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Agent chain — Task → Math Researcher → Math Solver.
Demonstrates:
- Building a two-agent sequential chain
- Streaming execution to capture per-agent prompts and responses
- Saving the communication log to JSON
Configure your LLM via environment variables:
LLM_API_KEY, LLM_BASE_URL, LLM_MODEL
Run:
python -m examples.math_chain_example
"""
import json
import os
from datetime import UTC, datetime
from pathlib import Path
from builder import BuilderConfig, GraphBuilder
from execution import MACPRunner, RunnerConfig, StreamEventType
from tools import create_openai_caller
# ── Graph construction ────────────────────────────────────────────────────────
def _build_graph():
"""Build: Task → Math Researcher → Math Solver."""
builder = GraphBuilder(BuilderConfig(include_task_node=True, validate=True))
builder.add_task(
query="Solve the equation: 2x - 3x² = 1",
description="Mathematical problem to solve",
)
builder.add_agent(
agent_id="math_researcher",
display_name="Math Researcher",
persona="a mathematical researcher",
description=(
"Outline the steps required to solve the problem but do NOT compute the final answer — only the plan."
),
)
builder.add_agent(
agent_id="math_solver",
display_name="Math Solver",
persona="a mathematics solver",
description="Follow the plan and output the CORRECT ANSWER.",
)
builder.connect_task_to_agents(agent_ids=["math_researcher"], bidirectional=False)
builder.add_workflow_edge("math_researcher", "math_solver")
return builder.build()
# ── Execution ─────────────────────────────────────────────────────────────────
def main():
graph = _build_graph()
llm_caller = create_openai_caller(
base_url=os.getenv("LLM_BASE_URL", "http://localhost:8000/v1"),
api_key=os.getenv("LLM_API_KEY", "your-api-key"),
model=os.getenv("LLM_MODEL", "gpt-4o-mini"),
temperature=0.7,
)
runner = MACPRunner(
llm_caller=llm_caller,
config=RunnerConfig(
timeout=120.0,
adaptive=False,
update_states=True,
broadcast_task_to_all=False,
),
)
node_data: dict[str, dict] = {}
final_answer = ""
final_agent = ""
total_tokens = 0
total_time = 0.0
execution_order: list[str] = []
print(f"Task: {graph.query}")
print("─" * 50)
for event in runner.stream(graph, final_agent_id="math_solver"):
etype = event.event_type
if etype == StreamEventType.AGENT_START:
aid = getattr(event, "agent_id", "")
name = getattr(event, "agent_name", aid)
print(f" ▶ {name} starting…")
node_data[aid] = {"agent_name": name, "response": ""}
elif etype == StreamEventType.AGENT_OUTPUT:
aid = getattr(event, "agent_id", "")
content = getattr(event, "content", "")
if aid in node_data:
node_data[aid]["response"] = content
execution_order.append(aid)
print(f" ✓ {node_data.get(aid, {}).get('agent_name', aid)}: {content[:120]}…")
elif etype == StreamEventType.AGENT_ERROR:
aid = getattr(event, "agent_id", "")
err = getattr(event, "error_message", "unknown")
execution_order.append(aid)
print(f" ✗ {aid}: ERROR — {err}")
elif etype == StreamEventType.RUN_END:
final_answer = getattr(event, "final_answer", "")
final_agent = getattr(event, "final_agent_id", "")
total_tokens = getattr(event, "total_tokens", 0)
total_time = getattr(event, "total_time", 0.0)
print("─" * 50)
print(f"Final answer (from '{final_agent}'):")
print(final_answer)
print(f"\nTokens: {total_tokens} | Time: {total_time:.2f}s")
# Save log
log = {
"timestamp": datetime.now(UTC).isoformat(),
"task": graph.query,
"execution_order": execution_order,
"total_tokens": total_tokens,
"total_time": total_time,
"nodes": node_data,
"final_answer": final_answer,
"final_agent": final_agent,
}
log_path = Path(__file__).parent / "math_chain_log.json"
log_path.write_text(json.dumps(log, ensure_ascii=False, indent=2), encoding="utf-8")
print(f"Log saved → {log_path}")
if __name__ == "__main__":
main()
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