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openclaw - 💡(How to fix) Fix [Feature]: Flowise low-code to replace all hardcoded flows [1 participants]

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openclaw/openclaw#69870•Fetched 2026-04-22 07:47:07
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Summary: Replace hardcoded agent flows with Flowise-managed runtime configurations, augmented by self-improving coding agents that generate, execute, evaluate, and iteratively refine both task logic and their own optimization strategies across runs and provides an easy for human interface to modify the previously hardcoded flows.

Root Cause

Summary: Replace hardcoded agent flows with Flowise-managed runtime configurations, augmented by self-improving coding agents that generate, execute, evaluate, and iteratively refine both task logic and their own optimization strategies across runs and provides an easy for human interface to modify the previously hardcoded flows.

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Summary

Summary: Replace hardcoded agent flows with Flowise-managed runtime configurations, augmented by self-improving coding agents that generate, execute, evaluate, and iteratively refine both task logic and their own optimization strategies across runs and provides an easy for human interface to modify the previously hardcoded flows.

Problem to solve

Currently, agent internal flows are hardcoded, requiring code changes, redeployment, and restarts for any modification. This makes iteration slow and prevents real-time experimentation or tuning.

It also blocks any form of self-improvement: agents cannot generate, execute, and evaluate new logic, nor persist improvements across runs. As a result, optimization remains limited by deployment cycles instead of flexible flows easy modifiable by end users.

In practice, this leads to delayed improvements, and inability to adapt behavior dynamically in changing conditions.

We want a meta-level programming where system is capable of modifying itself.

Proposed solution

Desired behavior

1. Runtime flow resolution

  • On agent invocation, OpenClaw:

    1. Fetches the active flow definition from Flowise
    2. Builds an execution graph (nodes, edges, conditions)
    3. Executes the flow instead of calling hardcoded logic

  • Flow selection:

    • By agent ID
    • Optionally by version, environment, or feature flag

2. Hot updates (no restart)

  • Flow updates in Flowise:

    • Become effective immediately for new executions
    • Do not affect in-flight runs

  • Implementation detail:

    • Maintain flow version binding per execution
    • New sessions → latest version
    • Existing sessions → pinned version

3. Versioning & rollout

  • Each flow has:
    • flow_id
    • version
    • status (draft, active, deprecated)

Alternatives considered

No response

Impact

Affected users / systems / channels

  • Developers / ML engineers maintaining agent flows
  • CI/CD pipelines (redeploy required for flow changes)
  • Runtime systems (need restart to apply updates)
  • Not Tech-savvy users

Severity

  • High
  • Any flow change requires code modification + redeployment
  • Slows down experimentation
  • Hardcoded inflexibility

Frequency

  • Frequent
  • Happens on most flow updates:
    • Logic adjustments
    • Adding/removing new tools or plugins
    • Fixing flow bugs

Consequences

  • Slower iteration
    • Each change requires build + deploy cycle
  • Operational overhead
    • Engineering involvement needed for simple flow edits
  • Service disruption risk
    • Restarts required to apply updates

Evidence/examples

No response

Additional information

https://github.com/FlowiseAI/Flowise https://github.com/facebookresearch/Hyperagents

extent analysis

TL;DR

Replace hardcoded agent flows with Flowise-managed runtime configurations to enable dynamic updates and self-improvement.

Guidance

  • Implement runtime flow resolution by fetching the active flow definition from Flowise and building an execution graph on agent invocation.
  • Use flow version binding per execution to enable hot updates without affecting in-flight runs.
  • Introduce versioning and rollout mechanisms with flow_id, version, and status to manage flow updates.
  • Consider the impact on developers, CI/CD pipelines, and runtime systems when implementing the new flow management system.

Example

# Example of fetching active flow definition from Flowise
def get_active_flow_definition(agent_id):
    # Fetch flow definition from Flowise
    flow_definition = Flowise.get_flow_definition(agent_id)
    # Build execution graph
    execution_graph = build_execution_graph(flow_definition)
    return execution_graph

Notes

The proposed solution requires significant changes to the existing system, and careful consideration of the impact on various stakeholders is necessary. The implementation details, such as flow version binding and hot updates, are crucial to ensuring a smooth transition.

Recommendation

Apply the proposed solution of replacing hardcoded agent flows with Flowise-managed runtime configurations, as it addresses the current limitations and enables dynamic updates and self-improvement. This approach allows for more flexibility and adaptability in changing conditions, reducing the need for code changes, redeployment, and restarts.

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#optimization#cache issue#memory leak#API versioning#request timeout

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