agentlensai 0.10.0

Python SDK for AgentLens — observability and audit trail for AI agents

AgentLens Python SDK

Python SDK for AgentLens — observability and audit trail for AI agents.

Installation

pip install agentlensai

Quick Start

Sync Client

from agentlensai import AgentLensClient, LogLlmCallParams, LlmMessage, TokenUsage

client = AgentLensClient("http://localhost:3400", api_key="als_your_key")

# Query events
result = client.query_events()
print(f"Total events: {result.total}")

# Get sessions
sessions = client.get_sessions()
for session in sessions.sessions:
    print(f"Session {session.id}: {session.status}")

# Log an LLM call
result = client.log_llm_call(
    session_id="ses_abc",
    agent_id="my-agent",
    params=LogLlmCallParams(
        provider="anthropic",
        model="claude-sonnet-4-20250514",
        messages=[LlmMessage(role="user", content="Hello!")],
        completion="Hi there! How can I help?",
        finish_reason="stop",
        usage=TokenUsage(input_tokens=10, output_tokens=8, total_tokens=18),
        cost_usd=0.001,
        latency_ms=850,
    ),
)
print(f"Logged LLM call: {result.call_id}")

# Get LLM analytics
analytics = client.get_llm_analytics()
print(f"Total LLM calls: {analytics.summary.total_calls}")
print(f"Total cost: ${analytics.summary.total_cost_usd:.2f}")

client.close()

Async Client

import asyncio
from agentlensai import AsyncAgentLensClient

async def main():
    async with AsyncAgentLensClient("http://localhost:3400", api_key="als_your_key") as client:
        # All the same methods, but async
        result = await client.query_events()
        health = await client.health()
        print(f"Server: {health.version}, Events: {result.total}")

asyncio.run(main())

Privacy-Aware Logging

# Redact sensitive prompts/completions while keeping metadata
result = client.log_llm_call(
    session_id="ses_abc",
    agent_id="my-agent",
    params=LogLlmCallParams(
        provider="openai",
        model="gpt-4o",
        messages=[LlmMessage(role="user", content="My SSN is 123-45-6789")],
        completion="I'll process that...",
        finish_reason="stop",
        usage=TokenUsage(input_tokens=15, output_tokens=10, total_tokens=25),
        cost_usd=0.002,
        latency_ms=1200,
        redact=True,  # Content replaced with [REDACTED], metadata preserved
    ),
)

Auto-Instrumentation (v0.4.0+)

One line of setup — every LLM call captured automatically. No code changes needed.

pip install agentlensai[openai]      # or agentlensai[anthropic] or agentlensai[all]

import agentlensai

# Automatically instruments OpenAI + Anthropic SDKs
agentlensai.init(
    url="http://localhost:3400",
    api_key="als_your_key",
    agent_id="my-agent",
)

# Every call is now captured — deterministic, not MCP-dependent
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
)
# ^ Automatically logged: model, tokens, cost, latency, prompts

# Works with Anthropic too
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
)
# ^ Also captured automatically

# Clean up when done
agentlensai.shutdown()

LangChain Integration

from agentlensai.integrations.langchain import AgentLensCallbackHandler

handler = AgentLensCallbackHandler()
chain.invoke(input, config={"callbacks": [handler]})
# ^ Every LLM call, tool call, and chain event captured

Supported Providers (v0.10.0+)

AgentLens supports 9 LLM providers with automatic instrumentation:

Provider	Install	Status
OpenAI	pip install agentlensai[openai]	✅ Sync + Async + Streaming
Anthropic	pip install agentlensai[anthropic]	✅ Sync + Async + Streaming
LiteLLM	pip install agentlensai[litellm]	✅ 100+ providers via proxy
AWS Bedrock	pip install agentlensai[bedrock]	✅ All Bedrock models
Google Vertex AI	pip install agentlensai[vertex]	✅ Vertex model garden
Google Gemini	pip install agentlensai[gemini]	✅ Gemini API
Mistral AI	pip install agentlensai[mistral]	✅ All Mistral models
Cohere	pip install agentlensai[cohere]	✅ v1 + v2 API
Ollama	pip install agentlensai[ollama]	✅ Local models (free)

Install all providers at once:

pip install agentlensai[all-providers]

Auto-Discovery

import agentlensai

# Automatically discovers and instruments ALL installed provider SDKs
agentlensai.init(
    url="http://localhost:3400",
    api_key="als_your_key",
    agent_id="my-agent",
    integrations="auto",  # default — instruments everything available
)

Or pick specific providers:

agentlensai.init(
    url="http://localhost:3400",
    integrations=["openai", "bedrock", "ollama"],
)

Provider Examples

Ollama (local, free):

import ollama
response = ollama.chat(model="llama3", messages=[{"role": "user", "content": "Hello"}])
# ^ Captured: model, tokens, latency (cost = $0)

AWS Bedrock:

import boto3
client = boto3.client("bedrock-runtime")
response = client.invoke_model(modelId="anthropic.claude-3-haiku-20240307-v1:0", body=...)
# ^ Captured with Bedrock-specific pricing

LiteLLM (100+ providers):

import litellm
response = litellm.completion(model="gpt-4", messages=[...])
# ^ Captured with built-in cost calculation

Migration from v0.4.0

The old instrument_openai() / instrument_anthropic() functions still work:

# Old way (still supported)
from agentlensai.integrations.openai import instrument_openai
instrument_openai()

# New way (recommended)
agentlensai.init(url="...", integrations="auto")

Key Guarantees

• Deterministic — Every call captured, not dependent on LLM behavior
• Fail-safe — If AgentLens server is down, your code still works normally
• Zero overhead — Events sent via background thread, doesn't block your calls
• Privacy — init(redact=True) strips content, keeps metadata

Features

• Auto-Instrumentation — One-liner setup for OpenAI, Anthropic, LangChain
• Sync & Async — Both AgentLensClient and AsyncAgentLensClient
• Typed — Full Pydantic v2 models, PEP 561 py.typed marker
• LLM Call Tracking — Log prompts, completions, tokens, costs, latency
• Privacy Redaction — Strip sensitive content while keeping analytics metadata
• Error Hierarchy — AgentLensError, AuthenticationError, NotFoundError, ValidationError, AgentLensConnectionError
• Context Managers — with / async with for automatic cleanup

API Reference

Method	Description
query_events(query?)	Query events with filters and pagination
get_event(id)	Get a single event by ID
get_sessions(query?)	Query sessions
get_session(id)	Get a single session
get_session_timeline(session_id)	Get session timeline with hash chain verification
log_llm_call(session_id, agent_id, params)	Log an LLM call with paired events
get_llm_analytics(params?)	Get LLM cost/usage analytics
health()	Check server health

Documentation

Full docs: amitpaz1.github.io/agentlens

Development

pip install -e ".[dev]"
pytest                    # 107 tests
mypy src/ --strict        # Type checking
ruff check src/ tests/    # Linting

License

MIT