pygentix 0.1.7

Composable AI agent framework — drop-in LLM tool-calling, structured output, and SQLAlchemy integration for any Python project.

pygentix

A composable Python framework for building AI agents with tool-calling, structured output, and SQLAlchemy integration — across any LLM provider.

pip install pygentix                    # core only
pip install pygentix[ollama]            # + Ollama backend
pip install pygentix[openai]            # + OpenAI (ChatGPT) backend
pip install pygentix[gemini]            # + Google Gemini backend
pip install pygentix[all]               # every backend

Azure OpenAI / Copilot uses the openai package — install pygentix[openai].

---

Quick Start

Pick a backend, register tools, and start a conversation:

from pygentix import Ollama

agent = Ollama(model="qwen2.5:7b")            # runs locally — no API key needed

@agent.uses
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"Sunny, 22 °C in {city}"

conv = agent.start_conversation()
response = conv.ask("What's the weather in Paris?")
print(response.message.content)
# → "It's sunny and 22 °C in Paris right now."

Every backend returns the same ChatResponse object, so switching providers is a one-line change:

from pygentix import ChatGPT, Gemini, Copilot

agent = ChatGPT(model="gpt-4o-mini")          # OpenAI
agent = Gemini(model="gemini-2.5-flash")       # Google
agent = Copilot(model="gpt-4o")               # Azure OpenAI

---

Backends

Class	Provider	Default model	Install extra
Ollama	Ollama (local)	qwen2.5:7b	ollama
ChatGPT	OpenAI	gpt-4o-mini	openai
Gemini	Google AI	gemini-2.5-flash	gemini
Copilot	Azure OpenAI	gpt-4o	openai

API keys

Cloud backends read their key from the environment (or accept it in the constructor). Ollama runs locally and needs no key.

Backend	Environment variable	Constructor kwarg
Ollama	(none — runs locally)	—
ChatGPT	OPENAI_API_KEY	api_key
Gemini	GEMINI_API_KEY	api_key
Copilot	AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT	api_key, endpoint

from pygentix import ChatGPT

agent = ChatGPT(api_key="sk-...")              # explicit
agent = ChatGPT()                              # reads OPENAI_API_KEY

---

Tool Calling

Decorate any Python function with @agent.uses to expose it as a tool the LLM can invoke:

from pygentix import Ollama

agent = Ollama()

@agent.uses
def search_docs(query: str) -> str:
    """Search the documentation for relevant articles."""
    return run_search(query)

@agent.uses
def send_email(to: str, subject: str, body: str) -> str:
    """Send an email to the specified address."""
    return mailer.send(to, subject, body)

conv = agent.start_conversation()
response = conv.ask("Find docs about authentication and email them to [e-mail]")

The framework introspects the function's signature and docstring to build the tool definition automatically. When the model decides to call a tool, the framework executes it and feeds the result back — looping until the model produces a final answer.

Parameterised @uses — serializer, description, name

@agent.uses also accepts optional keyword arguments so you can decorate an existing method (or a callable) without writing a thin wrapper around it:

from pygentix import ChatGPT

agent = ChatGPT()

@agent.uses(
    serializer=lambda rows: [r.model_dump(mode="json") for r in rows],
    name="get_open_tickets",
    description="Return every open support ticket, serialised as plain dicts.",
)
def fetch_open_tickets() -> list:
    return TicketRepo.query_open()

Kwarg	Purpose
serializer	Post-processes the return value before it reaches the LLM (e.g. ORM → JSON-safe dict).
description	Overrides func.__doc__ in the tool schema the LLM sees.
name	Overrides func.__name__ in the tool schema — handy for exposing repository methods under friendlier tool names.

All three forms are interchangeable:

@agent.uses                                        # bare decorator
@agent.uses(serializer=to_dicts, name="search")    # parameterised decorator
agent.uses(Repo.search, serializer=to_dicts)       # direct call

---

Tabular data: register, then reduce

When a tool would return a large list[dict] (query results, exports, analytics rows), sending every row to the model wastes tokens and context. pygentix provides an optional two-step tabular workflow:

1. tabular_register — store rows for the current Conversation (via active_conversation) and return a short JSON payload: dataset_id, row_count, and column names.
2. tabular_reduce — run filters, optional group_by, and metrics (sum, count, avg, min, max) on that dataset; only the small aggregate result goes back to the model.

Reduction is pure Python (no second LLM call on the bulk payload). Storage lives on the conversation object, so it is not shared across users or unrelated chats.

Register the tools

Call once after you construct the agent (same pattern as other add_tool usage):

from pygentix import Claude, register_tabular_tools

agent = Claude(name="MyAgent", api_key="...")
register_tabular_tools(agent)

Optional kwargs: max_rows_per_dataset (default 100_000), max_datasets (default 32, FIFO eviction), register_name / reduce_name if you need to avoid name clashes.

If the agent is registered by name before the instance exists, pass the name string — registrations queue the same way as @Agent.by_name("MyAgent").uses(...):

register_tabular_tools("MyAgent")
agent = Claude(name="MyAgent", ...)  # tools attach on construction

What the model sees

tabular_register(rows) — argument is a JSON array of objects. Returns a string of JSON like:

{
  "dataset_id": "ds_…",
  "row_count": 5000,
  "columns": ["id", "amount", "owner"],
  "hint": "Call tabular_reduce with this dataset_id …"
}

tabular_reduce(dataset_id, spec_json) — spec_json is a string containing a JSON object:

Key	Required	Meaning
metrics	yes	List of { "op": "sum"|"count"|"avg"|"min"|"max", "field"?: "…", "as"?: "…" }. For count, omit field to count rows; with field, count non-null values.
group_by	no	List of field names; each output row includes those keys plus metric columns.
where	no	List of { "field", "op", "value" } with op in eq, ne, gt, gte, lt, lte, in.

Field names must match ^[A-Za-z_][A-Za-z0-9_]*$.

Example spec_json (pretty-printed; send minified or as a single line from the app):

{
  "group_by": ["owner"],
  "metrics": [
    { "op": "sum", "field": "amount", "as": "total_amount" },
    { "op": "count", "as": "n" }
  ],
  "where": [{ "field": "amount", "op": "gte", "value": 100 }]
}

Response shape: {"rows": [ { … grouped keys + metrics … }, … ]}.

Typical integration pattern

Heavy work stays inside your own tool (DB query, CSV parse, etc.). You build list[dict] in Python, then either:

• Return only tabular_register(rows)’s JSON to the model and let it call tabular_reduce for the analysis it wants, or
• Call reduce_tabular_rows(rows, spec) yourself (same logic, no tool) if you do not need the model to choose the spec.

from pygentix.tabular import reduce_tabular_rows

rows = [{"owner": "a", "amount": 10.0}, {"owner": "b", "amount": 5.0}]
out = reduce_tabular_rows(
    rows,
    {
        "group_by": ["owner"],
        "metrics": [{"op": "sum", "field": "amount", "as": "t"}],
    },
)
# → [{'owner': 'a', 't': 10.0}, {'owner': 'b', 't': 5.0}]

Public symbols: register_tabular_tools, reduce_tabular_rows, TabularStore (for embedding the store elsewhere). See pygentix/tabular.py for full behaviour.

---

Vision / Image Understanding

Pass images alongside your question to any vision-capable model:

from pygentix import Ollama

agent = Ollama(model="llama3.2-vision")        # local vision model
conv = agent.start_conversation()

response = conv.ask("How many cats are in this photo?", images=["photo.jpeg"])
print(response.message.content)
# → "There are 3 cats in the photo."

The images parameter accepts a list of file paths and works across all backends:

Backend	Vision model examples
Ollama	llama3.2-vision, moondream
ChatGPT	gpt-4o, gpt-4o-mini
Gemini	gemini-2.5-flash, gemini-2.5-pro
Copilot	gpt-4o (via Azure)

---

PDF Document Parsing

Combine a vision model with PyMuPDF to extract structured information from PDF documents:

import fitz  # PyMuPDF — pip install pymupdf
from pygentix import Ollama, OutputAgent

class PDFAgent(Ollama, OutputAgent):
    pass

agent = PDFAgent(model="llama3.2-vision")

@agent.output
class InvoiceData:
    company: str
    invoice_number: str
    total: str
    client: str

# Render the first page to an image
doc = fitz.open("invoice.pdf")
page = doc[0]
pix = page.get_pixmap(dpi=200)
pix.save("invoice_page.png")
doc.close()

conv = agent.start_conversation()
response = conv.ask(
    "Extract the company name, invoice number, total amount, "
    "and client name from this invoice.",
    images=["invoice_page.png"],
)

parsed = agent.parse_output(response)
print(parsed.company)         # "TechCorp Solutions"
print(parsed.invoice_number)  # "INV-2026-001"
print(parsed.total)           # "$5,454.00"
print(parsed.client)          # "Acme Industries"

This pattern works for receipts, contracts, reports — any PDF you can render to an image.

---

Populating a Database with Generated Data

Let the LLM generate realistic data and write it directly to your database:

from sqlalchemy import Column, Integer, String, Float, Date, create_engine
from sqlalchemy.orm import declarative_base

from pygentix import Ollama, SqlAlchemyAgent

Base = declarative_base()

class Sale(Base):
    __tablename__ = "sales"
    id = Column(Integer, primary_key=True)
    product = Column(String)
    amount = Column(Float)
    date = Column(Date)

engine = create_engine("sqlite:///sales.db")
Base.metadata.create_all(engine)

class SalesAgent(Ollama, SqlAlchemyAgent):
    pass

agent = SalesAgent(engine=engine)
agent.writes(Sale)   # grants the model insert access

conv = agent.start_conversation()
conv.ask("Create 10 sales records with realistic product names, amounts between $10 and $500, and dates in 2026.")

# Verify the rows were inserted
from sqlalchemy.orm import Session
with Session(engine) as s:
    count = s.query(Sale).count()
    print(f"{count} sales created")  # 10 sales created
    for sale in s.query(Sale).all():
        print(f"  {sale.product}: ${sale.amount} on {sale.date}")

The agent introspects the ORM model's columns and generates run_insert calls automatically — no manual SQL or fixture files needed.

---

Structured Output

Use OutputAgent to guarantee responses follow a JSON schema:

from pygentix import Ollama, OutputAgent

class MyAgent(Ollama, OutputAgent):
    pass

agent = MyAgent()

@agent.output
class Answer:
    answer: str
    confidence: float = 0.0
    sources: list = []

conv = agent.start_conversation()
response = conv.ask("What is the capital of France?")

parsed = agent.parse_output(response)
print(parsed.answer)       # "Paris"
print(parsed.confidence)   # 0.95

The schema can also be a raw dict — pass any valid JSON Schema to agent.output({"type": "object", ...}).

---

SQLAlchemy Integration

SqlAlchemyAgent gives the LLM read/write access to your database through auto-generated tools:

from sqlalchemy import Column, Integer, String, create_engine
from sqlalchemy.orm import declarative_base

from pygentix import Ollama, OutputAgent, SqlAlchemyAgent

Base = declarative_base()

class Product(Base):
    __tablename__ = "products"
    id = Column(Integer, primary_key=True)
    name = Column(String)
    price = Column(Integer)

engine = create_engine("sqlite:///shop.db")
Base.metadata.create_all(engine)

class ShopAgent(Ollama, SqlAlchemyAgent, OutputAgent):
    pass

agent = ShopAgent(engine=engine)
agent.reads(Product)                  # enables run_query
agent.writes(Product)                 # enables run_insert, run_update, run_delete

@agent.output
class Response:
    answer: str
    data: list = []

conv = agent.start_conversation()
conv.ask("Add a product called 'Widget' priced at 9.99")
response = conv.ask("List all products under $20")

parsed = agent.parse_output(response)
for item in parsed.data:
    print(item)

The agent automatically generates run_query, run_insert, run_update, and run_delete tools, handles type coercion (strings → ints, dates, etc.), and serialises results back to the model.

---

Row-Level Security

Ensure users can only access their own data — even when the LLM generates the queries. pygentix supports three complementary layers.

Direct Scope (automatic WHERE injection)

Map a column on the table to a key in the conversation's scope. All CRUD operations are automatically constrained:

from pygentix import Ollama, SqlAlchemyAgent

class MyAgent(Ollama, SqlAlchemyAgent):
    pass

agent = MyAgent(engine=engine)
agent.reads(User, scope={"id": "current_user"})
agent.writes(User, scope={"id": "current_user"})

# Alice's session — she can only see and modify her own row
conv = agent.start_conversation(scope={"current_user": 5})
conv.ask("Update my name to Alice Smith")
# → UPDATE users SET name='Alice Smith' WHERE id=5
# Attempts to access other users' rows are silently filtered out

Inserts auto-set the scoped column; updates and deletes inject it into the filter. If the LLM tries to target a different user, it gets a PermissionError.

Scope Chains (multi-level relationships)

When ownership is inferred through foreign keys (e.g. User → Sale → SaleItem), declare the chain:

agent.reads(SaleItem, scope_chain=[
    ("sale_id", Sale),              # SaleItem.sale_id → JOIN Sale
    ("user_id", "current_user"),    # WHERE Sale.user_id = scope["current_user"]
])
agent.writes(SaleItem, scope_chain=[
    ("sale_id", Sale),
    ("user_id", "current_user"),
])

conv = agent.start_conversation(scope={"current_user": 5})
conv.ask("List my sale items")
# → SELECT ... FROM sale_items JOIN sales ON ... WHERE sales.user_id = 5

Chains can be arbitrarily deep — each tuple is (fk_column, TargetModel) except the last which is (scope_column, scope_key):

agent.reads(LineDetail, scope_chain=[
    ("item_id", SaleItem),          # JOIN SaleItem
    ("sale_id", Sale),              # JOIN Sale
    ("user_id", "current_user"),    # WHERE Sale.user_id = ...
])

Policy Callbacks (general-purpose gate)

For authorization logic beyond SQL — API calls, custom rules, role checks — register a policy callback. It runs before every tool execution on any agent type:

def my_policy(tool_name: str, arguments: dict, scope: dict) -> bool:
    """Return False to block the tool call."""
    if tool_name == "run_delete" and scope.get("role") != "admin":
        return False  # only admins can delete
    return True

conv = agent.start_conversation(
    scope={"current_user": 5, "role": "viewer"},
    policy=my_policy,
)
conv.ask("Delete all records")
# → LLM receives "Permission denied: run_delete blocked by policy"

Combining Scope and Policy

When both are defined, both run. The policy gate executes first — if it denies, the tool never reaches the database. If it allows, the scope filters are applied as usual:

conv = agent.start_conversation(
    scope={"current_user": 5, "role": "editor"},
    policy=my_policy,       # checked first
)
# 1. Policy: is this tool allowed for this role? ✓
# 2. Scope:  constrain query to current_user's data ✓

Scope-Aware Tools (works on any @uses)

SqlAlchemyAgent isn't the only thing that understands scope. Every tool registered with @agent.uses participates too: any parameter whose name matches a key in the active conversation scope is auto-filled at call time and hidden from the LLM-visible tool schema. The model literally cannot supply those arguments, so it cannot widen access by crafting different inputs.

from pygentix import ChatGPT

agent = ChatGPT()

@agent.uses(serializer=dump_list)
def get_my_opportunities(enterprise_id: str) -> list:
    """Return every opportunity in the logged-in user's enterprise."""
    return Opportunities.query_by_enterprise(enterprise_id)

conv = agent.start_conversation(scope={
    "user_id": "u_42",
    "enterprise_id": "ent_7",
    "role": "sales",
})
conv.ask("Show me my pipeline")
# Framework calls get_my_opportunities(enterprise_id="ent_7").
# The LLM sees get_my_opportunities() as a zero-argument tool.

Parameters not in scope remain visible to the LLM as normal arguments, so you can mix LLM-supplied and scope-supplied parameters freely:

@agent.uses
def search_my_tickets(enterprise_id: str, keyword: str) -> list:
    """Search tickets in the caller's enterprise by keyword."""
    return TicketRepo.search(enterprise_id, keyword)

# LLM sees: search_my_tickets(keyword: str)
# Framework injects enterprise_id from scope at call time.

This is an adapter / security boundary — the scope dict built from the authenticated session is the only place scoped values ever come from.

No scope = unrestricted (backward compatible)

If you don't pass scope or policy, everything works exactly as before — no filters, no restrictions.

---

Named Agents & Lazy Tool Registration

Large codebases often want to declare a tool next to the function it exposes (e.g. on a repository / model class) without centralising every registration in a single bootstrap module. Two library pieces make that safe regardless of import order:

1. Construct the agent with a name=. The instance registers itself in Agent.registry under that name.
2. Reference it from anywhere via Agent.by_name(...), which returns a lazy AgentRef. Its .uses(...) is the same decorator as Agent.uses — if the named agent already exists the registration runs immediately, otherwise it's queued and flushed as soon as an agent with that name is constructed.

# app/core/agent.py — built during startup
from pygentix import ChatGPT

agent = ChatGPT(name="CRMAgent", api_key=...)

# app/models/opportunity.py — declares its scoped tool in place
from pygentix import Agent

class Opportunities(Base):
    ...

    @staticmethod
    @Agent.by_name("CRMAgent").uses(
        serializer=dump_list,
        name="get_my_opportunities",
        description="Return every opportunity in the caller's enterprise.",
    )
    def get_all_by_enterprise_id(enterprise_id: str) -> list:
        ...

• Either module can be imported first. If models load before the agent is constructed, their registrations sit on Agent.pending_uses["CRMAgent"] and flush on construction.
• Agent.by_name(...) accepts exactly the same serializer / description / name kwargs as @agent.uses, because AgentRef.uses is Agent.uses (shared via attribute aliasing — not a reimplementation).
• Names must be unique: constructing two agents with the same name= raises ValueError.

When combined with scope-aware tools (see above), this pattern lets each model own its scoped tools while the endpoint only wires up authentication and starts the conversation:

# app/api/chat.py
from pygentix import Agent
from app.core.agent import agent   # makes sure CRMAgent exists
import app.models                  # triggers all @Agent.by_name("CRMAgent").uses decorators

@router.post("/chat")
def chat(..., current_user = Depends(verify_token)):
    scope = {"user_id": current_user.id, "enterprise_id": current_user.enterprise_id}
    conv = agent.start_conversation(scope=scope)
    ...

---

Task Scheduling

SchedulerAgent lets the LLM schedule tool calls and conversations for future execution — using natural language like "send this email tomorrow" or "check sales every Monday at 9am".

pip install pygentix[scheduler]   # adds croniter for cron expressions

Basic setup

from pygentix import Ollama, SchedulerAgent

class MyAgent(Ollama, SchedulerAgent):
    pass

agent = MyAgent(schedule_file="tasks.json", poll_interval=10)

@agent.uses
def send_email(to: str, subject: str, body: str) -> str:
    """Send an email."""
    return f"Email sent to {to}"

agent.start_scheduler()

conv = agent.start_conversation()
conv.ask("Send an email to [e-mail] saying hello tomorrow at 9am")
# LLM calls schedule_task("send_email", {"to": "[e-mail]", ...}, run_at="2026-04-04T09:00:00")

The scheduler auto-registers four LLM tools: schedule_task, schedule_conversation, list_scheduled_tasks, and cancel_scheduled_task. No manual tool wiring needed.

One-shot vs. recurring

# One-shot — direct tool call at a specific time
conv.ask("Send the report email at 5pm today")
# LLM calls: schedule_task("send_email", {...}, run_at="2026-04-03T17:00:00")

# Recurring — cron expression
conv.ask("Every Monday at 9am, send a summary email to the team")
# LLM calls: schedule_task("send_email", {...}, cron="0 9 * * 1")

Conversation replay (deferred reasoning)

When the LLM doesn't have all the data yet, it can schedule a future conversation instead of a direct call. At execution time, a fresh conversation runs so the LLM can reason about current data:

conv.ask("Every Friday at 6pm, check the latest sales numbers and email a report to [e-mail]")
# LLM calls: schedule_conversation("check latest sales and email report to [e-mail]", cron="0 18 * * 5")

Manual tick

For cron-based setups or testing, call tick() directly:

results = agent.tick()  # executes all due tasks right now

Task persistence

All scheduled tasks are persisted to a JSON file (default: scheduled_tasks.json). The file is human-readable and can be inspected or edited manually:

[
  {
    "id": "a1b2c3",
    "type": "tool_call",
    "function_name": "send_email",
    "arguments": {"to": "[e-mail]", "subject": "Hello"},
    "run_at": "2026-04-04T09:00:00",
    "cron": null,
    "status": "pending"
  }
]

Missed tasks

One-shot tasks whose run_at has passed while the process was down are marked as "missed" — they are not retried on startup. Recurring cron tasks simply wait for their next occurrence.

Lifecycle

agent.start_scheduler()   # start background polling thread
# ... application runs ...
agent.stop_scheduler()    # stop polling, join thread

---

Mixing Backends

Every agent is a composable mixin — swap the backend class and everything else stays the same:

from pygentix import Ollama, ChatGPT, Gemini, Copilot, SqlAlchemyAgent, OutputAgent

class LocalAgent(Ollama, SqlAlchemyAgent, OutputAgent):
    """Runs entirely on your machine via Ollama."""

class CloudAgent(ChatGPT, SqlAlchemyAgent, OutputAgent):
    """Uses OpenAI for inference."""

class GoogleAgent(Gemini, SqlAlchemyAgent, OutputAgent):
    """Uses Google Gemini for inference."""

class EnterpriseAgent(Copilot, SqlAlchemyAgent, OutputAgent):
    """Routes through your Azure OpenAI deployment."""

---

Multi-turn Conversations

A Conversation maintains the full message history, so follow-up questions have context:

from pygentix import Ollama, SqlAlchemyAgent

# ... define models, engine, etc.

agent = Ollama(engine=engine)
conv = agent.start_conversation()
conv.ask("Create a user named Alice with email [e-mail]")
conv.ask("Now create one for Bob at [e-mail]")
response = conv.ask("List all users")

---

Streaming Responses

Stream tokens as they arrive instead of waiting for the full response:

from pygentix import Ollama

agent = Ollama()
conv = agent.start_conversation()

for chunk in conv.ask_stream("Tell me a story about a robot"):
    print(chunk, end="", flush=True)

When tools are registered, the tool-call loop runs normally and the final answer is streamed. Every backend supports streaming natively (Ollama, OpenAI, Gemini, Azure).

---

Async Support

Use ask_async in async frameworks like FastAPI, Starlette, or Django:

import asyncio
from pygentix import Ollama

agent = Ollama()
conv = agent.start_conversation()

async def main():
    response = await conv.ask_async("What is the capital of France?")
    print(response.message.content)

asyncio.run(main())

By default, chat_async runs the sync method in a thread pool via asyncio.to_thread. Backends can override with native async clients for lower overhead.

---

MockAgent for Testing

Unit-test your application without hitting a real LLM:

from pygentix.testing import MockAgent

agent = MockAgent(responses=["Hello!", "Goodbye!"])
conv = agent.start_conversation()

r1 = conv.ask("Hi")     # → "Hello!"
r2 = conv.ask("Bye")    # → "Goodbye!"

MockAgent also supports tool-call simulation and usage metadata:

agent = MockAgent(responses=[
    {"tool_calls": [{"name": "get_weather", "arguments": {"city": "Paris"}}]},
    "It's sunny in Paris!",
])

@agent.uses
def get_weather(city: str) -> str:
    """Get weather."""
    return f"22°C in {city}"

conv = agent.start_conversation()
resp = conv.ask("Weather?")  # tool executes, then returns "It's sunny in Paris!"

---

Event Hooks

Register callbacks to observe tool calls, results, and LLM responses in real-time:

from pygentix import Ollama

agent = Ollama()

agent.on("tool_call", lambda name, args: print(f"→ Calling {name}({args})"))
agent.on("tool_result", lambda name, result: print(f"← {name} returned: {result}"))
agent.on("response", lambda resp: print(f"LLM: {resp.message.content[:80]}"))

@agent.uses
def search(query: str) -> str:
    """Search the web."""
    return f"Results for '{query}'..."

conv = agent.start_conversation()
conv.ask("Search for pygentix")
# → Calling search({'query': 'pygentix'})
# ← search returned: Results for 'pygentix'...
# LLM: Here are the search results for pygentix...

Hooks are ideal for logging, metrics, audit trails, and content filtering.

---

Conversation Save & Load

Persist conversations to JSON and restore them later:

from pygentix import Ollama
from pygentix.core import Conversation

agent = Ollama()
conv = agent.start_conversation()
conv.ask("My name is Alice")

# Save
json_str = conv.to_json()
# or: data = conv.to_dict()

# ... later, in another process ...
restored = Conversation.from_json(agent, json_str)
resp = restored.ask("What's my name?")
# → "Your name is Alice."

---

Token Usage Tracking

Every ChatResponse includes token counts when the backend reports them:

from pygentix import Ollama

agent = Ollama()
conv = agent.start_conversation()
response = conv.ask("Explain quantum computing in one sentence")

print(response.usage.prompt_tokens)      # e.g. 42
print(response.usage.completion_tokens)  # e.g. 28
print(response.usage.total_tokens)       # e.g. 70

All four backends (Ollama, ChatGPT, Gemini, Copilot) populate usage automatically.

---

Retry with Exponential Backoff

Transient API errors (rate-limits, timeouts, 500s) are retried automatically with exponential backoff:

from pygentix import ChatGPT

agent = ChatGPT(max_retries=5, retry_delay=2.0)

Retries apply to connection errors, timeouts, and HTTP status codes 429, 500, 502, 503, 504. Non-retriable errors (auth failures, validation) are raised immediately. The delay doubles after each attempt (2s → 4s → 8s → ...).

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Context Window Management

Prevent conversations from exceeding the model's context window by setting max_history:

from pygentix import Ollama

agent = Ollama()
conv = agent.start_conversation(max_history=20)

# After many turns, only the system prompt + last 20 messages are kept
for i in range(100):
    conv.ask(f"Question {i}")

len(conv.messages)  # ≤ 22 (system + 20 + current response)

The system prompt is always preserved. Oldest messages are dropped first.

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Structured Logging

pygentix uses Python's standard logging module under the "pygentix" logger. Enable it to see every message, tool call, and response:

import logging

logging.basicConfig(level=logging.DEBUG)
logging.getLogger("pygentix").setLevel(logging.DEBUG)

# Now every conv.ask() logs:
# INFO  pygentix: User: What's the weather?
# DEBUG pygentix: Calling tool get_weather({'city': 'Paris'})
# DEBUG pygentix: Tool get_weather → Sunny, 22°C
# INFO  pygentix: Assistant: It's sunny and 22°C in Paris.

Set to WARNING in production to silence informational logs.

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API Reference

Core

Symbol	Description
Agent	Abstract base class — subclass to create a backend
Agent.by_name(name)	Lazy handle (AgentRef) for registering tools against a named agent from anywhere
Agent.registry	Mapping of name → live Agent instance, populated when name= is passed to the constructor
ChatResponse	Normalized response every backend returns
Conversation	Multi-turn conversation with save/load, streaming, async
Function	Introspectable wrapper around a tool callable; supports optional serializer, description, name overrides
Usage	Token usage statistics (prompt, completion, total)

Backends

Symbol	Description
Ollama	Local inference via Ollama
ChatGPT	OpenAI Chat Completions
Gemini	Google Gemini (via google-genai)
Copilot	Azure OpenAI

Mixins & Utilities

Symbol	Description
OutputAgent	JSON schema enforcement for responses
SchedulerAgent	Schedule tool calls and conversations for future execution
SqlAlchemyAgent	Database CRUD tools from ORM models
MockAgent	Fake backend for unit testing (pygentix.testing)
register_tabular_tools	Adds tabular_register / tabular_reduce for large row lists (pygentix.tabular)
reduce_tabular_rows	Pure in-memory group / filter / metrics over list[dict]
TabularStore	Conversation-attached row store used by the tabular tools

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Development

git clone https://github.com/andreperussi/pygentix.git
cd pygentix
pip install -e ".[dev]"
pytest

Integration tests (tests/test_integration.py, tests/test_vision.py) always call local Ollama (no pytest skip). Pull qwen2.5:7b (integration default — stronger models often finish sooner in wall time than tiny ones because they need fewer tool/retry rounds; use PYGENTIX_OLLAMA_TEST_MODEL=qwen2.5:3b if you prefer the smallest weights) and llama3.2-vision (vision; override with PYGENTIX_OLLAMA_VISION_MODEL) before pytest. If startup hangs on ollama pull, set PYGENTIX_OLLAMA_NO_AUTO_PULL=1 and keep models pre-installed. PDF tests need PyMuPDF (pip install pymupdf).

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License

MIT