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your startup, wrapped in a local\nchat-style control panel (`app.py`) — no code editing required for\nday-to-day use.\n\n## The team (default roster)\n\n| Agent | Role |\n|---|---|\n| Chief of Staff & Head of Strategy | **Manager.** Reads your message, decides who's relevant, delegates, resolves conflicts, synthesizes the final answer. |\n| Market & Competitive Intelligence Analyst | Market size, segments, competitors, trends. |\n| Product Manager | What to build, what to defer, sequencing. |\n| Growth & Marketing Lead | Go-to-market, channels, growth experiments. |\n| Sales & Business Development Lead | Pipeline, buyer, pitch, partnerships. |\n| Finance & Operations Analyst | Cost, runway, unit economics, ops bottlenecks. |\n| Customer Success & Insights Lead | User reaction, adoption friction, retention/churn risk. |\n\nThis roster is just the starting point — **add, edit, or remove anyone, and\nreassign who manages the team, entirely from the web UI's Team tab.** Nothing\nis hardcoded: `crew.py` builds the crew from `src/startup_crew/config/agents.yaml`\nat runtime, so changes there (via the UI or by hand) take effect on the next\nmessage automatically.\n\n## Setup (already done if you set this up with Claude)\n\n```bash\nuv venv --python 3.12\nuv pip install --python .venv \"crewai[litellm]\" crewai-tools python-dotenv streamlit\n```\n\nYour active LLM/provider lives in `.env` as generic `LLM_MODEL` /\n`LLM_API_KEY` / `LLM_BASE_URL` values — set these from the web UI's \"LLM &\nModel\" tab rather than editing `.env` by hand.\n\n## Run the web UI\n\n```bash\ncd /c/Users/Prajwal/crewai-startup\n.venv/Scripts/streamlit run app.py\n```\n\nOpens at `http://localhost:8501` as a chat app:\n\n- **Chats (sidebar)** — each one is a separate \"project\" with its own memory.\n  Click **✚ New chat** to start a new thread, click any past chat to reopen\n  it, use **⋮** to rename or delete one. The crew sees everything said\n  earlier *in that chat* as context, so follow-ups build on prior answers\n  instead of starting cold. Every message, answer, and the full agent\n  delegation log are saved immediately to `projects/<id>.json` — nothing is\n  lost on refresh or restart.\n- **🗂 All projects** — browse/search every chat ever started (not just the\n  most recent ones in the sidebar), with created/updated timestamps, open,\n  and delete.\n- **🧑‍🤝‍🧑 Team** — add/edit/remove team members (role, goal,\n  backstory/personality), and reassign the manager. No code changes, ever.\n- **🔌 LLM & Model** — a priority-ordered **chain** of models/keys\n  (`llm_profiles.json`). Add multiple OpenRouter keys for the same free\n  model to dodge rate limits, or mix providers (OpenAI, Anthropic, Gemini,\n  local Ollama, custom endpoints). If #1 fails or times out, the crew\n  automatically retries the whole run with #2, and so on — reorder, edit,\n  test, or delete any entry. Takes effect immediately, no restart.\n- **ℹ️ About** — quick reference.\n\n## Run it from the terminal instead (single-shot, no memory)\n\n```bash\n.venv/Scripts/python -m src.startup_crew.main \"Should we launch a free tier or stay paid-only?\"\n```\n\nOr with no argument, to be prompted interactively:\n\n```bash\n.venv/Scripts/python -m src.startup_crew.main\n```\n\n## Notes on the free-tier model\n\nThe default provider (Nemotron 3 Ultra via OpenRouter's free tier) works, but\ncan be slow or occasionally return an \"overloaded\" / timeout error — that's\nthe shared free endpoint, not a bug here. If that gets in your way, use\n**🔌 LLM & Model** to switch to a paid provider/model for faster, more\nreliable responses.\n\n## Extending it further\n\n- **Give agents real tools** (web search, file/CSV reading, etc.): add\n  packages from `crewai_tools` (e.g. `SerperDevTool` for web search — needs\n  its own API key from serper.dev) to an agent's `tools=[...]` in `crew.py`'s\n  `_build_agents`.\n- **Change what the crew is asked**: edit `src/startup_crew/config/tasks.yaml`\n  — add more tasks, or change the existing ones. `{history}` is the running\n  conversation memory for the current chat; `context: [task_key, ...]` wires\n  one task's output into another's input.\n\n## Already using OpenCode?\n\n```bash\n.venv/Scripts/python connect_opencode.py            # import provider + model\n.venv/Scripts/python connect_opencode.py --list     # see what the key can serve\n```\n\n```bash\n.venv/Scripts/python connect_opencode.py --migrate   # swapped your keys already?\n```\n\nReads OpenCode's `auth.json`, asks the provider which models it will actually\nserve you, verifies one with a live call, and writes it into\n`llm_profiles.json` as the crew's **first-choice** profile. Your existing\nkeys stay on as fallback. Re-running replaces that profile rather than\nstacking duplicates. OAuth/subscription logins (Claude Pro, ChatGPT) are\nskipped — those only work inside OpenCode itself.\n\n`--migrate` handles a different case: you pasted a new provider's key over an\nold profile but left its `model` and `base_url` pointing at the old provider.\nAn `nvapi-...` key on an OpenRouter base_url is a guaranteed 401, and the\nerror message won't say why. It repoints each mismatched profile, re-verifies\nit with a live call, and is a no-op once everything lines up.\n\n## Slow models\n\nReasoning models (Nemotron 3 Ultra and similar) emit a long chain of thinking\ntokens before any answer, so one agent call can run for minutes and a full\ncrew run much longer. Every timeout in this project therefore defaults to\n**900 seconds** and is controlled by one environment variable:\n\n```bash\nLLM_TIMEOUT=1800        # in .env, to wait even longer\n```\n\nIt governs `crew.py`'s per-agent calls, the **Test** button in the LLM &\nModel tab, and `check_setup.py`. A short timeout does not fail fast here — it\naborts runs that were working and burns the whole fallback chain on a healthy\nmodel.\n\n## Check the setup\n\n```bash\n.venv/Scripts/python check_setup.py            # Linux/macOS: .venv/bin/python\n.venv/Scripts/python check_setup.py --quick    # skip the live crew run\n```\n\nChecks the Python version, every dependency, where the config is coming\nfrom, each API key with a real call, and finally runs a one-agent crew end\nto end. Exits 0 when everything passes. Run it locally, or from the hosted\napp's terminal, to tell a broken key apart from a broken install.\n\n## Deploy to Streamlit Community Cloud\n\n1. Go to [share.streamlit.io](https://share.streamlit.io) and sign in with GitHub.\n2. **New app** → pick this repo, branch `main`, main file `app.py`.\n   Under **Advanced settings**, set **Python 3.13**. This is not optional:\n   CrewAI declares `requires-python <3.14`, and on Python 3.14 its chromadb\n   dependency dies on import with\n   `unable to infer type for attribute \"chroma_server_nofile\"`. Pinning\n   pydantic does not help — the break is in 3.14 itself. Community Cloud\n   defaults to the newest Python, and **the version cannot be changed after\n   deployment** — you have to delete the app and redeploy to change it.\n3. Open **Advanced settings → Secrets** and paste the contents of\n   [`.streamlit/secrets.toml.example`](.streamlit/secrets.toml.example) with\n   your real key(s) filled in. `app.py` copies those into the environment at\n   startup, so no `.env` file is needed in the cloud.\n\n   Set **`LLM_PROFILES`** there too (a JSON array, see the example file) and\n   the whole priority chain — several keys for one free model, or a mix of\n   providers — is rebuilt on first run, so the **🔌 LLM & Model** tab comes\n   up already populated instead of empty. It is read only when\n   `llm_profiles.json` is absent, so edits you make in the UI are never\n   overwritten.\n4. Deploy. Dependencies come from `requirements.txt`.\n\n**Caveats on a hosted deployment:**\n\n- Streamlit Cloud's filesystem is ephemeral. Chats saved to `projects/`,\n  team edits written to `agents.yaml`, and LLM profiles in\n  `llm_profiles.json` are lost when the app sleeps or redeploys. For\n  durable state, point those paths at S3/a database, or keep running it\n  locally.\n- Community Cloud apps are public by default — anyone with the URL can use\n  your API keys. Restrict viewers in the app's settings, or deploy\n  somewhere private, before sharing the link.\n- CrewAI runs can take minutes; the free tier's 1 GB memory limit is tight\n  for large crews.\n\n## Local setup from a fresh clone\n\n```bash\ngit clone https://github.com/<your-user>/crewai-startup.git\ncd crewai-startup\npython -m venv .venv\n.venv/Scripts/pip install -r requirements.txt    # Linux/macOS: .venv/bin/pip\ncp .env.example .env                             # then fill in LLM_API_KEY\ncp llm_profiles.example.json llm_profiles.json\n.venv/Scripts/streamlit run app.py\n```\n\n`.env` and `llm_profiles.json` hold API keys and are gitignored — keep it\nthat way.\n","readmeExcerpt":"Startup Crew A CrewAI multi-agent team that helps run your startup, wrapped in a local chat-style control panel (app.py) — no code editing required for day-to-day use. 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