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owl
by camel-ai
Workforce-learning multi-agent framework for real-world task automation
Python
Updated Feb 6, 2026
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Summary
Implements an agent framework for orchestrating specialist agents to automate real-world web and task workflows. Uses a workforce-learning approach where agents learn from workforce-learning from execution traces and adapt policies for delegation and retry. Notable features include web interaction primitives, task-specific agent roles, and learning from historical runs to improve reliability.
The Value Proposition
As agents become more autonomous and delegate subtasks, knowing which agent decisions lead to failures or successes is critical for trust. Execution traces and learning signals so teams can iterate on agent roles and delegation strategies. This matters for multi-agent trust because it treats repeated runs as a source of reputation and operational improvement rather than a one-off execution.
Best For
Teams building production multi-agent automations that require role-based delegation, learning from traces, and improved task reliability.
Use Cases
- Automating web tasks where specialist agents handle scraping, form submission, and verification
- Iterating agent delegation policies by learning from past execution traces and failures
- Building production pipelines that route subtasks to role-based agents and improve success rates over time
Works With
openaihuggingfacelangchain
Topics
agentartificial-intelligencemulti-agent-systemstask-automationweb-interaction
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Keywords
multi-agent orchestrationagent delegationworkforce learningtask automation