Shipit Agent

SHIPIT Agent

A clean, powerful, open-source Python runtime for building tool-using AI agents.

One consistent API over every major LLM provider — with tools, skills, memory, MCP, a rule-based permission layer, prompt caching, deep multi-agent orchestration, RAG, and structured streaming events.

📖 Documentation · 📦 PyPI · Quick start · Changelog · Security

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What is SHIPIT Agent?

SHIPIT Agent is a small, explicit runtime for building production agents in Python. You bring an LLM; the runtime gives you the loop around it — tool calling, retries, streaming, memory, sessions, permissions, and cost tracking — plus a deep library of batteries (40+ built-in tools, 17 SaaS connectors, RAG, multi-agent orchestration, browser automation).

It is provider-agnostic by design: the same agent code runs on OpenAI, Anthropic, AWS Bedrock, Google Vertex/Gemini, Groq, Together, Ollama, or any of 100+ models through LiteLLM. Swap the model in one line — nothing else changes.

from shipit_agent import Agent
from shipit_agent.llms import build_llm_from_env

agent = Agent.with_builtins(llm=build_llm_from_env())   # any provider
print(agent.run("Find every TODO in this repo and summarize them.").output)

> The only hard dependency is pydantic. Everything else (a provider SDK, Playwright, a vector > store) is an optional extra you install when you need it. Python 3.11+ · MIT · 1850+ tests.

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Highlights

• 🤖 The Agent — one runtime: tool calling, retries, parallel tools, context compaction, and a

final-answer guarantee. Agent.with_builtins() ships the full tool catalogue.

• 🔌 Any LLM — OpenAI · Anthropic · Bedrock · Vertex · Gemini · Groq · Together · Ollama ·

OpenRouter · 100+ via LiteLLM. Native adapters where it matters, one interface everywhere.

• 🛡️ Control plane — a fast, rule-based permission engine (allow/deny/ask), plan mode

(read-only research before acting), and hooks that can block or rewrite any tool call.

• ⚡ Prompt caching — cross-provider cache-read accounting (Anthropic/Bedrock/Vertex

cache_control + OpenAI automatic caching) so repeated calls bill at a fraction of the cost.

• 🧰 Tools & connectors — 40+ built-in tools (bash, SQL, files, web search, code execution,

vision, PDF…) and 17 SaaS connectors (GitHub, Slack, Gmail, Jira, Salesforce, Stripe…).

• 🔗 MCP — connect Model Context Protocol servers over stdio, HTTP, or a persistent subprocess.
• 🧠 Deep agents — GoalAgent, ReflectiveAgent, Supervisor/Worker, ShipCrew, and the

create_deep_agent() factory for autonomous, multi-step, multi-agent work.

• 📚 Super RAG — hybrid vector + BM25 search with auto-cited sources and pluggable backends

(Chroma, Qdrant, pgvector).

• 🚀 Autopilot — long-running autonomous loops with a critic, artifacts, fan-out, and a scheduler.
• 🖥️ Computer use — drive a real browser via screenshots + a vision model (works in Jupyter).
• 📊 Production-ready — sessions, memory consolidation, structured output with validation-retry,

streaming events (+ SSE/WebSocket packets), tracing (file/OTel/LangSmith), and budgets.

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Installation

Requirements: Python 3.11+ (3.11 – 3.14 supported). The only hard dependency is pydantic; provider SDKs and heavier features are opt-in extras.

From PyPI (recommended)

pip install shipit-agent

Optional extras

Install only what you need — each extra pulls in the relevant third-party packages:

| Extra | Installs | For | | --- | --- | --- | | openai | openai | OpenAI / OpenAI-compatible | | anthropic | anthropic | native Anthropic (Claude) | | bedrock | boto3 | AWS Bedrock | | google | google-generativeai | Gemini | | groq / together / ollama | provider SDK | Groq / Together / Ollama | | litellm | litellm | 100+ models via one interface | | playwright | playwright | browser automation / computer use | | pdf | pypdf | the PDF tool | | sql | sqlalchemy | the SQL tool (add your own driver) | | rag-chroma / rag-qdrant / rag-pgvector | vector store | RAG backends | | rag-openai / rag-cohere / rag-sentence-transformers | embedder | RAG embeddings | | otel / langsmith | exporters | tracing | | all | everything | kitchen sink |

pip install "shipit-agent[anthropic]"        # one provider
pip install "shipit-agent[anthropic,playwright,rag-chroma]"   # combine
pip install "shipit-agent[all]"              # everything

> Browser automation / computer use also needs the Chromium binary: > ``bash > pip install "shipit-agent[playwright]" && playwright install chromium > ``

From source (development)

git clone https://github.com/shipiit/shipit_agent.git
cd shipit_agent
pip install -e ".[dev]"     # editable install with test/docs tooling
pytest -q                   # 1850+ tests
ruff check .

Alternatives: pip install . (non-editable), pip install -r requirements.txt, or poetry install.

Verify

import shipit_agent
print(shipit_agent.__version__)

> Notebook tip: if imports look out of date, your kernel may be using an older globally > installed copy. Run pip install -U shipit-agent (or pip install -e . from the repo) in the > kernel's environment.

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Environment setup

The fastest way to choose a model is environment variables — copy [.env.example](.env.example) to .env and fill in what you use:

# Pick the provider; build_llm_from_env() reads these:
SHIPIT_LLM_PROVIDER=bedrock            # openai | anthropic | bedrock | vertex | gemini | groq | together | ollama | litellm

# …then the provider's own credentials, e.g.:
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
AWS_REGION_NAME=us-east-1             # Bedrock uses your AWS region / profile (no key)
SHIPIT_BEDROCK_MODEL=bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0
GEMINI_API_KEY=...
GROQ_API_KEY=...
SHIPIT_LITELLM_MODEL=openrouter/openai/gpt-4o-mini    # for the litellm provider

from shipit_agent import Agent
from shipit_agent.llms import build_llm_from_env

agent = Agent.with_builtins(llm=build_llm_from_env())   # reads SHIPIT_LLM_PROVIDER + creds
print(agent.run("Hello, who are you?").output)

Run diagnostics any time with agent.doctor() to check provider config, credentials, and tools.

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Use any LLM provider

The agent never cares which model it talks to. Configure once via env, or instantiate an adapter directly:

from shipit_agent.llms import (
    build_llm_from_env, OpenAIChatLLM, AnthropicChatLLM,
    BedrockChatLLM, GeminiChatLLM, GroqChatLLM, LiteLLMChatLLM,
)

llm = build_llm_from_env("bedrock")                       # env-driven (prod)
llm = OpenAIChatLLM(model="gpt-4o")                       # native OpenAI
llm = AnthropicChatLLM(model="claude-opus-4-1")           # native Anthropic
llm = BedrockChatLLM(model="bedrock/us.meta.llama4-maverick-17b-instruct-v1:0")
llm = GeminiChatLLM(model="gemini/gemini-2.0-flash")
llm = GroqChatLLM(model="groq/llama-3.3-70b-versatile")
llm = LiteLLMChatLLM(model="together_ai/meta-llama/Llama-3.1-70B-Instruct-Turbo")

| Provider | Adapter | Env (SHIPIT_LLM_PROVIDER=) | Auth | | --- | --- | --- | --- | | OpenAI | OpenAIChatLLM | openai | OPENAI_API_KEY | | Anthropic | AnthropicChatLLM | anthropic | ANTHROPIC_API_KEY | | AWS Bedrock | BedrockChatLLM | bedrock | AWS region / profile | | Google Vertex | VertexAIChatLLM | vertex | service-account JSON | | Gemini | GeminiChatLLM | gemini | GEMINI_API_KEY | | Groq | GroqChatLLM | groq | GROQ_API_KEY | | Together | TogetherChatLLM | together | TOGETHERAI_API_KEY | | Ollama (local) | OllamaChatLLM | ollama | — | | LiteLLM / OpenRouter | LiteLLMChatLLM / LiteLLMProxyChatLLM | litellm | per provider |

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Core building blocks

Custom tools

Wrap any Python callable — the agent reads its signature and calls it when useful:

from shipit_agent import Agent, FunctionTool

def get_weather(city: str) -> str:
    """Current weather for a city."""
    return f"{city}: 22°C, clear"

agent = Agent.with_builtins(llm=llm, tools=[FunctionTool.from_callable(get_weather)])
agent.run("What's the weather in Tokyo — umbrella?")

Skills

Reusable behaviour templates that shape how the agent thinks and which tools it reaches for:

agent = Agent.with_builtins(
    llm=llm,
    skills=["code-workflow-assistant", "database-architect"],
    auto_use_skills=True,      # also match skills from the prompt
)

Sessions & memory

session = agent.chat_session(session_id="user-42")
session.send("My name is Ada. I build compilers.")
session.send("What was my name again?")          # → remembers across turns

Persist across processes with FileSessionStore, and distill conversations into durable facts with MemoryConsolidator.

Structured output

from pydantic import BaseModel

class Ticket(BaseModel):
    title: str; priority: str; tags: list[str]

result = agent.run("Triage: 'login broken on Safari'", output_schema=Ticket)
print(result.parsed)            # validated; auto-retries inside the same conversation

Streaming

for event in agent.stream("Write a haiku about shipping code"):
    if event.type == "text_delta":
        print(event.payload["chunk"], end="", flush=True)
    elif event.type == "tool_called":
        print("→", event.payload["name"])

Events also serialize to ready-made SSE / WebSocket packets for web UIs.

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The control plane

A Claude Code-style safety layer — rule-based, no extra LLM call.

from shipit_agent import Agent, PermissionEngine

agent = Agent.with_builtins(
    llm=llm,
    permissions=PermissionEngine(
        deny=["bash", "*_delete"],   # never run these
        ask=["sql"],                 # require approval
        allow=["read*", "grep*"],    # always fine
    ),
)

# Read-only "plan mode" — research and propose, take no action:
plan = agent.plan("Migrate the billing schema to multi-tenant.").output

• Modes: default, acceptEdits, plan, bypass.
• permission_callback(name, args) for programmatic human-in-the-loop approval.
• Blocking hooks — before_tool hooks can deny a call or rewrite its arguments;

on_user_prompt can redact prompts:

@hooks.on_before_tool
def guard(name, args):
    if name == "bash" and "rm -rf" in args.get("command", ""):
        return {"decision": "deny", "reason": "destructive command"}

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Performance: prompt caching

The runtime rebuilds the same system prompt + tool schemas each turn — the ideal cacheable prefix.

from shipit_agent.llms import AnthropicChatLLM
llm = AnthropicChatLLM("claude-opus-4-1", prompt_caching=True)   # default on for Claude

cache_control breakpoints are placed on tools + system prompt; responses surface cache_read_input_tokens / cache_creation_input_tokens, which flow into CostTracker. Caching spans Anthropic, Bedrock, Vertex (cache_control) and OpenAI (automatic) — cache reads bill at ~10% of input.

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Deep agents & orchestration

from shipit_agent import create_deep_agent, Goal

# Autonomous goal decomposition with a planner / explorer / coder / verifier loop:
agent = create_deep_agent(llm=llm, tools=[...])
result = agent.run(Goal(objective="Build and test a REST API for todos"))

GoalAgent (decompose → execute), ReflectiveAgent (self-improve to a quality bar), Supervisor + Worker (hierarchical), ShipCrew (role-based crews), AdaptiveAgent, and PersistentAgent (checkpoint + resume) are all first-class.

Super RAG

from shipit_agent import RAG, Agent
from shipit_agent.rag.embedder import HashingEmbedder

rag = RAG.default(embedder=HashingEmbedder())
rag.index_text("Payments run on Stripe; refunds settle in 5–7 days.", source="ops.md")

agent = Agent.with_builtins(llm=llm, rag=rag)     # retrieves, then answers with cited sources
print(agent.run("How long do refunds take?").rag_sources)

Hybrid vector + BM25 ranking, a document chunker, multiple embedders/rerankers, and pluggable backends (Chroma, Qdrant, pgvector).

Autopilot, computer use & connectors

# Drive a real browser (works in Jupyter):
from shipit_agent.computer_use import ComputerUseAgent, PlaywrightBrowserSession

with PlaywrightBrowserSession.launch(headless=True) as browser:
    ComputerUseAgent(llm=claude_llm, browser=browser,
                     goal="Find the iPhone 15 Pro price on apple.com").run()

Autopilot runs long, unattended jobs with a critic, artifacts, fan-out, and a scheduler. 17 SaaS connectors — GitHub, GitLab, Slack, Gmail, Google Drive/Sheets/Calendar, Jira, Linear, Notion, Confluence, HubSpot, Salesforce, Stripe, Zendesk, Figma, LinkedIn — share a credential store with built-in OAuth helpers.

MCP

from shipit_agent.mcp import RemoteMCPServer

agent = Agent.with_builtins(llm=llm, mcps=[RemoteMCPServer(name="fs", transport=...)])

Connect MCP servers over stdio, HTTP, or a persistent subprocess — their tools join the agent's tool set automatically.

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Observability & cost

• Tracing — FileTraceStore, OpenTelemetry, and LangSmith exporters.
• Cost & budgets — CostTracker prices every call from a model table; Budget enforces a

ceiling (and flags unknown-model pricing instead of silently billing $0).

• Verifier network — an optional cheap LLM that vetoes hallucinated tool calls and detects

stalling, complementing the rule-based permission engine.

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Examples & notebooks

• [examples/](examples/) — runnable scripts (basic agent, custom tools, parallel tools,

cost budgets, multi-turn memory, async runtime, secure tools, the verifier guard, and more).

• [notebooks/](notebooks/) — 60+ Jupyter notebooks covering agents, streaming, MCP,

connectors, deep agents, RAG, skills, autopilot, the control plane, prompt caching, and the memory tool.

python examples/run_multi_tool_agent.py

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Documentation

• 🌐 Full documentation site — searchable, with guides for every

subsystem.

• 📓 [CHANGELOG.md](CHANGELOG.md) · [release-notes/](release-notes/) — per-release detail.
• 🧰 [TOOLS.md](TOOLS.md) — the built-in tool catalogue.
• 🔐 [SECURITY.md](SECURITY.md) · ⚖️ [LICENSE.md](LICENSE.md) (MIT).

Contributing

Issues and PRs are welcome. Install the dev extras, keep the suite green, and run the linter:

pip install -e ".[dev]"
pytest -q
ruff check .

See [CONTRIBUTING.md](CONTRIBUTING.md) for the full guide.

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Built with Love. Powered by your choice of AI models.

Ship it fast. Ship it right.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: shipiit
• Source: shipiit/shipit_agent
• License: MIT
• Homepage: https://docs.shipiit.com/

Install and usage instructions live in the source repository linked above.