
Cogmem
io.github.writerslogic由 GitHub 账号 writerslogic 发布v2.7.1更新于 Oct 24, 2026
A self-improving, verifiable memory layer for AI coding agents.
概览
为 AI 编程代理提供本地记忆层,存储经过签名、可验证的经验并在会话间召回。
- 可以做什么
- Cogmem 为助手提供持久且可自我改进的编程记忆。它暴露八个工具——recall、note、status、verify、receipt、tree_head、progress 和 review_pending——以及实时用户模型和按项目状态等只读资源。记忆以 did:key 身份签名为 W3C 可验证凭证,并记录在哈希链式透明日志中,因此被篡改或注入的记忆可被检测并拒绝。它还会建模代理反复出现的错误,并在工具调用边界拦截。
- 适用场景
- 当你希望代理跨会话保留经验、决策和项目状态,而不是重复同样的错误时,值得添加。它适合重视记忆准确性和防篡改的编程工作流,且你愿意运行本地服务和钩子。
- 运行要求
- 通过 pip(cogmem)或克隆安装脚本本地安装;需要 Python 3.12+。MCP 服务器以 stdio 方式运行,命令为 cogmem mcp。可选 [recall] 附加组件通过 fastembed 提供本地语义召回。cogmem init 或 install.sh 会接入 Claude Code 钩子。学习流程需要 ANTHROPIC_API_KEY。数据位于 COGMEM_HOME(默认 ~/.claude/cogmem)。
安装
在 SourceWeft 中
- 打开 控制台中的 Cogmem,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
cogmem
A self-improving, verifiable memory layer for AI coding agents.
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A self-improving, verifiable memory layer for AI coding agents.
cogmem learns how you work across sessions so your agent gets more accurate and more autonomous over time: it stops repeating mistakes, keeps a live model of each project, and surfaces the right lesson at the right moment. Every memory is cryptographically signed and tamper-evident, so a poisoned or altered memory can be detected and rejected before it ever steers the agent.
Developed by WritersLogic — local-first recall; your memory and identity key stay on your machine (see Privacy).
Installation
From PyPI
pip install cogmem gives you the cogmem CLI and the MCP server (cogmem mcp, or uvx cogmem mcp on demand) — the verifiable-memory tools need only the core install. Add the [recall] extra for local semantic recall, then run cogmem init to wire the full learning loop (the SessionStart/UserPromptSubmit/Stop hooks and the index) into Claude Code. cogmem init is idempotent; re-run it any time.
Clone installer (turnkey, with the warm daemon)
The clone installer does everything pip install + cogmem init does, plus sets up the warm recall daemon (launchd/systemd) as a managed service:
Or in one line:
install.sh is idempotent — run it again any time to upgrade in place. It sets up
the code under ~/.claude/cogmem, a self-contained virtualenv with dependencies,
the cogmem CLI on your PATH, the Claude Code hooks, and a warm recall daemon
(a launchd agent on macOS, a systemd --user service on Linux). Requires
Python 3.12+; semantic recall runs on a local model (fastembed, no external
API). Pass --no-daemon or --no-hooks to skip those steps; set COGMEM_HOME
to install elsewhere — the CLI, engine, and hooks all resolve it at runtime, so
a non-default install keeps its memory and identity fully self-contained.
Custom Installation
Install to a different directory
Set COGMEM_HOME to place cogmem somewhere other than the default
~/.claude/cogmem:
Or with the one-liner:
The installer copies the code, creates the virtualenv, and symlinks the CLI to
~/.local/bin/cogmem (or wherever COGMEM_BIN points).
CLI path
Set COGMEM_BIN to control where the cogmem CLI symlink is placed:
If COGMEM_BIN is not on your PATH, the installer prints a warning. You can
always invoke cogmem directly from $COGMEM_HOME/cogmem.
How data directories and identity keys are resolved
At runtime the CLI and engine read COGMEM_HOME from the environment. When it is
unset they fall back to ~/.claude/cogmem. All runtime data lives under the
vault/ subdirectory:
The identity key is generated on first run (via cogmem status or any engine
operation) and persisted at $COGMEM_HOME/vault/identity/agent.key. The
corresponding did:key is derived from the Ed25519 public key. Moving or
reinstalling cogmem to a new COGMEM_HOME creates a fresh identity unless you
migrate the vault/ directory.
MCP client with a non-default install
The standard MCP client configuration works regardless of COGMEM_HOME because
the cogmem CLI resolves the environment variable at runtime:
If the CLI is not on your PATH, use the full path:
Or prefix with COGMEM_HOME in a shell wrapper:
Quick Start
MCP Integration
Run cogmem as an MCP server and connect any MCP-compatible client:
Eight tools are exposed: recall, note, status, verify, receipt, tree_head, progress, review_pending, plus read-only resources (the live user model and per-project state).
Claude Code Integration
install.sh wires cogmem into Claude Code automatically (idempotently merged into
~/.claude/settings.json) — no manual invocation required. Five hooks make the
memory loop run in the background:
Every hook is strictly fail-open: any error, timeout, or cold daemon injects
nothing and never blocks your prompt. The scripts live in ~/.claude/cogmem/hooks/;
re-run install.sh (or ./install.sh --no-daemon) to refresh the wiring.
Why cogmem? -- learns from outcomes, models failure modes, verifiable memory
Chat-memory systems (Mem0, Letta, Zep) store and retrieve facts. cogmem is built for coding agents and goes further on three axes:
It learns from outcomes. A feedback loop scores whether a recalled lesson actually helped, refines rules that prove wrong, and retires ones that mislead.
It models its own failure modes. cogmem tracks where the agent tends to go wrong in your work and intercepts known mistakes at the tool-call boundary — before they happen, not afterward.
Its memory is verifiable. Each memory is a W3C Verifiable Credential signed by the agent's did:key, recorded in a tamper-evident, SCITT-style transparency log. Agent memory is an attack surface; cogmem makes it auditable and poison-resistant.
Features -- two-layer memory, outcome feedback, self-model, project state, cross-project narrative, self-regulation, verifiable credentials
- Two-layer memory: always-loaded directives (scope-gated, human-approved) plus a semantic recall tail (local cross-encoder reranking, no data leaves the machine).
- Outcome feedback and self-refinement: memories earn or lose trust based on whether they actually helped; contradicted rules are corrected through a safe pipeline.
- Self-model and guard: a model of the agent's recurring mistakes, compiled into tripwires that intercept them at the
PreToolUseboundary. - Project-state model: a living per-project state (goal, claims, open questions, blockers) that gives situational continuity and reasons across time.
- Cross-project progress narrative: momentum, stalls, and dependencies across projects, surfaced as alerts.
- Self-regulation: recall thresholds tuned automatically against an eval harness.
- Verifiable Agent Memory:
did:keyidentity, W3C VC-signed memories, COSE_Sign1 SCITT signed statements (byte-compatible with HMS), a hash-chained transparency log with signed Merkle tree head and RFC 6962 inclusion receipts, optional poison-resistance enforcement. See PROVENANCE.md.
Verifiable Memory -- did:key identity, W3C VC, COSE/SCITT, hash-chained log, poison-resistance
cogmem treats every stored memory as a signed artifact:
did:keyidentity: each agent gets a persistent Ed25519 identity, exposed as a W3C DID.- W3C Verifiable Credentials: every memory is signed with
eddsa-jcs-2022Data Integrity proofs. - COSE_Sign1 / SCITT signed statements: byte-identical to the envelope format used by holographic-memory and crosstalk — independently verifiable by any of the three implementations.
- Hash-chained transparency log: append-only JSONL with SHA-256 chaining, a signed Merkle tree head, and RFC 6962-style inclusion receipts.
- Revocation: every credential carries a W3C Bitstring Status List entry; a demoted or retired memory is revoked in a signed status-list credential.
- Poison-resistance: altered or injected memories fail verification and are rejected before influencing the agent.
See PROVENANCE.md for the full specification.
Verify the C2PA sample yourself:
This proves the whole chain: agent identity (cawg.ica.credential_valid) bound to real cognition — a signed cogmem memory and a signed crosstalk reasoning audit, each an independently verifiable Ed25519 COSE/SCITT statement.
Standards alignment
cogmem is built on published standards, and it is precise about where it conforms versus where it is -style (compatible in shape and crypto, short of full profile conformance). The primitives are real Ed25519 signatures over real canonical byte structures — nothing here is mocked.
Toward full SCITT conformance. Three bounded steps, no new cryptography: (1) move iss/sub/content-type from the statement payload into the COSE protected header as CWT_Claims; (2) emit inclusion proofs as COSE Receipts (draft-ietf-cose-merkle-tree-proofs) in the statement's unprotected header; (3) make the external witness a mandatory Transparency-Service role distinct from the issuing agent. Steps 1–2 are re-encoding; step 3 is the architectural one, since a single-party log is a compatible format rather than meaningful transparency.
Privacy
cogmem is local-first by design. Memories, embeddings, and the identity key live on your machine, and semantic recall is fully local — the embedding and reranker models (fastembed) run on-device, so querying your memory never leaves the machine.
The learning pipeline is not local: acquisition, consolidation, the feedback judge, and the project/user-model synthesis send the relevant session transcript to the Anthropic API (ANTHROPIC_API_KEY). That is how rules are extracted and scored. If you need fully-offline operation, run with --no-hooks (recall still works) until a local-model extraction path lands. In short: recall is local; learning calls the API.
Part of the Agent-Provenance Stack
cogmem is one component of the WritersLogic verifiable agent-provenance pipeline — agent identity, memory, reasoning, and signed output, cryptographically bound end to end.
All four share one substrate — COSE_Sign1 / SCITT signed statements (Ed25519) and W3C DID identity — specified in UNIFIED-PROVENANCE.md.
Security
Found a vulnerability? Please report it privately — see SECURITY.md.
License
Apache-2.0 — see LICENSE.
来源:README.md,提交 e49b1d7
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0版本历史
1- v2.7.1最新Sep 16, 2026
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