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Agent Teams let you group coding agents (from Agent Hub), cloud models, and local providers into named roles, then run workflows that hand work between them.

Concepts

Member kinds:
  • agent — ollama launch agent from Agent Hub (claude, codex, hermes, …)
  • model — specific model + provider (gpt-4o + openai, gemini-2.0-flash + gemini)
  • provider — provider default model (ollama, groq, …)

Quick start

Starter teams and workflows are seeded on first use:
From fish:

Example team

Example workflow

Round-robin mode (v2)

Instead of explicit steps, members take turns until max_turns is reached. Outputs accumulate in {results}, {transcript}, and {last_result}.
Run:
If members is omitted, all team roles rotate in definition order.

Retries (v2)

Retry failed agent/LLM steps before marking a run failed. Per step:
Workflow or team defaults:
Priority: step → workflow defaults → team defaults. Retry count appears in CLI output (retries=N, attempts=N). Set TEAM_RETRY_BACKOFF=1 for exponential delay.

Per-step MCP (v2)

Enable MCP context per step (or inherit from workflow/team defaults):
For agent steps, MCP tool summaries are prepended to the prompt. For model/provider steps, they are added to the system prompt. Optional tool loop for model steps: set TEAM_MCP_TOOL_ROUNDS=1 and have the model emit JSON like {"mcp_tool": "github", "tool": "search", "arguments": {}}.

CLI reference

--resolve prints how each role maps to a concrete agent or LLM target.

Runtime behavior

  1. Load team + workflow YAML/JSON from config dir (or bundled templates).
  2. Resolve members — agents via Agent Hub catalog; models/providers via LLM registry.
  3. Recall unified memory when defaults.memory: unified.
  4. Execute steps:
    • Agent roles → answer_question with role context (lightweight; not full ollama launch).
    • Model/provider roles → LlmOrchestrator with a fixed provider/model chain.
    • Parallel blocks → thread pool (TEAM_MAX_PARALLEL, default 4).
    • Round-robin → rotate members for max_turns with shared transcript variables.
  5. Retry failed steps per retries / retry_delay (workflow or team defaults).
  6. Inject MCP tool summaries when mcp is enabled for a step.
  7. Pass outputs forward via template variables.

Bundled templates

Create from templates:

Environment

Scoped memory (v3)

Teams can opt into policy-filtered recall and scratchpad writes with provenance:
Per-step override: memory_scope: { write: run } on a workflow step.
Workflow runs print run_id and scratchpad_writes in CLI output. Teams and workflows are included in arka config backup.

Limitations

  • Agent steps use Arka chat routing, not full interactive ollama launch sessions.
  • Model-step MCP tool loops are opt-in (TEAM_MCP_TOOL_ROUNDS) and single-round by default.
  • Round-robin has no early-stop condition — runs until max_turns.
  • Workflow conditions and human-in-the-loop gates are not implemented.
  • Provider health checks run at LLM call time, not at team resolve time.
  • Agent Hub — shared MCP, memory, and agent catalog
  • LLM fallback — provider/model routing used by model/provider roles
  • Memory — unified memory injected into team runs