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swarm-ollama
by davidaparicio
Run Swarm multi-agent workflows locally using Ollama models
Python
Updated Apr 13, 2026
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Summary
Enables building and running multi-agent Swarm workflows against Ollama models for fully offline agent orchestration. Provides adapters and runtime glue so Swarm-style agents can send messages, delegate tasks, and aggregate results using a local Ollama server. Stands out for letting teams iterate on multi-agent topologies without cloud LLMs or API keys, making runs reproducible and private. Planning Pattern.
The Value Proposition
As agents get more autonomous, being able to run and test multi-agent setups offline is crucial for reproducibility, safety reviews, and privacy-conscious evaluation. Swarm for Ollama lowers the barrier to pre-production agent testing and localized evaluation, letting teams observe delegation patterns and failure modes before exposing agents to live services. That visibility is a practical first step toward tracking agent-to-agent reliability and building an agent track record. Human-in-the-Loop Pattern.
Ideal For
Teams prototyping or testing multi-agent orchestration locally without cloud LLMs, and researchers wanting reproducible agent interactions. Agent Registry Pattern Chain of Thought Pattern
Real-World Examples
- Iterate on multi-agent topologies locally when you need reproducible, offline testing
- Simulate agent delegation and coordination without cloud LLMs for privacy-sensitive evaluation
- Run pre-production agent interaction tests to observe failure modes and conversation logs
Works With
ollamapython
Topics
multi-agentmulti-agent-systemmulti-agent-systemsmulti-agentsollamaollama-apiollama-appswarm
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Keywords
multi-agent orchestrationmulti-agent trustagent delegationollama