{ "schema_version": "1.1", "id": "atlas-agent-memory", "slug": "agent-memory", "title": "Agent Memory", "url": "https://feed7.dev/atlas/agent-memory", "current_answer": "Mechanisms that let agents retain and recall project facts across sessions — memory servers, context files, caches.", "implementation_consequence": "Without memory, every session re-learns the project. With bad memory, every session inherits stale facts. The schema and eviction policy are the product.", "agent_context": "Persist per-project facts in a small auditable schema. Watch for unbounded growth. Context caching (Gemini) and memory servers (mcp-memory) are complementary, not competing.", "confidence": "editorial", "last_verified": null, "last_updated": "2026-06-30T00:00:00.000Z", "evidence": [ { "schema_version": "1.1", "id": "p3", "slug": "mcp-memory-server", "url": "https://feed7.dev/p/mcp-memory-server", "title": "mcp-memory: a working memory server for agent sessions", "why_included": "Persists agent memory across sessions with a small, auditable schema.", "summary": "Drop-in MCP server that stores per-project facts an agent can recall next session. Small JSON schema, local-first, no cloud dependency.", "practical_implication": "Your agent stops re-learning the project every session. Worth the 4-minute setup on any repo you touch weekly.", "agent_context": "mcp-memory stores per-project facts an agent can recall next session. Tested on macOS + Cursor: setup ~4 min, recall reliable for <500 facts. Limitation: no eviction policy yet.", "source": { "name": "GitHub", "url": "https://github.com/f7-labs/mcp-memory", "published_at": "2026-06-30T00:00:00.000Z" }, "source_class": "tool", "content_type": "GitHub Repo", "layer": "context", "domains": [ "coding" ], "topics": [ "mcp", "agent-memory" ], "verification": { "status": "founder_tested", "label": "Founder Tested", "method": "founder_test", "verified_at": "2026-06-28T00:00:00.000Z" }, "uncertainty": [ "No eviction policy; unbounded growth on large projects." ], "connected_context": null, "lifecycle": "New", "published_at": "2026-06-30T00:00:00.000Z", "modified_at": "2026-06-30T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/mcp-memory-server", "json": "https://feed7.dev/p/mcp-memory-server.json", "markdown": "https://feed7.dev/p/mcp-memory-server.md" } }, { "schema_version": "1.1", "id": "p9", "slug": "gemini-context-caching", "url": "https://feed7.dev/p/gemini-context-caching", "title": "Gemini agent SDK adds shared context caching across sessions", "why_included": "Cached context is billed once and reused across agent sessions — changes the economics of long system prompts.", "summary": "The agent SDK now exposes cross-session context caching with explicit TTL control. Cached tokens are ~10x cheaper on reuse.", "practical_implication": "Big static context (style guides, schemas, docs) belongs in the cache, not the prompt. Restructure bundles so stable material leads.", "agent_context": "Gemini SDK caches context across sessions with TTL control; cached tokens ~10x cheaper. Put stable material (guides, schemas) first so it caches; keep volatile material last.", "source": { "name": "Google", "url": "https://developers.googleblog.com/gemini-agent-sdk-context-cache", "published_at": "2026-06-29T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Docs Update", "layer": "context", "domains": [ "coding" ], "topics": [ "context-caching" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-06-29T00:00:00.000Z", "modified_at": "2026-06-29T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/gemini-context-caching", "json": "https://feed7.dev/p/gemini-context-caching.json", "markdown": "https://feed7.dev/p/gemini-context-caching.md" } }, { "schema_version": "1.1", "id": "p1", "slug": "claude-code-subagents", "url": "https://feed7.dev/p/claude-code-subagents", "title": "Claude Code ships subagents in isolated context windows", "why_included": "Long refactors no longer pollute the parent context — a child agent does the noisy work and reports back.", "summary": "Subagents get their own context window and a clean handoff contract. The parent session receives only the result summary and the list of touched files.", "practical_implication": "Fewer derailed sessions on big tasks. Route any task over ~20 file edits to a subagent and keep your planning context clean.", "agent_context": "Claude Code subagents run in isolated context. Use them for long refactors and research sweeps: the parent stays clean while a child does the noisy work, then reports which files remain. Prefer for tasks >20 file edits.", "source": { "name": "Anthropic", "url": "https://www.anthropic.com/engineering/claude-code-subagents", "published_at": "2026-07-02T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "tools", "domains": [ "coding" ], "topics": [ "subagents", "coding-agents" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-02T00:00:00.000Z", "modified_at": "2026-07-02T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/claude-code-subagents", "json": "https://feed7.dev/p/claude-code-subagents.json", "markdown": "https://feed7.dev/p/claude-code-subagents.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "Wearing the Agent: From Group Chats to Glasses — Sai Krishna Rallabandi", "url": "https://www.youtube.com/watch?v=s67bE2Ur3bY", "source_name": "AI Engineer", "published_at": "2026-07-29T22:58:19+00:00", "summary": "Shared agents need a different harness: action-boundary security, selective group memory, per-user privacy, and routing that decides both who receives information and when the agent should speak." }, { "title": "UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams", "url": "https://arxiv.org/abs/2607.26017v1", "source_name": "arXiv", "published_at": "2026-07-28T17:28:21+00:00", "summary": "UniMem routes novel experience into retrieval memory and consolidates recurring patterns into expandable parameters, offering a design for agents that learn across unlabelled task streams." }, { "title": "MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents", "url": "https://arxiv.org/abs/2607.25992v1", "source_name": "arXiv", "published_at": "2026-07-28T17:08:56+00:00", "summary": "MemLens scores individual agent memories, stores them by value, and exposes quality, latency, and token tradeoffs—an inspectable alternative to retaining every interaction equally." }, { "title": "AI Agents for Performance: Ship Faster, Pay Less — Rajat Shah, Netflix", "url": "https://www.youtube.com/watch?v=CgsWxRUY5Eo", "source_name": "AI Engineer", "published_at": "2026-07-28T00:59:04+00:00", "summary": "Netflix’s performance-agent playbook feeds production profiles and exact deployed code to agents, then relies on canaries and engineers to validate proposed optimizations." }, { "title": "Eviction as Estimation: A Fixed-Lag Smoothing View of Test-Time Memory, and When Measuring Beats Accumulating", "url": "https://arxiv.org/abs/2607.24667v1", "source_name": "arXiv", "published_at": "2026-07-27T17:08:27+00:00", "summary": "A delayed KV-eviction policy helps when memory reuse is sharp and observable, but loses its edge on natural-text benchmarks—a useful warning for agent-memory claims." }, { "title": "Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AI", "url": "https://www.youtube.com/watch?v=H7puB0RwJMM", "source_name": "AI Engineer", "published_at": "2026-07-23T04:00:02+00:00", "summary": "LLM synthesis breaks simple source pointers when facts merge, change, or outlive one input. Model provenance inside the context graph so agents can filter trust, debug derivation, and apply deletion policies." }, { "title": "WTF Is the Context Layer? The Missing Infrastructure for Production Agents — Prukalpa Sankar", "url": "https://www.youtube.com/watch?v=8G_1-3IO4ZQ", "source_name": "AI Engineer", "published_at": "2026-07-14T22:45:06+00:00", "summary": "Atlan’s agent experiments argue for shared, versioned context instead of per-agent memory: a portable layer for business facts, skills, norms, retrieval, and feedback across changing harnesses." }, { "title": "TencentCloud/TencentDB-Agent-Memory", "url": "https://github.com/TencentCloud/TencentDB-Agent-Memory", "source_name": "GitHub", "published_at": null, "summary": "An open-source memory hub turns agent conversations, workflows, docs, and code into governed assets that can be reused across sessions and roles, reducing repeated project setup." }, { "title": "The Factory That Dreams: 39 AI Agents, No Framework - Rushabh Doshi, Machinecraft", "url": "https://www.youtube.com/watch?v=jtzh-GBXBWc", "source_name": null, "published_at": null, "summary": "A 100-person factory built a multi-agent sales system around organized company memory, specialist roles, and human approval. The useful pattern is retrieval and governance, not custom model training." }, { "title": "Use any Chat SDK adapter with eve", "url": "https://vercel.com/changelog/eve-chat-sdk-channel", "source_name": null, "published_at": null, "summary": "eve’s Chat SDK channel lets one agent span messaging adapters while retaining threads, approval cards, proactive sends, webhook handling, and overridable defaults." }, { "title": "Effective harnesses for long-running agents", "url": "https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents", "source_name": null, "published_at": null, "summary": "Anthropic's harness pattern for multi-session agents: an initializer sets up the env, a JSON feature list, and progress files; each session then ships one feature, verified end-to-end and committed to git." }, { "title": "affaan-m/ECC", "url": "https://github.com/affaan-m/ECC", "source_name": "GitHub", "published_at": null, "summary": "ECC packages skills, hooks, memory, orchestration, and security controls for multiple coding-agent harnesses, but its breadth makes selective installation and verification essential." } ] }