Agent memory needs its OpenTelemetry moment
TL;DR Highlight
Agent memory formats are fragmented, so this post proposes an OpenTelemetry-style shared spec.
Who Should Read
A developer frustrated that memory from CLAUDE.md, Cursor rules and memory MCP servers can't be read across agent tools
Core Mechanics
- Four memory formats on one machine
- The author lists AGENTS.md/CLAUDE.md, Anthropic's memory tool (/memories), Copilot's repo facts, and a memory MCP server (JSONL knowledge graph).
- None of the four can read the others.
- OpenTelemetry agreed on four layers
- A data model, semantic conventions, a protocol (OTLP) and reference implementations were agreed on, while vendors compete on storage, querying and analysis.
- The author says agent memory is where observability was before 2019.
- Existing proposals focus on export
- MacPaw Portable Memory (.mem bundle), Open Memory Protocol (April 2026 draft, ZIP+manifest) and Cognee COGX exist.
- They mostly move memory between vendors, while live addressing, many tools sharing one place in a repo, remains open.
- Four questions every format answers
- Where is it true, what is its identity (update vs duplicate), what does it say, and when does it stop being true.
- The real fork is who writes it (person, model, extraction service), so a spec should record that.
- Example record: frontmatter plus Markdown
- Address, key, id, kind, written_by, lifecycle and attributes go in frontmatter, and the body is Markdown.
- The author says the field names are illustrative, not a proposal.
- Spec = conventions, contract, conformance tests
- It proposes a registry of address levels and attribute namespaces, write/read/list/search/archive operations over MCP, and conformance tests.
- Storage, ranking, embeddings and UI stay outside the spec as competitive areas.
Community Reactions
- Opinion: standardizing is too early
- Commenters said OTel came after decades of experience and standardizing now would add friction to experimentation.
- Another warned of a premature abstraction like SOAP or SGML.
- Counterpoint: LLMs read any format
- Commenters said agents already read memory files in various formats and a simple skill is enough.
- One expected CLAUDE.md pointing to AGENTS.md to perform about the same as native folders.
- Parallel session writes should be in the spec
- A commenter said one session seems able to silently overwrite another's memory.
- They cited Anthropic's agent memory research.
- Experience: agent memory is harmful
- One commenter said it remembers the wrong things and creates an inane focus, so they disable it on every harness.
- They tell the agent explicitly to record findings in a file when needed.
How to Apply
- List where your tools keep memory
- Today, search your repo and home directory for AGENTS.md, CLAUDE.md, .cursor/rules, /memories and MCP memory stores.
- Note scope, author type and expiry for each to spot duplicates.
- Add frontmatter to memory files
- Add fields like key, written_by and expires_at to your team's lesson notes.
- Use them to judge trust and prune stale notes.
- Consolidate memory write paths
- Have CLAUDE.md reference AGENTS.md and point agents to one write directory.
- Split files by key so parallel sessions don't write the same file.
Try It
# Paste into Claude Code or ChatGPT (simplified version of the post's idea: memory record audit)
Below are the agent memory files in our repo.
{{memory file contents}}
Analyze each memory with four questions and output a table.
1) Where is it true (address) 2) Identity (key, duplicates) 3) Content summary 4) When does it stop being true (expiry, evidence it is stale)
Also label the author type (person/agent/service).
Next step: propose merged files that combine duplicates and drop stale ones, with frontmatter (address, key, written_by, expires_at).Terminology
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