$ agent.remember(everything)

Inspired by human cognition. Each layer serves a distinct purpose, working together to give AI agents comprehensive memory capabilities.

1. 01
   short-term · context-aware

   Working Memory

   Temporary storage for immediate context and current task information. Like a human's mental scratchpad — holds what the agent needs right now and automatically clears when tasks complete.

2. 02
   event-based · temporal

   Episodic Memory

   Stores experiences and events with temporal context. Enables agents to recall past interactions, learn from outcomes, and build a rich history of what happened and why.

3. 03
   knowledge · concepts

   Semantic Memory

   The agent's encyclopedia. Stores factual knowledge, domain concepts, and general information. Knowledge that doesn't depend on when or where it was learned.

4. 04
   skills · procedures

   Procedural Memory

   Stores learned procedures, skills, and how-to knowledge. Enables agents to perform complex multi-step tasks reliably — muscle memory for AI.

ThinkingMemory is purpose-built memory infrastructure for AI agents.

1. 01

   Reasoning-Aware Retrieval

   Agents don't just search for keywords — they ask what they need to remember. ThinkingMemory understands the reasoning context and returns the most relevant memories, not just the most similar vectors.

   • Context-aware memory retrieval
   • Intent-based query understanding
   • Cross-layer memory fusion

   // Agent asks what it needs to remember memory.recall({ context: "user asking about pipeline failure", intent: "diagnose root cause", layers: ["episodic", "semantic"] }) // Returns contextually relevant memories // → past failure patterns (episodic) // → pipeline topology knowledge (semantic) // → resolution procedures (procedural)
2. 02

   Built-in Forgetting & Compression

   Just like human memory, not everything needs to be remembered forever. ThinkingMemory automatically compresses old memories, forgets irrelevant details, and consolidates patterns — keeping storage costs minimal and retrieval fast.

   • Intelligent memory decay algorithms
   • Pattern consolidation over time
   • Automatic storage optimization

   // Configure memory lifecycle memory.configure({ working: { ttl: "session" }, episodic: { compress_after: "7d", consolidate_after: "30d", forget_threshold: 0.1 }, semantic: { deduplicate: true }, procedural: { versioned: true } })
3. 03

   Agent-Agnostic APIs

   Works with any AI framework and any LLM. REST APIs and SDKs that plug into LangChain, CrewAI, AutoGen, custom agents, or anything else. Your memory infrastructure shouldn't lock you into a framework.

   • REST API + Python / JS SDKs
   • Framework-agnostic integration
   • Works with any LLM provider

   # works with any framework from thinkingmemory import Memory mem = Memory(agent_id="my-agent") # store a memory mem.store( layer="episodic", content="Pipeline X failed at 3am", metadata={"severity": "high"}, ) # retrieve with reasoning context memories = mem.recall("Why did the pipeline fail?")

What changes when memory is real infrastructure, not a sidecar.

1. ↘

   95% cost reduction

   Agents without memory repeat computations, re-fetch context, and waste tokens. Memory eliminates redundant processing, dramatically cutting API costs.

2. ≡

   Consistent behavior

   No more agents that forget previous interactions or give conflicting responses. Persistent memory ensures reliable, context-aware behavior across all sessions.

3. ↗

   Continuous improvement

   Agents learn from past experiences, improving performance over time without retraining. Every interaction makes the agent smarter and more capable.

4. ⇄

   Framework independence

   Swap frameworks without losing memory. Switch LLM providers without rebuilding. Your agent's knowledge persists regardless of the stack underneath.