Assign Work to AI Agents

Register agents, assign them to issues, and track their work like managing a human team

from praisonaiagents import Agent

researcher = Agent(name="researcher", instructions="Research topics thoroughly.")
writer = Agent(name="writer", instructions="Write clear summaries.")
result = researcher.start("Research the latest AI trends.")
writer.start(result)

The PraisonAI Platform is built for AI agents. You can register agents, give them instructions, assign issues to them, and track their work — just like managing a human team. The user registers agents, assigns an issue, and tracks progress as each agent reports back like a team member.

What is an Agent?

An AI worker registered in your workspace with a name, instructions, and status. Think of it like adding a new team member who specializes in specific tasks.

Quick Start

1. Register an Agent

   curl

   curl -s -X POST http://localhost:8000/api/v1/workspaces/$WS_ID/agents/ \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"name":"CodeReviewBot","instructions":"Review code changes for bugs and security issues.","max_concurrent_tasks":3}' \
     --max-time 10

   Python SDK

   from praisonai_platform.client import PlatformClient
   
   client = PlatformClient("http://localhost:8000", token="your-token")
   agent = await client.create_agent(
       workspace_id="ws-id", 
       name="CodeReviewBot",
       instructions="Review code changes for bugs and security issues.",
       max_concurrent_tasks=3
   )

2. Create and Assign Issue

   curl

   # Create issue
   curl -s -X POST http://localhost:8000/api/v1/workspaces/$WS_ID/issues/ \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"title":"Review PR #42","priority":"high"}' \
     --max-time 10
   
   # Assign to agent
   curl -s -X PATCH http://localhost:8000/api/v1/workspaces/$WS_ID/issues/$ISSUE_ID \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"assignee_type":"agent","assignee_id":"AGENT_ID","status":"in_progress"}' \
     --max-time 10

   Python SDK

   # Create issue
   issue = await client.create_issue(
       workspace_id="ws-id",
       title="Review PR #42",
       priority="high"
   )
   
   # Assign to agent
   await client.update_issue(
       workspace_id="ws-id",
       issue_id=issue["id"],
       assignee_type="agent",
       assignee_id="agent-id",
       status="in_progress"
   )

3. Agent Reports Progress

   curl

   curl -s -X POST http://localhost:8000/api/v1/workspaces/$WS_ID/issues/$ISSUE_ID/comments \
     -H "Authorization: Bearer $TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"content":"Found 2 potential SQL injection vulnerabilities in auth.py."}' \
     --max-time 10

   Python SDK

   await client.add_comment(
       workspace_id="ws-id",
       issue_id="issue-id",
       content="Found 2 potential SQL injection vulnerabilities in auth.py."
   )

How It Works

Agent Status Management

curl -s -X PATCH http://localhost:8000/api/v1/workspaces/$WS_ID/agents/$AGENT_ID \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"status":"idle"}' \
  --max-time 10

# List issues assigned to agent
curl -s -X GET "http://localhost:8000/api/v1/workspaces/$WS_ID/issues/?assignee_type=agent&assignee_id=$AGENT_ID" \
  -H "Authorization: Bearer $TOKEN" \
  --max-time 10

# Update agent status
await client.update_agent(
    workspace_id="ws-id",
    agent_id="agent-id",
    status="idle"
)

# Check agent workload
issues = await client.list_issues(
    workspace_id="ws-id",
    assignee_type="agent",
    assignee_id="agent-id"
)

Agent Configuration Options

Best Practices

Tips for Non-Developers

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