delfhos 0.6.0

Delfhos — AI agent SDK with typed connections and tool orchestration

Delfhos

Python SDK for building AI agents that use real tools — Gmail, SQL, Drive, Sheets, MCP servers, and your own functions — with safe, human-in-the-loop execution.

Full documentation at delfhos.com/docs

---

How it works

You describe a task in plain English. Delfhos:

1. Picks the relevant tools from the ones you configured
2. Writes Python code to accomplish the task
3. Executes that code in a sandbox against your real services
4. Retries automatically if something fails

You stay in control: restrict which actions each tool can take, and require approval before any write, send, or delete.

---

Install

pip install delfhos

API Key

Delfhos supports Gemini, OpenAI, and Anthropic models. Export the key for the provider you want to use:

export GOOGLE_API_KEY="..."    # Gemini
export OPENAI_API_KEY="..."    # OpenAI
export ANTHROPIC_API_KEY="..."  # Claude

---

Try it instantly (no credentials needed)

The sandbox tools come pre-loaded with dummy data so you can run your first agent right now:

from delfhos import Agent
from delfhos.sandbox import MockEmail, MockDatabase

agent = Agent(
    tools=[MockEmail(confirm=False), MockDatabase(confirm=False)],
    llm="gemini-3.1-flash-lite-preview",
)

agent.run(
    "Read my unread emails. If any mention a support ticket, "
    "look it up in the database and summarise the customer name, "
    "open tickets, and total order value."
)
agent.stop()

Or just run the included example:

python examples/hello_delfhos.py

---

Custom tools

Decorate any Python function with @tool and the agent can call it:

from delfhos import Agent, tool

@tool
def calculate_discount(price: float, pct: float) -> float:
    """Return price after applying a percentage discount."""
    return price * (1 - pct / 100)

agent = Agent(tools=[calculate_discount], llm="gemini-3.1-flash-lite-preview")
agent.run("What is the price of a $120 item with a 15% discount?")
agent.stop()

---

Built-in tools

from delfhos import Gmail, SQL, Sheets, Drive, Calendar, Docs, WebSearch, MCP

gmail = Gmail(oauth_credentials="client_secrets.json", allow=["read", "send"], confirm=["send"])
db    = SQL(url="postgresql://user:pass@host/db",       allow=["schema", "query"])
drive = Drive(oauth_credentials="client_secrets.json",  confirm=True)

agent = Agent(tools=[gmail, db, drive], llm="gemini-3.1-flash-lite-preview")
agent.run("Check unread emails and log any order mentions to the database.")
agent.stop()

allow — restrict which actions are available on the tool (["read", "send"], ["schema", "query"], …).
confirm — when human approval is required: True (all), False (none), or a list of specific actions.

---

Interactive chat

from delfhos import Agent, Chat, Gmail

agent = Agent(
    tools=[Gmail(oauth_credentials="client_secrets.json")],
    llm="gemini-3.1-flash-lite-preview",
    chat=Chat(summarizer_llm="gemini-3.1-flash-lite-preview"),
)

agent.run_chat()  # starts a terminal session — type /help for commands

---

Memory

from delfhos import Agent, Chat, Memory

agent = Agent(
    tools=[...],
    llm="gemini-3.1-flash-lite-preview",
    chat=Chat(keep=8, summarize=True, namespace="my_agent"),    # short-term
    memory=Memory(namespace="my_agent"),                         # long-term semantic
)

Embedding Models: See EMBEDDING_MODELS_GUIDE.md for comprehensive guide to 100+ supported models with auto-detection of model-specific requirements (trust_remote_code, instruction prefixes, etc.).

---

MCP servers

from delfhos import Agent, MCP

fs = MCP("server-filesystem", args=["."], allow=["read_file", "write_file"])
agent = Agent(tools=[fs], llm="gemini-3.1-flash-lite-preview")
agent.run("List all Python files and write a one-line summary for each.")
agent.stop()

---

Response object

agent.run() returns a Response with the result, status, cost, and trace:

r = agent.run("How many users signed up this week?")

print(r.text)        # agent's answer
print(r.status)      # True if task succeeded
print(r.cost_usd)    # cost in dollars (e.g. 0.0003)
print(r.duration_ms) # wall-clock time in milliseconds

---

Model support

Pass any model string from Gemini, OpenAI, or Anthropic:

# Gemini
agent = Agent(tools=[...], llm="gemini-2.0-flash-lite")
agent = Agent(tools=[...], llm="gemini-2.0-flash")

# OpenAI
agent = Agent(tools=[...], llm="gpt-5)
agent = Agent(tools=[...], llm="gpt-4o")

# Anthropic
agent = Agent(tools=[...], llm="claude-4-5-haiku")
agent = Agent(tools=[...], llm="claude-4-6-sonnet")

Use light_llm + heavy_llm to split fast prefiltering from heavier code generation:

agent = Agent(
    tools=[...],
    light_llm="gemini-3.1-flash-lite-preview",   # fast, cheap — for tool selection
    heavy_llm="claude-4-6-sonnet",         # stronger — for code generation
)

---

Context manager

The agent cleans up automatically when used as a context manager:

with Agent(tools=[...], llm="gemini-3.1-flash-lite-preview") as agent:
    agent.run("Summarise last week's sales and email it to the team.")

---

For the full API reference and advanced guides see DOCS.md or delfhos.com/docs.

License

Apache-2.0