delfhos 0.6.1

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

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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.

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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

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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

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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()

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Built-in tools

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

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.

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REST API Integration (APITool)

Connect any REST API with an OpenAPI 3.x specification — no custom code needed.

from delfhos import Agent, APITool

# From a public OpenAPI spec
petstore = APITool(
    spec="https://petstore3.swagger.io/api/v3/openapi.json",
    allow=["list_pets", "get_pet_by_id"],
    confirm=["create_pet", "delete_pet"],
)

# From a local spec with authentication
internal = APITool(
    spec="./openapi.yaml",
    base_url="https://api.internal.corp/v1",
    auth={"Authorization": "Bearer sk_..."},
)

# Inspect available endpoints
print(petstore.inspect())  # Compact: endpoint names
print(petstore.inspect(verbose=True))  # Detailed: methods, paths, descriptions

agent = Agent(tools=[petstore, internal], llm="gemini-2.5-flash")
agent.run("List all pets and create a new one named 'Buddy'")

Features:

• Automatic endpoint compilation from OpenAPI specs (no LLM needed)
• Path, query, and request body parameters extracted and typed
• Auth headers and query params injected per-endpoint
• $ref resolution for complex schemas
• allow= and confirm= support for fine-grained access control
• Caching: specs compiled once and cached to ~/delfhos/api_cache/

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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

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Memory & Long-term Context

Delfhos supports both session memory and persistent semantic memory with 100+ embedding models.

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
)

100+ Embedding Models: Automatic detection and compatibility for:

• Proprietary: OpenAI, Cohere, Anthropic, Google
• Open-source: Sentence-Transformers (MiniLM, all-MiniLM, all-mpnet, etc.)
• Specialized: BGE models (Alibaba), Jina, Nomic Embed, NV-Embed
• Local-first: Run models locally via Ollama or Hugging Face Transformers

Auto-detects model requirements:

• trust_remote_code toggles (for BGE, Jina, etc.)
• Instruction/prefix tokens (e.g., Nomic's "search_document:" prefix)
• Model dimensions (inferred after loading)

See EMBEDDING_MODELS_GUIDE.md for the full compatibility matrix.

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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()

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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

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Model support

Cloud providers: 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")

Local & custom models: Use LLMConfig for any OpenAI-compatible endpoint

from delfhos import Agent, LLMConfig

# Local Ollama model
agent = Agent(
    tools=[...],
    llm=LLMConfig(model="llama3.2", base_url="http://localhost:11434/v1")
)

# Enterprise vLLM server
agent = Agent(
    tools=[...],
    llm=LLMConfig(
        model="mistral-7b-instruct",
        base_url="https://llm.corp.internal/v1",
        api_key="internal-token"
    )
)

# Any OpenAI-compatible provider (Groq, Together, Anyscale, etc.)
agent = Agent(
    tools=[...],
    llm=LLMConfig(
        model="meta-llama/Llama-3-70b-chat-hf",
        base_url="https://api.together.xyz/v1",
        api_key="..."
    )
)

Dual-LLM optimization: Use fast local + strong cloud model

agent = Agent(
    tools=[...],
    light_llm=LLMConfig(model="qwen2.5:7b", base_url="http://localhost:11434/v1"),
    heavy_llm="gemini-2.5-flash",  # or Claude, OpenAI, etc.
)

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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.")

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

License

Apache-2.0