The problem: one shared agent maxes out fast. Separate agents per person means everyone starts from zero. We wanted the third option -- distributed compute with shared knowledge.
BendClaw is a runtime where multiple agents run in parallel, each on its own node, but all connected to one shared data layer. When one agent learns something, every other agent has it on the next run. No prompt engineering, no manual handoff.
What's inside:
- *Shared memory* -- all agents read and write to the same knowledge base. Context, learnings, history, fully connected. - *Cluster dispatch* -- agents fan out subtasks across nodes and collect results. Local and cloud nodes in one cluster. - *Self-evolving* -- every run automatically extracts knowledge and injects it into future runs. - *Trace and audit* -- every operation recorded. Who did what, which tools fired, what came back. All queryable. - *Secret-safe* -- secrets vault-isolated, never exposed to the LLM. Approval gates on sensitive actions. - *Token-aware* -- per-agent budgets with proactive alerts before anything runs out. - *100+ integrations* -- custom tools via Skills: write a description, point it at a script, done.
Rust, Apache-2.0. Hosted platform at https://evot.ai if you want to skip self-hosting.
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