GitHub user xintongsong edited a discussion: Planning Flink Agents 0.3
# Time Plan
- Target Feature Freeze: May 31
- Target Release: Jun 15
# Feature Plan
## Must
- Highlight Features
- Support for agent skills
- [Memory] Integrate with mature AI storage frameworks (Mem0)
- [Event log] Support per-event-type configurable log levels
- Support Python 3.12
- Demands from Production
- Support auto-arguments for tool using
- Support cross-language actions & events
- Important Experience Improvements
- Enhance the quickstart experience
- [Event log] Enable by default and display in UI
- Support async execution for cross-language resource access
## If possible
- Important feature, but lack concrete plan or capacity
- [Event log] Support querying and filtering
- Durable execution enhancement
- Eval
- Multimodal capabilities
- Nice to have features & improvements
- Loading MCP Tools & Prompts in runtime
- Support searching tools and prompts from the MCP server
- [Memory] MemoryRef auto resolving
- Structured information extraction
- User Unaware
- Refactor ActionExecutionOperator
- Refactor AgentPlan & resource cache
- Unify long-term memory management for Java & Python
- CI improvements
## Not likely (not sure about the necessity in event-driven agent scenarios)
- Support for multi-agent system & A2A
- Human-in-the-loop
- Serve as a service
- Sandbox
- Basic agent tools implementations like web search
- [Memory] Support for knowledge
# Brainstorm (previous)
## From @xintongsong
- Installation & Deployment
- Enhance the quickstart experience. - Currently, there are too many manual
steps, thus chances for users to make mistakes and run into problems.
- Agent Development
- Support Python 3.12
- Support cross-language actions & events
- Memory
- Support for knowledge. - Not entirely sure about this. What is the
relationship between Knowledge and ContextRetrievalAction?
- MemoryRef auto resolving - Using MemoryRef for Events and Resource
arguments just like using the referenced value
- Integrate with mature AI storage frameworks - E.g., mem0, reme, agents,
etc. Why build from scratch?
- Support auto-arguments for tool using - Not all arguments should be decided
by LLM. Avoid unnecessary randomness.
- Support searching tools and prompts from the MCP server
- Support for agent skills
- Support for multi-agent system
- Multimodal capabilities
- Human-in-the-loop
- Structured information extraction
- Agent Execution and Operation
- Durable execution enhancement - What if the job fails when the durable
execution code block is executed halfway? 2PC? User defined handling?
- Serve as a service - A service that accepts the request, put it into the
source, collect the output from the sink, and send the response.
- Event log
- Enable by default and display in UI
- Support per-event-type configurable log levels
- Support querying and filtering
- Eval
- How to quantitatively measure agent performance?
- How to compare performance of two different versions (prompt, model,
parameter, ...)?
- Tech Debt
- Refactor ActionExecutionOperator
- Refactor AgentPlan & resource cache
- Loading MCP Tools & Prompts in runtime - in case of updates on the MCP
server side
- Support async execution for cross-language resource access
- Unify long-term memory management for Java & Python
- CI
- Run the examples
- Submit jobs to a standalone flink cluster - in addition to the
mini-cluster
- Fault tolerant test - kill the TM and see if it recovers as expected
## From @alnzng
- Skills
- Multimodal
- Sandbox
- Human-in-the-Loop
## From @yanand0909
- A2A (Multi-Agent System)
- Basic agent tools implementations like web search
## From @JinkunLiu
- Improving the quick-start experience
GitHub link: https://github.com/apache/flink-agents/discussions/516
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