agentdecode 0.1.2

Free, open-source observability for AI agents. Trace LLM calls, catch silent failures, score output quality.

agentdecode

Free, open-source observability SDK for AI agents.

Trace every LLM call, tool invocation, and retrieval step your AI agent makes. Catch silent failures, score output quality automatically, and debug agent pipelines with full visibility.

Installation

pip install agentdecode

Quick Start

from agentdecode import AgentDecode

agent = AgentDecode(
    api_key="al_your_api_key",
    endpoint="https://agent-decode.vercel.app"
)

# Use as a context manager to group spans into a session
with agent.session("Customer Support Agent") as session:
    with session.span("classify_intent", span_type="llm") as span:
        span.model = "gpt-4o-mini"
        span.input = {"message": "Cancel my subscription"}
        # ... your LLM call here ...
        span.output = {"intent": "cancellation", "confidence": 0.97}
        span.input_tokens = 24
        span.output_tokens = 8
        span.cost_usd = 0.0001

    with session.span("lookup_account", span_type="tool") as span:
        span.input = {"user_id": "usr_9281"}
        # ... your DB call here ...
        span.output = {"plan": "pro", "months_active": 14}

    with session.span("generate_response", span_type="llm") as span:
        span.model = "gpt-4o"
        span.input = {"context": "Pro user, 14 months", "intent": "cancellation"}
        # ... your LLM call here ...
        span.output = {"response": "I understand you'd like to cancel..."}
        span.input_tokens = 85
        span.output_tokens = 120
        span.cost_usd = 0.003

# All spans are sent automatically when the session exits

Nested Spans (Parent-Child)

with agent.session("RAG Pipeline") as session:
    with session.span("orchestrator", span_type="agent") as parent:
        parent.input = {"query": "What is our refund policy?"}

        # Child spans — pass the parent to create hierarchy
        with session.span("search_docs", span_type="retrieval", parent=parent) as s:
            s.input = {"query": "refund policy", "top_k": 5}
            s.output = {"documents": ["doc1", "doc2"], "count": 2}

        with session.span("generate_answer", span_type="llm", parent=parent) as s:
            s.model = "gpt-4o"
            s.input = {"context": ["doc1", "doc2"], "question": "refund policy"}
            s.output = {"answer": "Our refund policy allows..."}

        parent.output = {"answer": "Our refund policy allows..."}

Decorator for Simple Tracing

@agent.trace("classify_intent", span_type="llm")
def classify(message: str) -> dict:
    # Your logic here
    return {"intent": "support", "confidence": 0.95}

# Calling this sends a single-span trace automatically
result = classify("I need help with my order")

Error Tracking

Exceptions inside spans are automatically captured with status: "error" and the exception message stored in error_message. The session is still sent so you can see exactly where things broke.

with agent.session("Risky Pipeline") as session:
    with session.span("flaky_api_call", span_type="tool") as span:
        span.input = {"url": "https://api.example.com/data"}
        response = requests.get("https://api.example.com/data")
        response.raise_for_status()  # If this throws, it's captured
        span.output = response.json()

API Reference

AgentDecode(api_key, endpoint)

Parameter	Type	Required	Description
api_key	str	✓	Your API key (starts with al_)
endpoint	str	✓	Your AgentDecode server URL

agent.session(name, session_id=None)

Returns a Session context manager. All spans created inside are batched and sent on exit.

session.span(name, span_type="tool", parent=None)

Returns a Span context manager. Set properties on the span object:

Property	Type	Description
input	any	Input data (any JSON-serializable value)
output	any	Output data (any JSON-serializable value)
model	str	Model name (e.g. "gpt-4o")
input_tokens	int	Input token count
output_tokens	int	Output token count
cost_usd	float	Cost in USD
error_message	str	Error description
metadata	dict	Custom key-value pairs

@agent.trace(name, span_type="agent")

Decorator that wraps a function in a single-span session. The function's arguments are captured as input and the return value as output.

Requirements

• Python ≥ 3.8
• Zero external dependencies (uses only Python stdlib)

License

MIT