delfhos 0.5.3

Delfhos — AI agent SDK with typed connections and tool orchestration

Delfhos

Delfhos is a Python SDK for building AI agents that use real tools (Gmail, SQL, Drive, MCP, custom functions) with clean orchestration and safe execution.

Install

pip install delfhos

From source:

git clone https://github.com/DavidFraifer/delfhos
cd delfhos
pip install -e .

Quick Start

from delfhos import Agent, WebSearch

agent = Agent(
    tools=[WebSearch(llm="gpt-5.4-mini")],
    llm="gemini-3.1-flash-lite-preview",
)

agent.run("What are the top 3 AI trends this week? Keep it short.")
agent.stop()

Core Concepts

• Agent: public entry point (from delfhos import Agent)
• tools: built-in tools (Gmail, SQL, etc), MCP tools, and @tool custom functions
• Chat: short-term conversation memory (SQLite persisted, requires chat=Chat(...) for run_chat())
• Memory: long-term semantic memory (SQLite + embeddings)
• allow: Action whitelist (allow=["read", "send"] restricts to specific actions)
• confirm: Approval requirement (True = all need approval, False = none, ["action"] = selective)

Built-in Tools

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

Examples:

# allow: restrict which actions are available
# confirm: when approval is required (True/False/list of actions)
gmail = Gmail(oauth_credentials="client_secrets.json", allow=["read", "send"], confirm=["send"])
db = SQL(url="postgresql://user:pass@host/mydb", allow=["schema", "query"], confirm=["write"])
drive = Drive(oauth_credentials="client_secrets.json", confirm=True)

agent = Agent(tools=[gmail, db, drive], llm="gemini-3.1-flash-lite-preview")

allow parameter:

• Restricts which actions are available on the tool
• Default: allow all actions
• Examples:
  • Gmail: allow=["read", "send"] (prevents other actions)
  • SQL: allow=["schema", "query"] (blocks write operations)
  • Sheets: allow=["read"] (read-only)

confirm parameter:

• Controls when human approval is required
• Default: True (approve all actions for safety)
• Values:
  • True: All actions require approval
  • False: No approval needed
  • List: Only specific actions require approval, e.g., confirm=["send", "delete"] or confirm=["write"]

Inspect tool capabilities:

• Class method: Gmail.inspect() (all available actions)
• Instance method: gmail.inspect() (per-instance connection info)
• Verbose: gmail.inspect(verbose=True) (detailed descriptions)

WebSearch special note:

• Requires an explicit LLM model: WebSearch(llm="gemini-3.1-flash-lite-preview") or WebSearch(llm="gpt-...")
• Can be a different model from the main agent LLM
• For parseable outputs, request format in the query (e.g., "Return ONLY JSON: {"rate": number}").

Custom Tools

from delfhos import Agent, tool

@tool
async def calculate_total(price: float, tax: float) -> float:
    return price * (1 + tax)

agent = Agent(
    tools=[calculate_total],
    llm="gemini-3.1-flash-lite-preview",
)

Interactive Chat Session

For synchronous, interactive terminal chat with full conversation history:

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")  # Required for run_chat()
)

agent.run_chat()  # Start interactive terminal session

Features:

• Type each message at the You > prompt.
• Agent responds with full context from conversation history.
• Commands: /help, /clear, /stop, /exit.
• Requires: chat=Chat(...) parameter at agent creation.

Memory (Chat + Long-Term)

from delfhos import Agent, Chat, Memory, WebSearch

agent = Agent(
    tools=[WebSearch(llm="gpt-5.4-mini")],
    chat=Chat(keep=8, summarize=True, namespace="support_agent"),
    memory=Memory(namespace="support_agent"),
    llm="gemini-3.1-flash-lite-preview",
)

Default storage paths:

• Chat: ~/delfhos/chat/<namespace>.db
• Memory: ~/delfhos/memory/<namespace>.db

Generated task code can persist durable facts with:

await memory.save("User prefers monthly billing", desc="final preference")

Approval and Safety

How allow and confirm interact:

allow = which actions are AVAILABLE
confirm = which of those available actions need APPROVAL

Examples:

# Example 1: Send requires approval, read is free
gmail = Gmail(oauth_credentials="oauth.json", allow=["read", "send"], confirm=["send"])
# Agent can: read (no approval), send (approval required)
# Agent cannot: delete, archive, etc.

# Example 2: Read-only, no approval needed
sheets = Sheets(oauth_credentials="oauth.json", allow=["read"], confirm=False)
# Agent can: read (no approval)
# Agent cannot: write, format, create, etc.

# Example 3: All writes need approval
db = SQL(url="postgresql://...", allow=["query", "write"], confirm=["write"])
# Agent can: query (no approval), write (approval required)
# Agent cannot: schema (if not in allow list)

# Example 4: Default—all available actions need approval
drive = Drive(oauth_credentials="oauth.json", confirm=True)  # no allow set = all actions available
# Agent can: all actions, but EACH action requires approval

Agent-level approval handler:

def my_approval_handler(request):
    # request has: action, tool, brief, reason
    return True  # auto-approve all

agent = Agent(
    tools=[...],
    llm="gemini-3.1-flash-lite-preview",
    on_confirm=my_approval_handler  # Replaces default console selector
)

When on_confirm is provided:

• Uses your custom approval handler instead of console prompts
• Can return True/False, (bool, reason), or dict with {"approved": bool, "response": str}
• Works with both tool-level confirm and agent-level approvals.

Optional Prefilter

When dealing with many tools, enable tool prefiltering to reduce token costs by ~60%:

agent = Agent(
    tools=[...],
    llm="gemini-3.1-flash-lite-preview",
    enable_prefilter=True  # Filter relevant tools before code generation
)

Default: enable_prefilter=False (all tools documented in prompts).

Model Support

Supported model families:

• gemini-*
• gpt-* (including o1/o3/o4 style IDs)
• claude-*

Example:

agent = Agent(tools=[...], llm="gemini-3.1-flash-lite-preview")

Pricing and Cost (USD)

Delfhos calculates LLM cost in USD from a user-editable pricing file:

• ~/delfhos/pricing.json

Behavior:

• The file is auto-created on first run.
• Final execution summary always shows total USD cost.
• In verbose mode, each LLM call also shows its individual USD cost.
• Trace exports include total cost and pricing source path.
• If a model is missing in pricing.json, Delfhos does not calculate USD cost for that model and logs a warning.

You can edit prices or add new models at any time:

{
    "_comment": "USD per 1M tokens. Edit rates or add models. Wildcards are supported, e.g. gpt-*.",
    "models": {
        "gemini-3.1-flash-lite-preview": {
            "input_per_million": 0.10,
            "output_per_million": 0.40
        },
        "gpt-4o-mini": {
            "input_per_million": 0.15,
            "output_per_million": 0.60
        },
        "claude-3-5-sonnet": {
            "input_per_million": 3.00,
            "output_per_million": 15.00
        },
        "gpt-*": {
            "input_per_million": 1.00,
            "output_per_million": 4.00
        }
    }
}

Error System

Delfhos errors include:

• stable code
• readable message
• fix hint

Example shape:

❌ [ERR-TOOL-001] Delfhos Error
----------------------------------------
Message: Tool 'web_search' failed during execution: network timeout
----------------------------------------
💡 Hint: Review the arguments sent to the tool...

Main families include:

• ERR-TOOL-*
• ERR-CONN-*
• ERR-ENV-*
• ERR-LLM-*
• ERR-MCP-*

Minimal MCP Example

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 python files and create a short summary file.")
agent.stop()

Inspecting MCP Server Setup

View connection formats and required environment variables:

from delfhos import MCP

github = MCP("server-github")
info = github.inspect()
# Returns:
# - server_name, short_name, version
# - connection_setup: args_format, env_format, headers_format, required_env_keys
# - example commands
print(info)

Common required env keys:

• server-github → GITHUB_TOKEN
• server-slack → SLACK_BOT_TOKEN
• server-asana → ASANA_TOKEN
• And 15+ others

License

Apache-2.0