feat: implement memory agents with session summaries, agentic management, and enhanced references #978
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…ent, and references - Add session summary configuration with auto-summarization every N turns - Implement agentic memory management with auto-classification and confidence thresholds - Add memory references with inline/footnote/metadata formatting options - Create MemoryTools class with remember(), update_memory(), forget(), search_memories() - Maintain full backward compatibility with existing memory functionality - Add comprehensive test suites for all new features Resolves #969 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: Mervin Praison <[email protected]>
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Summary of Changes
Hello @MervinPraison, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly enhances the memory capabilities of the PraisonAI agents by introducing advanced features for managing conversational context, intelligently storing and retrieving information, and providing traceable references. The changes are designed to be opt-in and fully backward compatible, allowing for a seamless upgrade path while empowering agents with more sophisticated memory management.
Highlights
- Session Summaries: Implemented automatic conversation summarization, allowing the system to generate LLM-based summaries every N turns and include session context in agent responses. This enhances the agent's ability to maintain conversational coherence over time.
- Agentic Memory Management: Introduced autonomous memory importance analysis using an LLM, enabling agents to perform intelligent CRUD operations (remember, update, forget, search) on their long-term memory. This includes auto-classification of information importance to prioritize critical facts.
- Memory References: Added support for structured reference metadata with confidence scores, allowing for multiple reference formats (inline, footnote, metadata). This provides enhanced search capabilities by linking responses directly to their source memories.
- Agent Tooling and Integration: Created a dedicated
MemoryToolsclass andget_memory_toolsfunction to expose these new memory functionalities as callable tools for agents, ensuring seamless integration with the existing agent system. New comprehensive test suites have also been added to validate these features and ensure backward compatibility.
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Code Review
This pull request introduces session summaries, agentic memory management, and memory references. The code is well-organized and includes tests. The review highlights a critical issue regarding potential data loss when updating memories, and an opportunity to improve test file maintainability.
| c.execute( | ||
| "UPDATE long_mem SET content = ?, meta = ? WHERE id = ?", | ||
| (new_fact, json.dumps({"updated": True, "updated_at": time.time()}), memory_id) | ||
| ) |
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The UPDATE statement overwrites the existing metadata. This will cause any pre-existing metadata associated with the memory entry to be lost. To prevent this data loss, fetch the existing metadata, update it, and then write it back.
# First, fetch existing metadata
c.execute("SELECT meta FROM long_mem WHERE id = ?", (memory_id,))
row = c.fetchone()
if not row:
return False # Or handle as an error
existing_meta = json.loads(row[0] or '{}')
existing_meta.update({"updated": True, "updated_at": time.time()})
# Then, update the record with the merged metadata
c.execute(
"UPDATE long_mem SET content = ?, meta = ? WHERE id = ?",
(new_fact, json.dumps(existing_meta), memory_id)
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| self.chroma_col.add( | ||
| documents=[new_fact], | ||
| metadatas=[{"updated": True, "updated_at": time.time()}], |
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Similar to the SQLite update, the metadata for the ChromaDB entry is being overwritten here. When you delete and re-add the document, preserve the original metadata and merge it with the update information to avoid data loss.
# get existing metadata
existing_metadata = self.chroma_col.get(ids=[memory_id], include=["metadatas"])
if existing_metadata and existing_metadata['metadatas']:
# Merge existing metadata with new metadata
updated_metadata = existing_metadata['metadatas'][0]
updated_metadata.update({"updated": True, "updated_at": time.time()})
else:
updated_metadata = {"updated": True, "updated_at": time.time()}
self.chroma_col.add(
documents=[new_fact],
metadatas=[updated_metadata],
ids=[memory_id],
embeddings=[embedding]
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| # Create an agent with memory tools | ||
| memory = Memory(memory_config, verbose=5) | ||
| from praisonaiagents.memory.tools import get_memory_tools |
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Imports should be at the top of the file to avoid circular dependency issues and improve readability.
from praisonaiagents.memory.tools import get_memory_tools
agent = Agent(![cursor[bot]](https://avatars.githubusercontent.com/in/1210556?s=60&v=4){kind=small}
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Bug: Undefined Variable in Conditional Logic
A NameError occurs in the remember() method when agentic memory is enabled but auto-classification is disabled. The importance_score variable is referenced in the metadata update but is only assigned when self.auto_classify is True, causing it to be undefined when self.auto_classify is False.
src/praisonai-agents/praisonaiagents/memory/memory.py#L1268-L1292
PraisonAI/src/praisonai-agents/praisonaiagents/memory/memory.py
Lines 1268 to 1292 in 2c5a455
Bug: Metadata Loss and Data Inconsistency
The update_memory method has two issues:
- It incorrectly replaces all existing metadata with only update flags in both the SQLite database and ChromaDB, leading to the loss of original metadata (e.g., quality, importance scores).
- In ChromaDB, if embedding generation fails after a memory is deleted for an update, the memory is not re-added, causing data inconsistency where the record exists in SQLite but is missing from the vector store.
src/praisonai-agents/praisonaiagents/memory/memory.py#L1299-L1329
PraisonAI/src/praisonai-agents/praisonaiagents/memory/memory.py
Lines 1299 to 1329 in 2c5a455
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@claude review this pull request and do a detailed analysis and fix this if the existing code doesn't have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. |
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Claude encountered an error —— View job I'll analyze this and get back to you. |
Implements all three proposed memory agents features from issue #969:
Features Implemented
Session Summaries
Agentic Memory Management
Memory References
Backward Compatibility
Closes #969
Generated with Claude Code