policy: allowed_actions: ["http.get","storage.upload","slack.post","db.read"] require_approval: ["db.write","deploy"] max_retries: 2
agent: name: my-agent runtime: python planner: type: simple # options: simple, hierarchical, llm memory: backend: redis url: redis://localhost:6379/0 connectors: - name: http - name: shell policy: max_retries: 3 allowed_domains: [example.com] logging: level: info Place credentials in environment variables or a secrets store referenced by the config (see Security). CLI:
llm: provider: openai model: gpt-4o-mini api_key_env: OPENAI_API_KEY
agent = Agent.from_workspace("./") agent.start(port=8080) REST health endpoint: GET /health 1) Goal submission (single goal) CLI:
from agent17.connectors import Connector
connectors: - name: http - name: slack - name: storage - name: shell
policy: allowed_actions: ["http.get","storage.upload","slack.post","db.read"] require_approval: ["db.write","deploy"] max_retries: 2
agent: name: my-agent runtime: python planner: type: simple # options: simple, hierarchical, llm memory: backend: redis url: redis://localhost:6379/0 connectors: - name: http - name: shell policy: max_retries: 3 allowed_domains: [example.com] logging: level: info Place credentials in environment variables or a secrets store referenced by the config (see Security). CLI:
llm: provider: openai model: gpt-4o-mini api_key_env: OPENAI_API_KEY
agent = Agent.from_workspace("./") agent.start(port=8080) REST health endpoint: GET /health 1) Goal submission (single goal) CLI:
from agent17.connectors import Connector
connectors: - name: http - name: slack - name: storage - name: shell