github-mirrors/tradingagents
1
0
Fork 0
mirror of https://github.com/TauricResearch/TradingAgents.git synced 2026-09-19 11:15:24 +03:00
Code Issues Packages Projects Releases Wiki Activity

fix(cli): read and write the decision log on the CLI path

Browse Source 
- shared create_run_state and record_decision for propagate() and the CLI #1332 #1347
This commit is contained in:
Yijia-Xiao
2026-09-14 23:12:17 +00:00
parent 2c1ba388d6
commit 4a9f196e92
4 changed files with 210 additions and 43 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
cli/main.py
View File

@@ -1110,18 +1110,10 @@ def run_analysis(checkpoint: bool | None = None):
)
update_display(layout, spinner_text, stats_handler=stats_handler, start_time=start_time)
# Initialize state and get graph args with callbacks.
# Resolve the instrument identity once here so all agents anchor to
# the real company (#814); the CLI builds state directly rather than
# going through propagate(), so this must happen on the CLI path too.
instrument_context = graph.resolve_instrument_context(
selections["ticker"], selections["asset_type"]
)
init_agent_state = graph.propagator.create_initial_state(
selections["ticker"],
selections["analysis_date"],
asset_type=selections["asset_type"],
instrument_context=instrument_context,
# The same initial state propagate() builds: settled decision log, past
# context and resolved instrument identity.
init_agent_state = graph.create_run_state(
selections["ticker"], selections["analysis_date"], selections["asset_type"]
)
# Pass callbacks to graph config for tool execution tracking
# (LLM tracking is handled separately via LLM constructor)
@@ -1243,8 +1235,16 @@ def run_analysis(checkpoint: bool | None = None):
trace.append(chunk)
# Clean run: drop this run's checkpoint so a later run starts fresh.
# A mid-stream failure skips this, keeping the checkpoint for resume.
# Streamed chunks are per-node deltas, not full state. Merge them
# so every report field populated across the run is present.
final_state = {}
for chunk in trace:
final_state.update(chunk)
# Clean run: log the decision, then drop this run's checkpoint so a
# later run starts fresh. A mid-stream failure skips both, keeping
# the checkpoint for resume.
graph.record_decision(selections["ticker"], selections["analysis_date"], final_state)
graph.clear_checkpoint_on_success(
selections["ticker"], selections["analysis_date"], selections["asset_type"]
)
@@ -1252,12 +1252,6 @@ def run_analysis(checkpoint: bool | None = None):
# Always restore the plain uncheckpointed graph, even on failure.
graph.end_checkpoint()
# Streamed chunks are per-node deltas, not full state. Merge them
# so every report field populated across the run is present.
final_state = {}
for chunk in trace:
final_state.update(chunk)
# Update all agent statuses to completed
for agent in message_buffer.agent_status:
message_buffer.update_agent_status(agent, "completed")