Content
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# 🔍 Chaukas SDK
**One line to instrument your agent and capture every event in an immutable, queryable audit trail.**
*Open-source SDK implementing [chaukas-spec](https://github.com/chaukasai/chaukas-spec) for standardized agent instrumentation*
[](https://pypi.org/project/chaukas-sdk/)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/Apache-2.0)
[]()
[]()
[Quick Start](#-quick-start) • [Documentation](#-documentation) • [Examples](#-examples) • [chaukas-spec](#-supported-frameworks) • [Community](#-community)
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---
## 🎯 Why Chaukas?
Building AI agents is hard. **Understanding what they're doing is harder.**
Chaukas SDK is an **open-source SDK** that implements the **[chaukas-spec](https://github.com/chaukasai/chaukas-spec)** — a standardized event schema for AI agent instrumentation. It gives you X-ray vision into your AI agents with **zero configuration**:
```python
import chaukas
chaukas.enable_chaukas() # That's it. You're done.
# Your existing agent code works unchanged
agent = Agent(name="assistant", model="gpt-4")
result = await agent.run(messages=[...])
```
**Instantly get:**
- 🎯 Complete execution traces with distributed tracing
- 🔄 Automatic retry detection and tracking (CrewAI, LangChain)
- 🛠️ Tool call monitoring and performance metrics
- 🤝 Multi-agent handoff visualization
- 🚨 Error tracking with full context
- 📊 LLM token usage and cost tracking
- 🔐 Policy enforcement and compliance logs
- 🎨 Beautiful, queryable event streams
## ✨ What Makes Chaukas Different
| Feature | Chaukas | Traditional APM | Manual Logging |
|---------|---------|-----------------|----------------|
| **Setup Time** | 1 line | Hours | Days |
| **Code Changes** | Zero | Extensive | Everywhere |
| **Agent-Native** | ✅ 100% | ❌ Adapted | ❌ Custom |
| **Event Coverage** | 🎉 [19/19 chaukas-spec](https://github.com/chaukasai/chaukas-spec) | ⚠️ Partial | 🤷 Up to you |
| **Standardized Schema** | ✅ [chaukas-spec](https://github.com/chaukasai/chaukas-spec) | ❌ Proprietary | ❌ None |
| **Multi-Agent Tracking** | ✅ Built-in | ❌ Manual | ❌ Complex |
| **MCP Protocol** | ✅ Native | ❌ No support | ❌ Manual |
| **Distributed Tracing** | ✅ Automatic | ⚠️ Requires setup | ❌ Hard |
| **Type Safety** | ✅ Full | ⚠️ Partial | ❌ None |
## 🚀 Quick Start
### Installation
```bash
pip install chaukas-sdk
```
### Configuration
Set your environment variables (or pass them programmatically):
```bash
export CHAUKAS_TENANT_ID="your-tenant"
export CHAUKAS_PROJECT_ID="your-project"
export CHAUKAS_ENDPOINT="https://api.chaukas.ai"
export CHAUKAS_API_KEY="your-api-key"
```
### Usage
#### OpenAI Agents
```python
import chaukas
from openai import OpenAI
from openai.agents import Agent
# Enable instrumentation
chaukas.enable_chaukas()
# Your code works exactly as before
client = OpenAI()
agent = Agent(
name="data-analyst",
instructions="You are a helpful data analyst.",
model="gpt-4o",
client=client
)
result = await agent.run(
messages=[{"role": "user", "content": "Analyze Q4 revenue"}]
)
# Chaukas automatically captures:
# ✅ Session start/end
# ✅ Agent lifecycle
# ✅ LLM invocations with tokens
# ✅ Tool calls and results
# ✅ Errors (18/19 event types - RETRY not supported, see below)
# ✅ Policy decisions
# ✅ State changes
```
> **Note:** OpenAI Agents SDK captures **18/19 event types (94.7%)**. RETRY events cannot be captured because the OpenAI SDK performs retries internally within its HTTP client layer, making them invisible to external instrumentation. All other frameworks (CrewAI, LangChain) support full 19/19 event coverage including RETRY detection.
#### CrewAI
```python
import chaukas
from crewai import Agent, Task, Crew, Process
chaukas.enable_chaukas()
# Define your crew
researcher = Agent(
role="Senior Research Analyst",
goal="Uncover cutting-edge developments in AI",
backstory="You're an expert at finding insights",
verbose=True
)
task = Task(
description="Research latest AI trends",
agent=researcher,
expected_output="A comprehensive report"
)
crew = Crew(
agents=[researcher],
tasks=[task],
process=Process.sequential
)
# Full observability out of the box
result = crew.kickoff()
```
#### Google ADK
```python
import chaukas
from adk import Agent
chaukas.enable_chaukas()
agent = Agent(name="assistant")
response = agent.run("Hello!")
```
#### LangChain
```python
import chaukas
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
chaukas.enable_chaukas()
# Your LangChain code works unchanged
prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
llm = ChatOpenAI(model="gpt-4")
chain = prompt | llm | StrOutputParser()
result = chain.invoke({"topic": "programming"})
# Chaukas automatically captures:
# ✅ Session start/end
# ✅ Chain lifecycle
# ✅ LLM invocations with tokens
# ✅ Tool calls (if using agents)
# ✅ RAG operations (retriever tracking)
# ✅ MCP tool calls (with mcp_server metadata)
# ✅ Streaming text output
# ✅ Custom events (SYSTEM)
# ✅ Errors and retries
```
## 📊 Supported Frameworks
Chaukas SDK implements the **[chaukas-spec](https://github.com/chaukasai/chaukas-spec)** — a standardized event schema with **19 event types** for AI agent observability.
| Framework | Version | Events | Status | Notes |
|-----------|---------|--------|--------|-------|
| **[OpenAI Agents](https://github.com/openai/openai-agents-python)** | `>=0.5.0,<1.0.0` | 🎉 **18/19** | 🟢 Production | Session mgmt, MCP protocol, policy tracking, state updates, errors |
| **[CrewAI](https://github.com/crewAIInc/crewAI)** | `>=1.4.1,<2.0.0` | 🎉 **19/19** | 🟢 Production | Event bus integration, multi-agent handoffs, knowledge sources, guardrails, flows |
| **[LangChain](https://github.com/langchain-ai/langchain)** / **[LangGraph](https://github.com/langchain-ai/langgraph)** | `>=0.1.0,<2.0` | 🎉 **17/19** | 🟢 Production | Runnable patching, chains, agents, tools, RAG, MCP, streaming, custom events. LangGraph works via LangChain integration. **Note:** LangChain 1.x requires Python ≥3.10 |
| **[Google ADK](https://github.com/google/adk-python)** | Latest | 🚧 **5/19** | 🟡 Under Construction | Basic agent & LLM tracking |
**Coming Soon**: AutoGen, Microsoft Semantic Kernel
*All frameworks implementing the complete [chaukas-spec](https://github.com/chaukasai/chaukas-spec) capture all 19 event types*
## 🎨 Event Types (chaukas-spec)
The **[chaukas-spec](https://github.com/chaukasai/chaukas-spec)** defines **19 standardized event types** for AI agent observability. Chaukas SDK captures all of them automatically:
### 🎭 Agent Lifecycle
```python
SESSION_START # User session begins
SESSION_END # Session completes
AGENT_START # Agent begins execution
AGENT_END # Agent finishes
AGENT_HANDOFF # Control transfers between agents
```
### 🧠 Model Operations
```python
MODEL_INVOCATION_START # LLM call initiated
MODEL_INVOCATION_END # LLM responds (includes tokens, cost)
```
### 🛠️ Tool Execution
```python
TOOL_CALL_START # Tool execution begins
TOOL_CALL_END # Tool completes with result
MCP_CALL_START # Model Context Protocol call starts
MCP_CALL_END # MCP operation completes
```
### 💬 I/O Tracking
```python
INPUT_RECEIVED # User input captured
OUTPUT_EMITTED # Agent output generated
```
### 🚨 Operational Intelligence
```python
ERROR # Error with full context
RETRY # Automatic retry detected (rate limits, timeouts)
POLICY_DECISION # Content filtering, guardrails enforced
DATA_ACCESS # Knowledge base, file, or API access
STATE_UPDATE # Agent configuration changes
SYSTEM_EVENT # Framework initialization, shutdown
```
## 🔥 Advanced Features
### Distributed Tracing
Every event includes full trace context:
```python
{
"event_id": "019a6700-adb9-718d-0bc9-0000415845aa",
"session_id": "019a6700-adb7-7a30-a548-000077453f71",
"trace_id": "019a6700-adb7-7ef3-1e46-0000ae993c28",
"span_id": "019a6700-adb9-706a-0a26-000073699939",
"parent_span_id": "019a6700-adb7-7b27-1858-0000ee8d895b",
"type": "EVENT_TYPE_TOOL_CALL_END",
"agent_id": "data-analyst",
"timestamp": "2025-01-08T12:34:56.789Z"
}
```
Visualize complete request flows across:
- Multiple agents
- LLM calls
- Tool invocations
- External API calls
### Intelligent Retry Detection
Chaukas automatically detects and tracks retries:
```python
# Your code
try:
result = await agent.run(messages)
except RateLimitError:
await asyncio.sleep(2) # Exponential backoff
result = await agent.run(messages) # Retry
# Chaukas captures:
# 1. ERROR event (rate limit)
# 2. RETRY event (attempt 1, exponential strategy, 2000ms delay)
# 3. MODEL_INVOCATION_START (retry attempt)
# 4. MODEL_INVOCATION_END (success)
```
### MCP Protocol Support
**Only SDK with native MCP instrumentation:**
```python
from agents import Agent
from agents.mcp import MCPServerStreamableHttp
# MCP server setup
mcp_server = MCPServerStreamableHttp(
url="http://localhost:8000",
server_name="documentation-server"
)
agent = Agent(
name="doc-agent",
model="gpt-4o",
mcp_servers=[mcp_server]
)
# Chaukas captures:
# - MCP_CALL_START (get_prompt request)
# - MCP_CALL_END (prompt retrieved, 245ms)
# - Full request/response payloads
```
### Policy Decision Tracking
Monitor content filtering and guardrails:
```python
# When OpenAI filters content
response = await agent.run(messages)
# Chaukas automatically captures:
{
"type": "EVENT_TYPE_POLICY_DECISION",
"policy_id": "openai_content_policy",
"outcome": "blocked",
"rule_ids": ["content_filter"],
"rationale": "Response blocked due to: content_filter",
"finish_reason": "content_filter"
}
```
### State Change Tracking
Track agent configuration changes:
```python
# Agent configuration updated
agent.temperature = 0.7
agent.instructions = "Be more creative"
# Chaukas captures the diff:
{
"type": "EVENT_TYPE_STATE_UPDATE",
"state_update": {
"temperature": {"old": 0.3, "new": 0.7},
"instructions": {
"old": "Be precise",
"new": "Be more creative"
}
}
}
```
### Multi-Agent Handoffs
Visualize agent collaboration:
```python
# CrewAI agent handoff
task.context = [previous_task]
# Chaukas captures:
{
"type": "EVENT_TYPE_AGENT_HANDOFF",
"from_agent_id": "researcher",
"to_agent_id": "writer",
"handoff_reason": "task_delegation",
"context_data": {...}
}
```
## ⚙️ Configuration
### Environment Variables
#### Required
```bash
CHAUKAS_TENANT_ID # Your tenant identifier
CHAUKAS_PROJECT_ID # Your project identifier
CHAUKAS_ENDPOINT # API endpoint (api mode)
CHAUKAS_API_KEY # Authentication key (api mode)
```
#### Optional
```bash
CHAUKAS_OUTPUT_MODE="api" # "api" or "file"
CHAUKAS_OUTPUT_FILE="events.jsonl" # File path (file mode)
CHAUKAS_BATCH_SIZE=20 # Events per batch
CHAUKAS_MAX_BATCH_BYTES=262144 # Max batch size (256KB)
CHAUKAS_FLUSH_INTERVAL=5.0 # Auto-flush interval (seconds)
CHAUKAS_TIMEOUT=30.0 # Request timeout (seconds)
CHAUKAS_BRANCH="main" # Git branch for context
CHAUKAS_TAGS="prod,us-east-1" # Custom tags
```
#### Framework-Specific
```bash
CREWAI_DISABLE_TELEMETRY=true # Disable CrewAI's telemetry
```
### Programmatic Configuration
```python
import chaukas
chaukas.enable_chaukas(
tenant_id="acme-corp",
project_id="production",
endpoint="https://observability.acme.com",
api_key="sk-proj-...",
session_id="custom-session-123", # Optional custom session
config={
"auto_detect": True, # Auto-detect installed SDKs
"enabled_integrations": [ # Or specify explicitly
"openai_agents",
"crewai"
],
"batch_size": 20, # Default batch size
"flush_interval": 10.0,
"timeout": 60.0,
}
)
```
### File Output Mode (Development)
Perfect for local development and testing:
```python
import os
os.environ["CHAUKAS_OUTPUT_MODE"] = "file"
os.environ["CHAUKAS_OUTPUT_FILE"] = "agent_events.jsonl"
import chaukas
chaukas.enable_chaukas()
# Events written to agent_events.jsonl
# Analyze with: cat agent_events.jsonl | jq .type | sort | uniq -c
```
## 📖 Examples
### Example 1: Debug LLM Token Usage
```python
import chaukas
chaukas.enable_chaukas()
# Run your agent
result = await agent.run(messages)
# Query events:
# cat events.jsonl | jq 'select(.type=="EVENT_TYPE_MODEL_INVOCATION_END") | .model_invocation.usage'
# Output:
{
"prompt_tokens": 234,
"completion_tokens": 456,
"total_tokens": 690,
"estimated_cost_usd": 0.0207
}
```
### Example 2: Track Multi-Agent Workflow
```python
import chaukas
from crewai import Agent, Task, Crew, Process
chaukas.enable_chaukas()
# Define a multi-agent crew
researcher = Agent(role="Researcher", goal="Find insights")
writer = Agent(role="Writer", goal="Write report")
research_task = Task(description="Research AI trends", agent=researcher)
writing_task = Task(
description="Write report",
agent=writer,
context=[research_task] # Handoff point
)
crew = Crew(agents=[researcher, writer], tasks=[research_task, writing_task])
result = crew.kickoff()
# Chaukas captures:
# 1. SESSION_START
# 2. AGENT_START (researcher)
# 3. MODEL_INVOCATION_* (researcher's LLM calls)
# 4. AGENT_END (researcher)
# 5. AGENT_HANDOFF (researcher → writer)
# 6. AGENT_START (writer)
# 7. MODEL_INVOCATION_* (writer's LLM calls)
# 8. AGENT_END (writer)
# 9. SESSION_END
```
### Example 3: Monitor Tool Execution
```python
import chaukas
from openai import OpenAI
from openai.agents import Agent
chaukas.enable_chaukas()
def search_database(query: str) -> str:
"""Search the product database."""
# Slow database query
import time
time.sleep(2)
return f"Results for: {query}"
agent = Agent(
name="support-agent",
model="gpt-4o",
tools=[search_database]
)
result = await agent.run(messages=[
{"role": "user", "content": "Find product XYZ"}
])
# Chaukas captures tool performance:
# TOOL_CALL_START → TOOL_CALL_END
# Duration: 2.1s (flag for optimization!)
```
### Example 4: Detect Rate Limit Issues
```python
import chaukas
chaukas.enable_chaukas()
# Your code encounters rate limits
for i in range(100):
try:
result = await agent.run(messages)
except RateLimitError as e:
await asyncio.sleep(2 ** i) # Exponential backoff
continue
# Query retry events:
# cat events.jsonl | jq 'select(.type=="EVENT_TYPE_RETRY")'
# Output shows patterns:
# - 15 retries in last hour
# - Average backoff: 4.2s
# - All due to rate limits (429)
# → Action: Implement request throttling
```
## 🏗️ Architecture
### How It Works
```
┌─────────────────────────────────────────────────────────────┐
│ Your Application │
│ │
│ ┌─────────────┐ ┌──────────────┐ │
│ │ OpenAI │ │ CrewAI │ │
│ │ Agent │ │ Crew │ │
│ └──────┬──────┘ └──────┬───────┘ │
│ │ │ │
│ └───────────┬───────────┘ │
│ │ │
│ ┌───────────▼───────────┐ │
│ │ Chaukas SDK │ (Monkey patching) │
│ │ - Auto-detection │ │
│ │ - Event capture │ │
│ │ - Distributed trace │ │
│ └───────────┬───────────┘ │
│ │ │
└─────────────────────┼──────────────────────────────────────┘
│
┌───────────▼───────────┐
│ Intelligent Batching │
│ - Adaptive sizing │
│ - Auto-retry │
│ - Memory-efficient │
└───────────┬───────────┘
│
┌───────────▼───────────┐
│ Transmission │
│ - gRPC (API mode) │
│ - File (Dev mode) │
└───────────┬───────────┘
│
┌───────────▼───────────┐
│ Chaukas Platform │
│ - Storage │
│ - Querying │
│ - Visualization │
└───────────────────────┘
```
### Event Flow
```python
Agent.run() called
│
├─→ SESSION_START (first call)
│
├─→ AGENT_START
│
├─→ INPUT_RECEIVED (user message)
│
├─→ MODEL_INVOCATION_START
│ │
│ └─→ [LLM processes]
│
├─→ MODEL_INVOCATION_END (with tokens)
│
├─→ TOOL_CALL_START (if tools requested)
│ │
│ └─→ [Tool executes]
│
├─→ TOOL_CALL_END (with result)
│
├─→ OUTPUT_EMITTED (agent response)
│
├─→ AGENT_END
│
└─→ SESSION_END (on cleanup)
```
### Distributed Tracing Hierarchy
```
Session (lifetime of user interaction)
│
├─ Trace (single request/response)
│ │
│ ├─ Agent Span (agent execution)
│ │ │
│ │ ├─ LLM Span (model call)
│ │ │
│ │ ├─ Tool Span (tool execution)
│ │ │ │
│ │ │ └─ MCP Span (MCP protocol call)
│ │ │
│ │ └─ Tool Span (another tool)
│ │
│ └─ Agent Span (handoff to second agent)
│ │
│ └─ LLM Span
│
└─ Trace (follow-up request)
└─ ...
```
## 🎯 Use Cases
### Production Monitoring
- Track agent reliability and uptime
- Monitor LLM token costs in real-time
- Detect performance regressions
- Alert on error spikes
### Debugging & Development
- Reproduce issues with full trace context
- Understand agent decision-making
- Optimize tool execution performance
- Test multi-agent workflows
### Compliance & Audit
- Immutable audit trail of all interactions
- Track policy enforcement decisions
- Monitor data access patterns
- Generate compliance reports
### Cost Optimization
- Identify expensive LLM calls
- Track token usage by agent/model
- Find opportunities for caching
- Optimize prompt engineering
## 🔧 Batching & Performance
### Adaptive Batching
Chaukas implements intelligent batching to optimize performance:
```python
┌─────────────────────────────────────────┐
│ Event Buffer │
│ │
│ Events accumulate until: │
│ • batch_size reached (default: 20) │
│ • max_batch_bytes reached (256KB) │
│ • flush_interval elapsed (5s) │
└─────────────┬───────────────────────────┘
│
▼
┌─────────────────┐
│ Send to Server │
└────┬────────────┘
│
Success? ────Yes──→ ✅ Done
│
No (503)
│
▼
┌────────────────────┐
│ Split batch in half│
│ Retry both halves │
└────────────────────┘
```
### Performance Characteristics
- **Overhead**: < 1% CPU impact
- **Memory**: ~10MB for 1000 events buffered
- **Latency**: < 5ms per event capture
- **Network**: Batched transmission reduces API calls by 95%
### Tuning for Your Use Case
```python
# High-volume production (optimize throughput)
chaukas.enable_chaukas(config={
"batch_size": 200,
"max_batch_bytes": 1_048_576, # 1MB
"flush_interval": 30.0
})
# Real-time debugging (optimize latency)
chaukas.enable_chaukas(config={
"batch_size": 1,
"flush_interval": 0.1
})
# Memory-constrained (optimize memory)
chaukas.enable_chaukas(config={
"batch_size": 10,
"max_batch_bytes": 65536, # 64KB
"flush_interval": 2.0
})
```
## 🐛 Troubleshooting
### Common Issues
#### CrewAI "Service Unavailable" Errors
**Problem**: Seeing "Transient error Service Unavailable" when using CrewAI
**Cause**: CrewAI's built-in telemetry trying to send data to their servers
**Solution**:
```bash
export CREWAI_DISABLE_TELEMETRY=true
```
This only disables CrewAI's telemetry. Chaukas continues capturing events normally.
#### Events Not Appearing
**Problem**: No events in output file or API
**Solution**:
```python
# Enable debug logging
import logging
logging.basicConfig(level=logging.DEBUG)
logging.getLogger("chaukas.sdk").setLevel(logging.DEBUG)
# Verify configuration
import chaukas
chaukas.enable_chaukas()
print(chaukas.get_config()) # Check settings
# Force flush before exit
chaukas.flush()
chaukas.disable_chaukas()
```
#### High Memory Usage
**Problem**: Memory consumption increasing over time
**Cause**: Large batches accumulating
**Solution**:
```python
# Reduce batch size and increase flush frequency
chaukas.enable_chaukas(config={
"batch_size": 10,
"max_batch_bytes": 65536,
"flush_interval": 1.0
})
```
#### 503 Errors from API
**Problem**: Server returning "high memory" errors
**Cause**: Batches too large
**Solution**: SDK automatically splits batches and retries. If persistent:
```python
chaukas.enable_chaukas(config={
"max_batch_bytes": 131072, # Reduce to 128KB
"batch_size": 50 # Smaller batch count
})
```
## 📚 Documentation
- **[chaukas-spec](https://github.com/chaukasai/chaukas-spec)** - Standardized event schema (19 event types)
- **[Examples Repository](./examples)** - Complete working examples for all supported frameworks
- **[OpenAI Examples](./examples/openai)** - OpenAI Agents integration examples and guides
- **[CrewAI Examples](./examples/crewai)** - CrewAI integration examples and guides
- **[LangChain Examples](./examples/langchain)** - LangChain integration examples and guides
- **[Google ADK Examples](./examples/adk)** - Google ADK integration examples
## 🧪 Development
### Setup
```bash
git clone https://github.com/chaukasai/chaukas-sdk
cd chaukas-sdk
pip install -e ".[dev]"
```
### Running Tests
```bash
# All tests
pytest
# With coverage
pytest --cov=chaukas
# Specific test file
pytest tests/test_openai_events.py -v
# Watch mode
pytest-watch
```
### Code Quality
```bash
# Format code
black src/ tests/ examples/
# Sort imports
isort src/ tests/ examples/
# Type checking
mypy src/chaukas/
# Run all checks
make lint
```
### Running Examples
```bash
# OpenAI Agents example
python examples/openai/openai_comprehensive_example.py
# CrewAI example
python examples/crewai/crewai_example.py
# Analyze captured events
cat events.jsonl | jq .type | sort | uniq -c
```
## 🤝 Contributing
We welcome contributions from the community! Whether you're:
- 🐛 Reporting bugs
- 💡 Requesting features
- 📖 Improving documentation
- 🔧 Contributing code
- ❓ Asking questions
Please read our [Contributing Guide](CONTRIBUTING.md) for detailed guidelines on:
- Setting up your development environment
- Coding standards and best practices
- Testing requirements
- Pull request process
### Quick Start for Contributors
1. **Fork and clone** the repository
2. **Install dependencies**: `pip install -e ".[dev]"`
3. **Make your changes** following our [coding standards](CONTRIBUTING.md#code-style)
4. **Run tests**: `make test && make lint`
5. **Submit a PR** using our [PR template](.github/PULL_REQUEST_TEMPLATE.md)
### Report Issues
Found a bug or have a feature request? Please use our issue templates:
- [Bug Report](.github/ISSUE_TEMPLATE/bug_report.yml)
- [Feature Request](.github/ISSUE_TEMPLATE/feature_request.yml)
- [Question](.github/ISSUE_TEMPLATE/question.yml)
### Community Guidelines
Please follow our [Code of Conduct](CODE_OF_CONDUCT.md) to keep our community welcoming and inclusive.
For security vulnerabilities, please see our [Security Policy](SECURITY.md).
## 🌟 Community
- **[GitHub Discussions](https://github.com/chaukasai/chaukas-sdk/discussions)** - Ask questions, share ideas
- **[GitHub Issues](https://github.com/chaukasai/chaukas-sdk/issues)** - Bug reports and feature requests
## 📬 Support
- **[GitHub Issues](https://github.com/chaukasai/chaukas-sdk/issues)** - Bug reports and feature requests
- **[Email](mailto:2153483+ranesidd@users.noreply.github.com)** - Direct support
- **[Examples](./examples)** - Working code examples and guides
## 📄 License
Apache 2.0 License - see [LICENSE](LICENSE) file for details
---
<div align="center">
**Built with ❤️ by the Chaukas team**
[Website](https://chaukas.ai) • [chaukas-spec](https://github.com/chaukasai/chaukas-spec) • [GitHub](https://github.com/chaukasai/chaukas-sdk)
</div>
