--- title: "Agent_Memory_Techniques vs honcho" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nirdiamant-agent-memory-techniques-vs-plastic-labs-honcho" tools: ["nirdiamant-agent-memory-techniques", "plastic-labs-honcho"] --- # Agent_Memory_Techniques vs honcho *GraphCanon updated Jul 11, 2026* ## Verdict Pick Agent_Memory_Techniques when agent_Memory_Techniques is primarily Jupyter Notebook; honcho is Python; pick honcho when honcho is primarily Python; Agent_Memory_Techniques is Jupyter Notebook. [Agent_Memory_Techniques](https://diamantai.substack.com/) reports 772 GitHub stars, 100 forks, and 2 open issues, last pushed Jul 4, 2026. [honcho](https://docs.honcho.dev) has 5.9k stars, 707 forks, and 161 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [Agent_Memory_Techniques's repository](https://github.com/NirDiamant/Agent_Memory_Techniques) and [honcho's repository](https://github.com/plastic-labs/honcho). | | [Agent_Memory_Techniques](/tools/nirdiamant-agent-memory-techniques.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Tagline | Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks | Memory library for building stateful agents | | Stars | 772 | 5,902 | | Forks | 100 | 707 | | Open issues | 2 | 161 | | Language | Jupyter Notebook | Python | | Adopt for | - | A Python memory library designed for building stateful AI agents with a focus on long-term and contextual memory management. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | AGPL-3.0: The software is free to use, distribute, and modify but requires that derivative works be similarly distributed as AGPL-3.0 under the same license. | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent_Memory_Techniques](/tools/nirdiamant-agent-memory-techniques.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Days since push | 6d | 0d | | Open issues (now) | 2 | 161 | | Owner type | User | Organization | | Full report | [trust report](/tools/nirdiamant-agent-memory-techniques/trust.md) | [trust report](/tools/plastic-labs-honcho/trust.md) | ## Decision facts: honcho - **Hosting:** self hosted - Honcho can be run locally using Docker, allowing for full control over the environment where it operates. - **Requirements:** - Requires setting up LLM API keys (Gemini, Anthropic, OpenAI) for certain functionalities.; - Python SDK needs to point to `http://localhost:8000` after Docker setup. - **Adopt for:** A Python memory library designed for building stateful AI agents with a focus on long-term and contextual memory management. - **License detail:** AGPL-3.0: The software is free to use, distribute, and modify but requires that derivative works be similarly distributed as AGPL-3.0 under the same license. ## Choose when ### Choose Agent_Memory_Techniques if… - Agent_Memory_Techniques is primarily Jupyter Notebook; honcho is Python. - License: Agent_Memory_Techniques is Apache-2.0, honcho is AGPL-3.0. - Tags unique to Agent_Memory_Techniques: episodic-memory, generative-ai, graphiti, knowledge-graph. ### Choose honcho if… - honcho is primarily Python; Agent_Memory_Techniques is Jupyter Notebook. - License: honcho is AGPL-3.0, Agent_Memory_Techniques is Apache-2.0. - Honcho can be run locally using Docker, allowing for full control over the environment where it operates. - Requirements: - Requires setting up LLM API keys (Gemini, Anthropic, OpenAI) for certain functionalities.; - Python SDK needs to point to `http://localhost:8000` after Docker setup.. - Tags unique to honcho: ai, ai-memory, context-engineering, continual-learning. - honcho ships Docker support for self-hosted deployment. - - You are developing stateful AI agents that require robust, contextual, and long-term memory capabilities. ## When NOT to use Agent_Memory_Techniques - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use honcho - - If your use case does not require long-term or contextual memory management, as honcho might introduce unnecessary complexity. - - In scenarios where a proprietary license is required, given honcho's AGPL-3.0 license may have implications for open-sourcing modifications. ## Common questions ### What is the difference between Agent_Memory_Techniques and honcho? Agent_Memory_Techniques: Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks. honcho: Memory library for building stateful agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent_Memory_Techniques over honcho? Choose Agent_Memory_Techniques over honcho when Agent_Memory_Techniques is primarily Jupyter Notebook; honcho is Python; License: Agent_Memory_Techniques is Apache-2.0, honcho is AGPL-3.0; Tags unique to Agent_Memory_Techniques: episodic-memory, generative-ai, graphiti, knowledge-graph. ### When should I choose honcho over Agent_Memory_Techniques? Choose honcho over Agent_Memory_Techniques when honcho is primarily Python; Agent_Memory_Techniques is Jupyter Notebook; License: honcho is AGPL-3.0, Agent_Memory_Techniques is Apache-2.0; Honcho can be run locally using Docker, allowing for full control over the environment where it operates; Requirements: - Requires setting up LLM API keys (Gemini, Anthropic, OpenAI) for certain functionalities.; - Python SDK needs to point to `http://localhost:8000` after Docker setup.; Tags unique to honcho: ai, ai-memory, context-engineering, continual-learning; honcho ships Docker support for self-hosted deployment; - You are developing stateful AI agents that require robust, contextual, and long-term memory capabilities. ### When should I avoid Agent_Memory_Techniques? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid honcho? - If your use case does not require long-term or contextual memory management, as honcho might introduce unnecessary complexity. - In scenarios where a proprietary license is required, given honcho's AGPL-3.0 license may have implications for open-sourcing modifications. ### Is Agent_Memory_Techniques or honcho more popular on GitHub? honcho has more GitHub stars (5,902 vs 772). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent_Memory_Techniques and honcho open source? Yes - both are open-source projects on GitHub (Agent_Memory_Techniques: Apache-2.0, honcho: AGPL-3.0). ### Where can I find alternatives to Agent_Memory_Techniques or honcho? GraphCanon lists graph-backed alternatives at [Agent_Memory_Techniques alternatives](/tools/nirdiamant-agent-memory-techniques/alternatives) and [honcho alternatives](/tools/plastic-labs-honcho/alternatives) ([Agent_Memory_Techniques markdown twin](/tools/nirdiamant-agent-memory-techniques/alternatives.md), [honcho markdown twin](/tools/plastic-labs-honcho/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/nirdiamant-agent-memory-techniques-vs-plastic-labs-honcho.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent_Memory_Techniques or honcho? Agent_Memory_Techniques: Very active. honcho: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Agent_Memory_Techniques and honcho? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent_Memory_Techniques trust report](/tools/nirdiamant-agent-memory-techniques/trust); [honcho trust report](/tools/plastic-labs-honcho/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nirdiamant-agent-memory-techniques`](/api/graphcanon/graph?tool=nirdiamant-agent-memory-techniques) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_