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Multi-Model Pipeline

Plan a role-based DAG, then route each assignment through an explicit worker-model pool. Preview the assignments with --dry-run before spending worker tokens.

Setup

pip install lionagi          # or: uv add lionagi
pip install matplotlib       # only for --show-graph
# claude — npm install -g @anthropic-ai/claude-code && claude login
# codex  — requires ChatGPT Plus/Pro (not an API key):
#          npm install -g @openai/codex && codex login

Command

li o flow claude/sonnet \
  "Research rate-limiting algorithms, implement one in Python, then review the implementation" \
  --workers codex/gpt-5.4-high,claude/sonnet,codex/gpt-5.3-codex-spark \
  --dry-run

The planner returns TaskAssignment entries, so the exact tasks and roles depend on the prompt. Dry-run output has two useful sections:

  • Plan (N assignments) lists each assignment's number, assignee, task, dependencies, and optional exit criteria.
  • Model + modes resolution shows the generated agent ID, selected worker model, and any role modes.

--workers is what makes this run multi-model. Model 1 goes to assignment 1, model 2 to assignment 2, and so on; the pool wraps if the planner creates more assignments. Assignments do not carry their own model field.

li o flow claude/sonnet \
  "Research rate-limiting algorithms, implement one in Python, then review the implementation" \
  --workers codex/gpt-5.4-high,claude/sonnet,codex/gpt-5.3-codex-spark \
  --save ./pipeline-out --show-graph

The live progress text and timing depend on the generated plan. Worker artifacts land under pipeline-out/<agent_id>/; --show-graph writes pipeline-out/flow_dag.png.

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