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Give AI agents self-hosted memory with search, graphs, tasks, and sync.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Postgram" yet — see the docs or source repo.
Store the following project background, glossary, and past decisions in Postgram as long-term memory for this AI agent, and organize them into searchable relationships by topic.
The system stores project knowledge in memory so the agent can retrieve and reuse it later.
Search Postgram for historical documents, tasks, and knowledge nodes related to "user permission change workflow," and return the most relevant results and their relationships.
Returns relevant documents, tasks, or knowledge graph nodes along with relationship clues between them.
Sync this set of product specs, meeting notes, and operating manuals into Postgram so the agent can answer questions based on them later.
The documents are synced into the system, enabling the agent to use them for later Q&A and task handling.
Developers or product teams can use it to store long-term knowledge, task history, and document content for AI agents, reducing context loss. It fits assistants that need persistent memory and searchable knowledge.
When a team wants to manage agent memory data on its own, it can use this tool to build self-hosted storage and retrieval capabilities. It suits environments that require deployment control and data management.
Research or engineering teams can connect documents, tasks, and knowledge nodes so AI agents can understand relationships between pieces of information. This is useful for tracing context and dependencies across materials.
It is a self-hosted memory tool for AI agents with hybrid search, knowledge graphs, tasks, and document sync. It can provide reusable knowledge and context for agents over time.
Based on the description, it is self-hosted. Specific installation steps, runtime requirements, and deployment details are not provided here; see the source repository.
From the description, it does more than document search by adding agent memory, knowledge graphs, tasks, and document sync. It is better suited for building a persistent knowledge layer for AI agents.
Provide AI agents with searchable long-term memory and conflict-aware recall.
Give coding agents persistent, evidence-backed project memory across sessions.
Provide Git-backed Markdown memory and clean search for AI agents.
Give AI agents persistent memory, knowledge graphs, recall, and relationship insights.
Give AI agents persistent, searchable memory with semantic recall and retrieval.
Provide persistent memory for AI agents across sessions and tasks.