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agents-and-societies

agents-and-societies

Build CAMEL ChatAgent workflows, BaseMessage setup, role-playing societies, Workforce orchestration, Task objects, and safe agent-loop debugging.

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Documentación de la Skill

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name: agents-and-societies description: "Build CAMEL ChatAgent workflows, BaseMessage setup, role-playing societies, Workforce orchestration, Task objects, and safe agent-loop debugging." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Agents and Societies

Use this sub-skill when the user needs to assemble CAMEL-AI agents or multi-agent workflows: ChatAgent setup, system messages, tool-enabled agents at a routing level, RolePlaying societies, Workforce orchestration, Task objects, response terminators, callbacks, memory hooks, and loop debugging.

Start Here

  1. Read references/api-reference.md to choose the right CAMEL classes and constructor options.
  2. Read references/workflows.md for practical recipes: single agent, role-playing society, workforce, pipeline mode, and CI dry-run validation.
  3. Run scripts/inspect_agent_basics.py to inspect installed CAMEL imports, signatures, message constructors, and dry-run object setup without model credentials.
  4. Read references/troubleshooting.md before debugging loops, missing API keys, termination, timeouts, callbacks, memory state, or task failures.

Route By Task

  • Single conversational agent: use ChatAgent with a string or BaseMessage system message, optional memory/window/token controls, and optional response terminators.
  • Message construction: use BaseMessage.make_system_message, make_assistant_message, and make_user_message for explicit roles, metadata, and multimodal attachments.
  • Tool-enabled agent design: attach tools, external_tools, timeouts, and masking at ChatAgent construction, then defer schema/toolkit details to ../tools-runtimes-and-services/.
  • Two-role society: use RolePlaying when an AI user and AI assistant should alternate turns, optionally with task specification, task planning, a critic, or pre-built ChatAgent instances.
  • Team orchestration: use Workforce when tasks need decomposition, assignment to workers, failure recovery, streaming callbacks, shared memory, or pipeline-mode dependencies.
  • Task state and decomposition: use Task for structured objectives, subtasks, dependencies, result propagation, and Workforce entry points.

Boundaries And Cross-Links

  • For provider credentials, ModelFactory, BaseModelBackend, model enums, and backend-specific configuration, use ../models-and-configuration/.
  • For FunctionTool, MCPToolkit, schema synthesis, sandboxed execution, and external services, use ../tools-runtimes-and-services/.
  • For ChatHistoryMemory, retrieval-backed memory, vector stores, and embedding internals, use ../memory-rag-and-data/.
  • For benchmark/data-generation recipes, use the sibling benchmark or datagen sub-skill if present; this sub-skill only covers agent workflow routing.

Safety Defaults

  • Prefer credential-free dry runs first: instantiate messages, tasks, agents with explicit local/dummy model planning, and inspect signatures before calling .step().
  • Always bound loops with max_iteration, response_terminators, role-play turn limits, or task_timeout_seconds before running in CI.
  • Keep tool execution bounded with tool_execution_timeout, mask_tool_output, and targeted tools; route tool schema failures to the tools sub-skill.
  • Treat share_memory=True, custom memory, and callbacks as workflow state surfaces that require explicit reset and logging plans.
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