# Verifik.io Python SDK
[](https://badge.fury.io/py/verifikio-sdk)
[](https://pypi.org/project/verifikio-sdk/)
[](https://pypi.org/project/verifikio-sdk/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/joaomdmoura/crewAI)
[](https://github.com/langchain-ai/langchain)
_The official Python SDK for Verifik.io – audit logs you can trust._
> **Also available:** [Node.js SDK](https://www.npmjs.com/package/@verifikio/sdk) - `npm install @verifikio/sdk`
📄 [Changelog](CHANGELOG.md) | 🚀 [v1.1.0 Release](CHANGELOG.md#110---2025-01-15)
## Installation
To install the official Verifik.io Python SDK:
```bash
pip install verifikio-sdk
```
For framework integrations:
```bash
# For CrewAI integration
pip install verifikio-sdk[crewai]
# For LangChain integration
pip install verifikio-sdk[langchain]
# For all integrations
pip install verifikio-sdk[all]
```
Verifik.io provides secure, immutable audit trails for AI agents using blockchain-style verification. Perfect for teams building with CrewAI, LangChain, AutoGPT, and other AI frameworks who need SOC 2 / ISO27001 compliance.
## Features
- 🔐 **Secure audit logging** with automatic hash chaining
- 🚀 **Developer-friendly** - Get started in minutes
- 📊 **Chain verification** - Ensure data integrity
- 🔑 **Simple authentication** with API keys
- 🐍 **Python 3.7+** compatible
- 📦 **Minimal dependencies** - Only requires `requests`
## Quick Start
```python
from verifikio import VerifikClient
# Initialize client with your API key
client = VerifikClient(api_key="verifik_live_abc123...")
# Log an AI agent event
response = client.log_event(
agent_name="data_processor",
action="process_customer_data",
inputs={"customer_id": "cust_123", "data_type": "profile"},
outputs={"status": "processed", "record_count": 1},
metadata={"processing_time": "1.2s"},
workflow_id="workflow_456",
status="success"
)
print(f"Audit log created: {response['id']}")
print(f"Hash: {response['hash']}")
```
## Framework Integrations
### CrewAI Integration
Automatically log all CrewAI agent actions and task executions:
```python
from verifikio import VerifikClient
from verifikio.integrations.crewai import VerifikCrewAIHandler
from crewai import Agent, Task, Crew
# Initialize Verifik.io
client = VerifikClient(api_key="verifik_live_xxx")
handler = VerifikCrewAIHandler(client, workflow_id="research-crew-001")
# Create agents with automatic logging
researcher = Agent(
role="Researcher",
goal="Research the latest AI trends",
backstory="You are an expert AI researcher",
callbacks=[handler]
)
writer = Agent(
role="Writer",
goal="Write engaging content about AI",
backstory="You are a technical writer",
callbacks=[handler]
)
# Create tasks - all executions are logged
research_task = Task(
description="Research GPT-4 capabilities",
agent=researcher,
callbacks=[handler]
)
write_task = Task(
description="Write an article about the findings",
agent=writer,
callbacks=[handler]
)
# Run crew - all actions are tracked
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
callbacks=[handler]
)
results = crew.kickoff()
```
### LangChain Integration
Automatically log LangChain chains, tools, and LLM calls:
```python
from verifikio import VerifikClient
from verifikio.integrations.langchain import VerifikLangChainHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
# Initialize Verifik.io
client = VerifikClient(api_key="verifik_live_xxx")
handler = VerifikLangChainHandler(
client,
workflow_id="qa-chain-001",
log_llm_calls=True # Log individual LLM calls
)
# Create LLM with logging
llm = OpenAI(temperature=0.7, callbacks=[handler])
# Create chain with logging
prompt = PromptTemplate(
input_variables=["topic"],
template="Write a short article about {topic}"
)
chain = LLMChain(
llm=llm,
prompt=prompt,
callbacks=[handler]
)
# All executions are automatically logged
result = chain.run("artificial intelligence in healthcare")
```
## API Reference
### VerifikClient
#### `__init__(api_key, base_url="https://api.verifik.io", timeout=30)`
Create a new Verifik.io client instance.
**Parameters:**
- `api_key` (str): Your Verifik.io API key (format: `verifik_live_*`)
- `base_url` (str, optional): Base URL for the API
- `timeout` (int, optional): Request timeout in seconds
```python
client = VerifikClient(api_key="verifik_live_abc123...")
```
#### `log_event(agent_name, action, **kwargs)`
Create a new audit log entry.
**Parameters:**
- `agent_name` (str): Name/identifier of the AI agent or service
- `action` (str): The action that was performed
- `inputs` (dict, optional): Input data/parameters for the action
- `outputs` (dict, optional): Output data/results from the action
- `metadata` (dict, optional): Additional metadata about the event
- `workflow_id` (str, optional): Workflow or session identifier
- `status` (str, optional): Status of the action (e.g., "success", "error", "pending")
**Returns:** `dict` - The created audit log entry
```python
response = client.log_event(
agent_name="email_agent",
action="send_notification",
inputs={"recipient": "user@example.com"},
outputs={"message_id": "msg_123"},
status="success"
)
```
#### `get_logs(limit=50, offset=0, workflow_id=None)`
Retrieve audit logs with pagination.
**Parameters:**
- `limit` (int, optional): Number of logs to return (max 100)
- `offset` (int, optional): Number of logs to skip
- `workflow_id` (str, optional): Filter by workflow ID
**Returns:** `dict` - Paginated list of audit logs
```python
logs = client.get_logs(limit=20, offset=0)
for log in logs['logs']:
print(f"Agent: {log['agentId']}, Action: {log['action']}")
```
#### `verify_chain()`
Verify the integrity of the audit log chain.
**Returns:** `dict` - Verification results
```python
verification = client.verify_chain()
print(f"Chain integrity: {verification['isValid']}")
```
#### `get_stats()`
Get account statistics and metrics.
**Returns:** `dict` - Account statistics
```python
stats = client.get_stats()
print(f"Total logs: {stats['totalLogs']}")
print(f"Chain integrity: {stats['chainIntegrity']}%")
```
## Framework Examples
### CrewAI Integration
```python
from verifikio import VerifikClient
from crewai import Agent, Task, Crew
# Initialize Verifik.io client
verifik = VerifikClient(api_key="verifik_live_abc123...")
# Create your CrewAI agents
researcher = Agent(
role='Research Analyst',
goal='Analyze market trends',
backstory='Expert in market analysis'
)
# Custom callback to log CrewAI events
def log_crew_event(agent_name, action, inputs=None, outputs=None, status="success"):
verifik.log_event(
agent_name=agent_name,
action=action,
inputs=inputs,
outputs=outputs,
metadata={"framework": "crewai"},
status=status
)
# Log when agent starts task
log_crew_event("research_analyst", "start_analysis", {"topic": "AI market"})
# ... run your CrewAI workflow ...
# Log completion
log_crew_event("research_analyst", "complete_analysis",
outputs={"findings": "Market growing 40% YoY"})
```
### LangChain Integration
```python
from verifikio import VerifikClient
from langchain.chains import LLMChain
from langchain.callbacks.base import BaseCallbackHandler
verifik = VerifikClient(api_key="verifik_live_abc123...")
class VerifikCallback(BaseCallbackHandler):
def on_chain_start(self, serialized, inputs, **kwargs):
verifik.log_event(
agent_name="langchain_agent",
action="chain_start",
inputs=inputs,
metadata={"chain_type": serialized.get("name", "unknown")}
)
def on_chain_end(self, outputs, **kwargs):
verifik.log_event(
agent_name="langchain_agent",
action="chain_end",
outputs=outputs,
status="success"
)
# Use the callback in your LangChain
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[VerifikCallback()])
```
## Error Handling
The SDK includes comprehensive error handling:
```python
from verifikio import VerifikClient, AuthenticationError, ValidationError, APIError
client = VerifikClient(api_key="verifik_live_abc123...")
try:
response = client.log_event(
agent_name="test_agent",
action="test_action"
)
except AuthenticationError:
print("Invalid API key")
except ValidationError as e:
print(f"Invalid parameters: {e}")
except APIError as e:
print(f"API error: {e}")
```
## Context Manager Support
The client supports context manager protocol for automatic cleanup:
```python
with VerifikClient(api_key="verifik_live_abc123...") as client:
client.log_event(
agent_name="context_agent",
action="test_action"
)
# Client session automatically closed
```
## Getting Your API Key
1. Sign up at [verifik.io](https://verifik.io)
2. Go to your Settings page
3. Generate a new API key
4. Copy the key (format: `verifik_live_...`)
## Development
### Running Tests
```bash
# Install development dependencies
pip install -e ".[dev]"
# Run tests
pytest tests/
# Run tests with coverage
pytest tests/ --cov=verifikio
```
### Code Quality
```bash
# Format code
black verifikio/
# Lint code
flake8 verifikio/
# Type checking
mypy verifikio/
```
## Contributing
1. Fork the repository
2. Create a feature branch: `git checkout -b feature-name`
3. Make your changes and add tests
4. Run the test suite: `pytest`
5. Submit a pull request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Support
- 📧 **Email**: support@verifik.io
- 📖 **Documentation**: https://docs.verifik.io
- 🐛 **Bug Reports**: https://github.com/verifik-io/verifikio-python/issues
- 💬 **Community**: https://discord.gg/verifik
## Changelog
### 1.0.0 (2024-01-14)
- Initial release
- Core audit logging functionality
- Chain verification
- Full API coverage
- Python 3.7+ support
Raw data
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"description": "# Verifik.io Python SDK\n\n[](https://badge.fury.io/py/verifikio-sdk)\n[](https://pypi.org/project/verifikio-sdk/)\n[](https://pypi.org/project/verifikio-sdk/)\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/joaomdmoura/crewAI)\n[](https://github.com/langchain-ai/langchain)\n\n_The official Python SDK for Verifik.io \u2013 audit logs you can trust._\n\n> **Also available:** [Node.js SDK](https://www.npmjs.com/package/@verifikio/sdk) - `npm install @verifikio/sdk`\n\n\ud83d\udcc4 [Changelog](CHANGELOG.md) | \ud83d\ude80 [v1.1.0 Release](CHANGELOG.md#110---2025-01-15)\n\n## Installation\n\nTo install the official Verifik.io Python SDK:\n\n```bash\npip install verifikio-sdk\n```\n\nFor framework integrations:\n\n```bash\n# For CrewAI integration\npip install verifikio-sdk[crewai]\n\n# For LangChain integration\npip install verifikio-sdk[langchain]\n\n# For all integrations\npip install verifikio-sdk[all]\n```\n\nVerifik.io provides secure, immutable audit trails for AI agents using blockchain-style verification. Perfect for teams building with CrewAI, LangChain, AutoGPT, and other AI frameworks who need SOC 2 / ISO27001 compliance.\n\n## Features\n\n- \ud83d\udd10 **Secure audit logging** with automatic hash chaining\n- \ud83d\ude80 **Developer-friendly** - Get started in minutes\n- \ud83d\udcca **Chain verification** - Ensure data integrity\n- \ud83d\udd11 **Simple authentication** with API keys\n- \ud83d\udc0d **Python 3.7+** compatible\n- \ud83d\udce6 **Minimal dependencies** - Only requires `requests`\n\n## Quick Start\n\n```python\nfrom verifikio import VerifikClient\n\n# Initialize client with your API key\nclient = VerifikClient(api_key=\"verifik_live_abc123...\")\n\n# Log an AI agent event\nresponse = client.log_event(\n agent_name=\"data_processor\",\n action=\"process_customer_data\",\n inputs={\"customer_id\": \"cust_123\", \"data_type\": \"profile\"},\n outputs={\"status\": \"processed\", \"record_count\": 1},\n metadata={\"processing_time\": \"1.2s\"},\n workflow_id=\"workflow_456\",\n status=\"success\"\n)\n\nprint(f\"Audit log created: {response['id']}\")\nprint(f\"Hash: {response['hash']}\")\n```\n\n## Framework Integrations\n\n### CrewAI Integration\n\nAutomatically log all CrewAI agent actions and task executions:\n\n```python\nfrom verifikio import VerifikClient\nfrom verifikio.integrations.crewai import VerifikCrewAIHandler\nfrom crewai import Agent, Task, Crew\n\n# Initialize Verifik.io\nclient = VerifikClient(api_key=\"verifik_live_xxx\")\nhandler = VerifikCrewAIHandler(client, workflow_id=\"research-crew-001\")\n\n# Create agents with automatic logging\nresearcher = Agent(\n role=\"Researcher\",\n goal=\"Research the latest AI trends\",\n backstory=\"You are an expert AI researcher\",\n callbacks=[handler]\n)\n\nwriter = Agent(\n role=\"Writer\", \n goal=\"Write engaging content about AI\",\n backstory=\"You are a technical writer\",\n callbacks=[handler]\n)\n\n# Create tasks - all executions are logged\nresearch_task = Task(\n description=\"Research GPT-4 capabilities\",\n agent=researcher,\n callbacks=[handler]\n)\n\nwrite_task = Task(\n description=\"Write an article about the findings\",\n agent=writer,\n callbacks=[handler]\n)\n\n# Run crew - all actions are tracked\ncrew = Crew(\n agents=[researcher, writer],\n tasks=[research_task, write_task],\n callbacks=[handler]\n)\n\nresults = crew.kickoff()\n```\n\n### LangChain Integration\n\nAutomatically log LangChain chains, tools, and LLM calls:\n\n```python\nfrom verifikio import VerifikClient\nfrom verifikio.integrations.langchain import VerifikLangChainHandler\nfrom langchain.chains import LLMChain\nfrom langchain.llms import OpenAI\nfrom langchain.prompts import PromptTemplate\n\n# Initialize Verifik.io\nclient = VerifikClient(api_key=\"verifik_live_xxx\")\nhandler = VerifikLangChainHandler(\n client, \n workflow_id=\"qa-chain-001\",\n log_llm_calls=True # Log individual LLM calls\n)\n\n# Create LLM with logging\nllm = OpenAI(temperature=0.7, callbacks=[handler])\n\n# Create chain with logging\nprompt = PromptTemplate(\n input_variables=[\"topic\"],\n template=\"Write a short article about {topic}\"\n)\n\nchain = LLMChain(\n llm=llm, \n prompt=prompt, \n callbacks=[handler]\n)\n\n# All executions are automatically logged\nresult = chain.run(\"artificial intelligence in healthcare\")\n```\n\n## API Reference\n\n### VerifikClient\n\n#### `__init__(api_key, base_url=\"https://api.verifik.io\", timeout=30)`\n\nCreate a new Verifik.io client instance.\n\n**Parameters:**\n- `api_key` (str): Your Verifik.io API key (format: `verifik_live_*`)\n- `base_url` (str, optional): Base URL for the API\n- `timeout` (int, optional): Request timeout in seconds\n\n```python\nclient = VerifikClient(api_key=\"verifik_live_abc123...\")\n```\n\n#### `log_event(agent_name, action, **kwargs)`\n\nCreate a new audit log entry.\n\n**Parameters:**\n- `agent_name` (str): Name/identifier of the AI agent or service\n- `action` (str): The action that was performed\n- `inputs` (dict, optional): Input data/parameters for the action\n- `outputs` (dict, optional): Output data/results from the action\n- `metadata` (dict, optional): Additional metadata about the event\n- `workflow_id` (str, optional): Workflow or session identifier\n- `status` (str, optional): Status of the action (e.g., \"success\", \"error\", \"pending\")\n\n**Returns:** `dict` - The created audit log entry\n\n```python\nresponse = client.log_event(\n agent_name=\"email_agent\",\n action=\"send_notification\",\n inputs={\"recipient\": \"user@example.com\"},\n outputs={\"message_id\": \"msg_123\"},\n status=\"success\"\n)\n```\n\n#### `get_logs(limit=50, offset=0, workflow_id=None)`\n\nRetrieve audit logs with pagination.\n\n**Parameters:**\n- `limit` (int, optional): Number of logs to return (max 100)\n- `offset` (int, optional): Number of logs to skip\n- `workflow_id` (str, optional): Filter by workflow ID\n\n**Returns:** `dict` - Paginated list of audit logs\n\n```python\nlogs = client.get_logs(limit=20, offset=0)\nfor log in logs['logs']:\n print(f\"Agent: {log['agentId']}, Action: {log['action']}\")\n```\n\n#### `verify_chain()`\n\nVerify the integrity of the audit log chain.\n\n**Returns:** `dict` - Verification results\n\n```python\nverification = client.verify_chain()\nprint(f\"Chain integrity: {verification['isValid']}\")\n```\n\n#### `get_stats()`\n\nGet account statistics and metrics.\n\n**Returns:** `dict` - Account statistics\n\n```python\nstats = client.get_stats()\nprint(f\"Total logs: {stats['totalLogs']}\")\nprint(f\"Chain integrity: {stats['chainIntegrity']}%\")\n```\n\n## Framework Examples\n\n### CrewAI Integration\n\n```python\nfrom verifikio import VerifikClient\nfrom crewai import Agent, Task, Crew\n\n# Initialize Verifik.io client\nverifik = VerifikClient(api_key=\"verifik_live_abc123...\")\n\n# Create your CrewAI agents\nresearcher = Agent(\n role='Research Analyst',\n goal='Analyze market trends',\n backstory='Expert in market analysis'\n)\n\n# Custom callback to log CrewAI events\ndef log_crew_event(agent_name, action, inputs=None, outputs=None, status=\"success\"):\n verifik.log_event(\n agent_name=agent_name,\n action=action,\n inputs=inputs,\n outputs=outputs,\n metadata={\"framework\": \"crewai\"},\n status=status\n )\n\n# Log when agent starts task\nlog_crew_event(\"research_analyst\", \"start_analysis\", {\"topic\": \"AI market\"})\n\n# ... run your CrewAI workflow ...\n\n# Log completion\nlog_crew_event(\"research_analyst\", \"complete_analysis\", \n outputs={\"findings\": \"Market growing 40% YoY\"})\n```\n\n### LangChain Integration\n\n```python\nfrom verifikio import VerifikClient\nfrom langchain.chains import LLMChain\nfrom langchain.callbacks.base import BaseCallbackHandler\n\nverifik = VerifikClient(api_key=\"verifik_live_abc123...\")\n\nclass VerifikCallback(BaseCallbackHandler):\n def on_chain_start(self, serialized, inputs, **kwargs):\n verifik.log_event(\n agent_name=\"langchain_agent\",\n action=\"chain_start\",\n inputs=inputs,\n metadata={\"chain_type\": serialized.get(\"name\", \"unknown\")}\n )\n \n def on_chain_end(self, outputs, **kwargs):\n verifik.log_event(\n agent_name=\"langchain_agent\",\n action=\"chain_end\",\n outputs=outputs,\n status=\"success\"\n )\n\n# Use the callback in your LangChain\nchain = LLMChain(llm=llm, prompt=prompt, callbacks=[VerifikCallback()])\n```\n\n## Error Handling\n\nThe SDK includes comprehensive error handling:\n\n```python\nfrom verifikio import VerifikClient, AuthenticationError, ValidationError, APIError\n\nclient = VerifikClient(api_key=\"verifik_live_abc123...\")\n\ntry:\n response = client.log_event(\n agent_name=\"test_agent\",\n action=\"test_action\"\n )\nexcept AuthenticationError:\n print(\"Invalid API key\")\nexcept ValidationError as e:\n print(f\"Invalid parameters: {e}\")\nexcept APIError as e:\n print(f\"API error: {e}\")\n```\n\n## Context Manager Support\n\nThe client supports context manager protocol for automatic cleanup:\n\n```python\nwith VerifikClient(api_key=\"verifik_live_abc123...\") as client:\n client.log_event(\n agent_name=\"context_agent\",\n action=\"test_action\"\n )\n# Client session automatically closed\n```\n\n## Getting Your API Key\n\n1. Sign up at [verifik.io](https://verifik.io)\n2. Go to your Settings page\n3. Generate a new API key\n4. Copy the key (format: `verifik_live_...`)\n\n## Development\n\n### Running Tests\n\n```bash\n# Install development dependencies\npip install -e \".[dev]\"\n\n# Run tests\npytest tests/\n\n# Run tests with coverage\npytest tests/ --cov=verifikio\n```\n\n### Code Quality\n\n```bash\n# Format code\nblack verifikio/\n\n# Lint code\nflake8 verifikio/\n\n# Type checking\nmypy verifikio/\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature-name`\n3. Make your changes and add tests\n4. Run the test suite: `pytest`\n5. Submit a pull request\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Support\n\n- \ud83d\udce7 **Email**: support@verifik.io\n- \ud83d\udcd6 **Documentation**: https://docs.verifik.io\n- \ud83d\udc1b **Bug Reports**: https://github.com/verifik-io/verifikio-python/issues\n- \ud83d\udcac **Community**: https://discord.gg/verifik\n\n## Changelog\n\n### 1.0.0 (2024-01-14)\n- Initial release\n- Core audit logging functionality\n- Chain verification\n- Full API coverage\n- Python 3.7+ support\n",
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"codeberg": false,
"github_user": "verifik-io",
"github_project": "verifikio-python",
"github_not_found": true,
"lcname": "verifikio-sdk"
}
JSON
