caura-ai/memclaw-build-fleet - 6 Help Wanted Issues

Open Issues Need Help

Document MEMCLAW_TRANSPORT=rest fallback mode with a runnable example about 3 hours ago

good first issue

A runnable reference implementation of multi-agent constraint propagation using MemClaw. 5 specialists share memory via MCP so decisions made upstream automatically govern downstream agents.

Python

#agent-fleet#agent-memory#ai-agents#ai-pipeline#constraint-propagation#llm#mcp#model-context-protocol#multi-agent#python#shared-memory

Document MEMCLAW_TRANSPORT=rest fallback mode with a runnable example about 3 hours ago

good first issue

caura-ai/memclaw-build-fleet

A runnable reference implementation of multi-agent constraint propagation using MemClaw. 5 specialists share memory via MCP so decisions made upstream automatically govern downstream agents.

Python

#agent-fleet#agent-memory#ai-agents#ai-pipeline#constraint-propagation#llm#mcp#model-context-protocol#multi-agent#python#shared-memory

--dry-run doesn't verify MCP tool-schema shape, only connectivity about 3 hours ago

good first issue

caura-ai/memclaw-build-fleet

A runnable reference implementation of multi-agent constraint propagation using MemClaw. 5 specialists share memory via MCP so decisions made upstream automatically govern downstream agents.

Python

#agent-fleet#agent-memory#ai-agents#ai-pipeline#constraint-propagation#llm#mcp#model-context-protocol#multi-agent#python#shared-memory

Add a unit test for per-agent tool allowlisting about 3 hours ago

good first issue

caura-ai/memclaw-build-fleet

A runnable reference implementation of multi-agent constraint propagation using MemClaw. 5 specialists share memory via MCP so decisions made upstream automatically govern downstream agents.

Python

#agent-fleet#agent-memory#ai-agents#ai-pipeline#constraint-propagation#llm#mcp#model-context-protocol#multi-agent#python#shared-memory

Add a 6th agent: Accessibility Auditor about 3 hours ago

good first issue

caura-ai/memclaw-build-fleet

A runnable reference implementation of multi-agent constraint propagation using MemClaw. 5 specialists share memory via MCP so decisions made upstream automatically govern downstream agents.

Python

#agent-fleet#agent-memory#ai-agents#ai-pipeline#constraint-propagation#llm#mcp#model-context-protocol#multi-agent#python#shared-memory

--json-output flag doesn't validate the output path before the run about 3 hours ago

good first issue

caura-ai/memclaw-build-fleet

A runnable reference implementation of multi-agent constraint propagation using MemClaw. 5 specialists share memory via MCP so decisions made upstream automatically govern downstream agents.

Python

#agent-fleet#agent-memory#ai-agents#ai-pipeline#constraint-propagation#llm#mcp#model-context-protocol#multi-agent#python#shared-memory