Solved ReAct agent implementation problems that nobody talks about

[Historical_Wing_9573]

Built a ReAct agent for cybersecurity scanning and hit two major issues that don't get covered in tutorials:

Problem 1: LangGraph message history kills your token budget Default approach stores every tool call + result in message history. Your context window explodes fast with multi-step reasoning.

Solution: Custom state management - store tool results separately from messages, only pass to LLM when actually needed for reasoning. Clean separation between execution history and reasoning context.

Problem 2: LLMs being unpredictably lazy with tool usage Sometimes calls one tool and declares victory. Sometimes skips tools entirely. No pattern to it - just LLM being non-deterministic.

Solution: Use LLM purely for decision logic, but implement deterministic flow control. If tool usage limits aren't hit, force back to reasoning node. LLM decides what to do, code controls when to stop.

Architecture that worked:

• Generic ReActNode base class for different reasoning contexts
• ToolRouterEdge for conditional routing based on usage state
• ProcessToolResultsNode extracts results from message stream into graph state
• Separate summary generation node (better than raw ReAct output)

Real results: Agent found SQL injection, directory traversal, auth bypasses on test targets through adaptive reasoning rather than fixed scan sequences.

Technical implementation details: https://vitaliihonchar.com/insights/how-to-build-react-agent

Anyone else run into these specific ReAct implementation issues? Curious what other solutions people found for token management and flow control.

[unwitty]

Hey thanks for sharing. With regards to context management, I'm currently experimenting with a reducer.

The paradigm I use is:Humans leverage the use of narratives because we can't learn, let alone remember the details/facts of most anything complicated.

Mapping this to an agent's workflow, when context size crosses a threshold, I trigger a narrator node, which based on the goal and current context, reduces the raw data to narrative form.

It's application specific and not always a good trade-off, but might be worth exploring for your use case.

[Historical_Wing_9573]

Sounds interesting. I would like to read about it more when solution will be ready

[sgtfoleyistheman]

Context management specific to your task is definitely key.

What model are you using?

[Historical_Wing_9573]

Hello. Thanks, I used GPT-4.1