Skip to main content
Glama
Praket7

agent-interop-runtime

by Praket7

Related Servers

Alternatives to agent-interop-runtime

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      B
      maintenance
      Enables coding agents from different vendors on the same machine to delegate tasks to each other over the A2A protocol, while also allowing the machine to act as an A2A peer.
      7
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      The simplest way to bridge and collaborate across AI Agent sessions like Claude Code, Codex, Gemini, or Cursor. It allows your agents to combine their strengths to solve your most difficult tasks without leaving their current context.
      16 npm
      67
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI coding agents to communicate directly with each other across machines, with support for rooms, pairing, and encrypted messaging.
      5 npm
      3
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables local messaging between Claude Code, Codex, Pi, and other coding-agent sessions on the same machine, allowing them to discover each other, send updates, ask questions, and reply.
      8
      9 npm
      2
      AGPL 3.0
    • F
      license
      Not graded
      quality
      A
      maintenance
      Coordinates AI coding agents across machines by sharing interface contracts, intent, and breaking-change alerts, enabling agents to negotiate changes before they break each other.
      -

    TDQS

    C2.7/5.0

    Scored across 46 tools

    Disambiguation2/5

    Several tools have overlapping boundaries: send_message, agent_send, and conversation_send all send prompts in different contexts, while watch_thread, watch_active_threads, get_thread_progress, and get_thread_progress_summary all expose live progress. The thread/session/conversation layering is substantial and requires careful reading to avoid misselection.

    Naming Consistency2/5

    Naming conventions are mixed: some tools use verb_noun (list_models, create_work), others use noun_verb (evidence_list, work_get, conversation_read), and there are inconsistent variants like set_model vs session_set_model and send_message vs agent_send. The lack of a single predictable pattern makes the 46-tool surface harder to navigate.

    Tool Count2/5

    46 tools is too many for a coherent agent-facing surface, even for a broad interop runtime. While the domain is genuinely large, the count forces agents to choose among dozens of closely related operations and would benefit from consolidation or grouping.

    Completeness3/5

    The set covers a broad runtime lifecycle: sessions, conversations, durable work, handoffs, reviews, permissions, and evidence. Notable gaps remain, however: evidence contents are said to be addressable by ID but there is no generic evidence read tool, and durable work/handoff/conversation records lack update, close, or delete operations.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues