Build self-learning and evolving LLM agents
AI agents are powerful, but they often remain static and fail to adapt to changing environments or learn from experience.
Whether it's adapting to new data patterns, learning from user interactions, or evolving decision-making strategies, Pulsar provides a powerful, standardized, adaptive framework to build agents that continuously improve and evolve.
How it works
Connect MCP Servers
Configure your agent to connect to multiple MCP servers using Claude's Model Context Protocol. Access distributed toolsets with uniform integration and full tool discovery capabilities.
Create Task Workflows
Build custom task types like planning and research using the TaskManager. Each task maintains persistent context, logs, and state tracking for complete workflow traceability.
Deploy with Memory
Launch your agent with built-in memory modules for long-term context accumulation. The agent extracts and stores structured information to enhance reasoning across multi-turn interactions.