agent-tracer 0.2.0

Universal tracing library for AI agent systems with LLM integration, context management, and export capabilities

agent-tracer

Universal tracing library for AI agent systems with LLM decision capture.

Version: 0.2.0 | Python: >= 3.11 | License: MIT

agent-tracer provides hierarchical tracing for any AI agent system. It captures execution flows, LLM decisions with reasoning and criteria, and artifacts, storing everything in PostgreSQL and/or JSON files on disk.

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Table of Contents

• Installation
• Quick Start
• Features
• Architecture
• API Reference
• Integrations
• Configuration
• Testing
• Contributing
• License

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Installation

Core package

pip install agent-tracer

Optional extras

pip install agent-tracer[llm]         # Anthropic + OpenAI client libraries
pip install agent-tracer[langchain]   # LangChain/LangGraph integration
pip install agent-tracer[exporters]   # Zipkin and Jaeger exporters
pip install agent-tracer[all]         # All optional dependencies

Development

pip install -e ".[dev]"

---

Quick Start

1. Basic tracing with TraceClient

from agent_tracer import TraceClient
from agent_tracer.storage import TraceStorageBackend

# File-only storage (no PostgreSQL required)
storage = TraceStorageBackend(db_conn=None, storage_dir="./traces")
client = TraceClient(storage)

# Start a trace
trace_id = client.start_trace(
    trigger_type="manual",
    triggered_by="[e-mail]",
    metadata={"purpose": "debugging", "environment": "production"}
)

# Create spans with steps and artifacts
with client.span("analysis_phase", "agent_execution") as span:
    client.add_step(
        name="load_data",
        step_type="data_fetch",
        input_data={"source": "database"},
        output_data={"records": 100}
    )

    client.add_artifact(
        name="analysis_result",
        artifact_type="report",
        content={"findings": ["issue1", "issue2"]}
    )

    client.add_decision(
        name="action_selected",
        reasoning="Multiple signals converge on the same action",
        criteria=["frequency", "impact", "correlation"],
        final_score=0.92
    )

# Finalize the trace
client.complete_trace(
    status="completed",
    summary={"total_issues": 2, "severity": "high"}
)

2. Decorator-based tracing

import asyncio
from agent_tracer import TraceClient, traced_agent, traced_llm_call
from agent_tracer.models import AgentDecision, DecisionCriteria, LLMContext
from agent_tracer.context import LLMContextCaptureMixin
from agent_tracer.storage import TraceStorageBackend

storage = TraceStorageBackend(db_conn=None, storage_dir="./traces")
trace_client = TraceClient(storage)

@traced_agent(trace_client, fail_safe=True)
class TaskPlannerAgent(LLMContextCaptureMixin):

    @traced_llm_call(trace_client)
    async def call_llm(self, prompt: str) -> AgentDecision:
        # Your LLM call here -- returns a structured decision
        return AgentDecision(
            action="create_subtasks",
            reasoning="Task is complex, parallel execution improves efficiency",
            confidence=0.87,
            alternatives_considered=["execute_sequentially", "delegate"],
            criteria=[
                DecisionCriteria(factor="complexity", score=0.9, weight=0.4),
                DecisionCriteria(factor="time_constraints", score=0.8, weight=0.3),
            ],
            context_used={"task": "implement auth"}
        )

    async def run(self, task: dict):
        decision = await self.call_llm(prompt=f"Plan: {task['description']}")
        return {"status": "completed", "action": decision.action}

# Tracing happens automatically
asyncio.run(TaskPlannerAgent().run({"description": "Implement auth", "sender": "system"}))

3. Fail-safe mode

Tracing errors never break your agent:

from agent_tracer.utils import FailSafeTraceClient

safe_client = FailSafeTraceClient(trace_client)

# If tracing fails (e.g., storage unavailable), the agent continues normally.
# Failures are logged and the client stops retrying for the current trace.
safe_client.start_trace(...)

---

Features

Hierarchical Tracing

Trace -> Span -> Step -> Artifact. Spans nest via parent/child relationships. Steps record individual actions. Artifacts attach data of any size (small data inline, large data on disk with preview).

Decision Capture

Record agent reasoning with structured AgentDecision models that include the chosen action, confidence score, evaluation criteria with weights, alternatives considered, and the context used.

Decorator Suite

• @traced_agent(client) -- Wraps an agent class. Automatically traces the run() method, captures context, and extracts decisions from AgentDecision return values.
• @traced_llm_call(client) -- Traces LLM calls with pre-call context capture (via LLMContextCaptureMixin) and post-call decision extraction.
• @traced_function(client) -- Traces any async function as a span.
• @traced_tool(client) -- Traces tool executions with input/output logging.

Context Propagation

Thread-safe and async-safe trace context via contextvars:

from agent_tracer.context import get_current_trace_id, set_current_trace_id

set_current_trace_id("trace_abc123")
print(get_current_trace_id())  # "trace_abc123"

Span stack management for nested hierarchies:

from agent_tracer.context import push_span, pop_span, get_current_span, get_span_stack

push_span("span_outer")
push_span("span_inner")
print(get_current_span())  # "span_inner"
print(get_span_stack())    # ["span_outer", "span_inner"]
pop_span()                 # returns "span_inner"

LLM Context Capture

Mix LLMContextCaptureMixin into your agent class to automatically capture model name, temperature, messages, estimated token count, available tools, and agent state before each LLM call.

Dual Storage

• PostgreSQL: Trace and span metadata stored in JSONB columns for flexible querying.
• Disk: Full trace JSON written to {storage_dir}/{trace_id}/trace.json. Large artifacts stored as individual files.
• File-only mode: Pass db_conn=None to skip PostgreSQL entirely.

Trace Export

Export traces to Zipkin or Jaeger format for visualization in distributed tracing UIs:

from agent_tracer.integrations.exporters import ZipkinExporter, JaegerExporter

# Zipkin (also works with Jaeger's Zipkin endpoint on port 9411)
zipkin = ZipkinExporter(service_name="my-agent")
zipkin.send_to_jaeger(trace_data)

# Jaeger native format
jaeger = JaegerExporter(service_name="my-agent")
jaeger.export_file("traces/trace_abc/trace.json", "jaeger_output.json")
jaeger.send_to_jaeger(trace_data, "http://localhost:14268/api/traces")

LangChain/LangGraph Integration

Drop-in callback handler that automatically traces node execution, LLM calls, and state transitions:

from agent_tracer.integrations import ComprehensiveTracingCallback

callback = ComprehensiveTracingCallback(trace_client, auto_parse_decisions=True)
# Add to your LangGraph agent's callbacks list

The callback captures chain start/end, LLM start/end with prompt and response, and parses LLM responses into AgentDecision objects using heuristic text analysis.

---

Architecture

Package Structure

agent_tracer/
  __init__.py          - Public API re-exports
  core/
    trace_client.py    - TraceClient: start/complete traces, spans, steps, artifacts
    schemas.py         - Pydantic models: Trace, Span, Step, Artifact, TriggerInfo
    utils.py           - Datetime helpers
  decorators/
    agent.py           - @traced_agent class decorator
    llm.py             - @traced_llm_call method decorator
    function.py        - @traced_function decorator
    tool.py            - @traced_tool decorator
  models/
    decisions.py       - AgentDecision, DecisionCriteria, LLMContext
  context/
    capture.py         - LLMContextCaptureMixin
    propagation.py     - get/set_current_trace_id, span stack (contextvars)
  utils/
    fail_safe.py       - FailSafeTraceClient wrapper
  storage/
    storage_backend.py - TraceStorageBackend (PostgreSQL + disk)
  integrations/
    langchain.py       - ComprehensiveTracingCallback
    exporters/
      zipkin.py        - ZipkinExporter
      jaeger.py        - JaegerExporter

Data Model

Trace
  |-- trace_id, trace_type, status, trigger, metadata, summary
  |-- created_at, completed_at, duration_ms
  |
  +-- Span (1..N)
        |-- span_id, parent_span_id, name, type, status
        |-- start_time, end_time, duration_ms, agent_metadata
        |
        +-- Step (0..N)
        |     |-- step_id, step_type, name, status
        |     |-- input, output, decision_logic
        |
        +-- Artifact (0..N)
              |-- artifact_id, name, artifact_type, content_type
              |-- storage (inline or external_ref), content, preview

Core Schemas (Pydantic v2)

Model	Purpose
Trace	Top-level execution container
Span	Execution phase or agent activity
Step	Individual action within a span
Artifact	Data/files attached to spans
TriggerInfo	What initiated the trace
TraceSummary	Summary metrics (total/failed spans)
AgentMetadata	Agent-specific span metadata
StorageInfo	Artifact storage location and size
AgentDecision	Structured decision with reasoning and criteria
DecisionCriteria	Single evaluation factor with score and weight
LLMContext	Pre-call context (model, temperature, messages, tools)

---

API Reference

TraceClient

client = TraceClient(storage_backend)

# Lifecycle
trace_id = client.start_trace(trigger_type, triggered_by, metadata, trace_type="agent_execution")
client.complete_trace(status, summary)

# Spans (context manager)
with client.span(name, span_type, agent_metadata=None) as span_data:
    ...

# Steps and artifacts
client.add_step(name, step_type, input_data, output_data)
client.add_artifact(name, artifact_type, content, metadata=None)
client.add_decision(name, reasoning, criteria, final_score)

Decorators

@traced_agent(trace_client, fail_safe=True)       # Class decorator for agents
@traced_llm_call(trace_client, fail_safe=True)     # Method decorator for LLM calls
@traced_function(trace_client, span_name=None)     # Decorator for async functions
@traced_tool(trace_client)                         # Decorator for tool executions

Context Propagation

set_current_trace_id(trace_id)        # Set trace ID in current async context
get_current_trace_id() -> str | None  # Get trace ID from current context

push_span(span_id)                    # Push span onto stack
pop_span() -> str | None              # Pop span from stack
get_current_span() -> str | None      # Peek at top span
get_span_stack() -> list[str]         # Get full span hierarchy

Imports

# Top-level (most common)
from agent_tracer import (
    TraceClient, traced_agent, traced_llm_call,
    AgentDecision, DecisionCriteria, LLMContext,
    FailSafeTraceClient,
)

# Sub-modules
from agent_tracer.decorators import traced_function, traced_tool
from agent_tracer.models import AgentDecision, DecisionCriteria, LLMContext
from agent_tracer.context import LLMContextCaptureMixin, get_current_trace_id
from agent_tracer.storage import TraceStorageBackend, TraceNotFoundError
from agent_tracer.utils import FailSafeTraceClient
from agent_tracer.integrations import ComprehensiveTracingCallback
from agent_tracer.integrations.exporters import ZipkinExporter, JaegerExporter

---

Integrations

LangChain / LangGraph

Requires the langchain extra:

pip install agent-tracer[langchain]

from agent_tracer.integrations import ComprehensiveTracingCallback

callback = ComprehensiveTracingCallback(
    trace_client,
    auto_parse_decisions=True,  # Parse LLM text into AgentDecision
    fail_safe=True,             # Never crash the agent
)

# The @traced_agent decorator auto-injects this callback when it detects
# a LangGraph agent (one with a `callbacks` list attribute).

Zipkin / Jaeger

Requires the exporters extra:

pip install agent-tracer[exporters]

Zipkin (also ingested by Jaeger on port 9411):

from agent_tracer.integrations.exporters import ZipkinExporter

exporter = ZipkinExporter(service_name="my-agent")
exporter.send_to_jaeger(trace_data, zipkin_url="http://localhost:9411/api/v2/spans")

Jaeger (native format via collector on port 14268):

from agent_tracer.integrations.exporters import JaegerExporter

exporter = JaegerExporter(service_name="my-agent")

# Export to file
exporter.export_file("traces/trace_abc/trace.json", "output.jaeger.json")

# Export entire directory
exporter.export_directory("./traces", "./jaeger_output")

# Send directly to Jaeger collector
exporter.send_to_jaeger(trace_data, "http://localhost:14268/api/traces")

---

Configuration

Storage Setup

File-only (no database required):

storage = TraceStorageBackend(db_conn=None, storage_dir="./traces")

PostgreSQL + file:

import psycopg2

conn = psycopg2.connect(
    host="localhost", port=5432,
    dbname="agent_traces", user="tracer", password="secret"
)
storage = TraceStorageBackend(db_conn=conn, storage_dir="./traces")

PostgreSQL Schema

The storage backend expects two tables:

CREATE TABLE agent_tracer_traces (
    trace_id        TEXT PRIMARY KEY,
    trace_type      TEXT,
    status          TEXT,
    trigger_type    TEXT,
    trigger_source  TEXT,
    triggered_by    TEXT,
    triggered_at    TIMESTAMPTZ,
    created_at      TIMESTAMPTZ,
    completed_at    TIMESTAMPTZ,
    duration_ms     INTEGER,
    total_spans     INTEGER,
    failed_spans    INTEGER,
    summary_metrics JSONB,
    metadata        JSONB,
    trace_document_url TEXT
);

CREATE TABLE agent_tracer_spans (
    span_id         TEXT PRIMARY KEY,
    trace_id        TEXT REFERENCES agent_tracer_traces(trace_id),
    parent_span_id  TEXT,
    name            TEXT,
    span_type       TEXT,
    status          TEXT,
    start_time      TIMESTAMPTZ,
    end_time        TIMESTAMPTZ,
    duration_ms     INTEGER,
    agent_metadata  JSONB
);

Artifact Storage

• Artifacts smaller than 10 KB are stored inline as JSON within the trace.
• Larger artifacts are written to {storage_dir}/{trace_id}/artifacts/{artifact_id}.txt with a 500-character preview kept inline.

---

Testing

# Run all tests
pytest -v

# With coverage
pytest --cov=agent_tracer --cov-report=term-missing

# Unit tests only
pytest tests/unit/ -v

Test configuration is in pyproject.toml under [tool.pytest.ini_options].

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Contributing

1. Fork the repository and create a feature branch.
2. Install development dependencies: pip install -e ".[dev]"
3. Write tests for any new functionality.
4. Run the test suite: pytest -v
5. Submit a pull request.

Code Style

• Type hints on all public APIs.
• Docstrings on all public classes and functions.
• Pydantic v2 models for data validation.
• Async-first decorator design (all decorators wrap async functions).

Dependencies

Core: pydantic>=2.0.0, psycopg2-binary>=2.9.9, python-dateutil>=2.8.0

Optional: anthropic, openai, langchain-core, requests

Dev: pytest, pytest-asyncio, pytest-cov, rich

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License

MIT License. See LICENSE for details.