agentmemory — Persistent memory for AI coding agents

Introduction: A New Kind of Coding Memory agentmemory banner image

In the fast-evolving world of AI coding agents, memory matters more than ever. agentmemory is a persistent memory layer designed for AI coding agents, so your agent remembers everything between sessions. It eliminates the perennial re-explanation loop and keeps your preferences, bugs, and architectural decisions alive across sessions. Built on the iii engine, agentmemory provides a unified memory server that can be wired to Claude Code, GitHub Copilot CLI, Cursor, Gemini CLI, Codex CLI, Hermes, OpenClaw, pi, OpenHuman, OpenCode, and many MCP clients. In short: one memory server, many agents, limitless continuity.

Banner and quick navigation

• This project is powered by iii and ships with a full suite of integration guides, quick starts, and real-time viewing tools.
• The banner image above sets the stage for a memory-driven approach to coding assistants.

Overview: What agentmemory Does

• Persistent memory across sessions: agentmemory quietly captures what your agent does, compresses it into searchable memory, and injects the right context when the next session starts. No re-teaching needed.
• Cross-agent memory sharing: one memory server serves every agent that supports MCP or REST API. Memories are shared across all wired tools and clients.
• Twelve hooks, 53 MCP tools, and eight skills: agentmemory comes with a rich toolkit that lets agents remember, recall, and reason about past work without manual intervention.
• Real-time visibility: a built-in viewer and iii console provide live insight into what the agent remembers and how it processes memory operations.

Images that illustrate the vision

• Banner: agentmemory banner image (banner.png) reinforces the idea of persistent memory.
• Demo and statistics: a quick glance at the demo, retrieval metrics, and tooling counts show practical benefits.
• Real-time view: the live viewer and console images illustrate how memory is captured, indexed, and accessed.

Works with Every Agent: A Universal Memory Layer

agentmemory is designed to work with any agent that supports hooks, MCP, or REST API. It presents a single memory server that all compatible agents can share.

Key compatible agents and integrations

• Claude Code: native plugin with 12 hooks and MCP support for seamless memory integration.
• Codex CLI: native plugin that exposes a comprehensive set of memory tools via MCP and plugin hooks.
• GitHub Copilot CLI: MCP-based integration with a plugin that wires Copilot to the memory server.
• OpenClaw: native plugin with MCP support and memory tools wired in.
• Hermes: native plugin with memory tooling integrated.
• pi: native plugin with a memory-backed workflow.
• OpenHuman: a Memory trait backend that connects to the memory server.
• Cursor: MCP server that connects to agentmemory for live memory and recall.
• Gemini CLI: memory-capable MCP server connection for rapid recall.
• OpenCode: 22 hooks plus MCP and plugin support to tap into memory.
• Cline: MCP server integration for memory-backed sessions.
• Goose, Kilo Code, Aider, Windsurf, Roo Code, Warp, and more: broad coverage across many agents, with standard MCP blocks and plugin wiring.
• OpenCode, Warp, and others: supports standard MCP blocks and plugin-based extension.

What changes with agentmemory

• You set up JWT authentication in a session; in subsequent sessions, the memory of the session context, tests, and preferences is already present.
• The system supports agent isolation modes via AGENTMEMORYAGENTSCOPE, enabling multi-agent setups where memory is scoped by agent identity.
• Memory is persistent, structured, and searchable across sessions, so the agent can recall history, test results, and preferences without re-teaching.

Quick Start: Try It in 30 Seconds

• Install the memory server globally:
• npm install -g @agentmemory/agentmemory
• Start the memory server and seed data:
• npx @agentmemory/agentmemory
• npx @agentmemory/agentmemory demo
• Wire a target agent (for example Claude Code):
• agentmemory connect claude-code
• Optional: install native skills to empower your agent with tool-use awareness:
• npx skills add rohitg00/agentmemory -y
• Alternative: use npx for a quick run without global install:
• npx @agentmemory/agentmemory
• Visit the real-time viewer at http://localhost:3113 to watch memory indexing in action.

Important setup notes

• If you used an older release with npx, you may need to force the latest version:
• npx -y @agentmemory/agentmemory@latest
• If you run into caching issues on macOS/Linux, clearing the npx cache can help:
• rm -rf ~/.npm/_npx

Session Replay and Memory Lifecycle

• Every session you record is replayable in the viewer. The Replay tab lets you scrub through prompts, tool calls, and tool results, with play/pause, speed control, and keyboard shortcuts.
• Import existing Claude Code JSONL transcripts to the viewer to enrich memory with prior work.
• The memory lifecycle features four tiers of consolidation: Working, Episodic, Semantic, and Procedural memory. This mirrors human memory: raw observations, session summaries, extracted patterns, and repeatable workflows.

Benchmarks and Performance Highlights

• Retrieval accuracy and recall
• In-house experiments show a strong retrieval signal for historical coding work, with high precision in top matches.
• The agentmemory hybrid approach achieves high recall and precise top-K matching, improving the usefulness of retrieved memories for subsequent sessions.
• Token efficiency and cost
• Agentmemory demonstrates significant token savings by compressing and indexing memories rather than pushing raw context into every prompt.
• Local embeddings and BM25-based retrieval complement dense embeddings to provide robust performance with lower operational costs.
• Integrations and test coverage
• The system ships with more than 50 native agents via the skills framework, and 53 MCP tools when connected to a live memory server.
• A comprehensive battery of tests (950+ tests) ensures stability across MPC interactions and memory operations.

vs Competitors: Why AgentMemory Stands Out

• agentmemory combines a memory engine with an MCP server, offering a unified memory layer that works across all agents and adapters.
• It delivers higher R@5 retrieval rates and more complete cross-agent memory sharing, compared with alternatives that offer only partial memory or rely on manual memory editing.
• The four-tier memory consolidation model (Working, Episodic, Semantic, Procedural) provides a richer and more adaptable memory structure than simple sticky notes or single-context memories.
• Real-time viewer and iii console provide comprehensive observability into memory operations, traces, and workflows.

How It Works: The Memory Pipeline

• Memory pipeline overview
• PostToolUse hook triggers - deduplication with SHA-256, privacy filtering to strip secrets, and storage of raw observations.
• Compression and extraction of structured facts, concepts, and narrative.
• Embedding creation using multiple providers (local and external) and indexing via BM25 and vector search.
• Session-level summarization and knowledge-graph extraction when enabled.
• SessionStart hook loading project profiles and attacking a hybrid search space (BM25 + vector + graph) for optimal recall.
• The 4-tier consolidation model
• Working: Real-time raw observations from tool usage.
• Episodic: Compressed session summaries.
• Semantic: Extracted facts and patterns.
• Procedural: Workflows and decision patterns.
• Privacy-first approach
• Secrets and keys are stripped before storage; sensitive data is protected while retaining useful memory signals.

Key Capture Points: What gets stored

• SessionStart: project path, session identifier.
• UserPromptSubmit: user prompts (privacy-filtered).
• PreToolUse: file access patterns and enriched context.
• PostToolUse: tool name, inputs, outputs.
• PostToolUseFailure: error context.
• PreCompact: memory re-injection before compaction.
• SubagentStart/Stop: sub-agent lifecycles.
• Stop: end-of-session summary.
• SessionEnd: overall session completion signal.

MCP Server: The 53-Tool Memory Toolkit

• 53 tools, 6 resources, 3 prompts, and 8 skills
• Core tools (always available): a core set including memoryrecall, memorysave, memorysmartsearch, memorysessions, memoryexport, memoryaudit, memorygovernance_delete, and more
• Extended tools (when connected to a live memory server): graphquery, memoryconsolidate, memoryClaudebridgesync, memoryteamshare, memoryteamfeed, memorysnapshotcreate, memoryactioncreate, memoryaction_update, and many more
• Standalone MCP: a lightweight shim for agents that cannot reach the host, enabling a consistent MCP interface with a local fallback set of tools
• Cross-agent sharing: MCP enables a shared memory layer so that multiple agents can benefit from a single, persistent memory store

Real-Time Viewer and iii Console: Observability in Action

• Real-Time Viewer
• Auto-starts on port 3113; live memory stream, session explorer, memory browser, and knowledge graph visualization
• The REST-served endpoint follows Bearer-token rules when an agent memory secret is present
• iii Console
• The console provides a live, architectural view of memory operations, including traces, flows, and function invocations
• Two windows: one for memory state and one for the iii engine, making it easy to debug memory operations
• The console supports flowing through memorysmartsearch, BM25 scans, embedding lookups, and re-ranker processes in a waterfall visualization
• The Workers page shows connected workers, including agentmemory, with metadata such as PID and runtime
• The Traces page provides a waterfall or flame view for per-span durations
• Visual cues
• The console and viewer work together to provide a complete picture of memory operations and their impact on agent behavior

Powered by iii: Architecture That Replaces Traditional Stack Components

• agentmemory is already a running iii instance
• Three primitives define the runtime: worker, function, trigger
• iii’s ecosystem replaces conventional stacks (Express/Fastify, Redis, Postgres, etc.) with unified primitives
• Observability and state management come from iii-state, iii-stream, and iii-observability workers
• Extending agentmemory is as simple as adding an iii worker, for example:
• iii worker add iii-pubsub
• iii worker add iii-cron
• iii worker add iii-queue
• iii worker add iii-observability
• iii worker add iii-sandbox
• iii worker add iii-database
• iii worker add mcp
• This approach means you don’t need separate monitoring, queues, or database layers—the iii components handle it all

Configuration and Local Models: Flexible, Local, and Cost-Aware

• LLM providers
• agentmemory auto-detects providers and supports a local no-LLM mode by default
• Local models (Ollama, LM Studio, vLLM, or llama.cpp) can be used to run compression and reasoning on-device
• Local models and selection
• The guide includes recommended models for memory tasks: qwen2.5-coder:7b, llama3.2:3b, mistral:7b-instruct, and others
• Cost-aware model selection helps balance performance and price for memory compression and reasoning
• Embeddings
• Local embeddings are shipped with the system via @xenova/transformers for offline operation
• Options include Gemini embeddings, OpenAI embeddings, and other providers depending on configuration
• AGENTMEMORY settings
• AGENTMEMORYURL and AGENTMEMORYSECRET control remote access and security
• AGENTMEMORY_TOOLS controls surface (core vs all 53 tools)
• Graph extraction and consolidation toggles allow you to tune memory behavior
• Multi-agent memory with AGENT_ID
• AGENTID and AGENTMEMORYAGENT_SCOPE provide two modes: shared (default) or isolated for strict separation
• Isolated mode filters recall results by agent tag to prevent cross-agent leakage

API and Endpoints: A Rich REST Surface

• 125 endpoints on port 3111
• Key endpoints include health checks, session management, memory capture, hybrid search, context generation, recall, remember, forget, enrich, and graph queries
• Security: endpoints can be protected with Authorization: Bearer tokens when AGENTMEMORY_SECRET is set
• The REST API exposes a comprehensive suite for programmatic access to memory operations, session management, and graph queries
• Documentation references point to the codebase for the full endpoint list (src/triggers/api.ts)

Deployment: One-Click Templates and Cloud Workflows

• One-click deployment templates for managed hosts
• Each template ships a self-contained Dockerfile that pulls agentmemory from npm and includes the iii engine
• Persistent storage is mounted at /data; the startup script configures a deploy-tuned memory server
• Numerous deployment options:
• Fly.io: one-click deploy
• Railway: deploy template
• Render: blueprint flow with automatic disk snapshots
• Coolify: self-hosted on a VPS with the same Docker Compose stack
• The deployment templates ensure that only port 3113 (viewer) is exposed to the host, while internal endpoints remain secure

Why agentmemory Matters: A Practical Perspective

• The fundamental problem: most coding agents forget everything at session end
• The memory layer flips this paradigm by providing a robust, searchable, and versioned memory graph
• The memory pipeline captures, compresses, and indexes information so the agent can recall context, test results, and design decisions
• Built-in memory (CLAUDE.md, notepads, etc.) is replaced by a scalable memory graph that grows with use
• The result: sessions flow more smoothly, fewer re-explain events occur, and the agent demonstrates higher recall and improved performance over time

What You Get with agentmemory

• A persistent memory layer for every supported agent
• A unified, scalable, and auditable memory graph
• Real-time visibility into memory activity via a viewer and console
• A robust MCP toolkit with 53 tools and 8 skills
• A path to broader automation and multi-agent collaboration through agent-scoped memory
• Local and cost-aware model options to reduce reliance on cloud services

Licensing and Credits

• Agentmemory is released under the Apache-2.0 license
• It is designed to be used with iii and a wide range of MCP-compliant or REST-based agents
• The project emphasizes openness, reproducibility, and collaboration across the AI coding agent ecosystem

Sections and Visual Aids: Quick Reference to the Figures

• Banner: The banner image (banner.png) sets the tone for persistent memory across sessions.
• Quick Start and Section Headers
• The section icons (section-quickstart.svg, section-benchmarks.svg, section-competitors.svg, etc.) help readers navigate the document quickly.
• Real-Time Viewer and Traces
• Real-Time Viewer image (assets/tags/section-viewer.svg) introduces the viewer’s capabilities.
• iii console traces (assets/iii-console/traces-waterfall.png) illustrate the memory operation flow and performance visualization.
• Workers and Live Stats
• Agents and workers are visible in the “Workers page” image (assets/iii-console/workers.png).
• Architectural Perspective
• The “Powered by iii” section showcases the architecture, with a dedicated design diagram (assets/tags/section-architecture.svg).

Conclusion: A Future-Proof Memory Layer for AI Coding Agents

agentmemory transforms the way developers and AI assistants collaborate. By providing a persistent, searchable, and multi-agent memory system, it eliminates the repetitive, error-prone cycle of re-explaining context and preferences. It enables memory-driven workflows, where past decisions, tests, and patterns inform future actions. All agents share the same memory, so improvements in one workflow flow through the entire ecosystem. With transparent real-time viewing, robust MCP tooling, and flexible local-model options, agentmemory is well positioned to become the standard memory layer for AI coding agents in production environments.

Appendix: Quick Reference Commands

• Install and run the memory server
• npm install -g @agentmemory/agentmemory
• npx @agentmemory/agentmemory
• Seed and demo sessions
• npx @agentmemory/agentmemory demo
• Connect an agent (example: Claude Code)
• agentmemory connect claude-code
• Global install and maintenance
• npm install -g @agentmemory/agentmemory
• agentmemory stop
• agentmemory remove
• agentmemory upgrade
• Local environment and security
• Create the env file at ~/.agentmemory/.env and customize keys
• AGENTMEMORY_URL to point to a running server when using a local or remote host

Images throughout the post

• Banner: agentmemory banner image
• Demo and stats: [assets/demo.gif], [assets/tags/stat-recall.svg], [assets/tags/stat-tokens.svg], [assets/tags/stat-tools.svg], [assets/tags/stat-hooks.svg], [assets/tags/stat-deps.svg], [assets/tags/stat-tests.svg]
• Real-time viewer: [assets/tags/section-viewer.svg], [assets/iii-console/workers.png], [assets/iii-console/traces-waterfall.png]
• Architecture and design: [assets/tags/section-architecture.svg], [assets/tags/section-how.svg], [assets/tags/section-config.svg]

If you’d like, I can tailor this blog post to a specific audience (engineers, product managers, or platform operators) or expand any section with deeper dive subsections and more implementation details.

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