agent-ledger-sdk 0.0.6

Official Python client for the Agent Ledger API

agent-ledger-sdk

Official Python SDK for Agent Ledger. Instrument your AI agents with session telemetry, capture every LLM/tool call, and enforce budget guardrails with a single synchronous HTTP client.

Portal: https://agent-ledger.thabo.xyz/

Table of contents

1. Features
2. Installation
3. Requirements
4. Quick start
5. Session lifecycle
6. Event schema
7. API reference
8. Error handling
9. Configuration & environments
10. Advanced usage
11. Testing & troubleshooting
12. Development
13. License

Features

• Zero-dependency interface beyond httpx, so you stay in control of transports, retries, and proxies.
• Typed dataclasses (BudgetGuardrailDetails) for precise guardrail reporting.
• Context-manager friendly client that mirrors the Agent Ledger API surface (/v1/sessions, /v1/events).
• Works anywhere CPython 3.9+ can run (serverless functions, notebooks, background workers).

Installation

pip install agent-ledger-sdk
# or with uv
uv add agent-ledger-sdk

Requirements

• Python 3.9 or newer.
• An Agent Ledger API key created in the dashboard.
• Network access to https://agent-ledger-api.azurewebsites.net.

Quick start

import os
import time

from agent_ledger_sdk import AgentLedgerClient, BudgetGuardrailError, run_session, instrument_tool
from openai import OpenAI

ledger = AgentLedgerClient(api_key=os.environ["AGENT_LEDGER_API_KEY"])
llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

try:
    with run_session(ledger, "support-bot") as (session_id, steps):
        started = time.perf_counter()
        completion = llm.responses.create(
            model="gpt-4o-mini",
            input=[{"role": "user", "content": "Draft a welcome email."}],
            temperature=0.2,
        )
        latency_ms = int((time.perf_counter() - started) * 1000)
        content = completion.output[0].content[0].text

        ledger.log_llm_call(
            session_id,
            {
                "stepIndex": steps.next(),
                "provider": "openai",
                "model": completion.model,
                "prompt": "Draft a welcome email.",
                "response": content,
                "tokensIn": completion.usage.input_tokens,
                "tokensOut": completion.usage.output_tokens,
                "latencyMs": latency_ms,
            },
        )

        # Tool wrapper example (logs tool_call + tool_result automatically)
        instrument_tool(
            ledger=ledger,
            session_id=session_id,
            step_index=steps.next(),
            tool_name="weather",
            tool_input={"city": "Boston"},
            run=lambda: {"temp_f": 61},
        )
except BudgetGuardrailError as exc:
    print("Budget exceeded", exc.details)
    raise
finally:
    ledger.close()

ℹ️ The SDK never proxies your LLM/tool calls. You run them directly and forward the metadata (prompt, response, tokens, latency) to Agent Ledger for analytics, guardrails, and diffing.

Session lifecycle

1. Start a session as soon as the workflow begins: ledger.start_session("agent-name").
2. Log events every time you:
   • call an LLM (log_llm_call).
   • invoke a tool (log_tool_call).
   • get a tool response (log_tool_result).
   • emit a custom diagnostic (log_events).
3. End the session with ledger.end_session(...) so the dashboard can show the outcome next to cost metrics.

Keep the session ID in scope (store it on the request context, job payload, or tracing span) so every downstream component can append telemetry.

Event schema

The Agent Ledger API expects camelCase keys. The Python SDK forwards your dictionaries as-is, so be sure to match the schema exactly.

Event	Required keys	Optional keys	Notes
LLM call	stepIndex, model, provider, prompt, response, tokensIn, tokensOut, latencyMs	—	Use log_llm_call. Agent Ledger prices the call automatically using provider/model/tokens.
Tool call	stepIndex, toolName, toolInput	—	Use log_tool_call right before invoking the tool.
Tool result	stepIndex, toolName, toolOutput, latencyMs	—	Use log_tool_result after receiving the output.
Custom	type, stepIndex, plus any attributes you want	Anything	Send via log_events for domain-specific signals.

Conventions:

• stepIndex is a monotonically increasing integer, making prompt/tool diffs straightforward.
• Keep payloads under ~64 KB per event for the best UI experience.
• Numbers should remain numeric (avoid serializing to strings) so the backend can aggregate costs.

API reference

AgentLedgerClient(api_key, *, timeout=10.0, transport=None)

Parameter	Type	Description
api_key	str (required)	Workspace API key. Raises ValueError if empty.
timeout	float	Passed to the underlying httpx.Client.
transport	`httpx.BaseTransport	None`

The client can be used as a context manager:

from agent_ledger_sdk import AgentLedgerClient

with AgentLedgerClient(api_key="sk-...", timeout=5) as ledger:
    session_id = ledger.start_session("batch-writer")
    ledger.end_session(session_id, "success")

start_session(agent_name: str) -> str

Creates a session row and returns its UUID.

end_session(session_id: str, status: Literal["success", "error"], *, error_message: Optional[str] = None) -> None

Marks a session as finished. When status == "error", include an error_message so the dashboard can surface it.

log_events(session_id: str, events: Iterable[Mapping[str, Any]]) -> None

Low-level ingestion helper. Accepts any iterable, clones each event into a list, and POSTs them to /v1/events. Each event must include a type field.

log_llm_call / log_tool_call / log_tool_result

Thin wrappers that add the correct type and delegate to log_events. Use them to keep your instrumentation consistent.

Error handling

• BudgetGuardrailError — raised when the API returns HTTP 429 with guardrail metadata. The details attribute includes:

  BudgetGuardrailDetails(
      agent_name="support-bot",
      daily_limit_usd=20.0,
      spent_today_usd=18.4,
      attempted_cost_usd=0.9,
      projected_cost_usd=19.3,
      remaining_budget_usd=0.7,
  )
  

  Use it to short-circuit further tool/LLM calls or trigger human intervention.

• AgentLedgerError — raised for any other non-success HTTP response (network failures, 4xx/5xx). Contains the server-provided error field when available.

Configuration & environments

• Provide AGENT_LEDGER_API_KEY (or load it from your secrets manager) and the SDK targets the hosted API automatically.
• Default endpoint → https://agent-ledger-api.azurewebsites.net.

For local experimentation, leave the SDK untouched and proxy traffic through tools like pytest-httpserver or MSW if you need to inspect requests.

Advanced usage

• Custom HTTP transport: inject an httpx.HTTPTransport or httpx_socks.SyncProxyTransport to route traffic through proxies or service meshes.
• Retries: wrap SDK calls with your preferred retry decorator (e.g., tenacity) if you need resilience around transient network errors.
• Thread safety: httpx.Client is threadsafe; reuse a single AgentLedgerClient across worker threads or WSGI/ASGI processes.
• Async workflows: if you need asyncio support today, instantiate the SDK inside an executor, or adapt it by swapping httpx.Client for httpx.AsyncClient (PRs welcome!).

Testing & troubleshooting

• In tests, intercept HTTP calls via transport injections or libraries such as pytest-httpserver/MSW so you can assert payloads.
• If the dashboard shows no events, double-check that your event dictionaries use camelCase keys.
• HTTP 401/403 errors mean the API key is missing or revoked—create a new key in the dashboard and update your secrets manager.
• Guardrail errors (HTTP 429) are expected when daily spend is exhausted; catch BudgetGuardrailError to degrade gracefully.

Development

cd packages/sdk-py
python -m venv .venv && source .venv/bin/activate
pip install -e .[dev]
pytest

License

MIT