内容

<a href="https://github.com/vstorm-co/agenticos"> <img src="assets/agenticos-banner-light.png" width="100%" alt="AgenticOS by Vstorm, the Sovereign Agentic AI Layer: AI agents your whole team can use and improve. Open source (Apache-2.0), self-hosted, built on Pydantic AI, with budgets, approvals, guardrails and activity built in. Links to the AgenticOS repository on GitHub."> </a> From the team behind this repo: <a href="https://github.com/vstorm-co/agenticos">AgenticOS</a>, the Sovereign Agentic AI Layer. AI agents your whole team can use and improve: open source (Apache-2.0), self-hosted, built on Pydantic AI. <h1 align="center">Pydantic Deep Agents</h1> Open-source Claude Code — that you can also build on. A self-hosted terminal AI assistant and the Python framework behind it. Use it today, or ship your own agent in one function call. Any model. 100% type-safe. MIT. <img src="assets/cli_demo_v2.gif" alt="Pydantic Deep Agents CLI demo" width="800"> <a href="https://vstorm-co.github.io/pydantic-deepagents/">Docs</a> · <a href="https://pypi.org/project/pydantic-deep/">PyPI</a> · <a href="#-live-run-forking--the-feature-no-one-else-has">Forking</a> · <a href="#-why-pydantic-deep">Why</a> · <a href="#-cli--terminal-ai-assistant">CLI</a> · <a href="#-framework--build-your-own-agent">Framework</a> · <a href="https://vstorm-co.github.io/pydantic-deepagents/examples/">Examples</a> <a href="https://pypi.org/project/pydantic-deep/"><img src="https://img.shields.io/pypi/v/pydantic-deep.svg" alt="PyPI version"></a> <a href="https://pepy.tech/projects/pydantic-deep"><img src="https://static.pepy.tech/badge/pydantic-deep/month" alt="PyPI Downloads"></a> <a href="https://github.com/vstorm-co/pydantic-deepagents/stargazers"><img src="https://img.shields.io/github/stars/vstorm-co/pydantic-deepagents?style=flat&logo=github&color=yellow" alt="GitHub Stars"></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.10+-blue?logo=python&logoColor=white" alt="Python 3.10+"></a> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a> <a href="https://coveralls.io/github/vstorm-co/pydantic-deepagents?branch=main"><img src="https://coveralls.io/repos/github/vstorm-co/pydantic-deepagents/badge.svg?branch=main" alt="Coverage Status"></a> <a href="https://github.com/vstorm-co/pydantic-deepagents/actions/workflows/ci.yml"><img src="https://github.com/vstorm-co/pydantic-deepagents/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <a href="https://www.bestpractices.dev/projects/12495"><img src="https://www.bestpractices.dev/projects/12495/badge" alt="OpenSSF Best Practices"></a> <a href="https://github.com/vstorm-co/pydantic-deepagents/blob/main/SECURITY.md"><img src="https://img.shields.io/badge/security-policy-blueviolet?logo=shieldsdotio&logoColor=white" alt="Security Policy"></a> <a href="https://github.com/pydantic/pydantic-ai"><img src="https://img.shields.io/badge/Powered%20by-Pydantic%20AI-E92063?logo=pydantic&logoColor=white" alt="Pydantic AI"></a> <a href="https://x.com/Kacper95682155"><img src="https://img.shields.io/badge/X-000000?logo=x&logoColor=white" alt="X"></a> --- **Pydantic Deep Agents is two things in one repo:** 🖥️ **A terminal AI assistant** — a self-hosted, open-source alternative to Claude Code. Install it, point it at any model, and it plans, edits files, runs commands, searches the web, remembers across sessions, spawns sub-agents, and connects to MCP servers. Almost everything Claude Code does — on the model *you* choose. 🐍 **A Python framework** — the *exact same harness* behind a single function call. `create_deep_agent()` hands a model a filesystem, shell, planning, memory, sub-agents, sandboxed execution, MCP, and unlimited context. Build your own assistant, research agent, or coding tool without rewiring the plumbing every time. Both run on **[Pydantic AI](https://github.com/pydantic/pydantic-ai)**, work with **any model** (Claude, GPT, Gemini, local), and are **100% type-safe** and MIT-licensed — and they share one trick nothing else has: [**Live Run Forking**](#-live-run-forking--the-feature-no-one-else-has), splitting a single run into parallel branches an AI judge merges back together. ## Two ways to use it ### 🖥️ 1. Use the assistant A Claude-Code-style TUI in your terminal, on **any** model — no Python setup (the script installs `uv` + the CLI for you): ```bash curl -fsSL https://raw.githubusercontent.com/vstorm-co/pydantic-deepagents/main/install.sh | bash pydantic-deep ``` > Windows / manual: `pip install "pydantic-deep[cli]"` ### 🐍 2. Build your own One function call gives you a full deep agent: ```bash pip install pydantic-deep ``` ```python from pydantic_deep import create_deep_agent agent = create_deep_agent(model="anthropic:claude-sonnet-4-6") result = await agent.run("Build a REST API for auth") ``` --- ## ⑂ Live Run Forking — the feature no one else has Claude Code can't do this. Aider can't. LangGraph and CrewAI can't. **It's the reason to use pydantic-deep.** When an agent hits a fork in the road — "should I refactor this with a decorator or a context manager?" — most tools force one bet. Pydantic Deep Agents lets the run **branch**: ``` ┌── branch A: "use a decorator" ── tests: 8/8 ✓ conf 0.71 agent.run("refactor auth") ──┬─┼── branch B: "use a context manager" ── tests: 6/8 ✗ conf 0.42 (shared history) │ └── branch C: "extract a base class" ── tests: 8/8 ✓ conf 0.55 │ └──► ⚖️ AI judge weighs quality + tests + consistency → adopts branch A, continues the run ``` Each branch is **fully isolated**: a copy-on-write filesystem overlay (reads fall through to the parent, writes stay local), its own steering message, and its own `budget_usd` cap. The coordinator resolves the fork with one of four acceptance modes — `manual`, `auto`, `auto_with_fallback` (default), or `vote` — and the winning branch's history is adopted as the parent run's continuation. **Framework — opt in with one flag:** ```python agent = create_deep_agent( model="anthropic:claude-sonnet-4-6", forking=True, # gives the agent: fork_run, inspect_branches, ) # merge_or_select, diff_branches, fork_cost, terminate_branch ``` **Or run a real test command against every branch and let exit codes decide the winner:** ```python from pydantic_deep import LiveForkCapability agent = create_deep_agent( forking=LiveForkCapability(test_command="pytest -q", test_timeout_s=120), ) # confidence = quality_spread·0.4 + test_pass_ratio·0.4 + internal_consistency·0.2 ``` **CLI — fork an in-flight conversation, watch branches stream live, merge the best:** ``` /fork # split the current run into N parallel branches >>A try a decorator # steer branch A >>B use a contextmgr # steer branch B /merge # resolve — manual picker, AI judge, or vote ``` Live per-branch panels stream each approach side by side; a judge screen scores them; you accept, review the diff, or decline. Configure branch count, budgets, per-branch models, and merge strategy with `/fork-config`. > 📖 Full reference: [docs/capabilities/live-fork.md](docs/capabilities/live-fork.md) --- ## 🆚 Why pydantic-deep? The only tool that is **a terminal assistant** *and* **a Python framework** *and* can **fork its own runs** — without giving up type safety or your choice of model. | | **Pydantic Deep** | Claude Code | Aider | LangGraph | CrewAI | |---|:---:|:---:|:---:|:---:|:---:| | Terminal TUI assistant | ✅ | ✅ | ✅ | — | — | | Python framework / library | ✅ | — | ~ | ✅ | ✅ | | **Live run forking + AI judge** | ✅ | — | — | — | — | | Multi-agent swarm + message bus | ✅ | ~ | — | ✅ | ✅ | | Any model / any provider | ✅ | Anthropic | ✅ | ✅ | ✅ | | Sandboxed Docker execution | ✅ | — | ~ | DIY | DIY | | Persistent memory + skills | ✅ | ✅ | — | DIY | ~ | | Type-safe structured output | ✅ | — | — | ~ | ~ | | MCP servers | ✅ | ✅ | — | ~ | ~ | | Self-hosted, open source | ✅ MIT | — | ✅ | ✅ | ✅ | ✅ first-class · ~ partial / via extensions · — not available · DIY you wire it yourself. Comparison reflects each project as of 2026-06; corrections welcome via PR. --- ## What's New - **2026-06-01**  **v0.3.24** — **Live Run Forking** — split an in-flight `agent.run()` into N parallel branches with copy-on-write isolation, per-branch budgets, a test-runner hook, and four merge modes (`manual` / `auto` / `auto_with_fallback` / `vote`). Opt in with `forking=True`. - **2026-06-01**  **v0.3.23** — **MCP client support** (framework + CLI). Connect GitHub, Figma (OAuth), Context7, DeepWiki, or any custom server. Import servers straight from Claude Code. New interactive `/mcp` command. Plus a full CLI presentation pass: clipboard image paste, real `+/-` diffs, tool icons, turn summaries. - **2026-06-01**  **v0.3.23** — **Automatic fallback-model retry** — `fallback_model=` wraps your primary in a `FallbackModel` chain; fires on API errors but never on auth errors. Plus a batteries-included **security hook preset** (`default_security_hook()`) and three new output styles (`markdown`, `json-only`, `bullet`). - **2026-04-22**  **v0.3.17** — LiteParse document parsing (`include_liteparse=True`) — PDFs, DOCX, XLSX, PPTX, and images with optional OCR, all local. - **2026-04-10**  **v0.3.5** — Headless runner (`pydantic-deep run`), Docker sandbox with named workspaces, browser automation via Playwright. > Full history: [CHANGELOG.md](CHANGELOG.md) --- ## 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, unlimited context, and — uniquely — the ability to fork. <table> <tr> <td>⑂ Live run forking</td> <td>Split a run into N isolated branches, each trying a different approach. AI judge or test results pick the winner. No other agent framework has this.</td> </tr> <tr> <td>🔧 Tool-calling</td> <td>File read/write/edit, shell execution, glob, grep, web search, web fetch, browser automation — wired up and ready.</td> </tr> <tr> <td>🤝 Multi-agent / swarm</td> <td>Spawn subagents for parallel workstreams. Shared TODO lists with claiming. Peer-to-peer message bus. Full team coordination.</td> </tr> <tr> <td>🧠 Persistent memory</td> <td>MEMORY.md persists across sessions. Auto-injected into the system prompt. Each agent has isolated memory by default.</td> </tr> <tr> <td>♾️ Unlimited context</td> <td>Auto-summarization when approaching the token budget. LLM-based or zero-cost sliding window. Never hits a context wall.</td> </tr> <tr> <td>🐳 Sandboxed execution</td> <td>Docker sandbox with named workspaces. Installed packages persist between sessions. Project dir mounted at /workspace.</td> </tr> <tr> <td>🗂️ Plan Mode</td> <td>Dedicated planner subagent asks clarifying questions and structures the work before execution begins. Headless-compatible.</td> </tr> <tr> <td>🔖 Checkpoints</td> <td>Save conversation state at any point. Rewind to any checkpoint. Fork sessions to explore alternative approaches.</td> </tr> <tr> <td>📚 Skills system</td> <td>Domain-specific knowledge loaded on demand from SKILL.md files. Built-in: code-review, refactor, test-writer, git-workflow, and more.</td> </tr> <tr> <td>📄 Document parsing</td> <td>Parse PDFs, DOCX, XLSX, PPTX, and images with optional OCR via LiteParse. Runs locally — no cloud services required.</td> </tr> <tr> <td>🔌 MCP</td> <td>Connect any Model Context Protocol server — GitHub, Figma (OAuth), Context7, DeepWiki, or custom. Import straight from Claude Code.</td> </tr> <tr> <td>⚡ Lifecycle hooks + security preset</td> <td>Claude Code-style PRE/POST_TOOL_USE hooks. Shell or Python handlers. <code>default_security_hook()</code> blocks destructive commands out of the box.</td> </tr> <tr> <td>📐 Structured output</td> <td>Type-safe Pydantic model responses via <code>output_type</code>. No JSON parsing. No <code>dict["key"]</code>. Full IDE autocomplete.</td> </tr> <tr> <td>🔁 Fallback models</td> <td>Primary model fails? <code>fallback_model=</code> hops to the next in the chain — on API errors, never on auth errors.</td> </tr> <tr> <td>🔄 Stuck loop detection</td> <td>Detects repeated identical tool calls, A-B-A-B alternating patterns, and no-op calls. Warns the model or stops the run.</td> </tr> <tr> <td>💰 Cost tracking</td> <td>Real-time token and USD cost tracking per run and cumulative. Hard budget limits with <code>BudgetExceededError</code>.</td> </tr> <tr> <td>✨ Self-improving</td> <td><code>/improve</code> analyzes past sessions and proposes updates to MEMORY.md, SOUL.md, and AGENTS.md.</td> </tr> <tr> <td>🏷️ 100% type-safe</td> <td>Pyright strict + MyPy strict. 100% test coverage. Every public API is fully typed — safe to use in production.</td> </tr> </table> Built natively on **[pydantic-ai](https://github.com/pydantic/pydantic-ai)** — uses the Capabilities API directly, inherits all pydantic-ai streaming, multi-model support, and Pydantic validation automatically. --- ## 🖥️ CLI — Terminal AI Assistant A Claude Code-style terminal AI assistant that works with **any model and any provider** — and forks. ### Install (macOS & Linux) ```bash curl -fsSL https://raw.githubusercontent.com/vstorm-co/pydantic-deepagents/main/install.sh | bash ``` No Python setup required — the script installs uv and the CLI automatically. Then: ```bash 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 | |:-:|---------| | ⑂ | **Live run forking** — split a run into branches, stream them side by side, merge the winner | | 💬 | Streaming chat with tool call visualization, icons, and real `+/-` diffs | | 📁 | File read / write / edit, shell execution, glob, grep | | 🤝 | Task planning, plan mode, and subagent delegation | | 🧠 | Persistent memory and self-improvement across sessions | | ♾️ | Context compression for unlimited conversations | | 🔖 | Checkpoints — save, rewind, and fork any session | | 🔌 | MCP servers via `/mcp` — GitHub, Figma (OAuth), and more; import from Claude Code | | 🌐 | Web search & fetch built-in · 🖥️ browser automation via Playwright (`--browser`) | | 🐳 | Docker sandbox — sandboxed execution with named workspaces | | 💭 | Extended thinking — `minimal` / `low` / `medium` / `high` / `xhigh` | | 📋 | Clipboard image paste (`Ctrl+V` / `/paste`) — multimodal prompts | | 💰 | 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 | | ✨ | `/fork`, `/merge`, `/improve`, `/skills`, `/mcp`, `/model`, `/theme`, `/compact`, and more | ### Usage ```bash # 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 # 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 # 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](docs/cli/index.md) for the full reference. --- ## 🐍 Framework — Build Your Own Agent ```bash pip install pydantic-deep ``` One function call gives you a production deep agent with planning, tool-calling, multi-agent delegation, persistent memory, unlimited context, forking, and cost tracking. Everything is a toggle: ```python from pydantic_deep import LocalWorkspace, create_deep_agent, create_default_deps agent = create_deep_agent( model="anthropic:claude-sonnet-4-6", workspace=LocalWorkspace("."), # where it works: memory (default), disk, Docker, … forking=True, # ⑂ split a run into parallel branches + AI judge 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 include_liteparse=True, # Document parsing — PDF, DOCX, XLSX + OCR web_search=True, # Tool-calling: web search thinking="high", # Extended thinking / reasoning effort context_manager=True, # Unlimited context via auto-summarization cost_tracking=True, # Token/USD budget enforcement fallback_model="openai:gpt-5.4", # auto-retry if the primary model fails include_checkpoints=True, # Save, rewind, and fork conversations ) deps = create_default_deps() 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"]`: ```python 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: ```python 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") ``` ### Claude Code-Style Lifecycle Hooks + Security Preset ```python from pydantic_deep import create_deep_agent, default_security_hook, Hook, HookEvent agent = create_deep_agent( hooks=[ *default_security_hook(), # blocks destructive shell, path traversal, secret leaks Hook( event=HookEvent.PRE_TOOL_USE, command="echo 'Tool: $TOOL_NAME args: $TOOL_INPUT' >> /tmp/audit.log", ), ], ) ``` ### MCP Servers Connect GitHub, Figma (OAuth), Context7, DeepWiki, or any custom server — auth handled for you: ```python from pydantic_deep import create_deep_agent, build_mcp_server, MCPServerConfig deepwiki = build_mcp_server( MCPServerConfig(name="deepwiki", transport="http", url="https://mcp.deepwiki.com/mcp") ) agent = create_deep_agent(mcp_servers=[deepwiki]) # curated defaults via builtin_mcp_servers() ``` ### 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 | | `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](https://agents.md/). > See the full [API reference](https://vstorm-co.github.io/pydantic-deepagents/api/toolsets/) for all options. --- ## 🔬 DeepResearch — Reference App A full-featured research deep agent with web UI — built entirely on Pydantic Deep Agents. <table> <tr> <td width="50%"> <a href="apps/deepresearch/"><img src="assets/planner_asks_question.png" alt="Planner subagent asks clarifying questions"></a> Plan Mode — planner asks clarifying questions </td> <td width="50%"> <a href="apps/deepresearch/"><img src="assets/spawn_subagents_deepresearch.png" alt="Parallel subagent research"></a> Multi-Agent Swarm — 5 subagents researching in parallel </td> </tr> <tr> <td width="50%"> <a href="apps/deepresearch/"><img src="assets/excalidraw_in_deepresearch.png" alt="Excalidraw canvas"></a> Excalidraw Canvas — live diagrams synced with agent </td> <td width="50%"> <a href="apps/deepresearch/"><img src="assets/display_files_deepresearch.png" alt="File browser"></a> File Browser — workspace files with inline preview </td> </tr> </table> Web search (Tavily, Brave, Jina), sandboxed code execution, Excalidraw diagrams, plan mode, report export. ```bash cd apps/deepresearch && uv sync && cp .env.example .env uv run deepresearch # → http://localhost:8080 ``` > See [apps/deepresearch/README.md](apps/deepresearch/README.md) for full setup. --- ## Architecture Pydantic Deep Agents uses pydantic-ai's native **Capabilities API** for all cross-cutting concerns — forking, hooks, memory, skills, context files, teams, and plan mode are all first-class pydantic-ai capabilities. ``` Pydantic Deep Agents +---------------------------------------------------------------------+ | | | +----------+ +----------+ +----------+ +----------+ +---------+ | | | Planning | |Filesystem| | Subagents| | Skills | | Teams | | | +----+-----+ +----+-----+ +----+-----+ +----+-----+ +----+----+ | | | | | | | | | +------------+-----+------+------------+------------+ | | | | | v | | Forking --> +------------------+ <-- Capabilities | | Summarization --> | Deep Agent | <-- Hooks | | Checkpointing --> | (pydantic-ai) | <-- Memory | | Cost Tracking --> | | <-- MCP | | +--------+---------+ | | | | | +-----------------+-----------------+ | | v v v | | +------------+ +------------+ +------------+ | | | State | | Local | | Docker | | | | Workspace | | Workspace | | Workspace | | | +------------+ +------------+ +------------+ | | | +---------------------------------------------------------------------+ ``` ### Modular Packages Every component is a standalone package — use only what you need: | Package | What It Does | |---------|--------------| | [pydantic-ai-backend](https://github.com/vstorm-co/pydantic-ai-backend) | Workspaces (Docker, sandboxd, Kubernetes, Daytona, in-memory) and the console toolset | | [pydantic-ai-todo](https://github.com/vstorm-co/pydantic-ai-todo) | Task planning with subtasks and dependencies | | [subagents-pydantic-ai](https://github.com/vstorm-co/subagents-pydantic-ai) | Sync/async delegation, background tasks, cancellation | | [summarization-pydantic-ai](https://github.com/vstorm-co/summarization-pydantic-ai) | LLM summaries or zero-cost sliding window | | [pydantic-ai-shields](https://github.com/vstorm-co/pydantic-ai-shields) | Cost tracking, input/output/tool blocking | --- ## Full Feature List <details> <summary>Expand</summary> ### Live Run Forking - Split an in-flight `agent.run()` into N parallel branches sharing history up to the fork point - Copy-on-write `BranchOverlay` filesystem isolation — reads fall through to parent, writes stay local - Per-branch steering messages, per-branch `budget_usd` caps, aggregate budget enforcement - Four merge modes: `manual`, `auto`, `auto_with_fallback` (default), `vote` - Autonomous `JudgeAgent` with structured `JudgeVerdict`; `compute_confidence` blends quality, test pass ratio, and consistency - Test-runner hook — run a shell command against each branch's snapshot; exit code feeds the judge - Agent tools: `fork_run`, `inspect_branches`, `merge_or_select`, `terminate_branch`, `diff_branches`, `fork_cost` - CLI: `/fork`, `/merge`, `/fork-config`, live per-branch streaming panels, judge screen, merge acceptance gate ### 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 AGENTS.md, CLAUDE.md, SOUL.md, .cursorrules, copilot-instructions, and more - **Checkpoints** — Save state, rewind or fork conversations. In-memory and file-based stores ### Production Features - **MCP** — Connect any Model Context Protocol server; import from Claude Code; OAuth + keystore auth - **Lifecycle hooks** — Claude Code-style PRE/POST_TOOL_USE. Shell commands or Python handlers - **Security preset** — `default_security_hook()` blocks destructive commands, path traversal, secret leaks - **Fallback models** — `fallback_model=` chains; fires on API errors, never on auth errors - **Structured output** — Type-safe responses with Pydantic models via `output_type` - **Cost tracking** — Token/USD budgets with automatic enforcement and real-time callbacks - **Output styles** — Built-in (concise, explanatory, formal, conversational, markdown, json-only, bullet) or custom - **Streaming · Image support · Human-in-the-loop confirmation workflows** ### CLI - Interactive TUI (Textual) with streaming, tool visualization, live fork panels, session management - Headless runner (`pydantic-deep run`) for CI/CD, benchmarks, scripted automation - 25+ slash commands: `/fork`, `/merge`, `/mcp`, `/improve`, `/compact`, `/diff`, `/model`, `/skills`, `/theme`, and more - `@filename` file references, `!command` shell passthrough, clipboard image paste - Tool approval dialogs with auto-approve · debug logging per session </details> --- ## Contributing ```bash 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 ``` See [CONTRIBUTING.md](CONTRIBUTING.md). Good first issues are [labeled here](https://github.com/vstorm-co/pydantic-deepagents/labels/good%20first%20issue). --- ## Vstorm OSS Ecosystem **pydantic-deep** is part of a broader open-source ecosystem for production AI agents: | Project | Description | Stars | |---------|-------------|-------| | **[AgenticOS](https://github.com/vstorm-co/agenticos)** | Open-source (Apache-2.0), self-hosted platform to build, share and govern AI agents across a company. | [![Stars](https://img.shields.io/github/stars/vstorm-co/agenticos?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/agenticos) | | **[full-stack-ai-agent-template](https://github.com/vstorm-co/full-stack-ai-agent-template)** | Zero to production AI app in 30 minutes. FastAPI + Next.js 15, 5 AI frameworks (incl. Pydantic Deep Agents), RAG pipeline, 75+ config options. | [![Stars](https://img.shields.io/github/stars/vstorm-co/full-stack-ai-agent-template?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/full-stack-ai-agent-template) | | **[pydantic-ai-shields](https://github.com/vstorm-co/pydantic-ai-shields)** | Drop-in guardrails for Pydantic AI agents. 5 infra + 5 content shields. | [![Stars](https://img.shields.io/github/stars/vstorm-co/pydantic-ai-shields?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/pydantic-ai-shields) | | **[subagents-pydantic-ai](https://github.com/vstorm-co/subagents-pydantic-ai)** | Declarative multi-agent orchestration with token tracking. | [![Stars](https://img.shields.io/github/stars/vstorm-co/subagents-pydantic-ai?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/subagents-pydantic-ai) | | **[summarization-pydantic-ai](https://github.com/vstorm-co/summarization-pydantic-ai)** | Smart context compression for long-running agents. | [![Stars](https://img.shields.io/github/stars/vstorm-co/summarization-pydantic-ai?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/summarization-pydantic-ai) | | **[pydantic-ai-backend](https://github.com/vstorm-co/pydantic-ai-backend)** | Sandboxed execution for AI agents. Docker + Daytona. | [![Stars](https://img.shields.io/github/stars/vstorm-co/pydantic-ai-backend?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/pydantic-ai-backend) | | **[content-skills](https://github.com/vstorm-co/content-skills)** | Claude Code content studio — blog, social, slides, video, infographics — all brand-aware. | [![Stars](https://img.shields.io/github/stars/vstorm-co/content-skills?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/content-skills) | | **[production-stack-skills](https://github.com/vstorm-co/production-stack-skills)** | Claude Code skills for production-grade FastAPI, PostgreSQL, Docker, and observability. | [![Stars](https://img.shields.io/github/stars/vstorm-co/production-stack-skills?style=flat&logo=github&color=yellow)](https://github.com/vstorm-co/production-stack-skills) | > **Want the full stack?** Use [full-stack-ai-agent-template](https://github.com/vstorm-co/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 [github.com/vstorm-co](https://github.com/vstorm-co) --- ## Star History If pydantic-deep saved you from wiring an agent harness by hand — **[give it a ⭐](https://github.com/vstorm-co/pydantic-deepagents)**. It's the single biggest thing that helps the project grow. <a href="https://www.star-history.com/#vstorm-co/pydantic-deepagents&type=date"> <img src="https://api.star-history.com/svg?repos=vstorm-co/pydantic-deepagents&type=date" alt="Star History" width="600"> </a> --- ## License MIT — see [LICENSE](LICENSE) --- <div align="center"> ### Need help shipping AI agents in production? We're <a href="https://vstorm.co">Vstorm</a> — an Applied Agentic AI Engineering Consultancy with 30+ production agent implementations. Pydantic Deep Agents is what we build them with. <a href="https://vstorm.co/contact-us/"> <img src="https://img.shields.io/badge/Talk%20to%20us%20%E2%86%92-0066FF?style=for-the-badge&logoColor=white" alt="Talk to us"> </a> Made with **care** by <a href="https://vstorm.co">Vstorm</a> </div>

pydantic-deepagents

内容

连接信息

猜你喜欢

pydantic-deepagents

微信扫一扫分享

需要认证

内容

连接信息

猜你喜欢