exagent 0.0.9

python library for creating agents

exagent

A small Python library for building LLM agents. Provides a minimal set of building blocks — tools, an agent loop, streaming, and observability hooks — without pulling in a large framework.

Works with OpenAI and Anthropic models out of the box.

Note: This project is under active development. APIs may change between versions.

Full documentation: sachindas246.github.io/exagent_docs

Features

• Tool calling with a @tool decorator that infers JSON schema from type hints
• Multi-step agent loop that chains tool calls automatically until the model is done
• Streaming or non-streaming — same API, flip a boolean
• Observability hooks for inspecting tool calls and per-iteration responses
• Provider-agnostic — OpenAI and Anthropic supported with a unified interface
• Multi-agent orchestration — route tasks across specialist agents automatically
• Small surface area — one Agent class, one @tool decorator, a handful of events

Installation

pip install exagent[openai]      # OpenAI only
pip install exagent[anthropic]   # Anthropic only
pip install exagent[all]         # both providers

Requires Python 3.10+.

Set your API key in the environment (or a .env file in your working directory):

export OPENAI_API_KEY=sk-...
# or
export ANTHROPIC_API_KEY=sk-ant-...

Quick start

from exagent import Agent, tool

@tool
def get_weather(city: str, units: str = "celsius") -> str:
    """Return current weather for a city."""
    # Stand-in for a real API call
    return f"{city}: 18°C, sunny"

class WeatherAgent(Agent):
    def __init__(self):
        self.system_description = "You are a helpful weather assistant."
        self.set_model("openai", "gpt-4.1-mini")
        self.add_tool(get_weather)
        super().__init__()

agent = WeatherAgent()
answer = agent.run("What's the weather in Paris?")
print(answer)

That's it. The agent will:

1. Send your prompt to the model along with the tool definition
2. Receive a tool call request
3. Execute the handler
4. Feed the result back to the model
5. Return the final text response

Defining tools

Any function decorated with @tool becomes a tool the model can call. The JSON schema is inferred from type hints; the description comes from the docstring.

from exagent import tool

@tool
def search_products(query: str, max_results: int = 10) -> list:
    """Search the product catalog.

    Returns a list of matching products.
    """
    ...

Supported type hints: str, int, float, bool, list[T], dict, Optional[T], Union[T, None].

Override the name or description if needed:

@tool(name="lookup_price", description="Get the current price of a product by SKU.")
def price(sku: str) -> float:
    ...

For tools that need full control over the schema, construct a Tool directly:

from exagent import Tool

my_tool = Tool(
    name="custom",
    description="...",
    parameters={"type": "object", "properties": {...}, "required": [...]},
    handler=lambda **kwargs: ...,
)
agent.add_tool(my_tool)

The agent loop

agent.run(prompt) drives a loop that handles multi-step tool chains automatically. Each iteration:

1. Sends the full conversation history + registered tools to the model
2. If the model requests tools, executes them and feeds results back as a user turn
3. If the model replies with plain text, returns it as the final answer

The loop stops when the model stops requesting tools, or when max_iterations (default 10) is reached.

Multi-step chaining

Tools chain naturally because each iteration sees the full history, including previous tool results. Example with three tools that depend on each other:

@tool
def find_user(email: str) -> dict:
    """Look up a user by email."""
    ...

@tool
def list_orders(user_id: str) -> list:
    """List all orders for a user."""
    ...

@tool
def get_order_status(order_id: str) -> dict:
    """Get the current status of an order."""
    ...

agent.add_tools([find_user, list_orders, get_order_status])
result = agent.run("What's the status of [e-mail]'s most recent order?")

The agent will call find_user → list_orders (using the user ID from step 1) → get_order_status (using the order ID from step 2) → then reply with a natural-language answer. The model decides the chain; the library just shuttles data between steps.

Two entry points: run() and stream()

agent.run(prompt) uses the provider's non-streaming API — one blocking request per turn — and returns the final text as a string:

text = agent.run("Summarize these docs")

agent.stream(prompt) uses the provider's streaming API and yields events as they arrive — ideal for showing live progress in a CLI or UI:

for event in agent.stream("Summarize these docs"):
    if event["type"] == "text_delta":
        print(event["text"], end="", flush=True)
    elif event["type"] == "done":
        print()

Both methods drive the same tool-calling loop. The only difference is how you consume the output.

Event types (from stream())

Event	When	Payload
text_delta	As tokens arrive	text: str
tool_call	When the model finalizes a tool call	tool_call: ToolCall
tool_result	After a tool handler runs	id, name, content, is_error
done	Loop complete	text: str (final assistant text)

Observability hooks

Both run() and stream() accept two optional callbacks for visibility into the loop:

def log_tool(tc):
    print(f"→ {tc.name}({tc.input})")

def log_iteration(i, response):
    print(f"[iter {i}] text={response.text[:60]!r} tools={len(response.tool_calls)}")

agent.run(
    "Check inventory for SKU-42 and reorder if below 10.",
    on_tool_call=log_tool,
    on_iteration=log_iteration,
)

• on_tool_call(tool_call) fires once per tool call, just before the handler runs
• on_iteration(iteration, response) fires at the end of each model turn with the full ProviderResponse (text, tool_calls, stop_reason). iteration is 1-indexed.

Hooks work in both streaming and non-streaming modes.

Providers

Switch providers via set_model(provider, model_name). Any model string the underlying SDK accepts works:

self.set_model("openai", "gpt-4.1-mini")
self.set_model("openai", "gpt-4.1")
self.set_model("anthropic", "claude-sonnet-4-5")
self.set_model("anthropic", "claude-opus-4-5")

Provider-specific keyword arguments pass through to the SDK client:

self.set_model("anthropic", "claude-sonnet-4-5", max_tokens=4096, api_key="...")

Configuration patterns

Subclassing (for reusable agents)

class SupportAgent(Agent):
    def __init__(self):
        self.system_description = "You are a customer support assistant."
        self.set_model("openai", "gpt-4.1-mini")
        self.add_tools([find_user, list_orders, get_order_status])
        super().__init__()

agent = SupportAgent()

Inline (for one-off use)

agent = Agent()
agent.system_description = "You are a helpful assistant."
agent.set_model("openai", "gpt-4.1-mini")
agent.add_tool(my_tool)
# Re-run post-init to rebuild the system prompt with tools/skills registered:
Agent.__init__(agent)

Skills (optional)

Skills are markdown files with YAML frontmatter that describe named capabilities:

---
name: writing
description: Use this skill when writing clearly for developers.
---

Additional body content here.

Load them onto an agent:

self.load_system_skill("skills/writing.md")
self.load_system_skills(["skills/writing.md", "skills/debugging.md"])

The skill's name and description are injected into the system prompt as a menu of capabilities the model should apply when relevant.

Example: agent with a skill

Given a skill file skills/concise.md:

---
name: concise-replies
description: Use this skill to keep all responses short and to the point.
---

Reply in plain language. Avoid bullet points unless the user asks for a list.
Never repeat the user's question back to them. Keep answers under three sentences.

Load it onto an agent:

from exagent import Agent, tool

@tool
def get_quote(topic: str) -> str:
    """Return a well-known quote on the given topic."""
    quotes = {
        "perseverance": "It does not matter how slowly you go as long as you do not stop.",
        "curiosity": "The important thing is not to stop questioning.",
    }
    return quotes.get(topic.lower(), "No quote found for that topic.")

class QuoteAgent(Agent):
    def __init__(self):
        self.system_description = "You are a helpful assistant that shares quotes."
        self.set_model("anthropic", "claude-sonnet-4-5")
        self.add_tool(get_quote)
        self.load_system_skill("skills/concise.md")
        super().__init__()

agent = QuoteAgent()
print(agent.run("Share a quote about curiosity."))

The concise-replies skill is listed in the system prompt. The model applies it when composing its reply — keeping the response short — without any extra prompt engineering on your part.

Orchestrator

OrchestratorAgent lets you build a router that delegates tasks to specialist agents automatically. Each registered agent is exposed to the orchestrator's LLM as a tool — the model reads their descriptions and calls whichever fits the task. If a task spans multiple specialists, it calls them in sequence.

from exagent import Agent, tool
from exagent.orchestrator import OrchestratorAgent

# --- Specialist agents ---

@tool
def calculate(expression: str) -> str:
    """Evaluate an arithmetic expression and return the result."""
    ...

class CalculatorAgent(Agent):
    def __init__(self):
        self.system_description = "You are a calculator assistant."
        self.set_model("openai", "gpt-4.1-mini")
        self.add_tool(calculate)
        super().__init__()

@tool
def word_count(text: str) -> str:
    """Count the number of words in the given text."""
    return f"{len(text.split())} words"

class TextAgent(Agent):
    def __init__(self):
        self.system_description = "You are a text processing assistant."
        self.set_model("openai", "gpt-4.1-mini")
        self.add_tool(word_count)
        super().__init__()

# --- Orchestrator ---

class MyOrchestrator(OrchestratorAgent):
    def __init__(self):
        self.set_model("openai", "gpt-4.1-mini")
        self.add_agent(
            CalculatorAgent(),
            name="calculator",
            description="Handles arithmetic and numeric calculations.",
        )
        self.add_agent(
            TextAgent(),
            name="text_processor",
            description="Handles word counts, character counts, and text operations.",
        )
        super().__init__()

orch = MyOrchestrator()
print(orch.run("What is 2847 * 193?"))           # → routed to calculator
print(orch.run("How many words in 'hello world'?"))  # → routed to text_processor

How routing works

The orchestrator's system prompt instructs the model to pick the best agent tool for each task. The model uses the description you provide at registration time to make that decision — write descriptions that are specific about what the agent can and cannot handle.

If a task needs two specialists, the model calls them in sequence within a single run() / stream() call:

User: "Calculate 15% of 840, then count the characters in the result."

Orchestrator LLM → calls calculator("840 * 0.15")  → "126.0"
               → calls text_processor("126.0")     → "4 characters"
               → replies: "15% of 840 is 126.0, which has 4 characters."

Registering agents

orchestrator.add_agent(agent, name="agent_name", description="...")

• name — the tool name the LLM uses to call this agent. Must be unique.
• description — what this agent handles and when to use it. If omitted, falls back to the agent's system_description.

Each sub-agent uses its own already-configured model. The orchestrator does not control which model the specialists use.

Streaming with the orchestrator

stream() works the same as on a regular agent. tool_call events show which specialist was chosen, and tool_result events carry what it returned:

for event in orch.stream("What is 128 * 64?"):
    if event["type"] == "text_delta":
        print(event["text"], end="", flush=True)
    elif event["type"] == "tool_call":
        print(f"\n[routing to] {event['tool_call'].name}")
    elif event["type"] == "tool_result":
        print(f"[agent result] {event['content']}")
    elif event["type"] == "done":
        print()

API reference

Agent

• agent.set_model(provider, model, **kwargs) — configure the LLM
• agent.add_tool(tool) / agent.add_tools([...]) — register tools (accepts Tool instances or raw functions)
• agent.load_system_skill(path) / agent.load_system_skills([paths]) — register skills from disk
• agent.run(prompt, max_iterations=10, on_tool_call=None, on_iteration=None) -> str — drive the loop with the non-streaming provider API and return the final text
• agent.stream(prompt, max_iterations=10, on_tool_call=None, on_iteration=None) -> Iterator[dict] — drive the loop with the streaming provider API and yield events
• agent.chat_history: list — full conversation history including tool calls and results

OrchestratorAgent

Extends Agent. Import from exagent.orchestrator.

• orchestrator.add_agent(agent, name, description=None) — register a specialist agent as a callable tool. description falls back to the agent's system_description if omitted.
• All Agent methods (run, stream, set_model, hooks) are inherited and work identically.

@tool decorator

@tool
@tool(name="custom_name", description="custom description")

Tool

• Tool(name, description, parameters, handler) — manual construction
• tool.run(arguments: dict) — invoke with a dict of arguments
• tool.to_anthropic() / tool.to_openai() — provider-specific tool definitions

Event shapes

{"type": "text_delta", "text": str}
{"type": "tool_call", "tool_call": ToolCall}
{"type": "tool_result", "id": str, "name": str, "content": str, "is_error": bool}
{"type": "done", "text": str}

License

MIT