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Merge branch 'master' into fix/1906-skillorchestra-imgs-kwargs-propagation
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.github/workflows/RELEASE.yml

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- uses: actions/checkout@v6
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- name: Install poetry
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run: pipx install poetry==$POETRY_VERSION
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- name: Set up Python 3.9
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- name: Set up Python 3.10
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uses: actions/setup-python@v6
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with:
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python-version: "3.9"
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python-version: "3.10"
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cache: "poetry"
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- name: Build project for distribution
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run: poetry build

.github/workflows/python-package.yml

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build:
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runs-on: ubuntu-latest
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timeout-minutes: 20
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strategy:
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fail-fast: false
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matrix:
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python -m pip install --upgrade pip
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python -m pip install flake8 pytest
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if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
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# The tests import swarms, so the package under test has to be installed.
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python -m pip install -e . --no-deps
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- name: Lint with flake8
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run: |
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# stop the build if there are Python syntax errors or undefined names

.github/workflows/test-main-features.yml

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- name: Install dependencies
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run: |
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poetry install --with test --no-dev
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poetry install --only main,test
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- name: Set up environment variables
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run: |
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- name: Run Main Features Tests
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run: |
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cd /Users/swarms_wd/Desktop/research/swarms
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poetry run python tests/test_main_features.py
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- name: Upload test results

.github/workflows/tests.yml

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jobs:
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test:
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runs-on: ubuntu-latest
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timeout-minutes: 20
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- uses: actions/checkout@v6

CLAUDE.md

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| `context_compression` | bool | `True` | Auto-summarise when near context limit (v12) |
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| `persistent_memory` | bool | `False` | Read/write MEMORY.md across restarts (v12); opt in explicitly |
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| `temperature` | float | `0.5` | Sampling temperature |
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| `max_tokens` | int | `16000` | Max tokens per LLM call. Note: currently overwritten during setup by the model's own limit, so passing it has no effect |
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| `max_tokens` | int | model's max output | Max tokens per LLM call. Unset resolves to the model's own output limit |
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| `reasoning_effort` | str | `None` | `"low"`, `"medium"`, `"high"` for reasoning models |
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| `thinking_tokens` | int | `None` | Extended thinking budget (Claude) |
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| `output_type` | str | `"str-all-except-first"` | How to format returned output |
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| `"HeavySwarm"` | Intensive multi-loop deep analysis |
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| `"RoundRobin"` | Round-robin task distribution |
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| `"PlannerWorkerSwarm"` | Planner + worker delegation |
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| `"BatchedGridWorkflow"` | Grid-based batch execution |
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| `"LLMCouncil"` | LLM-based council decisions |
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| `"AutoSwarmBuilder"` | Auto-configures everything |
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| `"auto"` | Router selects swarm_type automatically |
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| High-stakes ruling with deliberation | `CouncilAsAJudge` |
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| Structured adversarial debate | `DebateWithJudge` |
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| Deep research, many loops | `HeavySwarm` |
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| Don't know yet / rapid prototyping | `AutoSwarmBuilder` or `SwarmRouter(swarm_type="auto")` |
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| Don't know yet / rapid prototyping | `AutoSwarmBuilder` |
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| Need to switch architectures easily | `SwarmRouter` |
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**Don't instantiate heavyweight structures inside tight loops** — create agents and workflows once, reuse them across calls.
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**Don't pass `tools=[]` (empty list)** — pass `tools=None` instead. An empty list can confuse schema generation.
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**Don't use `streaming_on=True` and `streaming_callback` together on the same agent**`streaming_on` streams to stdout; `streaming_callback` streams to your function. Pick one.
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**Don't set `context_compression=False` on very long autonomous sessions** — without compression the agent will eventually hit the context limit and raise an error.

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