- create_sentiment_analyst pre-fetches news, StockTwits and Reddit before
prompting, and TestSentimentAnalystAgent invoked it unstubbed, so every run
made live requests and a real 429 stalled the suite for minutes
- stub the three sources as the sibling prompt tests already do; the file drops
from ~84s to under a second, and the suite no longer depends on Reddit or
Yahoo being reachable
- the Trader received only the digested investment plan, so its entry / stop /
sizing levels were not anchored to real price structure (ATR, support and
resistance, current price)
- inject the market report and instruct the Trader to take concrete price levels
from it and direction/strategy from the plan; when the market analyst was not
selected the report is empty, so the section and grounding note are omitted #1167
A weak model can write a placeholder ('None', 'N/A') into an optional price
field, tripping schema validation. Coerce null-ish strings to None on the
trader/PM float fields; real numeric strings still parse.
- Local servers (LM Studio, vLLM) reject the object-form tool_choice langchain
sends for function calling. The generic openai_compatible provider now binds
the schema as a tool without forcing tool_choice.
- A structured call can return no parsed result (a thinking model answering in
plain text); fall back to free text with a clear reason instead of an opaque
render error.
Clear the deferred full-repo lint backlog so the whole tree passes the strict
ruff select (E,W,F,I,B,UP,C4,SIM). Mechanical fixes dominate: import sorting,
pep585/604 annotations, dropped dead imports, and whitespace. The few semantic
changes are behavior-preserving: declare __all__ on the agent_utils and
alpha_vantage re-export hubs; expand 'from x import *' to explicit names; use
immutable tuple defaults instead of mutable list defaults; contextlib.suppress
for try/except/pass; and narrow an over-broad assertRaises.
The analyst emitted free-form prose, so its sentiment header varied by
provider and run and downstream consumers needed drifting regex. Extend
the structured-output pattern the trio already uses: a SentimentReport
schema (band + 0-10 score + confidence + narrative) rendered to a
deterministic header, with a free-text fallback for providers that lack
native structured output.
#796
Extends the canonical structured-output pattern from the Portfolio Manager
to the other two decision-making agents. Each of the three agents now
returns a typed Pydantic instance via llm.with_structured_output() in a
single primary call, and a render helper turns the result into the same
markdown shape downstream agents and saved reports already consume.
- ResearchPlan: 5-tier recommendation, conversational rationale, concrete
strategic actions for the trader.
- TraderProposal: 3-tier action (transaction direction is naturally Buy /
Hold / Sell — position sizing happens later at the Portfolio Manager),
reasoning, and optional entry_price / stop_loss / position_sizing.
Rendered output preserves the trailing "FINAL TRANSACTION PROPOSAL:
**BUY/HOLD/SELL**" line for backward compatibility with the analyst
stop-signal text.
- PortfolioDecision: 5-tier rating, executive summary, investment thesis,
optional price_target / time_horizon (unchanged).
The shared try-structured-then-fallback pattern is extracted into
tradingagents/agents/utils/structured.py (bind_structured +
invoke_structured_or_freetext) so all three agents go through the same
code path and log the same warning when a provider lacks structured
output and the agent falls back to free-text generation.
Net effect for users: every saved markdown report (research/manager.md,
trading/trader.md, portfolio/decision.md) now has consistent section
headers across runs and providers, easier to scan.
Net effect for the runtime: the rating extraction round-trip is gone —
the rating comes from the structured response itself, not a second
LLM call. SignalProcessor was already simplified to a heuristic adapter
in the previous commit.
11 new tests in tests/test_structured_agents.py cover the Trader and
Research Manager render functions, structured-output happy paths, and
free-text fallback. Full suite: 88 tests pass in ~2s without API keys.
