agent-observability-langchain 1.2.1
LangChain integration for Agent Observability - structured logging, cost tracking, and compliance for AI agents
Requires Python: >=3.9
Keywords: langchain, agent, observability, logging, compliance, audit, ai-agent, llm, cost-tracking, monitoring, analytics, structured-logging

# Agent Observability for LangChain

[![PyPI version](https://badge.fury.io/py/agent-observability-langchain.svg)](https://pypi.org/project/agent-observability-langchain/)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
[![LangChain](https://img.shields.io/badge/LangChain-Tool-green.svg)](https://python.langchain.com/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

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

## Installation

```bash
pip install agent-observability-langchain
```

## Quick Start

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

```python
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:

```bash
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

```bash
export AGENT_OBS_API_KEY=ao_live_your_key_here
```

### Step 3: Add to Your Agent

```python
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:

```python
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):

```python
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

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

### 4. With LangGraph

```python
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

```python
# 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

```python
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

```python
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:

```python
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](https://pypi.org/project/agent-observability/)** — Core Python SDK
- **[agent-observability-mcp](https://www.npmjs.com/package/agent-observability-mcp)** — Claude Desktop MCP integration
- **[agent-observability-autogpt](https://pypi.org/project/agent-observability-autogpt/)** — AutoGPT plugin
- **[agent-observability-crewai](https://pypi.org/project/agent-observability-crewai/)** — CrewAI integration

## License

MIT License — see [LICENSE](LICENSE) for details.

## Links

- [API Documentation](https://api-production-0c55.up.railway.app/docs)
- [OpenAPI Spec](https://api-production-0c55.up.railway.app/openapi.json)
- [Pricing](https://api-production-0c55.up.railway.app/pricing.json)

## Contributing

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