Increase import_actions_from_memory for NFT agents

[kongzii]

No description provided.

[coderabbitai]

Walkthrough

The pull request modifies the DeployableAgentNFTGameAbstract class in the deploy_nft_treasury_game.py file by increasing the import_actions_from_memory attribute from 10 to 100. This change potentially allows the agent to import and retain a larger number of past actions from memory during its operation, expanding the agent's action retention capabilities.

Changes

File	Change Summary
prediction_market_agent/agents/microchain_agent/nft_treasury_game/deploy_nft_treasury_game.py	Updated import_actions_from_memory attribute from 10 to 100 in DeployableAgentNFTGameAbstract class

Possibly related PRs

• Fix NFT contract usage #645: Addition of retrieve_total_number_of_keys method to the DeployableAgentNFTGameAbstract class, which is related to the memory action import modification

Suggested reviewers

• gabrielfior

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📥 Commits

Reviewing files that changed from the base of the PR and between 2189f11 and 5caab90.

📒 Files selected for processing (1)

• prediction_market_agent/agents/microchain_agent/nft_treasury_game/deploy_nft_treasury_game.py (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (4)

• GitHub Check: test-build-image
• GitHub Check: mypy
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• GitHub Check: pytest

🔇 Additional comments (2)

prediction_market_agent/agents/microchain_agent/nft_treasury_game/deploy_nft_treasury_game.py (2)

41-41: Approve the increase in memory capacity with monitoring recommendation.

The 10x increase in import_actions_from_memory (10 → 100) will allow agents to maintain a larger history of actions, potentially leading to better decision-making in the NFT game. However, since this affects all NFT agents, we should monitor its impact.

Let's verify the memory usage implications:

Follow-up Actions:

1. Monitor memory usage patterns of the agents with the increased capacity
2. Validate that the GPT-4 mini model can effectively process the larger context
3. Consider adding memory usage metrics or logging

✅ Verification successful

Verified: Memory increase is safe and well-bounded

The increase to 100 actions is safe because:

• The system has proper memory bounds with max_tokens=8196
• Memory fetching is properly implemented with null-safety checks
• Even with stricter token limits (e.g., Replicate's 4096), the system can handle the increased context

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Check for memory-related configurations and potential issues

# Check for any memory-related configurations or limitations in the codebase
rg -i "memory|history|context|token.*limit" --type python

# Look for any existing memory monitoring or management code
ast-grep --pattern 'def $_(memory|history|context)' 

Length of output: 144

---

Script:

#!/bin/bash
# Search for the specific variable and its usage
rg -A 3 -B 3 "import_actions_from_memory" 

# Search for memory and token related configurations
rg -A 2 -B 2 -i "(memory_size|token.*limit|max.*tokens|context.*window)"

# Look for memory management in class definitions
ast-grep --pattern 'class $_ {
  $$$
  memory
  $$$
}'

Length of output: 5747

---

Line range hint 93-116: Verify reflection phase handling with increased memory.

The increased memory capacity will provide more historical context during the game completion reflection phase. This is beneficial as it allows for more comprehensive learning, but we should verify that the reflection phase can handle the larger context effectively.

Let's check the reflection phase implementation:

✅ Verification successful

Reflection phase handles increased memory capacity appropriately

The implementation uses Chroma vector store with similarity search, limiting to top 50 most relevant memories, which ensures efficient processing regardless of history size. The switch to GPT-4 aligns with the memory function's design.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Description: Verify reflection phase handling

# Check implementation of CheckAllPastActionsGivenContext
ast-grep --pattern 'class CheckAllPastActionsGivenContext {
  $$$
}'

# Look for any context window or token limit configurations
rg -i "token.*limit|context.*window" --type python

Length of output: 154

---

Script:

#!/bin/bash
# Search for CheckAllPastActionsGivenContext class and its usages
ast-grep --pattern 'class CheckAllPastActionsGivenContext'
ast-grep --pattern 'import $$$CheckAllPastActionsGivenContext$$$'

# Look for token limits and context windows with correct file extension
rg -i "token.*limit|context.*window" -g "*.py"

# Find files that reference CheckAllPastActionsGivenContext
rg "CheckAllPastActionsGivenContext" -g "*.py"

Length of output: 981

---

Script:

#!/bin/bash
# Check implementation details
ast-grep --pattern 'class LongTermMemoryBasedFunction {
  $$$
}'

# Get CheckAllPastActionsGivenContext implementation
cat prediction_market_agent/agents/microchain_agent/memory_functions.py

Length of output: 3955

✨ Finishing Touches

• 📝 Generate Docstrings (Beta)

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[kongzii]

Just looking over the agent's history, with 10 as previously, that's not a lot of history to work with (just 5 function calls + outputs). I'm hoping that with 100 agent will be able to plan better.