swarmcore 0.2.0

Coordinate AI agents in a workflow

SwarmCore

Scalable AI agent coordination. Compose agents into sequential and parallel flows with automatic context management that stays lean as the swarm grows.

pip install swarmcore

Quickstart

import asyncio
from swarmcore import Agent, Swarm

researcher = Agent(name="researcher", instructions="Research the topic.", model="anthropic/claude-sonnet-4-20250514")
writer = Agent(name="writer", instructions="Write a report from the research.", model="anthropic/claude-sonnet-4-20250514")

result = asyncio.run(Swarm(flow=researcher >> writer).run("AI agent trends in 2025"))
print(result.output)

The researcher runs first, its output goes into shared context, and the writer sees it automatically.

Tiered Context

Most multi-agent frameworks dump every prior agent's full output into the next prompt. That blows up at scale. SwarmCore keeps it tight:

• Immediately preceding step → full output
• Everything earlier → summaries only
• Need more? → agents call expand_context at runtime to pull any prior agent's full output

agent_1 >> agent_2 >> ... >> agent_10

agent_10 sees: agents 1-8 [SUMMARIES] + agent_9 [FULL]
               (can expand any earlier agent on demand)

The tool and prompt hint are injected automatically. Agents produce summaries via <summary> tags in their output — if omitted, the full output is used instead.

Flows

>> for sequential, | for parallel:

planner >> writer                        # sequential
(researcher | critic) >> writer          # parallel then sequential
planner >> (researcher | critic) >> writer  # mixed

Or the functional API:

from swarmcore import chain, parallel
Swarm(flow=chain(planner, parallel(researcher, critic), writer))

Parallel agents share the same context snapshot — they don't see each other's outputs.

Tools

Plain Python functions:

def search_web(query: str) -> str:
    """Search the web for information."""
    return results

agent = Agent(name="researcher", instructions="...", tools=[search_web])

Type hints and docstrings are converted to tool schemas automatically. Sync and async functions both work.

Models

Any LiteLLM-compatible model:

Agent(name="a", instructions="...", model="anthropic/claude-sonnet-4-20250514")
Agent(name="b", instructions="...", model="openai/gpt-4o")
Agent(name="c", instructions="...", model="ollama/llama3")
Agent(name="d", instructions="...", model="groq/llama-3.1-8b-instant")

API

Agent(name, instructions, model, tools=None)

Param	Type	Default	Description
name	str	required	Identifier used in context keys
instructions	str	required	System prompt
model	str	"anthropic/claude-sonnet-4-20250514"	LiteLLM model string
tools	list[Callable]	None	Tool functions

Swarm(flow)

Param	Type	Description
flow	Flow	Execution plan from operators or chain()/parallel()

SwarmResult

Field	Type	Description
output	str	Final agent's output
context	dict[str, str]	All outputs keyed by agent name
history	list[AgentResult]	Ordered execution results

AgentResult

Field	Type	Description
agent_name	str	Agent that produced this result
output	str	Text output (summary tags stripped)
summary	str	Summary from <summary> tags, or full output
model	str	Model used
duration_seconds	float	Wall-clock time
token_usage	TokenUsage	Token counts

License

MIT