agent-observability-langchain 1.2.0

LangChain integration for Agent Observability - structured logging, cost tracking, and compliance for AI agents

Agent Observability for LangChain

LangChain integration for the Agent Observability platform — structured logging, cost tracking, and compliance audit trails for production AI agents.

Installation

pip install agent-observability-langchain

Quick Start

Step 1: Get Your Free API Key (One-Time)

import requests

response = requests.post(
    "https://api-production-0c55.up.railway.app/v1/register",
    json={"agent_id": "my-langchain-agent"}
)
api_key = response.json()["api_key"]
print(f"Your API key: {api_key}")
# Save this! Example: ao_live_abc123...

Or via curl:

curl -X POST https://api-production-0c55.up.railway.app/v1/register \
  -H "Content-Type: application/json" \
  -d '{"agent_id": "my-langchain-agent"}'

Step 2: Set Environment Variable

export AGENT_OBS_API_KEY=ao_live_your_key_here

Step 3: Add to Your Agent

from langchain.agents import initialize_agent, AgentType
from langchain_openai import ChatOpenAI
from agent_observability_langchain import AgentObservabilityTool

# Create observability tool
obs_tool = AgentObservabilityTool()

# Create agent with observability
agent = initialize_agent(
    tools=[obs_tool],
    llm=ChatOpenAI(model="gpt-4"),
    agent=AgentType.OPENAI_FUNCTIONS,
    verbose=True
)

# Use your agent — it can now log events for compliance
result = agent.run(
    "Log an API call to OpenAI with cost $0.05 and latency 1200ms"
)

Features

Feature	Description
Structured Logging	Track all agent decisions and API calls with metadata
Cost Tracking	Monitor spending per agent, task, and LLM provider
Performance Analytics	Latency percentiles, error rates, throughput
Compliance Ready	SOC 2, GDPR, HIPAA audit trail requirements
Queryable History	Search logs by agent, time, event type, severity
LangChain Native	Works with agents, chains, and LangGraph

Use Cases

1. Cost Analysis

Track spending across all your agents and LLM providers:

from agent_observability_langchain import AgentObservabilityTool

tool = AgentObservabilityTool(agent_id="research-bot-v2")

# Log each API call with cost
tool.invoke({
    "event_type": "api_call",
    "metadata": {
        "provider": "openai",
        "model": "gpt-4",
        "cost_usd": 0.03,
        "latency_ms": 1250,
        "tokens": 1500
    }
})

2. Decision Auditing

For regulated industries (finance, healthcare, legal):

tool.invoke({
    "event_type": "decision",
    "metadata": {
        "decision": "approve_loan",
        "confidence": 0.92,
        "factors": ["credit_score", "income", "debt_ratio"],
        "customer_id": "cust_12345"
    },
    "severity": "info"
})

3. Error Tracking

tool.invoke({
    "event_type": "error",
    "metadata": {
        "error_type": "RateLimitError",
        "provider": "openai",
        "retry_count": 3,
        "resolved": False
    },
    "severity": "error"
})

4. With LangGraph

from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from agent_observability_langchain import AgentObservabilityTool

# Create agent with observability
agent = create_react_agent(
    ChatOpenAI(model="gpt-4"),
    tools=[AgentObservabilityTool()]
)

result = agent.invoke({
    "messages": [("user", "Research AI trends and log the costs")]
})

5. Custom Agent ID per Task

# Different agent IDs for different workflows
research_tool = AgentObservabilityTool(agent_id="research-agent")
writer_tool = AgentObservabilityTool(agent_id="writer-agent")
reviewer_tool = AgentObservabilityTool(agent_id="reviewer-agent")

API Reference

AgentObservabilityTool

AgentObservabilityTool(
    api_key: str = None,        # Or set AGENT_OBS_API_KEY env var
    agent_id: str = "langchain-agent",  # Your agent identifier
    api_base: str = None        # Override API URL (for self-hosted)
)

Input Schema

Parameter	Type	Required	Description
event_type	str	Yes	api_call, decision, transaction, error, state_change
metadata	dict	No	Event data (provider, cost_usd, latency_ms, etc.)
severity	str	No	debug, info, warning, error, critical (default: info)

Common Metadata Fields

Field	Type	Description
provider	str	LLM provider (openai, anthropic, cohere, etc.)
model	str	Model name (gpt-4, claude-3-opus, etc.)
cost_usd	float	Cost in USD
latency_ms	int	Response time in milliseconds
tokens	int	Total token count
input_tokens	int	Input/prompt tokens
output_tokens	int	Output/completion tokens
error_message	str	Error details (for error events)
decision	str	Decision made (for decision events)
confidence	float	Confidence score (0-1)

Pricing

Tier	Logs/Month	Price	Best For
Free	100,000	$0	Development, small projects
Starter	1,000,000	$29/month	Production agents
Professional	10,000,000	$199/month	Enterprise, high-volume

Full pricing: https://api-production-0c55.up.railway.app/pricing.json

Convenience Function

from agent_observability_langchain import create_observability_tool

# Quick creation
tool = create_observability_tool(agent_id="my-agent")

Environment Variables

Variable	Description	Required
AGENT_OBS_API_KEY	Your API key	Yes (or pass to constructor)
AGENT_OBS_BASE_URL	Override API URL	No

Error Handling

The tool handles errors gracefully and returns error messages instead of raising exceptions:

result = tool.invoke({
    "event_type": "api_call",
    "metadata": {"provider": "openai"}
})

if "Failed" in result:
    print(f"Logging failed: {result}")
else:
    print(f"Success: {result}")

Related Packages

• agent-observability — Core Python SDK
• agent-observability-mcp — Claude Desktop MCP integration
• agent-observability-autogpt — AutoGPT plugin
• agent-observability-crewai — CrewAI integration

License

MIT License — see LICENSE for details.

Links

• API Documentation
• OpenAPI Spec
• Pricing
• GitHub Repository

Contributing

Contributions welcome! Please open an issue or submit a pull request.