agent() Agents

Use agent() to build an RLM agent with a typed final response.

from axllm import agent

helper = agent('question:string -> answer:string')

Agents coordinate tools, child agents, runtime sessions, memories, skills, context policies, discovery, recall, shared fields, traces, usage, and final typed responses.

What It Does

agent() creates a structured agent program. The agent planner/executor/responder loop can call tools, delegate to child agents, inspect runtime state, ask for clarification, discover tools or skills, recall memory, and finish with a typed output object.

Core Call Shape

helper = agent(signature, options)
result = helper.forward(aiClient, inputs)

Common Patterns

• Start with a signature that names the final answer fields.
• Add fn() tools for host data and side effects.
• Add child agents to the same callable list as tools.
• Use namespaces to keep tool calls readable.
• Enable discovery when available tools are too numerous to include in full.
• Save and restore state around clarification.
• Use context policies for long-running sessions.

Minimal agent

from axllm import agent

assistant = agent("question:string -> answer:string", {"contextFields": []})
out = assistant.forward(client, {"question": "Capital of France?"})

Namespaced tools and discovery

Use a flat functions list for small stable sets: local fn() tools, child agents, MCP clients, and runtime providers can all live beside each other. The actor sees those callables directly.

from axllm import agent

assistant = agent("question:string -> answer:string", {"contextFields": []})
out = assistant.forward(client, {"question": "Capital of France?"})

Use grouped functions when the catalog is large or easier to reason about by domain. Each group gives the actor a namespace plus module-level selection criteria; with functionDiscovery: true, concrete schemas are loaded only after the actor calls discover(...).

from axllm import agent

assistant = agent("question:string -> answer:string", {"contextFields": []})
out = assistant.forward(client, {"question": "Capital of France?"})

Grouped mode keeps big catalogs out of the prompt until needed. Keep the top-level list either flat or grouped. If a child agent belongs inside a group, pass childAgent.getFunction() inside the group’s functions list.

Memory, skills, and context policy

from axllm import agent

assistant = agent("question:string -> answer:string", {"contextFields": []})
out = assistant.forward(client, {"question": "Capital of France?"})

from axllm import agent

assistant = agent("question:string -> answer:string", {"contextFields": []})
out = assistant.forward(client, {"question": "Capital of France?"})

Connect MCP servers

MCP clients can be passed as tool providers after initialization. Use the flat form when the server exposes a small, obvious tool set.

client = AxMCPClient(transport)
client.init()
assistant = agent("request:string -> response:string", {
    "functions": client.to_function(),
    "functionDiscovery": True,
    "contextFields": [],
})

Use grouped discovery when an MCP server has many tools, prompts, or resources. The group gives the actor a namespace and selection criteria before it asks to see detailed schemas.

assistant = agent("request:string -> response:string", {
    "functions": [{
        "namespace": "memory",
        "title": "Memory MCP",
        "selectionCriteria": "Use for persistent memory lookup.",
        "functions": client.to_function(),
    }],
    "functionDiscovery": True,
    "contextFields": [],
})

Production Notes

Trace actor turns, tool calls, child-agent calls, clarification, discovery, recall, context growth, token usage, and final typed outputs. Keep host functions narrow and typed. Let fatal infrastructure errors bubble; let task uncertainty become clarification or a typed final answer.

See Tools, agent() API, and MCP.