ainative-agent-framework
# AINative Agent Framework
## OpenClaw Agent Swarm
AINative uses OpenClaw as its local agent gateway. 9 specialized agents are available:
| Agent | ID | Specialty |
|-------|-----|-----------|
| Main | `main` | Orchestration, routing, default |
| Atlas Redwood | `atlas` | Infrastructure & deployment |
| Lyra Chen-Sato | `lyra` | Frontend & UI |
| Sage Okafor | `sage` | Backend & APIs |
| Vega Martinez | `vega` | Data & analytics |
| Nova Sinclair | `nova` | Security & auth |
| Luma Harrington | `luma` | Documentation |
| Helios Mercer | `helios` | Performance & optimization |
| Aurora Vale | `aurora` | Testing & QA |
## Dispatch Tasks via CLI
```bash
# Route to best agent automatically
openclaw agent --agent main --message "Review the auth endpoint for SQL injection"
# Target a specific agent
openclaw agent --agent aurora --message "Write tests for the billing module"
openclaw agent --agent sage --message "Add rate limiting to the credits endpoint"
openclaw agent --agent nova --message "Audit API key storage for security issues"
openclaw agent --agent atlas --message "Check Railway deploy logs for errors"
```
## Dispatch via Cody Script
```bash
# Status check
python3 scripts/cody_openclaw.py status
python3 scripts/cody_openclaw.py agents
# Dispatch a task
python3 scripts/cody_openclaw.py dispatch --agent aurora --task "Run test suite for billing module"
python3 scripts/cody_openclaw.py dispatch --agent sage --task "Implement POST /api/v1/echo/register"
# Send a direct message
python3 scripts/cody_openclaw.py message --agent main --message "What is the current test coverage?"
```
## ACP (Agent Communication Protocol)
```bash
# Connect to ACP session directly
openclaw acp --session agent:main:main --token YOUR_GATEWAY_TOKEN
# Via cody script
python3 scripts/cody_openclaw.py acp --session agent:main:main
```
## Python Agent Pattern
Build your own agent that calls AINative APIs:
```python
import requests
class AINativeAgent:
def __init__(self, api_key: str, system_prompt: str):
self.api_key = api_key
self.system_prompt = system_prompt
self.messages = []
def think(self, user_input: str) -> str:
self.messages.append({"role": "user", "content": user_input})
resp = requests.post(
"https://api.ainative.studio/v1/public/chat/completions",
headers={"X-API-Key": self.api_key},
json={
"model": "claude-3-5-sonnet-20241022",
"messages": [
{"role": "system", "content": self.system_prompt},
*self.messages
],
"max_tokens": 2048,
}
).json()
reply = resp["choices"][0]["message"]["content"]
self.messages.append({"role": "assistant", "content": reply})
return reply
def remember(self, fact: str):
"""Persist something to ZeroMemory."""
requests.post(
"https://api.ainative.studio/api/v1/public/memory/v2/remember",
headers={"X-API-Key": self.api_key},
json={"content": fact, "memory_type": "episodic"}
)
def recall(self, query: str) -> list:
"""Retrieve relevant memories."""
resp = requests.post(
"https://api.ainative.studio/api/v1/public/memory/v2/recall",
headers={"X-API-Key": self.api_key},
json={"query": query, "limit": 5}
).json()
return [m["content"] for m in resp.get("memories", [])]
# Usage
agent = AINativeAgent("ak_your_key", "You are a helpful coding assistant.")
response = agent.think("How do I implement rate limiting in FastAPI?")
agent.remember(f"User asked about rate limiting: {response[:100]}")
```
## Multi-Agent Handoff Pattern
```python
def route_task(task: str) -> str:
"""Route task to the right OpenClaw agent."""
routing = {
"test": "aurora",
"security": "nova",
"deploy": "atlas",
"frontend": "lyra",
"backend": "sage",
"performance": "helios",
"data": "vega",
"docs": "luma",
}
for keyword, agent_id in routing.items():
if keyword in task.lower():
return agent_id
return "main"
import subprocess
def dispatch(task: str):
agent_id = route_task(task)
subprocess.run(["openclaw", "agent", "--agent", agent_id, "--message", task])
```
## RLHF Feedback
Collect feedback to improve agent quality:
```bash
# Via MCP tool
zerodb-rlhf-feedback
```
```python
requests.post(
"https://api.ainative.studio/api/v1/public/zerodb/rlhf/feedback",
headers={"X-API-Key": "ak_your_key"},
json={
"session_id": "sess-123",
"rating": 5,
"feedback": "Agent correctly identified the SQL injection vector",
}
)
```
## Monitor Agent Swarm
```bash
# Real-time logs
python3 scripts/cody_openclaw.py logs --follow
# Status dashboard
openclaw status
```
## References
- `scripts/cody_openclaw.py` — Cody's OpenClaw control script
- `.claude/commands/openclaw-dispatch.md` — /openclaw-dispatch command
- `.openclaw/openclaw.json` — Local gateway configuration
- `src/backend/app/services/` — Backend agent services
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