From 6bc0cb7a8ddd388807d35ae85b53bcd3e4129767 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Mon, 13 Oct 2025 18:47:50 +0800 Subject: [PATCH 01/31] feat(research_agent): initially implement ResearchAgent with knowledge integration and SEC filing fetching --- .env.example | 1 + python/pyproject.toml | 8 +- python/uv.lock | 1164 ++++++++++++++++- .../agents/research_agent/__main__.py | 9 + .../valuecell/agents/research_agent/core.py | 53 + .../agents/research_agent/knowledge.py | 25 + .../agents/research_agent/sources.py | 23 + .../valuecell/agents/research_agent/types.py | 0 python/valuecell/agents/research_agent/vdb.py | 15 + 9 files changed, 1268 insertions(+), 30 deletions(-) create mode 100644 python/valuecell/agents/research_agent/__main__.py create mode 100644 python/valuecell/agents/research_agent/core.py create mode 100644 python/valuecell/agents/research_agent/knowledge.py create mode 100644 python/valuecell/agents/research_agent/sources.py create mode 100644 python/valuecell/agents/research_agent/types.py create mode 100644 python/valuecell/agents/research_agent/vdb.py diff --git a/.env.example b/.env.example index 055aaffc3..6bdb5dfd1 100644 --- a/.env.example +++ b/.env.example @@ -21,6 +21,7 @@ AGENT_DEBUG_MODE=false # Model Provider settings # Get your key from: https://openrouter.ai/ OPENROUTER_API_KEY= +GOOGLE_API_KEY= # Model IDs for OpenRouter PLANNER_MODEL_ID=google/gemini-2.5-pro diff --git a/python/pyproject.toml b/python/pyproject.toml index 7d91c7adc..8a7877863 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -22,6 +22,12 @@ dependencies = [ "edgartools>=4.12.2", "sqlalchemy>=2.0.43", "aiosqlite>=0.19.0", + "lancedb>=0.25.2", + "sentence-transformers>=5.1.1", + "unstructured>=0.18.15", + "markdown>=3.9", + "loguru>=0.7.3", + "tantivy>=0.25.0", ] [project.optional-dependencies] @@ -109,4 +115,4 @@ skip_glob = [ "**/tests/**", "docs/**", 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+if __name__ == "__main__": + agent = create_wrapped_agent(ResearchAgent) + asyncio.run(agent.serve()) \ No newline at end of file diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py new file mode 100644 index 000000000..ea19f37d4 --- /dev/null +++ b/python/valuecell/agents/research_agent/core.py @@ -0,0 +1,53 @@ +import os +from typing import AsyncGenerator, Iterator + +from agno.agent import Agent, RunOutputEvent +from agno.models.google import Gemini +from edgar import set_identity +from loguru import logger + +from valuecell.core.agent.responses import streaming +from valuecell.core.types import BaseAgent, StreamResponse + +from .knowledge import knowledge + +set_identity(os.getenv("SEC_EMAIL") or "your.name@example.com") + + +class ResearchAgent(BaseAgent): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.knowledge_research_agent = Agent( + model=Gemini(id="gemini-2.5-flash"), + knowledge=knowledge, + search_knowledge=True, + debug_mode=True, + ) + + async def analyze_from_knowledge( + self, query: str + ) -> AsyncGenerator[StreamResponse, None]: + response_stream: Iterator[RunOutputEvent] = ( + await self.knowledge_research_agent.arun( + query, stream=True, stream_intermediate_steps=True + ) + ) + async for event in response_stream: + if event.event == "RunContent": + yield streaming.message_chunk(event.content) + elif event.event == "ToolCallStarted": + yield streaming.tool_call_started( + event.tool.tool_call_id, event.tool.tool_name + ) + elif event.event == "ToolCallCompleted": + yield streaming.tool_call_completed( + event.tool.result, event.tool.tool_call_id, event.tool.tool_name + ) + logger.info("Financial data analysis completed") + + yield streaming.done() + + async def stream( + self, query: str, session_id: str, task_id: str + ) -> AsyncGenerator[StreamResponse, None]: + pass diff --git a/python/valuecell/agents/research_agent/knowledge.py b/python/valuecell/agents/research_agent/knowledge.py new file mode 100644 index 000000000..0b6297307 --- /dev/null +++ b/python/valuecell/agents/research_agent/knowledge.py @@ -0,0 +1,25 @@ +from pathlib import Path +from typing import Optional + +from agno.knowledge.chunking.markdown import MarkdownChunking +from agno.knowledge.knowledge import Knowledge +from agno.knowledge.reader.markdown_reader import MarkdownReader + +from .vdb import vector_db + +knowledge = Knowledge( + vector_db=vector_db, + max_results=10, +) +reader = MarkdownReader(chunking_strategy=MarkdownChunking()) + + +async def insert_md_file_to_knowledge( + name: str, path: Path, metadata: Optional[dict] = None +): + await knowledge.add_content_async( + name=name, + path=path, + metadata=metadata, + reader=reader, + ) diff --git a/python/valuecell/agents/research_agent/sources.py b/python/valuecell/agents/research_agent/sources.py new file mode 100644 index 000000000..3da541c45 --- /dev/null +++ b/python/valuecell/agents/research_agent/sources.py @@ -0,0 +1,23 @@ +from pathlib import Path +from typing import List, Optional + +from edgar import Company +from edgar.enums import FormType + + +async def fetch_sec_filings( + cik_or_ticker: str, + form: FormType = FormType.QUARTERLY_REPORT, + year: Optional[int | List[int]] = None, + quarter: Optional[int | List[int]] = None, +): + company = Company(cik_or_ticker) + filings = company.get_filings(form=form, year=year, quarter=quarter) + + for filing in filings: + filing_date = filing.filing_date.strftime("%Y-%m-%d") + period_of_report = filing.period_of_report + content = filing.document.markdown() + + path = Path(f"./{filing.company}-{filing_date}-{filing.form}.md") + path.write_text(content) \ No newline at end of file diff --git a/python/valuecell/agents/research_agent/types.py b/python/valuecell/agents/research_agent/types.py new file mode 100644 index 000000000..e69de29bb diff --git a/python/valuecell/agents/research_agent/vdb.py b/python/valuecell/agents/research_agent/vdb.py new file mode 100644 index 000000000..10b2aeba2 --- /dev/null +++ b/python/valuecell/agents/research_agent/vdb.py @@ -0,0 +1,15 @@ +from agno.knowledge.embedder.sentence_transformer import SentenceTransformerEmbedder +from agno.knowledge.reranker.sentence_transformer import SentenceTransformerReranker +from agno.vectordb.lancedb import LanceDb +from agno.vectordb.search import SearchType + +embedder = SentenceTransformerEmbedder(id="all-mpnet-base-v2", dimensions=768) +reranker = SentenceTransformerReranker(model="BAAI/bge-reranker-v2-m3", top_n=8) + +vector_db = LanceDb( + table_name="knowledge", + uri="./lancedb", + embedder=embedder, + reranker=reranker, + search_type=SearchType.hybrid, +) From e73fdff3a0003945014c67e2a320536e704b8105 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 09:55:53 +0800 Subject: [PATCH 02/31] refactor: update LanceDb initialization and improve utility functions for path resolution --- python/valuecell/agents/research_agent/vdb.py | 6 ++++-- python/valuecell/utils/db.py | 14 +++++++++++--- 2 files changed, 15 insertions(+), 5 deletions(-) diff --git a/python/valuecell/agents/research_agent/vdb.py b/python/valuecell/agents/research_agent/vdb.py index 10b2aeba2..20f326037 100644 --- a/python/valuecell/agents/research_agent/vdb.py +++ b/python/valuecell/agents/research_agent/vdb.py @@ -3,12 +3,14 @@ from agno.vectordb.lancedb import LanceDb from agno.vectordb.search import SearchType +from valuecell.utils.db import resolve_lancedb_uri + embedder = SentenceTransformerEmbedder(id="all-mpnet-base-v2", dimensions=768) reranker = SentenceTransformerReranker(model="BAAI/bge-reranker-v2-m3", top_n=8) vector_db = LanceDb( - table_name="knowledge", - uri="./lancedb", + table_name="research_agent_knowledge_base", + uri=resolve_lancedb_uri(), embedder=embedder, reranker=reranker, search_type=SearchType.hybrid, diff --git a/python/valuecell/utils/db.py b/python/valuecell/utils/db.py index 07481293a..6f850a779 100644 --- a/python/valuecell/utils/db.py +++ b/python/valuecell/utils/db.py @@ -1,7 +1,7 @@ import os -def default_db_path() -> str: +def _repo_root() -> str: """Resolve repository root and return default DB path valuecell.db. Layout assumption: this file is at repo_root/python/valuecell/utils/db.py @@ -9,8 +9,16 @@ def default_db_path() -> str: """ here = os.path.dirname(__file__) repo_root = os.path.abspath(os.path.join(here, "..", "..", "..")) - return os.path.join(repo_root, "valuecell.db") + return repo_root def resolve_db_path() -> str: - return os.environ.get("VALUECELL_SQLITE_DB") or default_db_path() + return os.environ.get("VALUECELL_SQLITE_DB") or os.path.join( + _repo_root(), "valuecell.db" + ) + + +def resolve_lancedb_uri() -> str: + return os.environ.get("VALUECELL_LANCEDB_URI") or os.path.join( + _repo_root(), "lancedb" + ) From 151a7ced4e11fdedb8aedb0e8a1307281d00d871 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 09:56:07 +0800 Subject: [PATCH 03/31] feat: integrate aiofiles for asynchronous file handling and enhance get_knowledge_path utility --- .gitignore | 8 +++++- python/pyproject.toml | 1 + python/uv.lock | 2 ++ .../agents/research_agent/sources.py | 26 +++++++++++++++---- python/valuecell/utils/path.py | 12 +++++++++ 5 files changed, 43 insertions(+), 6 deletions(-) diff --git a/.gitignore b/.gitignore index 75326d9a3..f2ff65b09 100644 --- a/.gitignore +++ b/.gitignore @@ -213,6 +213,7 @@ __marimo__/ *.db-shm *.sqlite *.sqlite3 +./lancedb # Downloads *csv @@ -220,4 +221,9 @@ __marimo__/ *xls # MACOS -.DS_Store \ No newline at end of file +.DS_Store + +# Local files +logs +*.log +.knowledge \ No newline at end of file diff --git a/python/pyproject.toml b/python/pyproject.toml index 8a7877863..4b9dbebb9 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -28,6 +28,7 @@ dependencies = [ "markdown>=3.9", "loguru>=0.7.3", "tantivy>=0.25.0", + "aiofiles>=24.1.0", ] [project.optional-dependencies] diff --git a/python/uv.lock b/python/uv.lock index 9b034c834..c24388b4c 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -3081,6 +3081,7 @@ source = { editable = "." } dependencies = [ { name = "a2a-sdk", extra = ["http-server"] }, { name = "agno", extra = ["openai"] }, + { name = "aiofiles" }, { name = "aiosqlite" }, { name = "akshare" }, { name = "edgartools" }, @@ -3136,6 +3137,7 @@ test = [ requires-dist = [ { name = "a2a-sdk", extras = ["http-server"], specifier = ">=0.3.4" }, { name = "agno", extras = ["openai"], specifier = ">=2.0,<3.0" }, + { name = "aiofiles", specifier = ">=24.1.0" }, { name = "aiosqlite", specifier = ">=0.19.0" }, { name = "akshare", specifier = ">=1.17.44" }, { name = "diff-cover", marker = "extra == 'dev'", specifier = ">=9.0.0" }, diff --git a/python/valuecell/agents/research_agent/sources.py b/python/valuecell/agents/research_agent/sources.py index 3da541c45..c87f9fd66 100644 --- a/python/valuecell/agents/research_agent/sources.py +++ b/python/valuecell/agents/research_agent/sources.py @@ -1,9 +1,12 @@ from pathlib import Path from typing import List, Optional +import aiofiles from edgar import Company from edgar.enums import FormType +from valuecell.utils.path import get_knowledge_path + async def fetch_sec_filings( cik_or_ticker: str, @@ -14,10 +17,23 @@ async def fetch_sec_filings( company = Company(cik_or_ticker) filings = company.get_filings(form=form, year=year, quarter=quarter) + res = [] for filing in filings: - filing_date = filing.filing_date.strftime("%Y-%m-%d") - period_of_report = filing.period_of_report - content = filing.document.markdown() + filing_date: str = filing.filing_date.strftime("%Y-%m-%d") + period_of_report: str = filing.period_of_report + content: str = filing.document.markdown() + + name = f"{filing.company}-{filing_date}-{filing.form}" + path = Path(get_knowledge_path()) / f"{name}.md" + metadata = { + "doc_type": filing.form, + "company": filing.company, + "period_of_report": period_of_report, + "filing_date": filing_date, + } + async with aiofiles.open(path, "w") as file: + await file.write(content) + + res.append((name, path, metadata)) - path = Path(f"./{filing.company}-{filing_date}-{filing.form}.md") - path.write_text(content) \ No newline at end of file + return res diff --git a/python/valuecell/utils/path.py b/python/valuecell/utils/path.py index 57d83ba72..bde0e4d79 100644 --- a/python/valuecell/utils/path.py +++ b/python/valuecell/utils/path.py @@ -36,3 +36,15 @@ def get_agent_card_path() -> str: root_path = get_root_path() agent_card_path = Path(root_path) / "configs" / "agent_cards" return str(agent_card_path) + + +def get_knowledge_path() -> str: + """ + Returns the path to the knowledge directory located in the project root. + + Returns: + str: Absolute path of the knowledge directory + """ + root_path = get_root_path() + knowledge_path = Path(root_path) / ".knowledge" + return str(knowledge_path) From b12c41c620e9effc911e143697db13a378a9f970 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 10:40:36 +0800 Subject: [PATCH 04/31] feat: enhance dependency management and add agent debug mode utility --- python/pyproject.toml | 4 +-- python/uv.lock | 34 +++++++++++++++++---- python/valuecell/core/coordinate/planner.py | 3 +- python/valuecell/utils/env.py | 5 +++ 4 files changed, 36 insertions(+), 10 deletions(-) create mode 100644 python/valuecell/utils/env.py diff --git a/python/pyproject.toml b/python/pyproject.toml index 4b9dbebb9..e9e6142c5 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -18,16 +18,14 @@ dependencies = [ "yfinance>=0.2.65", "requests>=2.32.5", "akshare>=1.17.44", - "agno[openai]>=2.0,<3.0", + "agno[openai, google, lancedb]>=2.0,<3.0", "edgartools>=4.12.2", "sqlalchemy>=2.0.43", "aiosqlite>=0.19.0", - "lancedb>=0.25.2", "sentence-transformers>=5.1.1", "unstructured>=0.18.15", "markdown>=3.9", "loguru>=0.7.3", - "tantivy>=0.25.0", "aiofiles>=24.1.0", ] diff --git a/python/uv.lock b/python/uv.lock index c24388b4c..9ebb7bdbf 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -54,6 +54,13 @@ wheels = [ ] [package.optional-dependencies] +google = [ + { name = "google-genai" }, +] +lancedb = [ + { name = "lancedb" }, + { name = "tantivy" }, +] openai = [ { name = "openai" }, ] @@ -812,6 +819,25 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/17/63/b19553b658a1692443c62bd07e5868adaa0ad746a0751ba62c59568cd45b/google_auth-2.40.3-py2.py3-none-any.whl", hash = "sha256:1370d4593e86213563547f97a92752fc658456fe4514c809544f330fed45a7ca", size = 216137, upload-time = "2025-06-04T18:04:55.573Z" }, ] +[[package]] +name = "google-genai" +version = "1.43.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "google-auth" }, + { name = "httpx" }, + { name = "pydantic" }, + { name = "requests" }, + { name = "tenacity" }, + { name = "typing-extensions" }, + { name = "websockets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c1/75/992ca4462682949750709678b8efbc865222c9a16cf34504b69c5459606c/google_genai-1.43.0.tar.gz", hash = "sha256:84eb219d320759c5882bc2cdb4e2ac84544d00f5d12c7892c79fb03d71bfc9a4", size = 236132, upload-time = "2025-10-10T23:16:40.131Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/61/85/e90dda488d5044e6e4cd1b49e7e7f0cc7f4a2a1c8004e88a5122d42ea024/google_genai-1.43.0-py3-none-any.whl", hash = "sha256:be1d4b1acab268125d536fd81b73c38694a70cb08266759089154718924434fd", size = 236733, upload-time = "2025-10-10T23:16:38.809Z" }, +] + [[package]] name = "googleapis-common-protos" version = "1.70.0" @@ -3080,13 +3106,12 @@ version = "0.1.0" source = { editable = "." } dependencies = [ { name = "a2a-sdk", extra = ["http-server"] }, - { name = "agno", extra = ["openai"] }, + { name = "agno", extra = ["google", "lancedb", "openai"] }, { name = "aiofiles" }, { name = "aiosqlite" }, { name = "akshare" }, { name = "edgartools" }, { name = "fastapi" }, - { name = "lancedb" }, { name = "loguru" }, { name = "markdown" }, { name = "pydantic" }, @@ -3095,7 +3120,6 @@ dependencies = [ { name = "requests" }, { name = "sentence-transformers" }, { name = "sqlalchemy" }, - { name = "tantivy" }, { name = "unstructured" }, { name = "uvicorn" }, { name = "yfinance" }, @@ -3136,14 +3160,13 @@ test = [ [package.metadata] requires-dist = [ { name = "a2a-sdk", extras = ["http-server"], specifier = ">=0.3.4" }, - { name = "agno", extras = ["openai"], specifier = ">=2.0,<3.0" }, + { name = "agno", extras = ["openai", "google", "lancedb"], specifier = ">=2.0,<3.0" }, { name = "aiofiles", specifier = ">=24.1.0" }, { name = "aiosqlite", specifier = ">=0.19.0" }, { name = "akshare", specifier = ">=1.17.44" }, { name = "diff-cover", marker = "extra == 'dev'", specifier = ">=9.0.0" }, { name = "edgartools", specifier = ">=4.12.2" }, { name = "fastapi", specifier = ">=0.104.0" }, - { name = "lancedb", specifier = ">=0.25.2" }, { name = "loguru", specifier = ">=0.7.3" }, { name = "markdown", specifier = ">=3.9" }, { name = "pydantic", specifier = ">=2.0.0" }, @@ -3156,7 +3179,6 @@ requires-dist = [ { name = "ruff", marker = "extra == 'dev'" }, { name = "sentence-transformers", specifier = ">=5.1.1" }, { name = "sqlalchemy", specifier = ">=2.0.43" }, - { name = "tantivy", specifier = ">=0.25.0" }, { name = "unstructured", specifier = ">=0.18.15" }, { name = "uvicorn", specifier = ">=0.24.0" }, { name = "yfinance", specifier = ">=0.2.65" }, diff --git a/python/valuecell/core/coordinate/planner.py b/python/valuecell/core/coordinate/planner.py index 7f8d7890e..51e4442fb 100644 --- a/python/valuecell/core/coordinate/planner.py +++ b/python/valuecell/core/coordinate/planner.py @@ -28,6 +28,7 @@ from valuecell.core.task import Task, TaskPattern, TaskStatus from valuecell.core.types import UserInput from valuecell.utils import generate_uuid +from valuecell.utils.env import agent_debug_mode_enabled from .models import ExecutionPlan, PlannerInput, PlannerResponse @@ -153,7 +154,7 @@ async def _analyze_input_and_create_tasks( self.tool_get_agent_description, ], markdown=False, - debug_mode=os.getenv("AGENT_DEBUG_MODE", "false").lower() == "true", + debug_mode=agent_debug_mode_enabled(), instructions=[ create_prompt_with_datetime(PLANNER_INSTRUCTIONS), ], diff --git a/python/valuecell/utils/env.py b/python/valuecell/utils/env.py new file mode 100644 index 000000000..e2162088a --- /dev/null +++ b/python/valuecell/utils/env.py @@ -0,0 +1,5 @@ +import os + + +def agent_debug_mode_enabled() -> bool: + return os.getenv("AGENT_DEBUG_MODE", "false").lower() == "true" From b9e4056c671991befd714ccc3af8f9a9cc6e461f Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 11:29:32 +0800 Subject: [PATCH 05/31] refactor: replace custom path resolution with get_repo_root_path utility --- python/valuecell/core/coordinate/planner.py | 10 ++----- .../core/coordinate/planner_prompts.py | 29 ------------------- python/valuecell/utils/db.py | 15 ++-------- python/valuecell/utils/path.py | 18 ++++++++++-- 4 files changed, 21 insertions(+), 51 deletions(-) diff --git a/python/valuecell/core/coordinate/planner.py b/python/valuecell/core/coordinate/planner.py index 51e4442fb..f7373cdb5 100644 --- a/python/valuecell/core/coordinate/planner.py +++ b/python/valuecell/core/coordinate/planner.py @@ -21,10 +21,7 @@ from agno.models.openrouter import OpenRouter from valuecell.core.agent.connect import RemoteConnections -from valuecell.core.coordinate.planner_prompts import ( - PLANNER_INSTRUCTIONS, - create_prompt_with_datetime, -) +from valuecell.core.coordinate.planner_prompts import PLANNER_INSTRUCTIONS from valuecell.core.task import Task, TaskPattern, TaskStatus from valuecell.core.types import UserInput from valuecell.utils import generate_uuid @@ -155,9 +152,8 @@ async def _analyze_input_and_create_tasks( ], markdown=False, debug_mode=agent_debug_mode_enabled(), - instructions=[ - create_prompt_with_datetime(PLANNER_INSTRUCTIONS), - ], + instructions=[PLANNER_INSTRUCTIONS], + add_datetime_to_context=True, ) # Execute planning with the agent diff --git a/python/valuecell/core/coordinate/planner_prompts.py b/python/valuecell/core/coordinate/planner_prompts.py index ac1968884..5a54e6042 100644 --- a/python/valuecell/core/coordinate/planner_prompts.py +++ b/python/valuecell/core/coordinate/planner_prompts.py @@ -6,35 +6,6 @@ ExecutionPlanner when calling the LLM-based planning agent. """ -from datetime import datetime -from textwrap import dedent - - -def create_prompt_with_datetime(base_prompt: str) -> str: - """ - Inject the current date/time into a base prompt string. - - The planner benefits from a stable timestamp in its instructions so it can - make time-sensitive decisions (for example, when interpreting requests - mentioning "today", "this week", etc.). This helper formats the current - local date/time and appends it to the provided prompt text. - - Args: - base_prompt: The base instructions that should receive the timestamp. - - Returns: - A dedented, multi-line prompt string that includes the current date/time. - """ - now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - return dedent( - f""" - {base_prompt} - - **Other Important Context** - - Current date and time: {now} - """ - ) - # noqa: E501 PLANNER_INSTRUCTIONS = """ diff --git a/python/valuecell/utils/db.py b/python/valuecell/utils/db.py index 6f850a779..16c2138e9 100644 --- a/python/valuecell/utils/db.py +++ b/python/valuecell/utils/db.py @@ -1,24 +1,15 @@ import os - -def _repo_root() -> str: - """Resolve repository root and return default DB path valuecell.db. - - Layout assumption: this file is at repo_root/python/valuecell/utils/db.py - We walk up 3 levels to reach repo_root. - """ - here = os.path.dirname(__file__) - repo_root = os.path.abspath(os.path.join(here, "..", "..", "..")) - return repo_root +from .path import get_repo_root_path def resolve_db_path() -> str: return os.environ.get("VALUECELL_SQLITE_DB") or os.path.join( - _repo_root(), "valuecell.db" + get_repo_root_path(), "valuecell.db" ) def resolve_lancedb_uri() -> str: return os.environ.get("VALUECELL_LANCEDB_URI") or os.path.join( - _repo_root(), "lancedb" + get_repo_root_path(), "lancedb" ) diff --git a/python/valuecell/utils/path.py b/python/valuecell/utils/path.py index bde0e4d79..61d2da619 100644 --- a/python/valuecell/utils/path.py +++ b/python/valuecell/utils/path.py @@ -1,7 +1,8 @@ +import os from pathlib import Path -def get_root_path() -> str: +def get_python_root_path() -> str: """ Returns the root directory of the current Python project (where pyproject.toml is located) @@ -26,6 +27,17 @@ def get_root_path() -> str: ) +def get_repo_root_path() -> str: + """Resolve repository root and return default DB path valuecell.db. + + Layout assumption: this file is at repo_root/python/valuecell/utils/db.py + We walk up 3 levels to reach repo_root. + """ + here = os.path.dirname(__file__) + repo_root = os.path.abspath(os.path.join(here, "..", "..", "..")) + return repo_root + + def get_agent_card_path() -> str: """ Returns the path to the agent card JSON file located in the configs/agent_cards directory. @@ -33,7 +45,7 @@ def get_agent_card_path() -> str: Returns: str: Absolute path of the agent card JSON file """ - root_path = get_root_path() + root_path = get_python_root_path() agent_card_path = Path(root_path) / "configs" / "agent_cards" return str(agent_card_path) @@ -45,6 +57,6 @@ def get_knowledge_path() -> str: Returns: str: Absolute path of the knowledge directory """ - root_path = get_root_path() + root_path = get_repo_root_path() knowledge_path = Path(root_path) / ".knowledge" return str(knowledge_path) From d4b45e57101f045454c61be30cbfdd817130b366 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 11:30:25 +0800 Subject: [PATCH 06/31] feat(research_agent): implement ResearchAgent with SEC filing fetching and knowledge integration; enhance fetch_sec_filings function and add instruction prompts --- .gitignore | 3 +- .../research_agent/{types.py => __init__.py} | 0 .../valuecell/agents/research_agent/core.py | 70 +++++++++++++++---- .../agents/research_agent/knowledge.py | 4 +- .../agents/research_agent/prompts.py | 48 +++++++++++++ .../agents/research_agent/schemas.py | 17 +++++ .../agents/research_agent/sources.py | 49 ++++++++++--- 7 files changed, 165 insertions(+), 26 deletions(-) rename python/valuecell/agents/research_agent/{types.py => __init__.py} (100%) create mode 100644 python/valuecell/agents/research_agent/prompts.py create mode 100644 python/valuecell/agents/research_agent/schemas.py diff --git a/.gitignore b/.gitignore index f2ff65b09..13f193291 100644 --- a/.gitignore +++ b/.gitignore @@ -213,7 +213,8 @@ __marimo__/ *.db-shm *.sqlite *.sqlite3 -./lancedb +lancedb +.lancedb # Downloads *csv diff --git a/python/valuecell/agents/research_agent/types.py b/python/valuecell/agents/research_agent/__init__.py similarity index 100% rename from python/valuecell/agents/research_agent/types.py rename to python/valuecell/agents/research_agent/__init__.py diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py index ea19f37d4..9fafbeeca 100644 --- a/python/valuecell/agents/research_agent/core.py +++ b/python/valuecell/agents/research_agent/core.py @@ -3,15 +3,20 @@ from agno.agent import Agent, RunOutputEvent from agno.models.google import Gemini +from agno.team import Team from edgar import set_identity from loguru import logger from valuecell.core.agent.responses import streaming from valuecell.core.types import BaseAgent, StreamResponse +from valuecell.utils.env import agent_debug_mode_enabled -from .knowledge import knowledge +from valuecell.agents.research_agent.knowledge import knowledge +from valuecell.agents.research_agent.sources import fetch_sec_filings +from valuecell.agents.research_agent.prompts import KNOWLEDGE_AGENT_INSTRUCTION -set_identity(os.getenv("SEC_EMAIL") or "your.name@example.com") + +set_identity(os.getenv("SEC_EMAIL")) class ResearchAgent(BaseAgent): @@ -19,18 +24,50 @@ def __init__(self, **kwargs): super().__init__(**kwargs) self.knowledge_research_agent = Agent( model=Gemini(id="gemini-2.5-flash"), + tools=[fetch_sec_filings], knowledge=knowledge, search_knowledge=True, - debug_mode=True, + instructions=[KNOWLEDGE_AGENT_INSTRUCTION], + add_datetime_to_context=True, + debug_mode=agent_debug_mode_enabled(), + ) + self.general_research_agent = Agent( + model=Gemini(id="gemini-2.5-flash", search=True), + debug_mode=agent_debug_mode_enabled(), + ) + self.research_team = Team( + name="Stock Company Financial Research Team", + members=[self.knowledge_research_agent, self.general_research_agent], + model=Gemini(id="gemini-2.5-flash"), + instructions="Coordinate with team members to provide comprehensive information. Delegate tasks based on the user's request.", ) - async def analyze_from_knowledge( - self, query: str + # async def analyze_from_knowledge( + # self, query: str + # ) -> AsyncGenerator[StreamResponse, None]: + # response_stream: Iterator[RunOutputEvent] = self.knowledge_research_agent.arun( + # query, stream=True, stream_intermediate_steps=True + # ) + # async for event in response_stream: + # if event.event == "RunContent": + # yield streaming.message_chunk(event.content) + # elif event.event == "ToolCallStarted": + # yield streaming.tool_call_started( + # event.tool.tool_call_id, event.tool.tool_name + # ) + # elif event.event == "ToolCallCompleted": + # yield streaming.tool_call_completed( + # event.tool.result, event.tool.tool_call_id, event.tool.tool_name + # ) + # logger.info("Financial data analysis completed") + + # yield streaming.done() + + async def stream( + self, query: str, session_id: str, task_id: str ) -> AsyncGenerator[StreamResponse, None]: - response_stream: Iterator[RunOutputEvent] = ( - await self.knowledge_research_agent.arun( - query, stream=True, stream_intermediate_steps=True - ) + response_stream: Iterator[RunOutputEvent] = self.knowledge_research_agent.arun( + query, stream=True, stream_intermediate_steps=True ) async for event in response_stream: if event.event == "RunContent": @@ -47,7 +84,14 @@ async def analyze_from_knowledge( yield streaming.done() - async def stream( - self, query: str, session_id: str, task_id: str - ) -> AsyncGenerator[StreamResponse, None]: - pass + +if __name__ == "__main__": + import asyncio + + async def main(): + agent = ResearchAgent() + query = "Provide a summary of Apple's latest quarterly report." + async for response in agent.stream(query, "test_session", "test_task"): + print(response) + + asyncio.run(main()) diff --git a/python/valuecell/agents/research_agent/knowledge.py b/python/valuecell/agents/research_agent/knowledge.py index 0b6297307..fd07ab881 100644 --- a/python/valuecell/agents/research_agent/knowledge.py +++ b/python/valuecell/agents/research_agent/knowledge.py @@ -11,7 +11,7 @@ vector_db=vector_db, max_results=10, ) -reader = MarkdownReader(chunking_strategy=MarkdownChunking()) +md_reader = MarkdownReader(chunking_strategy=MarkdownChunking()) async def insert_md_file_to_knowledge( @@ -21,5 +21,5 @@ async def insert_md_file_to_knowledge( name=name, path=path, metadata=metadata, - reader=reader, + reader=md_reader, ) diff --git a/python/valuecell/agents/research_agent/prompts.py b/python/valuecell/agents/research_agent/prompts.py new file mode 100644 index 000000000..e0fa08bc7 --- /dev/null +++ b/python/valuecell/agents/research_agent/prompts.py @@ -0,0 +1,48 @@ +KNOWLEDGE_AGENT_INSTRUCTION = """ +Purpose +------- +You are a financial research assistant. Your primary objective is to satisfy the user's information request about a company's financials, filings, or performance with accurate, sourceable, and actionable answers. + +Tools +----- +- fetch_sec_filings(ticker_or_cik, form, year?, quarter?): Use this when primary-source facts are needed (e.g., reported revenue, net income, footnotes). Provide exact parameters when invoking the tool. +- Knowledge base search: Use the agent's internal knowledge index to find summaries, historical context, analyst commentary, and previously ingested documents. + +Retrieval & Analysis Steps +-------------------------- +1. Clarify: If the user's request lacks a ticker/CIK, form type, or time range, ask a single clarifying question. +2. Primary check: If the user requests factual items (financial line items, footnote detail, MD&A text), call `fetch_sec_filings` with specific filters to retrieve the relevant filings. +3. Post-fetch knowledge search (required): Immediately after calling `fetch_sec_filings`, run a knowledge-base search for the same company and time period. Use the search results to: + - confirm or enrich extracted facts, + - surface relevant analyst commentary or historical context, + - detect any pre-existing summaries already ingested that relate to the same filing. +4. Read & extract: From retrieved filings and knowledge results, extract exact phrasing or numeric values. Prefer the filing table or MD&A for numeric facts. +5. Synthesize: Combine extracted facts with knowledge-base results to provide context (trends, historical comparisons, interpretations). If the knowledge base contradicts filings, prioritize filings and explain the discrepancy. + +Output Format +------------- +Always return a structured answer with these sections when applicable: +- Summary: one-paragraph concise answer. +- Key facts: bullet list of sourced facts (each fact followed by source: filing name + date). +- Calculation / Steps: show any calculations or assumptions used. +- Context: brief historical or qualitative context from the knowledge base. If `fetch_sec_filings` was used, explicitly annotate which context lines came from the knowledge search that followed the fetch. +- Next steps: if data is missing or incomplete, recommend concrete follow-ups (e.g., call `fetch_sec_filings` with X/Y, check 10-K footnote Z, update knowledge corpus). + +Tone & Constraints +------------------ +- Be concise, factual, and source-focused. Avoid speculation. When unsure, quantify uncertainty (e.g., "~5% uncertain because..."). +- Cite filings explicitly when using them as evidence. + +Examples +-------- +Example 1 (user asks for revenue): +- Action: call `fetch_sec_filings('AAPL', '10-Q', year=2025, quarter=2)` +- Output: Summary + Key facts ("Revenue: $X — Apple-2025-07-31-10-Q"), Calculation/Steps if needed, Context, Next steps. + +Example 2 (user asks for interpretation): +- Action: search knowledge base for analyst notes and recent filings; do NOT call fetch_sec_filings unless you need a primary source quote or table. +- Output: Summary + Context + Sources. + +If you decide to call `fetch_sec_filings`, include the exact tool call you will make (parameters) in a short preamble before producing the answer. +""" + diff --git a/python/valuecell/agents/research_agent/schemas.py b/python/valuecell/agents/research_agent/schemas.py new file mode 100644 index 000000000..5f87aeace --- /dev/null +++ b/python/valuecell/agents/research_agent/schemas.py @@ -0,0 +1,17 @@ +from pathlib import Path +from dataclasses import dataclass + + +@dataclass +class SECFilingMetadata: + doc_type: str + company: str + period_of_report: str + filing_date: str + + +@dataclass +class SECFilingResult: + name: str + path: Path + metadata: SECFilingMetadata diff --git a/python/valuecell/agents/research_agent/sources.py b/python/valuecell/agents/research_agent/sources.py index c87f9fd66..7fea0a5ea 100644 --- a/python/valuecell/agents/research_agent/sources.py +++ b/python/valuecell/agents/research_agent/sources.py @@ -3,19 +3,41 @@ import aiofiles from edgar import Company -from edgar.enums import FormType +from edgar.entity.filings import EntityFilings from valuecell.utils.path import get_knowledge_path +from .knowledge import insert_md_file_to_knowledge +from .schemas import SECFilingMetadata, SECFilingResult + async def fetch_sec_filings( cik_or_ticker: str, - form: FormType = FormType.QUARTERLY_REPORT, + form: List[str] | str = "10-Q", year: Optional[int | List[int]] = None, quarter: Optional[int | List[int]] = None, ): + """Fetch SEC filings for a given company. + If year and quarter are provided, filter filings accordingly. If not, fetch the latest filings. + + Args: + cik_or_ticker (str): CIK or ticker symbol of the company. Never introduce backticks, quotes, or spaces. + form (List[str] | str, optional): Type of SEC filing form to fetch. + - Defaults to "10-Q". Can be a list of forms (e.g. ["10-K", "10-Q"]). + - Choices include "10-K", "10-Q". + year (Optional[int | List[int]], optional): Year or list of years to filter filings. Defaults to None. + quarter (Optional[int | List[int]], optional): Quarter or list of quarters to filter filings. Defaults to None. + + Returns: + List[Tuple[str, Path, dict]]: A list of tuples containing the name, path, and metadata of each fetched filing. + """ company = Company(cik_or_ticker) - filings = company.get_filings(form=form, year=year, quarter=quarter) + if year and quarter: + filings = company.get_filings(form=form, year=year, quarter=quarter) + else: + filings = company.get_filings(form=form).latest() + if not isinstance(filings, EntityFilings): + filings = [filings] res = [] for filing in filings: @@ -25,15 +47,22 @@ async def fetch_sec_filings( name = f"{filing.company}-{filing_date}-{filing.form}" path = Path(get_knowledge_path()) / f"{name}.md" - metadata = { - "doc_type": filing.form, - "company": filing.company, - "period_of_report": period_of_report, - "filing_date": filing_date, - } + metadata = SECFilingMetadata( + doc_type=filing.form, + company=filing.company, + period_of_report=period_of_report, + filing_date=filing_date, + ) async with aiofiles.open(path, "w") as file: await file.write(content) - res.append((name, path, metadata)) + sec_filing_result = SECFilingResult(name, path, metadata) + res.append(sec_filing_result) + # asyncio.create_task( + # insert_md_file_to_knowledge(name=name, path=path, metadata=metadata.__dict__) + # ) + await insert_md_file_to_knowledge( + name=name, path=path, metadata=metadata.__dict__ + ) return res From 03a992de3b5c4d8023c8e6e27331d9dd035ddd8e Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 11:53:09 +0800 Subject: [PATCH 07/31] refactor: streamline SEC email configuration --- .../valuecell/agents/research_agent/core.py | 45 +++---------------- python/valuecell/agents/sec_agent.py | 2 +- 2 files changed, 6 insertions(+), 41 deletions(-) diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py index 9fafbeeca..5283b8605 100644 --- a/python/valuecell/agents/research_agent/core.py +++ b/python/valuecell/agents/research_agent/core.py @@ -3,21 +3,16 @@ from agno.agent import Agent, RunOutputEvent from agno.models.google import Gemini -from agno.team import Team from edgar import set_identity from loguru import logger +from valuecell.agents.research_agent.knowledge import knowledge +from valuecell.agents.research_agent.prompts import KNOWLEDGE_AGENT_INSTRUCTION +from valuecell.agents.research_agent.sources import fetch_sec_filings from valuecell.core.agent.responses import streaming from valuecell.core.types import BaseAgent, StreamResponse from valuecell.utils.env import agent_debug_mode_enabled -from valuecell.agents.research_agent.knowledge import knowledge -from valuecell.agents.research_agent.sources import fetch_sec_filings -from valuecell.agents.research_agent.prompts import KNOWLEDGE_AGENT_INSTRUCTION - - -set_identity(os.getenv("SEC_EMAIL")) - class ResearchAgent(BaseAgent): def __init__(self, **kwargs): @@ -31,37 +26,7 @@ def __init__(self, **kwargs): add_datetime_to_context=True, debug_mode=agent_debug_mode_enabled(), ) - self.general_research_agent = Agent( - model=Gemini(id="gemini-2.5-flash", search=True), - debug_mode=agent_debug_mode_enabled(), - ) - self.research_team = Team( - name="Stock Company Financial Research Team", - members=[self.knowledge_research_agent, self.general_research_agent], - model=Gemini(id="gemini-2.5-flash"), - instructions="Coordinate with team members to provide comprehensive information. Delegate tasks based on the user's request.", - ) - - # async def analyze_from_knowledge( - # self, query: str - # ) -> AsyncGenerator[StreamResponse, None]: - # response_stream: Iterator[RunOutputEvent] = self.knowledge_research_agent.arun( - # query, stream=True, stream_intermediate_steps=True - # ) - # async for event in response_stream: - # if event.event == "RunContent": - # yield streaming.message_chunk(event.content) - # elif event.event == "ToolCallStarted": - # yield streaming.tool_call_started( - # event.tool.tool_call_id, event.tool.tool_name - # ) - # elif event.event == "ToolCallCompleted": - # yield streaming.tool_call_completed( - # event.tool.result, event.tool.tool_call_id, event.tool.tool_name - # ) - # logger.info("Financial data analysis completed") - - # yield streaming.done() + set_identity(os.getenv("SEC_EMAIL")) async def stream( self, query: str, session_id: str, task_id: str @@ -90,7 +55,7 @@ async def stream( async def main(): agent = ResearchAgent() - query = "Provide a summary of Apple's latest quarterly report." + query = "Provide a summary of Apple's 2024 all quarterly report." async for response in agent.stream(query, "test_session", "test_task"): print(response) diff --git a/python/valuecell/agents/sec_agent.py b/python/valuecell/agents/sec_agent.py index a609852c8..131b4731a 100644 --- a/python/valuecell/agents/sec_agent.py +++ b/python/valuecell/agents/sec_agent.py @@ -67,7 +67,7 @@ class Sec13FundAgentConfig: """Configuration management class for SEC 13F Agent""" def __init__(self): - self.sec_email = os.getenv("SEC_EMAIL", "your.name@example.com") + self.sec_email = os.getenv("SEC_EMAIL") self.parser_model_id = os.getenv("SEC_PARSER_MODEL_ID", "openai/gpt-4o-mini") self.analysis_model_id = os.getenv( "SEC_ANALYSIS_MODEL_ID", "google/gemini-2.5-pro" From 0eceffcdde0f2a6952d9e5ec21ab75f480a362fd Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 13:35:14 +0800 Subject: [PATCH 08/31] refactor(fetch_sec_filings): improve file naming and metadata handling for SEC filings --- .../agents/research_agent/sources.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/python/valuecell/agents/research_agent/sources.py b/python/valuecell/agents/research_agent/sources.py index 7fea0a5ea..c04099423 100644 --- a/python/valuecell/agents/research_agent/sources.py +++ b/python/valuecell/agents/research_agent/sources.py @@ -44,25 +44,26 @@ async def fetch_sec_filings( filing_date: str = filing.filing_date.strftime("%Y-%m-%d") period_of_report: str = filing.period_of_report content: str = filing.document.markdown() + doc_type: str = filing.form + company_name: str = filing.company - name = f"{filing.company}-{filing_date}-{filing.form}" - path = Path(get_knowledge_path()) / f"{name}.md" + orig_doc = filing.document.document + md_doc = orig_doc.replace(filing.document.extension, ".md") + file_name = f"{doc_type}_{md_doc}" + path = Path(get_knowledge_path()) / file_name metadata = SECFilingMetadata( - doc_type=filing.form, - company=filing.company, + doc_type=doc_type, + company=company_name, period_of_report=period_of_report, filing_date=filing_date, ) async with aiofiles.open(path, "w") as file: await file.write(content) - sec_filing_result = SECFilingResult(name, path, metadata) + sec_filing_result = SECFilingResult(file_name, path, metadata) res.append(sec_filing_result) - # asyncio.create_task( - # insert_md_file_to_knowledge(name=name, path=path, metadata=metadata.__dict__) - # ) await insert_md_file_to_knowledge( - name=name, path=path, metadata=metadata.__dict__ + name=file_name, path=path, metadata=metadata.__dict__ ) return res From a33d4e73b8ffb612fa01c6f69dab03b770b17437 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 16:02:59 +0800 Subject: [PATCH 09/31] refactor(prompts): enhance instruction clarity and structure for knowledge agent --- .../agents/research_agent/prompts.py | 36 ++++++++----------- 1 file changed, 15 insertions(+), 21 deletions(-) diff --git a/python/valuecell/agents/research_agent/prompts.py b/python/valuecell/agents/research_agent/prompts.py index e0fa08bc7..bb01f3f50 100644 --- a/python/valuecell/agents/research_agent/prompts.py +++ b/python/valuecell/agents/research_agent/prompts.py @@ -1,15 +1,12 @@ KNOWLEDGE_AGENT_INSTRUCTION = """ -Purpose -------- +Purpose: You are a financial research assistant. Your primary objective is to satisfy the user's information request about a company's financials, filings, or performance with accurate, sourceable, and actionable answers. -Tools ------ +Tools: - fetch_sec_filings(ticker_or_cik, form, year?, quarter?): Use this when primary-source facts are needed (e.g., reported revenue, net income, footnotes). Provide exact parameters when invoking the tool. - Knowledge base search: Use the agent's internal knowledge index to find summaries, historical context, analyst commentary, and previously ingested documents. -Retrieval & Analysis Steps --------------------------- +Retrieval & Analysis Steps: 1. Clarify: If the user's request lacks a ticker/CIK, form type, or time range, ask a single clarifying question. 2. Primary check: If the user requests factual items (financial line items, footnote detail, MD&A text), call `fetch_sec_filings` with specific filters to retrieve the relevant filings. 3. Post-fetch knowledge search (required): Immediately after calling `fetch_sec_filings`, run a knowledge-base search for the same company and time period. Use the search results to: @@ -19,29 +16,26 @@ 4. Read & extract: From retrieved filings and knowledge results, extract exact phrasing or numeric values. Prefer the filing table or MD&A for numeric facts. 5. Synthesize: Combine extracted facts with knowledge-base results to provide context (trends, historical comparisons, interpretations). If the knowledge base contradicts filings, prioritize filings and explain the discrepancy. -Output Format -------------- -Always return a structured answer with these sections when applicable: -- Summary: one-paragraph concise answer. -- Key facts: bullet list of sourced facts (each fact followed by source: filing name + date). -- Calculation / Steps: show any calculations or assumptions used. -- Context: brief historical or qualitative context from the knowledge base. If `fetch_sec_filings` was used, explicitly annotate which context lines came from the knowledge search that followed the fetch. -- Next steps: if data is missing or incomplete, recommend concrete follow-ups (e.g., call `fetch_sec_filings` with X/Y, check 10-K footnote Z, update knowledge corpus). - -Tone & Constraints ------------------- +Output Format: +Always return a structured answer with these sections when applicable, and for any factual statement, calculation, or context, provide a source in the following markdown link format: [filing name + date](file://). +- Summary: one-paragraph concise answer. If the summary contains factual data, include the source in markdown link format. +- Key facts: bullet list of sourced facts (each fact followed by source: [filing name + date](file://)). +- Calculation / Steps: show any calculations or assumptions used, and for any numbers or facts, provide the source in markdown link format. +- Context: brief historical or qualitative context from the knowledge base or filings. For any referenced data or statement, include the source in markdown link format. If `fetch_sec_filings` was used, explicitly annotate which context lines came from the knowledge search that followed the fetch, and provide sources for both. +- Next steps: if data is missing or incomplete, recommend concrete follow-ups (e.g., call `fetch_sec_filings` with X/Y, check 10-K footnote Z, update knowledge corpus), and if referencing missing data, specify the expected source format. + +Tone & Constraints: - Be concise, factual, and source-focused. Avoid speculation. When unsure, quantify uncertainty (e.g., "~5% uncertain because..."). - Cite filings explicitly when using them as evidence. -Examples --------- +Examples: Example 1 (user asks for revenue): - Action: call `fetch_sec_filings('AAPL', '10-Q', year=2025, quarter=2)` -- Output: Summary + Key facts ("Revenue: $X — Apple-2025-07-31-10-Q"), Calculation/Steps if needed, Context, Next steps. +- Output: Summary (with source: [Apple-2025-07-31-10-Q](file://)), Key facts (each with source), Calculation/Steps (with source if using numbers from filings), Context (with source if referencing filings or knowledge base), Next steps (with expected source if referencing missing data). Example 2 (user asks for interpretation): - Action: search knowledge base for analyst notes and recent filings; do NOT call fetch_sec_filings unless you need a primary source quote or table. -- Output: Summary + Context + Sources. +- Output: Summary (with source if referencing facts), Context (with sources for all referenced data), Sources (all in markdown link format). If you decide to call `fetch_sec_filings`, include the exact tool call you will make (parameters) in a short preamble before producing the answer. """ From 15e6c7cc8616bc7cf7ea7a235ebaf24cbad3dc03 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 16:25:58 +0800 Subject: [PATCH 10/31] refactor: update query to include annual reports and enhance knowledge agent instructions with structured tags --- python/valuecell/agents/research_agent/core.py | 2 +- .../valuecell/agents/research_agent/prompts.py | 18 ++++++++++++------ .../valuecell/agents/research_agent/sources.py | 2 +- python/valuecell/agents/research_agent/vdb.py | 1 + 4 files changed, 15 insertions(+), 8 deletions(-) diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py index 5283b8605..94bf69583 100644 --- a/python/valuecell/agents/research_agent/core.py +++ b/python/valuecell/agents/research_agent/core.py @@ -55,7 +55,7 @@ async def stream( async def main(): agent = ResearchAgent() - query = "Provide a summary of Apple's 2024 all quarterly report." + query = "Provide a summary of Apple's 2024 all quarterly and annual reports." async for response in agent.stream(query, "test_session", "test_task"): print(response) diff --git a/python/valuecell/agents/research_agent/prompts.py b/python/valuecell/agents/research_agent/prompts.py index bb01f3f50..45c9007d5 100644 --- a/python/valuecell/agents/research_agent/prompts.py +++ b/python/valuecell/agents/research_agent/prompts.py @@ -1,12 +1,14 @@ KNOWLEDGE_AGENT_INSTRUCTION = """ -Purpose: + You are a financial research assistant. Your primary objective is to satisfy the user's information request about a company's financials, filings, or performance with accurate, sourceable, and actionable answers. + -Tools: + - fetch_sec_filings(ticker_or_cik, form, year?, quarter?): Use this when primary-source facts are needed (e.g., reported revenue, net income, footnotes). Provide exact parameters when invoking the tool. - Knowledge base search: Use the agent's internal knowledge index to find summaries, historical context, analyst commentary, and previously ingested documents. + -Retrieval & Analysis Steps: + 1. Clarify: If the user's request lacks a ticker/CIK, form type, or time range, ask a single clarifying question. 2. Primary check: If the user requests factual items (financial line items, footnote detail, MD&A text), call `fetch_sec_filings` with specific filters to retrieve the relevant filings. 3. Post-fetch knowledge search (required): Immediately after calling `fetch_sec_filings`, run a knowledge-base search for the same company and time period. Use the search results to: @@ -15,20 +17,23 @@ - detect any pre-existing summaries already ingested that relate to the same filing. 4. Read & extract: From retrieved filings and knowledge results, extract exact phrasing or numeric values. Prefer the filing table or MD&A for numeric facts. 5. Synthesize: Combine extracted facts with knowledge-base results to provide context (trends, historical comparisons, interpretations). If the knowledge base contradicts filings, prioritize filings and explain the discrepancy. + -Output Format: + Always return a structured answer with these sections when applicable, and for any factual statement, calculation, or context, provide a source in the following markdown link format: [filing name + date](file://). - Summary: one-paragraph concise answer. If the summary contains factual data, include the source in markdown link format. - Key facts: bullet list of sourced facts (each fact followed by source: [filing name + date](file://)). - Calculation / Steps: show any calculations or assumptions used, and for any numbers or facts, provide the source in markdown link format. - Context: brief historical or qualitative context from the knowledge base or filings. For any referenced data or statement, include the source in markdown link format. If `fetch_sec_filings` was used, explicitly annotate which context lines came from the knowledge search that followed the fetch, and provide sources for both. - Next steps: if data is missing or incomplete, recommend concrete follow-ups (e.g., call `fetch_sec_filings` with X/Y, check 10-K footnote Z, update knowledge corpus), and if referencing missing data, specify the expected source format. + -Tone & Constraints: + - Be concise, factual, and source-focused. Avoid speculation. When unsure, quantify uncertainty (e.g., "~5% uncertain because..."). - Cite filings explicitly when using them as evidence. + -Examples: + Example 1 (user asks for revenue): - Action: call `fetch_sec_filings('AAPL', '10-Q', year=2025, quarter=2)` - Output: Summary (with source: [Apple-2025-07-31-10-Q](file://)), Key facts (each with source), Calculation/Steps (with source if using numbers from filings), Context (with source if referencing filings or knowledge base), Next steps (with expected source if referencing missing data). @@ -38,5 +43,6 @@ - Output: Summary (with source if referencing facts), Context (with sources for all referenced data), Sources (all in markdown link format). If you decide to call `fetch_sec_filings`, include the exact tool call you will make (parameters) in a short preamble before producing the answer. + """ diff --git a/python/valuecell/agents/research_agent/sources.py b/python/valuecell/agents/research_agent/sources.py index c04099423..dca4d8fd5 100644 --- a/python/valuecell/agents/research_agent/sources.py +++ b/python/valuecell/agents/research_agent/sources.py @@ -32,7 +32,7 @@ async def fetch_sec_filings( List[Tuple[str, Path, dict]]: A list of tuples containing the name, path, and metadata of each fetched filing. """ company = Company(cik_or_ticker) - if year and quarter: + if year or quarter: filings = company.get_filings(form=form, year=year, quarter=quarter) else: filings = company.get_filings(form=form).latest() diff --git a/python/valuecell/agents/research_agent/vdb.py b/python/valuecell/agents/research_agent/vdb.py index 20f326037..61af96741 100644 --- a/python/valuecell/agents/research_agent/vdb.py +++ b/python/valuecell/agents/research_agent/vdb.py @@ -14,4 +14,5 @@ embedder=embedder, reranker=reranker, search_type=SearchType.hybrid, + use_tantivy=False, ) From 868a9ec06b79569dbe939b4d98efab84babe113e Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 16:32:29 +0800 Subject: [PATCH 11/31] feat: add ResearchAgent configuration for SEC filing analysis and monitoring --- .../agent_cards/stock_research_agent.json | 57 +++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 python/configs/agent_cards/stock_research_agent.json diff --git a/python/configs/agent_cards/stock_research_agent.json b/python/configs/agent_cards/stock_research_agent.json new file mode 100644 index 000000000..691afb8f6 --- /dev/null +++ b/python/configs/agent_cards/stock_research_agent.json @@ -0,0 +1,57 @@ +{ + "name": "ResearchAgent", + "display_name": "Research Agent", + "url": "http://localhost:10004/", + "description": "Research Agent analyzes SEC filings (10-K, 10-Q, 13F, 8-K) and internal knowledge to produce sourceable, actionable summaries and data extracts. It can extract financial line items, reconcile filings with knowledge search results, summarize holdings from 13F filings, and recommend follow-ups with exact tool calls.", + "skills": [ + { + "id": "extract_financials", + "name": "Extract financial line items", + "description": "Retrieve and extract numeric financial line items (revenue, net income, EPS, cash flow) and cite the filing source.", + "examples": [ + "What was Apple's revenue in Q4 2024? Provide the filing source.", + "Show net income and EPS for Tesla in the most recent 10-Q." + ], + "tags": ["10-K","10-Q","financials","filings"] + }, + { + "id": "analyze_13f_filings", + "name": "Analyze 13F holdings", + "description": "Parse 13F filings to extract institutional holdings, position sizes, and changes over time.", + "examples": [ + "What are the top holdings of Berkshire Hathaway in the latest 13F filing?", + "How has Vanguard's top 10 holdings changed over the last four quarters?" + ], + "tags": ["13F","holdings","institutional"] + }, + { + "id": "synthesize_context", + "name": "Synthesize context and commentary", + "description": "Combine extracted filing data with knowledge-base search results to provide interpretation, analyst commentary, and historical context, always citing sources.", + "examples": [ + "Summarize analyst commentary on Apple's guidance and cite sources.", + "Compare revenue trends for company X over the last three years with sources." + ], + "tags": ["analysis","context","knowledge-base"] + }, + { + "id": "monitor_and_alert", + "name": "Monitor filings and notify", + "description": "Track company filings and notify users of new relevant SEC documents, with a short sourceable summary and suggested follow-ups.", + "examples": [ + "Notify me when Tesla files its next 10-Q and provide a one-paragraph summary with sources.", + "Alert me to significant changes in Berkshire Hathaway's 13F holdings." + ], + "tags": ["monitor","alerts","tracking"] + } + ], + "enabled": true, + "metadata": { + "version": "1.0.0", + "author": "ValueCell Team", + "tags": [ + "sec filings", + "fund-analysis" + ] + } +} \ No newline at end of file From 5108eda0f7205ecf1e17b7c036bfea3267958a25 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 16:34:35 +0800 Subject: [PATCH 12/31] format codebase --- python/valuecell/agents/research_agent/__main__.py | 2 +- python/valuecell/agents/research_agent/prompts.py | 1 - python/valuecell/agents/research_agent/schemas.py | 2 +- python/valuecell/core/coordinate/planner_prompts.py | 1 - 4 files changed, 2 insertions(+), 4 deletions(-) diff --git a/python/valuecell/agents/research_agent/__main__.py b/python/valuecell/agents/research_agent/__main__.py index 35a25bc48..e5a2e3442 100644 --- a/python/valuecell/agents/research_agent/__main__.py +++ b/python/valuecell/agents/research_agent/__main__.py @@ -6,4 +6,4 @@ if __name__ == "__main__": agent = create_wrapped_agent(ResearchAgent) - asyncio.run(agent.serve()) \ No newline at end of file + asyncio.run(agent.serve()) diff --git a/python/valuecell/agents/research_agent/prompts.py b/python/valuecell/agents/research_agent/prompts.py index 45c9007d5..84d9f5777 100644 --- a/python/valuecell/agents/research_agent/prompts.py +++ b/python/valuecell/agents/research_agent/prompts.py @@ -45,4 +45,3 @@ If you decide to call `fetch_sec_filings`, include the exact tool call you will make (parameters) in a short preamble before producing the answer. """ - diff --git a/python/valuecell/agents/research_agent/schemas.py b/python/valuecell/agents/research_agent/schemas.py index 5f87aeace..3f343588d 100644 --- a/python/valuecell/agents/research_agent/schemas.py +++ b/python/valuecell/agents/research_agent/schemas.py @@ -1,5 +1,5 @@ -from pathlib import Path from dataclasses import dataclass +from pathlib import Path @dataclass diff --git a/python/valuecell/core/coordinate/planner_prompts.py b/python/valuecell/core/coordinate/planner_prompts.py index 5a54e6042..1eea89599 100644 --- a/python/valuecell/core/coordinate/planner_prompts.py +++ b/python/valuecell/core/coordinate/planner_prompts.py @@ -6,7 +6,6 @@ ExecutionPlanner when calling the LLM-based planning agent. """ - # noqa: E501 PLANNER_INSTRUCTIONS = """ You are an AI Agent execution planner that analyzes user requests and creates executable task plans using available agents. 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[[package]] name = "typer" version = "0.17.4" @@ -3118,7 +2597,6 @@ dependencies = [ { name = "python-dateutil" }, { name = "pytz" }, { name = "requests" }, - { name = "sentence-transformers" }, { name = "sqlalchemy" }, { name = "unstructured" }, { name = "uvicorn" }, @@ -3177,7 +2655,6 @@ requires-dist = [ { name = "pytz", specifier = ">=2023.3" }, { name = "requests", specifier = ">=2.32.5" }, { name = "ruff", marker = "extra == 'dev'" }, - { name = "sentence-transformers", specifier = ">=5.1.1" }, { name = "sqlalchemy", specifier = ">=2.0.43" }, { name = "unstructured", specifier = ">=0.18.15" }, { name = "uvicorn", specifier = ">=0.24.0" }, diff --git a/python/valuecell/agents/research_agent/vdb.py b/python/valuecell/agents/research_agent/vdb.py index 61af96741..7ed726d19 100644 --- a/python/valuecell/agents/research_agent/vdb.py +++ b/python/valuecell/agents/research_agent/vdb.py @@ -1,18 +1,18 @@ -from agno.knowledge.embedder.sentence_transformer import SentenceTransformerEmbedder -from agno.knowledge.reranker.sentence_transformer import SentenceTransformerReranker +from agno.knowledge.embedder.google import GeminiEmbedder from agno.vectordb.lancedb import LanceDb from agno.vectordb.search import SearchType from valuecell.utils.db import resolve_lancedb_uri -embedder = SentenceTransformerEmbedder(id="all-mpnet-base-v2", dimensions=768) -reranker = SentenceTransformerReranker(model="BAAI/bge-reranker-v2-m3", top_n=8) +# embedder = SentenceTransformerEmbedder(id="all-MiniLM-L6-v2", dimensions=384) +# reranker = SentenceTransformerReranker(model="BAAI/bge-reranker-v2-m3", top_n=8) +embedder = GeminiEmbedder(id="gemini-embedding-001", dimensions=1536) vector_db = LanceDb( table_name="research_agent_knowledge_base", uri=resolve_lancedb_uri(), embedder=embedder, - reranker=reranker, + # reranker=reranker, search_type=SearchType.hybrid, use_tantivy=False, ) From 988ec43d80e9324ab5e3ff3b33fdacb3438ddee1 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 18:22:21 +0800 Subject: [PATCH 14/31] fix: correct metadata key for tool result in agent response handling --- python/valuecell/core/agent/decorator.py | 2 +- python/valuecell/core/coordinate/response_router.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/python/valuecell/core/agent/decorator.py b/python/valuecell/core/agent/decorator.py index 7180abbed..e22477048 100644 --- a/python/valuecell/core/agent/decorator.py +++ b/python/valuecell/core/agent/decorator.py @@ -179,7 +179,7 @@ async def execute(self, context: RequestContext, event_queue: EventQueue) -> Non if EventPredicates.is_tool_call(response_event): metadata["tool_call_id"] = response.metadata.get("tool_call_id") metadata["tool_name"] = response.metadata.get("tool_name") - metadata["tool_result"] = response.metadata.get("content") + metadata["tool_result"] = response.metadata.get("tool_result") await updater.update_status( TaskState.working, message=new_agent_text_message(response.content or ""), diff --git a/python/valuecell/core/coordinate/response_router.py b/python/valuecell/core/coordinate/response_router.py index e5d62d3fa..da61c4b21 100644 --- a/python/valuecell/core/coordinate/response_router.py +++ b/python/valuecell/core/coordinate/response_router.py @@ -102,8 +102,8 @@ async def handle_status_update( tool_name = event.metadata.get("tool_name", "unknown_tool_name") tool_result = None - if "tool_result" in event.metadata: - tool_result = get_message_text(event.metadata.get("tool_result", "")) + if "tool_result" in event.metadata and event.metadata["tool_result"]: + tool_result = event.metadata.get("tool_result") responses.append( response_factory.tool_call( conversation_id=task.conversation_id, From 784eba5d9f220645ebfa5cfab99a326b1df65d38 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 18:22:37 +0800 Subject: [PATCH 15/31] refactor: enhance ResearchAgent initialization and stream method parameters --- python/valuecell/agents/research_agent/core.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py index 94bf69583..d97f2f4c2 100644 --- a/python/valuecell/agents/research_agent/core.py +++ b/python/valuecell/agents/research_agent/core.py @@ -2,6 +2,7 @@ from typing import AsyncGenerator, Iterator from agno.agent import Agent, RunOutputEvent +from agno.db.in_memory import InMemoryDb from agno.models.google import Gemini from edgar import set_identity from loguru import logger @@ -19,20 +20,28 @@ def __init__(self, **kwargs): super().__init__(**kwargs) self.knowledge_research_agent = Agent( model=Gemini(id="gemini-2.5-flash"), + instructions=[KNOWLEDGE_AGENT_INSTRUCTION], tools=[fetch_sec_filings], knowledge=knowledge, + db=InMemoryDb(), + # context search_knowledge=True, - instructions=[KNOWLEDGE_AGENT_INSTRUCTION], add_datetime_to_context=True, + add_history_to_context=True, + num_history_runs=3, + # configuration debug_mode=agent_debug_mode_enabled(), ) set_identity(os.getenv("SEC_EMAIL")) async def stream( - self, query: str, session_id: str, task_id: str + self, query: str, conversation_id: str, task_id: str ) -> AsyncGenerator[StreamResponse, None]: response_stream: Iterator[RunOutputEvent] = self.knowledge_research_agent.arun( - query, stream=True, stream_intermediate_steps=True + query, + stream=True, + stream_intermediate_steps=True, + session_id=conversation_id, ) async for event in response_stream: if event.event == "RunContent": From c4d568878f40ef7eac9c112de4d74ce43da319fd Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Tue, 14 Oct 2025 18:22:41 +0800 Subject: [PATCH 16/31] feat: enhance planner instructions with structured guidelines and requirements --- .../core/coordinate/planner_prompts.py | 30 +++++++++++++++---- 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/python/valuecell/core/coordinate/planner_prompts.py b/python/valuecell/core/coordinate/planner_prompts.py index 1eea89599..efe2b8435 100644 --- a/python/valuecell/core/coordinate/planner_prompts.py +++ b/python/valuecell/core/coordinate/planner_prompts.py @@ -8,9 +8,11 @@ # noqa: E501 PLANNER_INSTRUCTIONS = """ + You are an AI Agent execution planner that analyzes user requests and creates executable task plans using available agents. + -## Core Process + 1. **Understand capabilities**: Call `get_agent_card` with the target agent name 2. **Assess completeness**: Determine if the user request contains sufficient information 3. **Clarify if needed**: Call `get_user_input` only when essential information is missing @@ -20,26 +22,39 @@ - Make your best guess before asking for clarification - If response is still ambiguous after clarification, make your best guess and proceed 4. **Generate plan**: Create a structured execution plan with clear, actionable tasks + -## Task Creation Guidelines + -### Query Optimization + - Transform vague requests into clear, specific, actionable queries - Tailor language to target agent capabilities - Use formatting (`**bold**`) to highlight critical details (stock symbols, dates, names) - Be precise and avoid ambiguous language + -### Task Patterns + - **ONCE**: Single execution with immediate results (default) - **RECURRING**: Periodic execution for ongoing monitoring/updates - Use only when user explicitly requests regular updates - Always confirm intent before creating recurring tasks: "Do you want regular updates on this?" + -## Response Requirements + +- If user specifies a target agent name, do not split user query into multiple tasks; create a single task for the specified agent. +- Avoid splitting tasks into excessively fine-grained steps. Tasks should be actionable by the target agent without requiring manual orchestration of many micro-steps. +- Aim for a small set of clear tasks (typical target: 1–5 tasks) for straightforward requests. For complex research, group related micro-steps under a single task with an internal subtask description. +- Do NOT create separate tasks for trivial UI interactions or internal implementation details (e.g., "open page", "click button"). Instead, express the goal the agent should achieve (e.g., "Retrieve Q4 2024 revenue from the 10-Q and cite the filing"). +- When a user requests very deep or multi-stage research, it's acceptable to create a short sequence (e.g., 3–8 tasks) but prefer grouping and clear handoffs. +- If unsure about granularity, prefer slightly larger tasks and include explicit guidance in the task's query about intermediate checks or tolerances. + + + + **Output valid JSON only (no markdown, backticks, or comments):** -```json + { "tasks": [ { @@ -51,4 +66,7 @@ "adequate": true/false, "reason": "Brief explanation of planning decision" } + + + """ From 46aa0a87d4d3bd0986d5bb73f8c53f0555821b9d Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 10:30:46 +0800 Subject: [PATCH 17/31] feat: add context awareness guidelines for conversational continuity and query handling --- .../core/coordinate/planner_prompts.py | 65 +++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/python/valuecell/core/coordinate/planner_prompts.py b/python/valuecell/core/coordinate/planner_prompts.py index efe2b8435..ecd193efe 100644 --- a/python/valuecell/core/coordinate/planner_prompts.py +++ b/python/valuecell/core/coordinate/planner_prompts.py @@ -24,6 +24,51 @@ 4. **Generate plan**: Create a structured execution plan with clear, actionable tasks + +**Recognize conversational continuity and implicit context:** + +Multi-turn conversation patterns: +- **Affirmative continuations**: Short responses like "yes", "ok", "sure", "go ahead", "proceed", "sounds good" typically mean: + * User approves proceeding with the last discussed plan or suggestion + * User confirms a clarifying question you asked + * Set adequate=true and create the plan that was being discussed + +- **Expansion requests**: Phrases like "go on", "continue", "tell me more", "what else", "elaborate", "dig deeper" indicate: + * User wants more detail on the most recent topic + * User is asking to expand on the last response + * Infer the subject from context (e.g., last mentioned company, metric, or topic) + * If a specific agent was just used or mentioned, route the expansion request to that agent + +- **Follow-up questions**: Questions like "what about X?", "how about Y?", "and risks?", "profitability?" suggest: + * User is asking about a related aspect of the current topic + * Assume the same company, timeframe, or context as the previous discussion + * Example: After discussing Apple revenue, "what about margins?" should be understood as "What about Apple's margins?" + +- **Implicit references**: Pronouns and short references like "it", "they", "that", "this company", "same period": + * Refer to the most recently mentioned entity + * Maintain context from the implied previous interaction + * Don't request clarification unless genuinely ambiguous + +**When to set adequate=false (request clarification):** +- ONLY when the query is genuinely unintelligible or lacks critical information that cannot be reasonably inferred +- Examples of legitimate clarification needs: + * No company specified for a financial query and no prior context to infer from + * Ambiguous timeframe when precision is essential (e.g., "recent" could mean yesterday or this year) + * Conflicting or contradictory instructions + +**When to set adequate=true (proceed with task):** +- Short conversational continuations that have clear intent (affirmations, expansions, follow-ups) +- Vague but actionable requests where you can make a reasonable assumption +- Follow-up questions where the subject can be logically inferred +- Any query where proceeding with a sensible interpretation is better than blocking for clarification + +**Best practices:** +- Bias toward action: If there's a reasonable interpretation, use it +- Trust conversational flow: Users expect continuity in dialogue +- Make informed assumptions: Use agent names, keywords, or patterns to infer context +- Preserve momentum: Don't interrupt fluid conversations with unnecessary clarifications + + @@ -31,8 +76,28 @@ - Tailor language to target agent capabilities - Use formatting (`**bold**`) to highlight critical details (stock symbols, dates, names) - Be precise and avoid ambiguous language +- For continuation requests (e.g., "go on", "tell me more"), formulate the query to explicitly reference what should be expanded: + * Good: "Provide additional analysis on **Apple's** Q2 2024 profitability metrics beyond revenue" + * Avoid: "Tell me more" (too vague for the executing agent) + +When the user's input suggests continuation or expansion: +1. **Identify the implied subject**: Infer company name, topic, timeframe, or agent from conversational patterns +2. **Expand the query**: Convert short requests into explicit, actionable tasks +3. **Preserve intent**: Maintain the user's focus (e.g., if they asked about revenue and now say "what about margins?", keep the same company and period) +4. **Route appropriately**: + - If a specific agent was implied or previously mentioned, route to that agent + - If the topic is clear (e.g., "risks" → use research_agent; "price" → use market data agent) + - When uncertain, default to the most versatile agent for the domain + +Examples of conversational query transformation: +- User: "go on" → "Continue the analysis of **[last mentioned company]** with additional metrics on [last discussed topic]" +- User: "what about margins?" (after revenue discussion) → "Analyze **[same company]**'s gross and operating margins for [same period]" +- User: "yes, proceed" (after suggesting a plan) → Execute the tasks from that suggested plan +- User: "tell me more about risks" → "Provide detailed risk analysis for **[implied company]** covering operational, financial, and market risks" + + - **ONCE**: Single execution with immediate results (default) - **RECURRING**: Periodic execution for ongoing monitoring/updates From ac78d06d98e86889d527fcf22d1b330b9153bd9e Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 10:30:54 +0800 Subject: [PATCH 18/31] feat: add tool usage guidelines and response planning structure for financial queries --- .../agents/research_agent/prompts.py | 155 ++++++++++++++++-- 1 file changed, 140 insertions(+), 15 deletions(-) diff --git a/python/valuecell/agents/research_agent/prompts.py b/python/valuecell/agents/research_agent/prompts.py index 84d9f5777..7f87bb2c7 100644 --- a/python/valuecell/agents/research_agent/prompts.py +++ b/python/valuecell/agents/research_agent/prompts.py @@ -8,6 +8,24 @@ - Knowledge base search: Use the agent's internal knowledge index to find summaries, historical context, analyst commentary, and previously ingested documents. + +Efficient tool calling: +1. Batch parameters: When the user asks for multi-period data (e.g., "revenue for Q1-Q4 2024"), prefer a SINGLE call with broader parameters (e.g., year=2024 without quarter filter) rather than 4 separate quarterly calls. +2. Limit concurrent calls: Avoid making more than 3 `fetch_sec_filings` calls in a single response. If more data is needed: + - Prioritize the most recent or most relevant periods + - Use knowledge base search to fill gaps + - Suggest follow-up queries for additional details +3. Smart defaults: If year/quarter are unspecified, default to the most recent available data rather than calling multiple periods. +4. Knowledge base first: For broad questions or interpretive queries, search the knowledge base before calling fetch_sec_filings. Only fetch new filings if the knowledge base lacks the specific data needed. + + + +Before answering, briefly plan your approach: +1. Query type: Is this factual (specific numbers), analytical (trends/comparisons), or exploratory (broad understanding)? +2. Tool strategy: Do I need fetch_sec_filings? How many calls? Can I batch parameters or use knowledge base instead? +3. Output style: What level of detail and technical depth is appropriate for this query? + + 1. Clarify: If the user's request lacks a ticker/CIK, form type, or time range, ask a single clarifying question. 2. Primary check: If the user requests factual items (financial line items, footnote detail, MD&A text), call `fetch_sec_filings` with specific filters to retrieve the relevant filings. @@ -20,28 +38,135 @@ -Always return a structured answer with these sections when applicable, and for any factual statement, calculation, or context, provide a source in the following markdown link format: [filing name + date](file://). -- Summary: one-paragraph concise answer. If the summary contains factual data, include the source in markdown link format. -- Key facts: bullet list of sourced facts (each fact followed by source: [filing name + date](file://)). -- Calculation / Steps: show any calculations or assumptions used, and for any numbers or facts, provide the source in markdown link format. -- Context: brief historical or qualitative context from the knowledge base or filings. For any referenced data or statement, include the source in markdown link format. If `fetch_sec_filings` was used, explicitly annotate which context lines came from the knowledge search that followed the fetch, and provide sources for both. -- Next steps: if data is missing or incomplete, recommend concrete follow-ups (e.g., call `fetch_sec_filings` with X/Y, check 10-K footnote Z, update knowledge corpus), and if referencing missing data, specify the expected source format. +Adapt your response style based on the query type and user needs. Your answer should be clear, readable, and appropriately detailed. + +**For factual queries** (e.g., "What was Apple's Q2 2024 revenue?"): +- Lead with a direct answer in plain language (e.g., "Apple's Q2 2024 revenue was $X billion") +- Follow with 2-3 key supporting facts with sources: [brief descriptor](file://path) +- Add brief context only if it clarifies the answer (e.g., year-over-year comparison) +- Keep it concise (2-3 paragraphs max) +- Example structure: + * Opening: Direct answer with source + * Supporting details: 2-3 related metrics or context points + * Brief interpretation if relevant + +**For analytical queries** (e.g., "How is Apple's profitability trending?", "What's driving margin changes?"): +- Start with an interpretive summary (1-2 paragraphs) that tells the story +- Weave data points and sources into the narrative naturally +- Explain what the numbers mean in business terms (e.g., "This 5% margin increase suggests improving operational efficiency") +- Compare to industry norms, historical patterns, or company guidance when relevant +- Define technical terms on first use (e.g., "gross margin (revenue minus cost of goods sold)") +- Use headers to organize longer responses by theme +- Example structure: + * Opening: What's happening and why it matters + * Evidence: Data-backed explanation with sources + * Context: Industry/historical comparison + * Implications: What this means for the business + +**For exploratory queries** (e.g., "What should I know about Tesla's business risks?", "Give me an overview of Microsoft's AI strategy"): +- Organize by themes or topics with clear headers +- Use a conversational, accessible tone +- Prioritize insights over raw data dumps +- Cite sources but don't let citations disrupt readability +- Highlight what's most important for understanding the big picture +- Make connections between different pieces of information +- Example structure: + * Brief overview (1-2 sentences) + * Thematic sections with headers + * Key takeaways at the end + +**Source citation rules:** +- Always provide sources for specific numbers, quotes, or factual claims +- Format: [brief descriptor](file://path) - e.g., [Q2 2024 10-Q](file://...), [2024 Annual Report](file://...) +- Integrate citations naturally in text, or group at the end if citing many documents +- When using both knowledge base and fresh filings, clarify which is which (e.g., "According to the Q2 10-Q...", "Previously analyzed data shows...") +- For calculations, cite the source of each input number + +**Accessibility principles:** +- Define financial jargon on first use (e.g., "EBITDA (earnings before interest, taxes, depreciation, and amortization)") +- Use analogies or comparisons to make numbers relatable (e.g., "a 15% increase, the highest growth rate in 5 years") +- Don't assume the user knows SEC filing structures—explain when referencing specific sections +- When showing calculations, explain the logic in words before showing the math +- Adjust technical depth based on query complexity—simple questions deserve simple answers -- Be concise, factual, and source-focused. Avoid speculation. When unsure, quantify uncertainty (e.g., "~5% uncertain because..."). -- Cite filings explicitly when using them as evidence. +- Be clear, factual, and source-focused. Avoid speculation unless explicitly labeled as interpretation. +- Cite sources for all specific data points, but integrate citations naturally into readable prose. +- When unsure about data quality or completeness, be transparent (e.g., "Based on available filings, X appears to be Y, though Z may affect this"). +- Prioritize clarity over formality—write as if explaining to a colleague. +- If data is missing or incomplete, suggest concrete next steps (e.g., "To get quarterly breakdown, fetch Q1-Q4 10-Qs for 2024", "Check 10-K footnote 12 for detailed segment data"). + +After providing your answer, consider ending with a brief, contextual follow-up suggestion to encourage continued exploration. Keep it natural, specific, and actionable. + +**Good follow-up patterns:** +- "Would you like me to compare this to [competitor/prior period/benchmark]?" +- "I can also analyze [related metric] or dig into [specific aspect] if helpful." +- "Interested in [related topic A] or [related topic B]?" +- "Happy to drill down into [specific area] if you'd like more depth." + +**When to include:** +- When the response successfully answers the question and deeper analysis is available +- When there are natural adjacent topics the user might care about + +**When NOT to include:** +- When data is missing or incomplete +- When you've already provided exhaustive detail +- When the user's query is clearly final + +Keep it brief (1 sentence) and place it at the very end after all analysis and citations. + + -Example 1 (user asks for revenue): -- Action: call `fetch_sec_filings('AAPL', '10-Q', year=2025, quarter=2)` -- Output: Summary (with source: [Apple-2025-07-31-10-Q](file://)), Key facts (each with source), Calculation/Steps (with source if using numbers from filings), Context (with source if referencing filings or knowledge base), Next steps (with expected source if referencing missing data). +Example 1 - Factual query (user asks "What was Tesla's Q3 2024 revenue?"): +Tool plan: Call fetch_sec_filings('TSLA', '10-Q', year=2024, quarter=3) once, then search knowledge base. + +Response: +"Tesla reported revenue of $25.2 billion in Q3 2024 [Q3 2024 10-Q](file://...), representing 8% year-over-year growth. The automotive segment contributed $20.0 billion (79% of total revenue), while energy generation and storage added $2.4 billion [same source]. + +This marks Tesla's third consecutive quarter of revenue growth after a flat Q4 2023, driven primarily by increased vehicle deliveries in China and Europe [knowledge base: Q3 2024 analysis]. + +Would you like me to break down the automotive revenue by region, or analyze how margins are trending alongside this growth?" + +--- + +Example 2 - Analytical query (user asks "How is Apple's gross margin trending?"): +Tool plan: Check knowledge base first for recent margin data; if needed, fetch 10-Q/10-K for last 4 quarters with ONE call (year=2024). + +Response: +"**Improving margin trajectory** +Apple's gross margin has expanded steadily over the past year, reaching 46.2% in Q2 2024 compared to 43.8% a year earlier [Q2 2024 10-Q](file://...). This 240 basis point improvement reflects two key factors: + +**Product mix shift**: Services revenue (which carries ~70% margins) grew to represent 24% of total revenue, up from 21% in Q2 2023 [same source]. As higher-margin services scale, they lift overall company margins. + +**Operational efficiency**: The company reduced component costs through supplier negotiations and improved manufacturing yields, particularly for iPhone 15 Pro models [knowledge base: Q2 earnings analysis]. + +Industry context: Apple's margin expansion outpaces the consumer electronics sector average of 35-40%, positioning the company at the premium end of the market [knowledge base: sector comparison data]. + +I can also dig into operating margins or compare to other big tech companies like Microsoft or Google if that would be useful." + +--- + +Example 3 - Exploratory query (user asks "What are the main risks Microsoft faces?"): +Tool plan: Search knowledge base for risk factors; only fetch latest 10-K if knowledge base lacks current info. + +Response: +"**Key business risks for Microsoft** + +**Competition in cloud/AI**: Microsoft faces intense competition from Amazon (AWS) and Google Cloud in the cloud infrastructure market. The company's Azure growth rate has decelerated from 50%+ to ~30% year-over-year as the market matures [Q4 2024 10-K, Risk Factors section](file://...). + +**Regulatory scrutiny**: Ongoing antitrust investigations in the EU and US could result in fines or operational restrictions, particularly around Teams bundling and Azure market practices [same source]. + +**Cybersecurity obligations**: As a major infrastructure provider, Microsoft faces increasing liability for security breaches and must invest heavily in threat prevention [10-K](file://...). + +**AI investment uncertainty**: The company is investing billions in AI/LLM infrastructure with uncertain ROI timelines. If monetization lags expectations, margins could compress [knowledge base: analyst commentary]. + +Happy to drill down into any of these risk areas—regulatory issues, cloud competition, or AI investment economics—or pull specific details from the latest 10-K if you'd like more depth." -Example 2 (user asks for interpretation): -- Action: search knowledge base for analyst notes and recent filings; do NOT call fetch_sec_filings unless you need a primary source quote or table. -- Output: Summary (with source if referencing facts), Context (with sources for all referenced data), Sources (all in markdown link format). +--- -If you decide to call `fetch_sec_filings`, include the exact tool call you will make (parameters) in a short preamble before producing the answer. +Note: In all examples, tool calls are batched when possible, sources are cited naturally, and the response style matches the query type. Each response ends with a contextual, actionable follow-up suggestion to encourage continued exploration. """ From 3fc56fcf3b0eb79b462985dc1b5d77a3ddcfbca2 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 10:38:20 +0800 Subject: [PATCH 19/31] fix: ci --- .../core/coordinate/tests/test_response_router.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/python/valuecell/core/coordinate/tests/test_response_router.py b/python/valuecell/core/coordinate/tests/test_response_router.py index 86fe61dbe..7c374dc9b 100644 --- a/python/valuecell/core/coordinate/tests/test_response_router.py +++ b/python/valuecell/core/coordinate/tests/test_response_router.py @@ -252,19 +252,24 @@ async def test_tool_call_event(self): agent_name="test-agent", ) thread_id = "thread-123" - m = Message( - message_id="m1", role=Role.agent, parts=[TextPart(text="Tool result")] - ) + message_text = "Tool result" event = TaskStatusUpdateEvent( context_id="ctx-123", task_id="task-123", final=False, - status=TaskStatus(state=TaskState.working, message=m), + status=TaskStatus( + state=TaskState.working, + message=Message( + message_id="m1", + role=Role.agent, + parts=[TextPart(text=message_text)], + ), + ), metadata={ "response_event": "tool_call_started", "tool_call_id": "call-123", "tool_name": "test_tool", - "tool_result": m, + "tool_result": message_text, }, ) From c27ae9050675b2cc4fe1cc29df2e59c5261af271 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 10:42:36 +0800 Subject: [PATCH 20/31] fix: coverage --- .../core/agent/tests/test_decorator.py | 74 +++++++++++++++++-- 1 file changed, 66 insertions(+), 8 deletions(-) diff --git a/python/valuecell/core/agent/tests/test_decorator.py b/python/valuecell/core/agent/tests/test_decorator.py index 946ac732f..59efe2bd0 100644 --- a/python/valuecell/core/agent/tests/test_decorator.py +++ b/python/valuecell/core/agent/tests/test_decorator.py @@ -45,6 +45,25 @@ async def notify(self, query, context_id, task_id): ) +class MockToolCallAgent(BaseAgent): + """Mock agent that returns tool call responses.""" + + def __init__(self): + self.stream_called = False + + async def stream(self, query, context_id, task_id): + self.stream_called = True + yield StreamResponse( + event=StreamResponseEvent.TOOL_CALL_STARTED, + content="Tool call started", + metadata={ + "tool_call_id": "call-123", + "tool_name": "test_tool", + "tool_result": "Tool result data" + } + ) + + class TestGenericAgentExecutor: """Test GenericAgentExecutor class.""" @@ -197,18 +216,57 @@ async def failing_stream(*args): mock_updater.complete.assert_called_once() @pytest.mark.asyncio - async def test_cancel_raises_error(self): - """Test cancel method raises ServerError.""" - agent = MockAgent() + async def test_execute_handles_tool_call_response(self): + """Test execute method handles tool call responses properly.""" + agent = MockToolCallAgent() executor = GenericAgentExecutor(agent) + # Mock request context context = MagicMock() - event_queue = MagicMock() + context.get_user_input.return_value = "test query" + context.current_task = MagicMock() + context.current_task.id = "task-123" + context.current_task.context_id = "context-456" + context.metadata = {} + context.message = MagicMock() + context.message.metadata = {} + + # Mock event queue + event_queue = MagicMock(spec=EventQueue) + event_queue.enqueue_event = AsyncMock() + + # Mock task updater + with patch("valuecell.core.agent.decorator.TaskUpdater") as mock_updater_class: + mock_updater = MagicMock() + mock_updater.update_status = AsyncMock() + mock_updater.complete = AsyncMock() + mock_updater_class.return_value = mock_updater + + await executor.execute(context, event_queue) + + # Verify agent.stream was called + assert agent.stream_called - with pytest.raises( - Exception - ): # Should raise ServerError/UnsupportedOperationError - await executor.cancel(context, event_queue) + # Verify task updater was used + mock_updater_class.assert_called_once_with( + event_queue, "task-123", "context-456" + ) + + # Verify tool call metadata was passed correctly + tool_call_calls = [ + call for call in mock_updater.update_status.call_args_list + if len(call[1]) > 1 and call[1].get('metadata', {}).get('tool_call_id') == 'call-123' + ] + assert len(tool_call_calls) == 1 + + # Check that metadata contains tool_result (line 182 coverage) + metadata = tool_call_calls[0][1]['metadata'] + assert 'tool_result' in metadata + assert metadata['tool_result'] == 'Tool result data' + assert metadata['tool_call_id'] == 'call-123' + assert metadata['tool_name'] == 'test_tool' + + mock_updater.complete.assert_called_once() class TestCreateAgentExecutor: From e3f16e83d3f04054e9e3806f5d2dcf1dd1619c1f Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 11:09:32 +0800 Subject: [PATCH 21/31] feat: refine agent targeting policy and enhance task creation guidelines for improved query handling --- .../core/coordinate/planner_prompts.py | 81 ++++--------------- 1 file changed, 17 insertions(+), 64 deletions(-) diff --git a/python/valuecell/core/coordinate/planner_prompts.py b/python/valuecell/core/coordinate/planner_prompts.py index ecd193efe..8dc2cd39b 100644 --- a/python/valuecell/core/coordinate/planner_prompts.py +++ b/python/valuecell/core/coordinate/planner_prompts.py @@ -13,61 +13,31 @@ -1. **Understand capabilities**: Call `get_agent_card` with the target agent name +**If user specifies a target agent:** +1. **Verify agent exists**: Call `get_agent_card` to confirm the agent is available +2. **Check principle-level validity**: Ensure the query is not fundamentally out-of-scope or unexecutable +3. **Forward as-is**: Create a single task with the user's query unchanged (unless a critical issue exists) +4. **Generate plan**: Return the task plan immediately + +**If no target agent specified:** +1. **Understand capabilities**: Call `get_agent_card` to explore available agents 2. **Assess completeness**: Determine if the user request contains sufficient information 3. **Clarify if needed**: Call `get_user_input` only when essential information is missing - - Don't ask user for information that can be inferred or researched (e.g., current date, time ranges, stock symbols, ciks) + - Don't ask for information that can be inferred or researched (e.g., current date, time ranges, stock symbols, ciks) - Don't ask for non-essential details or information already provided - Proceed directly if the request is reasonably complete - Make your best guess before asking for clarification - - If response is still ambiguous after clarification, make your best guess and proceed 4. **Generate plan**: Create a structured execution plan with clear, actionable tasks - -**Recognize conversational continuity and implicit context:** - -Multi-turn conversation patterns: -- **Affirmative continuations**: Short responses like "yes", "ok", "sure", "go ahead", "proceed", "sounds good" typically mean: - * User approves proceeding with the last discussed plan or suggestion - * User confirms a clarifying question you asked - * Set adequate=true and create the plan that was being discussed - -- **Expansion requests**: Phrases like "go on", "continue", "tell me more", "what else", "elaborate", "dig deeper" indicate: - * User wants more detail on the most recent topic - * User is asking to expand on the last response - * Infer the subject from context (e.g., last mentioned company, metric, or topic) - * If a specific agent was just used or mentioned, route the expansion request to that agent - -- **Follow-up questions**: Questions like "what about X?", "how about Y?", "and risks?", "profitability?" suggest: - * User is asking about a related aspect of the current topic - * Assume the same company, timeframe, or context as the previous discussion - * Example: After discussing Apple revenue, "what about margins?" should be understood as "What about Apple's margins?" - -- **Implicit references**: Pronouns and short references like "it", "they", "that", "this company", "same period": - * Refer to the most recently mentioned entity - * Maintain context from the implied previous interaction - * Don't request clarification unless genuinely ambiguous - -**When to set adequate=false (request clarification):** -- ONLY when the query is genuinely unintelligible or lacks critical information that cannot be reasonably inferred -- Examples of legitimate clarification needs: - * No company specified for a financial query and no prior context to infer from - * Ambiguous timeframe when precision is essential (e.g., "recent" could mean yesterday or this year) - * Conflicting or contradictory instructions - -**When to set adequate=true (proceed with task):** -- Short conversational continuations that have clear intent (affirmations, expansions, follow-ups) -- Vague but actionable requests where you can make a reasonable assumption -- Follow-up questions where the subject can be logically inferred -- Any query where proceeding with a sensible interpretation is better than blocking for clarification - -**Best practices:** -- Bias toward action: If there's a reasonable interpretation, use it -- Trust conversational flow: Users expect continuity in dialogue -- Make informed assumptions: Use agent names, keywords, or patterns to infer context -- Preserve momentum: Don't interrupt fluid conversations with unnecessary clarifications - + +If the user specifies a target agent name, the planner should only check for principle-level issues: +- Do not rewrite or expand the query unless it contains a fundamental problem (e.g., illegal, unsupported, or out-of-scope for the agent). +- Do not clarify, split, or optimize the query for style or detail—just forward it as-is to the target agent. +- Only rewrite or block the query if it is ambiguous to the point of being unexecutable, or if it asks for something the agent fundamentally cannot do. +- If the query is valid for the agent's core function, pass it through unchanged. +- If the user does not specify a target agent, follow normal planning and task creation guidelines. + @@ -81,23 +51,6 @@ * Avoid: "Tell me more" (too vague for the executing agent) - -When the user's input suggests continuation or expansion: -1. **Identify the implied subject**: Infer company name, topic, timeframe, or agent from conversational patterns -2. **Expand the query**: Convert short requests into explicit, actionable tasks -3. **Preserve intent**: Maintain the user's focus (e.g., if they asked about revenue and now say "what about margins?", keep the same company and period) -4. **Route appropriately**: - - If a specific agent was implied or previously mentioned, route to that agent - - If the topic is clear (e.g., "risks" → use research_agent; "price" → use market data agent) - - When uncertain, default to the most versatile agent for the domain - -Examples of conversational query transformation: -- User: "go on" → "Continue the analysis of **[last mentioned company]** with additional metrics on [last discussed topic]" -- User: "what about margins?" (after revenue discussion) → "Analyze **[same company]**'s gross and operating margins for [same period]" -- User: "yes, proceed" (after suggesting a plan) → Execute the tasks from that suggested plan -- User: "tell me more about risks" → "Provide detailed risk analysis for **[implied company]** covering operational, financial, and market risks" - - - **ONCE**: Single execution with immediate results (default) - **RECURRING**: Periodic execution for ongoing monitoring/updates From cd679bca9ed52a40417b3a221a8f6017743ff05c Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 11:19:07 +0800 Subject: [PATCH 22/31] feat: enable session summaries and read chat history for improved context management --- python/valuecell/agents/research_agent/core.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py index d97f2f4c2..cbe3edf28 100644 --- a/python/valuecell/agents/research_agent/core.py +++ b/python/valuecell/agents/research_agent/core.py @@ -29,6 +29,8 @@ def __init__(self, **kwargs): add_datetime_to_context=True, add_history_to_context=True, num_history_runs=3, + read_chat_history=True, + enable_session_summaries=True, # configuration debug_mode=agent_debug_mode_enabled(), ) From 0664189ec51e7e520b8cbc34a73acf7b64e11d4a Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 11:26:39 +0800 Subject: [PATCH 23/31] feat: add expected output format for knowledge agent and update prompts for clarity --- python/valuecell/agents/research_agent/core.py | 3 ++- python/valuecell/agents/research_agent/prompts.py | 4 +++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py index cbe3edf28..ebdfb7157 100644 --- a/python/valuecell/agents/research_agent/core.py +++ b/python/valuecell/agents/research_agent/core.py @@ -8,7 +8,7 @@ from loguru import logger from valuecell.agents.research_agent.knowledge import knowledge -from valuecell.agents.research_agent.prompts import KNOWLEDGE_AGENT_INSTRUCTION +from valuecell.agents.research_agent.prompts import KNOWLEDGE_AGENT_INSTRUCTION, KNOWLEDGE_AGENT_EXPECTED_OUTPUT from valuecell.agents.research_agent.sources import fetch_sec_filings from valuecell.core.agent.responses import streaming from valuecell.core.types import BaseAgent, StreamResponse @@ -21,6 +21,7 @@ def __init__(self, **kwargs): self.knowledge_research_agent = Agent( model=Gemini(id="gemini-2.5-flash"), instructions=[KNOWLEDGE_AGENT_INSTRUCTION], + expected_output=KNOWLEDGE_AGENT_EXPECTED_OUTPUT, tools=[fetch_sec_filings], knowledge=knowledge, db=InMemoryDb(), diff --git a/python/valuecell/agents/research_agent/prompts.py b/python/valuecell/agents/research_agent/prompts.py index 7f87bb2c7..4120b4000 100644 --- a/python/valuecell/agents/research_agent/prompts.py +++ b/python/valuecell/agents/research_agent/prompts.py @@ -36,7 +36,9 @@ 4. Read & extract: From retrieved filings and knowledge results, extract exact phrasing or numeric values. Prefer the filing table or MD&A for numeric facts. 5. Synthesize: Combine extracted facts with knowledge-base results to provide context (trends, historical comparisons, interpretations). If the knowledge base contradicts filings, prioritize filings and explain the discrepancy. +""" +KNOWLEDGE_AGENT_EXPECTED_OUTPUT = """ Adapt your response style based on the query type and user needs. Your answer should be clear, readable, and appropriately detailed. @@ -169,4 +171,4 @@ Note: In all examples, tool calls are batched when possible, sources are cited naturally, and the response style matches the query type. Each response ends with a contextual, actionable follow-up suggestion to encourage continued exploration. -""" +""" \ No newline at end of file From 016815cb26cc6f6f93a1dd36616bb22ea36f2d67 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 13:33:55 +0800 Subject: [PATCH 24/31] fix: update planner model ID --- .env.example | 2 +- python/valuecell/core/coordinate/planner.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.env.example b/.env.example index 6bdb5dfd1..7ff9bfa32 100644 --- a/.env.example +++ b/.env.example @@ -24,7 +24,7 @@ OPENROUTER_API_KEY= GOOGLE_API_KEY= # Model IDs for OpenRouter -PLANNER_MODEL_ID=google/gemini-2.5-pro +PLANNER_MODEL_ID=google/gemini-2.5-flash SEC_PARSER_MODEL_ID=openai/gpt-4o-mini SEC_ANALYSIS_MODEL_ID=deepseek/deepseek-chat-v3-0324 AI_HEDGE_FUND_PARSER_MODEL_ID=google/gemini-2.5-flash diff --git a/python/valuecell/core/coordinate/planner.py b/python/valuecell/core/coordinate/planner.py index f7373cdb5..0eb602567 100644 --- a/python/valuecell/core/coordinate/planner.py +++ b/python/valuecell/core/coordinate/planner.py @@ -142,7 +142,7 @@ async def _analyze_input_and_create_tasks( # Create planning agent with appropriate tools and instructions agent = Agent( model=OpenRouter( - id=os.getenv("PLANNER_MODEL_ID", "google/gemini-2.5-pro"), + id=os.getenv("PLANNER_MODEL_ID", "google/gemini-2.5-flash"), max_tokens=None, ), tools=[ From 3a801a282b1d2ed6c60a7d2028cc471959c60f89 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 13:34:19 +0800 Subject: [PATCH 25/31] format --- .../valuecell/agents/research_agent/core.py | 5 ++++- .../agents/research_agent/prompts.py | 2 +- .../core/agent/tests/test_decorator.py | 20 ++++++++++--------- 3 files changed, 16 insertions(+), 11 deletions(-) diff --git a/python/valuecell/agents/research_agent/core.py b/python/valuecell/agents/research_agent/core.py index ebdfb7157..28bfab52c 100644 --- a/python/valuecell/agents/research_agent/core.py +++ b/python/valuecell/agents/research_agent/core.py @@ -8,7 +8,10 @@ from loguru import logger from valuecell.agents.research_agent.knowledge import knowledge -from valuecell.agents.research_agent.prompts import KNOWLEDGE_AGENT_INSTRUCTION, KNOWLEDGE_AGENT_EXPECTED_OUTPUT +from valuecell.agents.research_agent.prompts import ( + KNOWLEDGE_AGENT_EXPECTED_OUTPUT, + KNOWLEDGE_AGENT_INSTRUCTION, +) from valuecell.agents.research_agent.sources import fetch_sec_filings from valuecell.core.agent.responses import streaming from valuecell.core.types import BaseAgent, StreamResponse diff --git a/python/valuecell/agents/research_agent/prompts.py b/python/valuecell/agents/research_agent/prompts.py index 4120b4000..9df126b8b 100644 --- a/python/valuecell/agents/research_agent/prompts.py +++ b/python/valuecell/agents/research_agent/prompts.py @@ -171,4 +171,4 @@ Note: In all examples, tool calls are batched when possible, sources are cited naturally, and the response style matches the query type. Each response ends with a contextual, actionable follow-up suggestion to encourage continued exploration. -""" \ No newline at end of file +""" diff --git a/python/valuecell/core/agent/tests/test_decorator.py b/python/valuecell/core/agent/tests/test_decorator.py index 59efe2bd0..925a6034a 100644 --- a/python/valuecell/core/agent/tests/test_decorator.py +++ b/python/valuecell/core/agent/tests/test_decorator.py @@ -59,8 +59,8 @@ async def stream(self, query, context_id, task_id): metadata={ "tool_call_id": "call-123", "tool_name": "test_tool", - "tool_result": "Tool result data" - } + "tool_result": "Tool result data", + }, ) @@ -254,17 +254,19 @@ async def test_execute_handles_tool_call_response(self): # Verify tool call metadata was passed correctly tool_call_calls = [ - call for call in mock_updater.update_status.call_args_list - if len(call[1]) > 1 and call[1].get('metadata', {}).get('tool_call_id') == 'call-123' + call + for call in mock_updater.update_status.call_args_list + if len(call[1]) > 1 + and call[1].get("metadata", {}).get("tool_call_id") == "call-123" ] assert len(tool_call_calls) == 1 # Check that metadata contains tool_result (line 182 coverage) - metadata = tool_call_calls[0][1]['metadata'] - assert 'tool_result' in metadata - assert metadata['tool_result'] == 'Tool result data' - assert metadata['tool_call_id'] == 'call-123' - assert metadata['tool_name'] == 'test_tool' + metadata = tool_call_calls[0][1]["metadata"] + assert "tool_result" in metadata + assert metadata["tool_result"] == "Tool result data" + assert metadata["tool_call_id"] == "call-123" + assert metadata["tool_name"] == "test_tool" mock_updater.complete.assert_called_once() From 3c827dc3e1448511df8850cd50c6240822276e5f Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 15:03:21 +0800 Subject: [PATCH 26/31] feat: enhance execution planner with context management and session summary features --- python/valuecell/core/coordinate/planner.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/python/valuecell/core/coordinate/planner.py b/python/valuecell/core/coordinate/planner.py index 0eb602567..9e5c1764c 100644 --- a/python/valuecell/core/coordinate/planner.py +++ b/python/valuecell/core/coordinate/planner.py @@ -18,6 +18,7 @@ from a2a.types import AgentCard from agno.agent import Agent +from agno.db.in_memory import InMemoryDb from agno.models.openrouter import OpenRouter from valuecell.core.agent.connect import RemoteConnections @@ -153,7 +154,13 @@ async def _analyze_input_and_create_tasks( markdown=False, debug_mode=agent_debug_mode_enabled(), instructions=[PLANNER_INSTRUCTIONS], + # context + db=InMemoryDb(), add_datetime_to_context=True, + add_history_to_context=True, + num_history_runs=3, + read_chat_history=True, + enable_session_summaries=True, ) # Execute planning with the agent From 4ad14b6eff085b59a1ab9fc624043e782db08221 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 15:06:05 +0800 Subject: [PATCH 27/31] feat: add conversation ID handling in execution plan creation --- python/valuecell/core/coordinate/planner.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/python/valuecell/core/coordinate/planner.py b/python/valuecell/core/coordinate/planner.py index 9e5c1764c..0a901a0df 100644 --- a/python/valuecell/core/coordinate/planner.py +++ b/python/valuecell/core/coordinate/planner.py @@ -106,9 +106,10 @@ async def create_plan( Returns: ExecutionPlan: A structured plan with tasks for execution. """ + conversation_id = (user_input.meta.conversation_id,) plan = ExecutionPlan( plan_id=generate_uuid("plan"), - conversation_id=user_input.meta.conversation_id, + conversation_id=conversation_id, user_id=user_input.meta.user_id, orig_query=user_input.query, # Store the original query created_at=datetime.now().isoformat(), @@ -116,14 +117,19 @@ async def create_plan( # Analyze input and create appropriate tasks tasks = await self._analyze_input_and_create_tasks( - user_input, user_input_callback + user_input, + conversation_id, + user_input_callback, ) plan.tasks = tasks return plan async def _analyze_input_and_create_tasks( - self, user_input: UserInput, user_input_callback: Optional[Callable] = None + self, + user_input: UserInput, + conversation_id: str, + user_input_callback: Optional[Callable] = None, ) -> List[Task]: """ Analyze user input and produce a list of `Task` objects. @@ -135,6 +141,7 @@ async def _analyze_input_and_create_tasks( Args: user_input: The original user input to analyze. + conversation_id: Conversation this planning belongs to. user_input_callback: Optional async callback used for Human-in-the-Loop. Returns: @@ -168,7 +175,8 @@ async def _analyze_input_and_create_tasks( PlannerInput( desired_agent_name=user_input.desired_agent_name, query=user_input.query, - ) + ), + session_id=conversation_id, ) # Handle user input requests through Human-in-the-Loop workflow From b24b873d70c85d2b57ff73029342cf9a597da338 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 15:25:23 +0800 Subject: [PATCH 28/31] fix typo --- python/valuecell/core/coordinate/planner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/valuecell/core/coordinate/planner.py b/python/valuecell/core/coordinate/planner.py index 0a901a0df..19cdaf720 100644 --- a/python/valuecell/core/coordinate/planner.py +++ b/python/valuecell/core/coordinate/planner.py @@ -106,7 +106,7 @@ async def create_plan( Returns: ExecutionPlan: A structured plan with tasks for execution. """ - conversation_id = (user_input.meta.conversation_id,) + conversation_id = user_input.meta.conversation_id plan = ExecutionPlan( plan_id=generate_uuid("plan"), conversation_id=conversation_id, From afe54b391ff352e3176e992354c7d802d86f8f1f Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 15:47:07 +0800 Subject: [PATCH 29/31] feat: modify investment research agent configuration with skills and metadata --- ...stock_research_agent.json => investment_research_agent.json} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename python/configs/agent_cards/{stock_research_agent.json => investment_research_agent.json} (98%) diff --git a/python/configs/agent_cards/stock_research_agent.json b/python/configs/agent_cards/investment_research_agent.json similarity index 98% rename from python/configs/agent_cards/stock_research_agent.json rename to python/configs/agent_cards/investment_research_agent.json index 691afb8f6..cdcd0fc5e 100644 --- a/python/configs/agent_cards/stock_research_agent.json +++ b/python/configs/agent_cards/investment_research_agent.json @@ -51,7 +51,7 @@ "author": "ValueCell Team", "tags": [ "sec filings", - "fund-analysis" + "fundamental analysis" ] } } \ No newline at end of file From 643155e9008c2f7f0ff303d18a3364c6a92e7f2e Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 15:47:45 +0800 Subject: [PATCH 30/31] fix: remove duplicated log file pattern from .gitignore --- .gitignore | 1 - 1 file changed, 1 deletion(-) diff --git a/.gitignore b/.gitignore index 13f193291..0c0b5625b 100644 --- a/.gitignore +++ b/.gitignore @@ -226,5 +226,4 @@ lancedb # Local files logs -*.log .knowledge \ No newline at end of file From 8259192688cdc096f1973a5d5848b67960cc8555 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 15 Oct 2025 15:48:12 +0800 Subject: [PATCH 31/31] docs: enhance docstring for fetch_sec_filings with detailed filing choices --- python/valuecell/agents/research_agent/sources.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/python/valuecell/agents/research_agent/sources.py b/python/valuecell/agents/research_agent/sources.py index dca4d8fd5..d4bfdc63e 100644 --- a/python/valuecell/agents/research_agent/sources.py +++ b/python/valuecell/agents/research_agent/sources.py @@ -24,7 +24,10 @@ async def fetch_sec_filings( cik_or_ticker (str): CIK or ticker symbol of the company. Never introduce backticks, quotes, or spaces. form (List[str] | str, optional): Type of SEC filing form to fetch. - Defaults to "10-Q". Can be a list of forms (e.g. ["10-K", "10-Q"]). - - Choices include "10-K", "10-Q". + - Choices explained: + - "10-K": Annual report + - "10-Q": Quarterly report + - "8-K": Current report for unscheduled material events or corporate changes year (Optional[int | List[int]], optional): Year or list of years to filter filings. Defaults to None. quarter (Optional[int | List[int]], optional): Quarter or list of quarters to filter filings. Defaults to None.