Merge pull request #1 from agno-agi/refactor/project-name

refactor: change name to investment-team

Author: ashpreetbedi

CLAUDE.md

@@ -4,7 +4,7 @@ This file provides context for Claude Code when working with this repository.
 
 ## Project Overview
 
-Agentic Investment Committee (AIC) — A multi-agent system built with Agno that simulates a professional investment committee deploying $10M into public equities. Demonstrates 5 multi-agent architectures, three-layer knowledge, and institutional learning.
+Agentic Investment Team — A multi-agent system built with Agno that simulates a professional investment team deploying $10M into public equities. Demonstrates 5 multi-agent architectures, three-layer knowledge, and institutional learning.
 
 ## Architecture
 
@@ -17,7 +17,7 @@ AgentOS (app/main.py)
 │   ├── Risk Officer            — downside scenarios, portfolio exposure (YFinance)
 │   ├── Knowledge Agent         — research library (RAG) + memo archive (FileTools)
 │   ├── Memo Writer             — synthesizes analysis into formal memos (FileTools)
-│   └── Committee Chair         — final decisions, capital allocation (Opus 4.6)
+│   └── Committee Chair         — final decisions, capital allocation (Gemini 3.1 Pro)
 │
 ├── Teams (4 architectures)
 │   ├── Coordinate Team         — Chair orchestrates analysts dynamically
@@ -37,14 +37,14 @@ AgentOS (app/main.py)
 ```
 
 All specialist agents use:
-- Claude Sonnet 4.6 model (`claude-sonnet-4-6`)
+- Gemini 3 Flash model (`gemini-3-flash-preview`)
 - PostgreSQL database (pgvector) for persistence
 - Committee context (Layer 1) in system prompt
 - Shared knowledge base (Layer 2) for RAG
 - Shared learnings (institutional learning)
 
 Committee Chair and team leaders use:
-- Claude Opus 4.6 model (`claude-opus-4-6`)
+- Gemini 3.1 Pro model (`gemini-3.1-pro-preview`)
 
 ## Key Files
 
@@ -60,7 +60,7 @@ Committee Chair and team leaders use:
 | `agents/risk_officer.py` | Risk Officer — YFinance |
 | `agents/knowledge_agent.py` | Knowledge Agent — RAG + FileTools |
 | `agents/memo_writer.py` | Memo Writer — FileTools (save) |
-| `agents/committee_chair.py` | Committee Chair — Opus 4.6, no tools |
+| `agents/committee_chair.py` | Committee Chair — Gemini 3.1 Pro, no tools |
 | `teams/coordinate_team.py` | Coordinate team (dynamic orchestration) |
 | `teams/route_team.py` | Route team (single dispatch) |
 | `teams/broadcast_team.py` | Broadcast team (parallel evaluation) |
@@ -98,17 +98,17 @@ All agents follow this structure:
 
 ```python
 from agno.agent import Agent
-from agno.models.anthropic import Claude
+from agno.models.google import Gemini
 from agno.learn import LearningMachine, LearnedKnowledgeConfig, LearningMode
 
 from context import COMMITTEE_CONTEXT
-from agents.settings import committee_knowledge, committee_learnings
+from agents.settings import team_knowledge, team_learnings
 from db import get_postgres_db
 
 agent_db = get_postgres_db()
 
 instructions = f"""\
-You are the [Role] on a $10M investment committee.
+You are the [Role] on a $10M investment team.
 
 ## Committee Rules (ALWAYS FOLLOW)
 
@@ -121,14 +121,14 @@ You are the [Role] on a $10M investment committee.
 my_agent = Agent(
     id="my-agent",
     name="My Agent",
-    model=Claude(id="claude-sonnet-4-6"),
+    model=Gemini(id="gemini-3-flash-preview"),
     db=agent_db,
     instructions=instructions,
     tools=[...],
-    knowledge=committee_knowledge,
+    knowledge=team_knowledge,
     search_knowledge=True,
     learning=LearningMachine(
-        knowledge=committee_learnings,
+        knowledge=team_learnings,
         learned_knowledge=LearnedKnowledgeConfig(
             mode=LearningMode.AGENTIC,
             namespace="global",
@@ -146,7 +146,7 @@ my_agent = Agent(
 
 1. **Never duplicate knowledge instances** — always import from `agents.settings`
 2. **All instructions include `COMMITTEE_CONTEXT`** via f-string (Layer 1)
-3. **Opus for Chair/team leaders, Sonnet for specialists**
+3. **Gemini Pro for Chair/team leaders, Gemini Flash for specialists**
 4. **Memos = files (FileTools), Research = vectors (PgVector)** — never mix
 5. **`committee_chair` is NOT a member** of Coordinate/Broadcast/Task teams (the team `model=` acts as chair). It IS a member of Route team and the final Workflow step.
 6. **No learning config** on: Memo Writer, Committee Chair, Knowledge Agent, Route team
@@ -161,7 +161,7 @@ my_agent = Agent(
 
 ```python
 # Shared knowledge instances (import from agents.settings)
-from agents.settings import committee_knowledge, committee_learnings
+from agents.settings import team_knowledge, team_learnings
 
 # Agent database (no contents_table needed)
 agent_db = get_postgres_db()
@@ -177,7 +177,7 @@ from db import db_url, get_postgres_db, create_knowledge
 from context import COMMITTEE_CONTEXT
 
 # Shared settings
-from agents.settings import committee_knowledge, committee_learnings, MEMOS_DIR, EXA_MCP_URL
+from agents.settings import team_knowledge, team_learnings, MEMOS_DIR, EXA_MCP_URL
 
 # Agents
 from agents import (
@@ -217,8 +217,7 @@ python -m app.load_knowledge --recreate  # Drop and reload
 ## Environment Variables
 
 Required:
-- `ANTHROPIC_API_KEY` — for Claude models
-- `OPENAI_API_KEY` — for embeddings
+- `GOOGLE_API_KEY` — for Gemini models and embeddings
 - `EXA_API_KEY` — for Exa web search
 
 Optional:
@@ -236,10 +235,10 @@ Optional:
 | What | Layer | Storage | Table/Location |
 |------|-------|---------|----------------|
 | Investment mandate, risk policy | Layer 1 | Filesystem → prompt | `context/*.md` |
-| Company research, sector analysis | Layer 2 | PgVector | `committee_knowledge` |
-| Research document contents | Layer 2 | PostgreSQL | `committee_knowledge_contents` |
+| Company research, sector analysis | Layer 2 | PgVector | `team_knowledge` |
+| Research document contents | Layer 2 | PostgreSQL | `team_knowledge_contents` |
 | Past investment memos | Layer 3 | Filesystem | `memos/*.md` |
-| Discovered patterns, corrections | Learning | PgVector | `committee_learnings` |
-| Learning contents | Learning | PostgreSQL | `committee_learnings_contents` |
+| Discovered patterns, corrections | Learning | PgVector | `team_learnings` |
+| Learning contents | Learning | PostgreSQL | `team_learnings_contents` |
 | Session history | — | PostgreSQL | Automatic (Agno) |
 | Agent memory | — | PostgreSQL | Automatic (Agno) |

README.md

@@ -1,6 +1,6 @@
-# Agentic Investment Committee
+# Agentic Investment Team
 
-An AI investment committee built with [Agno](https://docs.agno.com) that demonstrates 5 multi-agent architectures working together to evaluate stocks, manage risk, and make portfolio decisions.
+An AI investment team built with [Agno](https://docs.agno.com) that demonstrates 5 multi-agent architectures working together to evaluate stocks, manage risk, and make portfolio decisions.
 
 7 specialist agents collaborate through 4 team configurations and a deterministic workflow — all backed by a three-layer knowledge system and institutional learning.
 
@@ -15,7 +15,7 @@ AgentOS
 │   ├── Risk Officer          ── YFinance + mandate enforcement
 │   ├── Knowledge Agent       ── RAG search + memo file navigation
 │   ├── Memo Writer           ── Writes investment memos to disk
-│   └── Committee Chair       ── Final decision-maker (Opus 4.6)
+│   └── Committee Chair       ── Final decision-maker (Gemini 3.1 Pro)
 │
 ├── Teams (4)
 │   ├── Coordinate Team       ── Dynamic multi-agent orchestration
@@ -40,13 +40,12 @@ AgentOS
 ### 1. Clone and configure
 
 ```sh
-git clone https://github.com/agno-agi/investment-committee.git investment-committee
-cd investment-committee
+git clone https://github.com/agno-agi/investment-team.git investment-team
+cd investment-team
 
 cp example.env .env
 # Edit .env and add your API keys
-# ANTHROPIC_API_KEY=sk-ant-***
-# OPENAI_API_KEY=sk-***
+# GOOGLE_API_KEY=***
 # EXA_API_KEY=*** # Optional -- Exa MCP is free (thank you!)
 ```
 
@@ -61,7 +60,7 @@ This starts PostgreSQL (with pgvector) and the API server.
 ### 3. Load research into the knowledge base
 
 ```sh
-docker exec -it aic-api python -m app.load_knowledge
+docker exec -it investment-team-api python -m app.load_knowledge
 ```
 
 This loads company profiles and sector analyses into PgVector for RAG search. Only needs to run once — documents are skipped if they already exist.
@@ -110,13 +109,13 @@ What does our research say about semiconductors?
 
 | Agent | Model | Tools | Purpose |
 |-------|-------|-------|---------|
-| Market Analyst | Claude Sonnet 4.6 | Exa MCP, YFinance | Macro environment, news, market conditions |
-| Financial Analyst | Claude Sonnet 4.6 | YFinance | Valuation, fundamentals, analyst estimates |
-| Technical Analyst | Claude Sonnet 4.6 | YFinance | Price action, indicators, support/resistance |
-| Risk Officer | Claude Sonnet 4.6 | YFinance | Position sizing, mandate compliance, risk limits |
-| Knowledge Agent | Claude Sonnet 4.6 | FileTools (read-only) | RAG over research library + memo file browsing |
-| Memo Writer | Claude Sonnet 4.6 | FileTools (read/write) | Drafts and saves standardized investment memos |
-| Committee Chair | Claude Opus 4.6 | None | Final BUY/HOLD/PASS decisions with conviction scores |
+| Market Analyst | Gemini 3 Flash | Exa MCP, YFinance | Macro environment, news, market conditions |
+| Financial Analyst | Gemini 3 Flash | YFinance | Valuation, fundamentals, analyst estimates |
+| Technical Analyst | Gemini 3 Flash | YFinance | Price action, indicators, support/resistance |
+| Risk Officer | Gemini 3 Flash | YFinance | Position sizing, mandate compliance, risk limits |
+| Knowledge Agent | Gemini 3 Flash | FileTools (read-only) | RAG over research library + memo file browsing |
+| Memo Writer | Gemini 3 Flash | FileTools (read/write) | Drafts and saves standardized investment memos |
+| Committee Chair | Gemini 3.1 Pro | None | Final BUY/HOLD/PASS decisions with conviction scores |
 
 ## Teams
 
@@ -142,7 +141,7 @@ Each step's output feeds into the next, producing a complete investment memo wit
 ## Project Structure
 
 ```
-investment-committee/
+investment-team/
 ├── agents/                     # 7 specialist agents
 │   ├── settings.py             # Shared knowledge instances (import, never recreate)
 │   ├── market_analyst.py
@@ -200,12 +199,12 @@ railway run python -m app.load_knowledge
 
 **View logs:**
 ```sh
-railway logs --service investment-committee
+railway logs --service investment-team
 ```
 
 **Redeploy after changes:**
 ```sh
-railway up --service investment-committee -d
+railway up --service investment-team -d
 ```
 
 **Open dashboard:**
@@ -224,7 +223,7 @@ curl -LsSf https://astral.sh/uv/install.sh | sh
 source .venv/bin/activate
 
 # Start PostgreSQL (required)
-docker compose up -d aic-db
+docker compose up -d investment-team-db
 
 # Load research
 python -m app.load_knowledge
@@ -258,8 +257,7 @@ python -m app.load_knowledge --recreate # Drop and reload all
 
 | Variable | Required | Default | Description |
 |----------|----------|---------|-------------|
-| `ANTHROPIC_API_KEY` | Yes | — | Claude models for all agents |
-| `OPENAI_API_KEY` | Yes | — | Embeddings (text-embedding-3-small) |
+| `GOOGLE_API_KEY` | Yes | — | Gemini models + embeddings |
 | `EXA_API_KEY` | Yes | — | Web search for Market Analyst |
 | `PARALLEL_API_KEY` | No | — | ParallelTools for Market Analyst |
 | `RUNTIME_ENV` | No | `prd` | Set to `dev` for auto-reload |

agents/committee_chair.py

@@ -3,21 +3,21 @@
 ---------------
 
 Final decision-maker and capital allocator.
-Model: Opus 4.6. Tools: None.
+Model: Gemini 3.1 Pro. Tools: None.
 """
 
 from agno.agent import Agent
 from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode
-from agno.models.anthropic import Claude
+from agno.models.google import Gemini
 
-from agents.settings import committee_knowledge, committee_learnings
+from agents.settings import team_knowledge, team_learnings
 from context import COMMITTEE_CONTEXT
 from db import get_postgres_db
 
 agent_db = get_postgres_db()
 
 instructions = f"""\
-You are the Committee Chair of a $10M investment committee.
+You are the Committee Chair of a $10M investment team.
 
 ## Committee Rules (ALWAYS FOLLOW)
 
@@ -56,13 +56,13 @@
 committee_chair = Agent(
     id="committee-chair",
     name="Committee Chair",
-    model=Claude(id="claude-opus-4-6"),
+    model=Gemini(id="gemini-3.1-pro-preview"),
     db=agent_db,
     instructions=instructions,
-    knowledge=committee_knowledge,
+    knowledge=team_knowledge,
     search_knowledge=True,
     learning=LearningMachine(
-        knowledge=committee_learnings,
+        knowledge=team_learnings,
         learned_knowledge=LearnedKnowledgeConfig(
             mode=LearningMode.AGENTIC,
             namespace="global",

agents/financial_analyst.py

@@ -8,17 +8,17 @@
 
 from agno.agent import Agent
 from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode
-from agno.models.anthropic import Claude
+from agno.models.google import Gemini
 from agno.tools.yfinance import YFinanceTools
 
-from agents.settings import committee_knowledge, committee_learnings
+from agents.settings import team_knowledge, team_learnings
 from context import COMMITTEE_CONTEXT
 from db import get_postgres_db
 
 agent_db = get_postgres_db()
 
 instructions = f"""\
-You are the Financial Analyst on a $10M investment committee.
+You are the Financial Analyst on a $10M investment team.
 
 ## Committee Rules (ALWAYS FOLLOW)
 
@@ -49,14 +49,14 @@
 financial_analyst = Agent(
     id="financial-analyst",
     name="Financial Analyst",
-    model=Claude(id="claude-sonnet-4-6"),
+    model=Gemini(id="gemini-3-flash-preview"),
     db=agent_db,
     instructions=instructions,
     tools=[YFinanceTools()],
-    knowledge=committee_knowledge,
+    knowledge=team_knowledge,
     search_knowledge=True,
     learning=LearningMachine(
-        knowledge=committee_learnings,
+        knowledge=team_learnings,
         learned_knowledge=LearnedKnowledgeConfig(
             mode=LearningMode.AGENTIC,
             namespace="global",

agents/knowledge_agent.py

@@ -2,24 +2,24 @@
 Knowledge Agent
 ---------------
 
-Committee librarian with two retrieval modes:
+Team librarian with two retrieval modes:
 - Research Library (vector search / RAG) for company and sector research
 - Memo Archive (file navigation) for past investment memos
 """
 
 from agno.agent import Agent
-from agno.models.anthropic import Claude
+from agno.models.google import Gemini
 from agno.tools.file import FileTools
 
-from agents.settings import MEMOS_DIR, committee_knowledge
+from agents.settings import MEMOS_DIR, team_knowledge
 from context import COMMITTEE_CONTEXT
 from db import get_postgres_db
 
 agent_db = get_postgres_db()
 
 instructions = f"""\
-You are the Knowledge Agent on a $10M investment committee. You serve as the
-committee's librarian with two retrieval capabilities.
+You are the Knowledge Agent on a $10M investment team. You serve as the
+team's librarian with two retrieval capabilities.
 
 ## Committee Rules (ALWAYS FOLLOW)
 
@@ -57,7 +57,7 @@
 knowledge_agent = Agent(
     id="knowledge-agent",
     name="Knowledge Agent",
-    model=Claude(id="claude-sonnet-4-6"),
+    model=Gemini(id="gemini-3-flash-preview"),
     db=agent_db,
     instructions=instructions,
     tools=[
@@ -70,7 +70,7 @@
             enable_delete_file=False,
         )
     ],
-    knowledge=committee_knowledge,
+    knowledge=team_knowledge,
     search_knowledge=True,
     add_datetime_to_context=True,
     add_history_to_context=True,