8 Best ThinkGPT Alternatives in 2026 (Open Source)
ThinkGPT — Agent techniques to augment your LLM and push it beyong its limits. vs LangChain Memory/LlamaIndex: purpose-built Chain of Thought library combining memory, self-refinement, knowledge compression, and inference — focused on making LLMs 'think' rather than just retrieve
These 8 open-source tools do the same job. They are ordered by how closely they match ThinkGPT, with live GitHub data so you can see which projects are actively maintained.
| Tool | GitHub stars | Stars / 30d | Last commit |
|---|---|---|---|
| ThinkGPT(original) | 1.6k | +0 | 2023-05-16 |
| Letta | 25.0k | +515 | 2026-09-10 |
| Mem0 | 66.4k | +2,429 | 2026-09-25 |
| Memary | 2.7k | +12 | 2024-10-18 |
| Cognee | 31.2k | +2,656 | 2026-09-29 |
| AI Legion | 1.4k | +1 | 2025-05-27 |
| DeerFlow | 83.3k | +5,343 | 2026-09-30 |
| smolagents | 29.6k | +531 | 2026-09-30 |
| Lagent | 2.3k | +7 | 2026-04-20 |
1. Letta
Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.
What sets it apart: Pioneered virtual context management for LLMs — enabling unlimited conversation history through intelligent memory paging
Best for: Long-running AI agents with persistent memory; Research on memory architectures for LLMs
2. Mem0
Universal memory layer for AI Agents
What sets it apart: Unlike Zep (session-focused memory) or ChatGPT's built-in memory (closed, limited), Mem0 provides a standalone, open-source memory layer with proven +26% accuracy gains over OpenAI Memory, multi-level (user/session/agent) state management, and 90% token reduction via intelligent memory retrieval.
Best for: AI assistant developers who need persistent, personalized memory across conversations without building custom infrastructure; Customer support chatbots that need to recall past tickets and user preferences
3. Memary
The Open Source Memory Layer For Autonomous Agents
What sets it apart: vs LangChain Memory / Mem0: graph-database-backed memory system emulating human memory (breadth + depth tracking) — agents automatically build and query knowledge graphs rather than flat conversation history
Best for: Building persistent, context-aware AI agents with evolving memory; User preference tracking and personalization across sessions; Multi-user agent management with separate knowledge contexts
4. Cognee
Knowledge Engine for AI Agent Memory in 6 lines of code
What sets it apart: Unlike Mem0 (conversation memory) or Chroma (pure vector search), Cognee builds an evolving knowledge graph from documents, combining vector + graph search with cognitive science approaches, ontology grounding, and cross-agent knowledge sharing — making it AI memory infrastructure rather than just a vector database.
Best for: AI agent developers who need persistent, learning memory that combines vector search with knowledge graph relationships; Enterprise use cases requiring tenant isolation, audit trails, and cross-agent knowledge sharing
5. AI Legion
An LLM-powered autonomous agent platform
What sets it apart: Multi-agent platform where autonomous LLM agents with persistent memory collaborate through console interaction, learning from their own mistakes
Best for: multi-agent-experimentation; exploring-agent-self-organization; autonomous-task-delegation
6. DeerFlow
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of ta
What sets it apart: vs AutoGPT: purpose-built for deep research with sub-agent orchestration and sandbox; vs LangGraph: higher-level harness with built-in memory, sandbox, and skill system rather than bare graph framework
Best for: Deep research and exploration tasks; Building multi-agent systems with sub-agent orchestration; Teams wanting coding agent integration (Claude Code/Codex)
7. smolagents
🤗 smolagents: a barebones library for agents that think in code.
What sets it apart: vs LangChain: code-first agent design uses 30% fewer tokens by writing Python instead of JSON tool calls; vs CrewAI: lighter ~1000 lines core with HuggingFace Hub integration for sharing agents/tools
Best for: Building code-writing AI agents with sandboxed execution; HuggingFace ecosystem users wanting agent capabilities; Multi-modal agent applications
8. Lagent
A lightweight framework for building LLM-based agents
What sets it apart: vs LangChain/CrewAI: PyTorch-inspired design with intuitive layer composition, dual sync/async interfaces, and built-in session-isolated memory for concurrent agent workloads
Best for: Multi-agent workflows with iterative self-refinement; Research with InternLM/Qwen models and custom agents