deer-workflow is an open-source graph engineering runtime for teams building structured AI agent systems. Its core idea is simple: keep orchestration logic in TypeScript, then delegate semantic work to replaceable agent runtimes. That separation matters because many AI workflow stacks mix control flow, prompts, tool calls, retries, and model-specific behavior in one hard-to-debug layer. deer-workflow gives builders a clearer place to model the graph while still leaving room to swap or update the
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#2866
Popularity Rank
1 / 55
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Emerging
Adoption Stage
10/28/2026
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Recently added to the ecosystem
Score: 100/100
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True Memory Fragments: lazy, source-bound code memory for AI coding agents
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AI Agent Platform built in Rust — Multi-model, MCP tools, 14+ channel integrations. Self-host or use teai.io
jaggernaut007
Unified MCP server: hybrid search + code graph + semantic memory. 15 tools, <350MB RAM, fully local. No API keys.
Repowise indexes code, call graphs, git history, tests, documentation, dead code, code-health scores, and architecture decisions so developers and AI coding agents can query a repository with evidence-backed context instead of repeatedly rediscovering the same code.
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