openclaw - 💡(How to fix) Fix [Feature]: 跨 Agent 通信机制优化:Session 发现与角色身份标识 [1 participants]

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openclaw/openclaw#59402Fetched 2026-04-08 02:24:14
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Code Example

┌──────┐    任务分发    ┌──────┐    专业处理    ┌──────┐
│ 用户  │ ───────────→ │ Agent A │ ───────────→ │ Agent B└──────┘                └──────┘                └──────┘
   ▲                                              │
   │              直接回复结果                      │
   └──────────────────────────────────────────────┘

---

# 期望的官方 API
openclaw sessions list --agent <agent-name> --channel <channel>

# 或
openclaw agent get-session --agent <agent-name> --target <user-id>

---

# Agent A 调用
openclaw agent send --to <agent-name> --message "<内容>" --identity "A"

# Agent B 回复用户
openclaw agent reply-to-user --message "<内容>" --identity "B"

---

# 查询目标 Agent 的活跃 session
openclaw sessions lookup --agent <agent-name> --channel <channel> --target <user-id>

---

{
  "sourceAgent": "A",
  "isAgentMessage": true,
  "targetUser": "user:xxx"
}
RAW_BUFFERClick to expand / collapse

Summary

问题描述

在使用 OpenClaw 进行多 Agent 协作时,当前缺乏可靠的跨 Agent 直接通信机制。现有方案存在两个核心问题:

  1. Session ID 发现机制缺失 — Agent A 无法可靠获取 Agent B 的 sessionID
  2. 角色身份混淆风险 — 长上下文场景中,模型注意力缺失导致无法区分"用户消息"与"Agent 间消息"

核心目标

实现非子代理的跨 Agent 协作方案,解决以下痛点:

1. 保持 Agent 上下文连续性

  • 子代理 (sessions_spawn) 任务完成后会话即销毁,无法保留历史上下文
  • 独立 Agent 需要长期维护专属 session,积累领域知识和任务记忆
  • 跨 Agent 调用时应复用已有 session,而非创建临时子代理

2. 维持记忆连贯性

  • 每个 Agent 拥有独立的 MEMORY.md 和记忆系统
  • 跨 Agent 协作时,各 Agent 应在自己 session 内记录关键信息
  • 避免子代理模式下"任务完成即遗忘"的问题

3. 直接通知用户

  • Agent B 完成任务后,应能直接回复到用户 channel
  • 避免消息经过 Agent A 转发导致上下文污染
  • 用户可在原对话通道收到最终结果,体验更流畅

期望工作流

┌──────┐    任务分发    ┌──────┐    专业处理    ┌──────┐
│ 用户  │ ───────────→ │ Agent A │ ───────────→ │ Agent B │
└──────┘                └──────┘                └──────┘
   ▲                                              │
   │              直接回复结果                      │
   └──────────────────────────────────────────────┘

流程说明:

  1. 用户向 Agent A 发起请求
  2. Agent A 通过 sessions_send 将子任务发送给 Agent B(复用 B 的已有 session)
  3. Agent B 处理完成后,使用 openclaw message send 直接回复到用户 channel
  4. Agent A 和 Agent B 各自在独立 session 中保留完整上下文和记忆

Problem to solve

当前限制

问题 1:Session ID 获取不可靠 ❌

现状:

  • 无官方 API 供 Agent A 查询 Agent B 的活跃 session
  • 通过 skills 查询 session 列表 + 筛选 channel 内容的方式不可靠
  • 无法保证获取到正确的、最新的 sessionID

期望:

# 期望的官方 API
openclaw sessions list --agent <agent-name> --channel <channel>

# 或
openclaw agent get-session --agent <agent-name> --target <user-id>

问题 2:角色身份混淆 ⚠️

现状:

  • Agent B 回复到 A 的 session 时,A 可能将 B 的回复误认为是用户消息
  • 长上下文场景中,模型注意力缺失导致"角色声明"被忽略
  • 短期记忆失忆时,无法区分消息来源(用户 vs 其他 Agent)

期望:

  • 官方提供跨 Agent 消息的元数据标识(如 message.source = "agent:B"
  • 或提供专用的跨 Agent 通信通道,与用户消息隔离

Proposed solution

方案 A:官方跨 Agent 通信 API

# Agent A 调用
openclaw agent send --to <agent-name> --message "<内容>" --identity "A"

# Agent B 回复用户
openclaw agent reply-to-user --message "<内容>" --identity "B"

方案 B:Session 发现服务

# 查询目标 Agent 的活跃 session
openclaw sessions lookup --agent <agent-name> --channel <channel> --target <user-id>

方案 C:消息元数据增强

sessions_sendsessions_spawn 中增加身份标识字段:

{
  "sourceAgent": "A",
  "isAgentMessage": true,
  "targetUser": "user:xxx"
}

Alternatives considered

No response

Impact

当前痛点:

  • 多 Agent 协作只能依赖子代理模式,无法保留长期上下文和记忆
  • 复杂任务分发场景下,Agent 间通信容易混淆角色身份
  • 用户无法在原始对话通道直接收到最终结果,体验不流畅

预期收益:

  • ✅ 各 Agent 可独立维护专属 session,积累领域知识
  • ✅ 跨 Agent 调用复用已有 session,保持记忆连贯性
  • ✅ 任务完成后可直接回复用户,体验更流畅
  • ✅ 消息来源清晰,避免角色混淆

典型使用场景:

  • 跨专业 Agent 的任务编排(调研 → 分析 → 报告)
  • 需要持久化记忆的多阶段工作流
  • 需要各 Agent 保持独立上下文的协作场景

Evidence/examples

No response

Additional information

No response

extent analysis

TL;DR

To address the limitations in cross-Agent collaboration, consider implementing a session discovery service or utilizing an official cross-Agent communication API to enable reliable and direct communication between Agents.

Guidance

  • Identify a reliable method for Agent A to obtain Agent B's session ID, such as an official API or a session discovery service.
  • Implement a mechanism to distinguish between user messages and Agent messages, such as adding metadata to messages or using a dedicated communication channel.
  • Consider using a persistent session for each Agent to maintain context and memory across tasks.
  • Evaluate the proposed solutions, such as official cross-Agent communication APIs or session discovery services, to determine the best approach for your specific use case.

Example

# Example of a session discovery service
openclaw sessions lookup --agent <agent-name> --channel <channel> --target <user-id>

# Example of an official cross-Agent communication API
openclaw agent send --to <agent-name> --message "<内容>" --identity "A"

Notes

The ideal solution will depend on the specific requirements and constraints of your project. It is essential to carefully evaluate the proposed solutions and consider factors such as scalability, reliability, and security.

Recommendation

Apply a workaround, such as implementing a session discovery service or utilizing a dedicated communication channel, to address the current limitations in cross-Agent collaboration. This approach will allow you to maintain context and memory across tasks while ensuring reliable and direct communication between Agents.

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