeReview)
result = await agent.run("Review the auth module", deps=deps)
print(result.output.score)  # fully typed

Multi-Agent Swarm

Spawn isolated subagents for parallel workstreams. Each subagent is a full deep agent with its own tool-calling, memory, and context:

agent = create_deep_agent(
    subagents=[
        {
            "name": "researcher",
            "description": "Researches topics using web search",
            "instructions": "Search the web, synthesize findings, cite sources.",
        },
        {
            "name": "code-reviewer",
            "description": "Reviews code for quality, security, and performance",
            "instructions": "Check for security issues, N+1 queries, missing tests...",
        },
    ],
)
# Main agent delegates: task(description="Review auth.py", subagent_type="code-reviewer")

Unlimited Context

Auto-summarization keeps long-running agents within the token budget:

from pydantic_deep import create_summarization_processor

processor = create_summarization_processor(
    trigger=("tokens", 100000),  # compress at 100k tokens
    keep=("messages", 20),       # keep last 20 messages verbatim
)
agent = create_deep_agent(history_processors=[processor])

Claude Code-Style Lifecycle Hooks

from pydantic_deep import Hook, HookEvent

agent = create_deep_agent(
    hooks=[
        Hook(
            event=HookEvent.PRE_TOOL_USE,
            command="echo 'Tool: $TOOL_NAME args: $TOOL_INPUT' >> /tmp/audit.log",
        ),
    ],
)

MCP Servers

from pydantic_ai.capabilities import MCP

agent = create_deep_agent(
    capabilities=[MCP(url="https://mcp.example.com/api")],
)

Context Files

Pydantic Deep Agents auto-discovers and injects project-specific context into every conversation:

File	Purpose	Who Sees It
AGENTS.md	Project conventions, architecture, instructions	Main agent + all subagents
CLAUDE.md	Claude Code project instructions	Main agent + all subagents
SOUL.md	Agent personality, style, communication preferences	Main agent only
.cursorrules	Cursor editor conventions	Main agent only
.github/copilot-instructions.md	GitHub Copilot instructions	Main agent only
CONVENTIONS.md	Project coding conventions	Main agent only
CODING_GUIDELINES.md	Coding guidelines	Main agent only
MEMORY.md	Persistent memory — read/write/update tools	Per-agent (isolated)

Compatible with Claude Code, Cursor, GitHub Copilot, and other agent frameworks. AGENTS.md follows the agents.md spec.

See the full API reference for all options.

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🔬 DeepResearch — Reference App

A full-featured research deep agent with web UI — built entirely on Pydantic Deep Agents.

Plan Mode — planner asks clarifying questions	Multi-Agent Swarm — 5 subagents researching in parallel
Excalidraw Canvas — live diagrams synced with agent	File Browser — workspace files with inline preview

Web search (Tavily, Brave, Jina), sandboxed code execution, Excalidraw diagrams, plan mode, report export.

cd apps/deepresearch && uv sync && cp .env.example .env
uv run deepresearch    # → http://localhost:8080

See apps/deepresearch/README.md for full setup.

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Architecture

Pydantic Deep Agents uses pydantic-ai's native Capabilities API for all cross-cutting concerns — hooks, memory, skills, context files, teams, and plan mode are all first-class pydantic-ai capabilities.

Capabilities

Capability	Package	What It Does
CostTracking	pydantic-ai-shields	Token/USD budget enforcement and real-time cost callbacks
ContextManagerCapability	summarization-pydantic-ai	Unlimited context via auto-summarization
LimitWarnerCapability	summarization-pydantic-ai	URGENT/CRITICAL warnings when context limits approach
StuckLoopDetection	pydantic-deep	Detects and breaks repetitive agent loops
EvictionCapability	pydantic-deep	Intercepts large tool outputs before they enter history
PatchToolCallsCapability	pydantic-deep	Fixes orphaned tool calls/results in history
HooksCapability	pydantic-deep	Claude Code-style PRE/POST_TOOL_USE lifecycle hooks
CheckpointMiddleware	pydantic-deep	Save, rewind, and fork conversation state
WebSearch / WebFetch	pydantic-ai built-in	Tool-calling: web search and URL fetching
SkillsCapability	pydantic-deep	Domain-specific skills from SKILL.md files
MemoryCapability	pydantic-deep	Persistent memory across sessions
TeamCapability	pydantic-deep	Multi-agent swarm — shared TODOs, message bus
PlanCapability	pydantic-deep	Structured planning before execution

Modular Packages

Every component is a standalone package — use only what you need:

Package	What It Does
pydantic-ai-backend	File storage, Docker sandbox, console toolset
pydantic-ai-todo	Task planning with subtasks and dependencies
subagents-pydantic-ai	Sync/async delegation, background tasks, cancellation
summarization-pydantic-ai	LLM summaries or zero-cost sliding window
pydantic-ai-shields	Cost tracking, input/output/tool blocking

                         Pydantic Deep Agents
+---------------------------------------------------------------------+
|                                                                     |
|   +----------+ +----------+ +----------+ +----------+ +---------+   |
|   | Planning | |Filesystem| | Subagents| |  Skills  | |  Teams  |   |
|   +----+-----+ +----+-----+ +----+-----+ +----+-----+ +----+----+   |
|        |            |            |            |            |        |
|        +------------+-----+------+------------+------------+        |
|                           |                                         |
|                           v                                         |
|  Summarization --> +------------------+ <-- Capabilities            |
|  Checkpointing --> |    Deep Agent    | <-- Hooks                   |
|  Cost Tracking --> |   (pydantic-ai)  | <-- Memory                  |
|  Loop Detect   --> |                  | <-- Limit Warner            |
|                    +--------+---------+                             |
|                             |                                       |
|           +-----------------+-----------------+                     |
|           v                 v                 v                     |
|    +------------+    +------------+    +------------+               |
|    |   State    |    |   Local    |    |   Docker   |               |
|    |  Backend   |    |  Backend   |    |  Sandbox   |               |
|    +------------+    +------------+    +------------+               |
|                                                                     |
+---------------------------------------------------------------------+

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Full Feature List

Expand

Tool-Calling

• ls, read_file, write_file, edit_file, glob, grep, execute — full filesystem access
• Docker sandbox with named workspaces — sandboxed execution, packages persist between sessions
• Web search (DuckDuckGo, Tavily, Brave) and web fetch
• Browser automation via Playwright — navigate, click, type_text, screenshot, execute_js, and more

Deep Agent Architecture

• Planning — Task tracking with subtasks, dependencies, and cycle detection
• Subagents / Multi-agent swarm — Sync/async delegation, background task management, soft/hard cancellation
• Agent Teams — Shared TODO lists with claiming and dependency tracking, peer-to-peer message bus
• Plan Mode — Dedicated planner subagent for structured planning before execution
• Persistent memory — MEMORY.md that persists across sessions, auto-injected into system prompt
• Self-improving — /improve analyzes past sessions, proposes updates to context files

Context & Memory

• Unlimited context — Auto-summarization when approaching token budget (LLM-based or sliding window)
• Context limit warnings — Model receives URGENT/CRITICAL messages when approaching 70% context usage
• Eviction capability — Intercepts large tool outputs via after_tool_execute before they enter history
• Context files — Auto-discover and inject AGENTS.md, CLAUDE.md, SOUL.md, .cursorrules, copilot-instructions, CONVENTIONS.md, CODING_GUIDELINES.md
• Checkpoints — Save state, rewind or fork conversations. In-memory and file-based stores. Per-run isolation via for_run()

Reliability

• Stuck loop detection — Detects repeated identical calls, A-B-A-B alternating, and no-op patterns. Warns or stops the agent
• Orphan repair — Fixes orphaned tool calls/results in conversation history before each model request
• Context limit warnings — Injects URGENT/CRITICAL messages so the model knows to wrap up

Production Features

• MCP — Connect any Model Context Protocol server
• Lifecycle hooks — Claude Code-style PRE/POST_TOOL_USE. Shell commands or Python handlers
• Structured output — Type-safe responses with Pydantic models via output_type
• Cost tracking — Token/USD budgets with automatic enforcement and real-time callbacks
• Streaming — Full streaming support for real-time responses
• Image support — Multi-modal analysis with image inputs
• Human-in-the-loop — Confirmation workflows for sensitive operations
• Output styles — Built-in (concise, explanatory, formal, conversational) or custom

CLI

• Interactive TUI (Textual) with streaming, tool visualization, session management
• Headless runner (pydantic-deep run) for CI/CD, benchmarks, scripted automation
• 20+ slash commands: /improve, /compact, /diff, /model, /provider, /skills, /theme, and more
• @filename file references, !command shell passthrough
• Tool approval dialogs with auto-approve
• Debug logging per session

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Contributing

git clone https://github.com/vstorm-co/pydantic-deepagents.git
cd pydantic-deepagents
make install
make test   # 100% coverage required
make all    # lint + typecheck + test

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Vstorm OSS Ecosystem

pydantic-deepagents is part of a broader open-source ecosystem for production AI agents:

Project	Description
full-stack-ai-agent-template	Zero to production AI app in 30 minutes. FastAPI + Next.js 15, 6 AI frameworks (incl. pydantic-deep), RAG pipeline, 75+ config options.
pydantic-ai-shields	Drop-in guardrails for Pydantic AI agents. 5 infra + 5 content shields.
pydantic-ai-subagents	Declarative multi-agent orchestration with token tracking.
pydantic-ai-summarization	Smart context compression for long-running agents.
pydantic-ai-backend	Sandboxed execution for AI agents. Docker + Daytona.
content-skills	Claude Code content studio — blog, social, slides, video, infographics — all brand-aware.
production-stack-skills	Claude Code skills for production-grade FastAPI, PostgreSQL, Docker, and observability.

Want the full stack? Use full-stack-ai-agent-template — it ships pydantic-deep integrated with FastAPI, Next.js, auth, WebSocket streaming, and RAG out of the box.

Browse all projects at oss.vstorm.co

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Star History

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License

MIT — see LICENSE

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