Myelinate

Your agents make the same mistakes twice. Myelinate makes them learn.

Myelinate plugs into any agent framework and turns feedback your team already has (tests, CI, review bots, user corrections) into skills the agent uses on the next run. No fine-tuning, no prompt edits. Every change is reviewed, tested on live traffic and reversible.

Does it work?

In the house-rules benchmark, Claude Haiku 5.5 has to follow team conventions it was never told: commit format, migration rules, support-reply style and docstring rules. It learns only from CI-style verifier feedback. On tasks it never trained on, its pass rate went from 0% to 91% after 24 runs, for about $1.25 of learning.

Try it

pip install "myelinate[server]"
MYELINATE_ENV=dev myelinate serve          # REST + MCP gateway on :8080, interactive API docs at /docs

In your agent's codebase, install only the client (pip install myelinate). It depends on just httpx and pydantic, so it fits next to pinned framework versions. The [maf] adapter works with Microsoft Agent Framework 1.15 and later.

pip install "myelinate[maf]"               # client + Microsoft Agent Framework adapter

See it learn (the demo above; a few cents with your Anthropic key):

git clone https://github.com/notaaaronfr/myelinate && cd myelinate && uv sync --extra server
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env && set -a && . ./.env && set +a
uv run python examples/demo_learning.py

Connect your agent: Microsoft Agent Framework · OpenClaw · MCP · OpenTelemetry (zero code) · LangGraph, CrewAI and others · Full documentation

Myelin is the sheath the brain builds around pathways that get used, and that work, so they fire faster and more reliably. Myelinate does the same for agents: rules that help get reinforced, rules that hurt get corrected or retired, and nothing changes without evidence.

How it works

A drop-in component that gives any agent framework a self-improvement loop: learned skills, persistent memory, reflection, and skill evolution. Every change is governed, measured and reversible.

You attach it to Microsoft Agent Framework, OpenClaw, LangGraph, CrewAI, ADK or anything else. The agent stays in its own framework and makes two calls per run: recall before it and record after it. Frameworks that emit OpenTelemetry need no code at all.

The design combines the strongest published mechanisms (ACE, Kayba ACE, GEPA, SkillOpt, Trace2Skill, WikiSkill, ReasoningBank, Mem0, Zep/Graphiti, Honcho, Hermes Agent, Letta, LangMem, Voyager, AWM). It adds what none of them have: live canary rollout, a holdout control group that measures causal lift, and enterprise governance.

 agent (MAF · OpenClaw · LangGraph · CrewAI · ADK · custom)          OTel GenAI / OpenInference traces
   │ recall ──────────────┐            ┌──────────── record              │ (zero-code)
   ▼                      ▼            ▼                                 ▼
 ┌──────────────────────────── Myelinate (REST · MCP · OTLP) ────────────────────────────┐
 │ authN/Z → tenant isolation → rate limit → audit                                                    │
 │ RECALL   hybrid BM25+dense (RRF) × skill posterior → MMR → canary arm / holdout → stable, cacheable │
 │ RECORD   PII redaction → usage + reward credit (Beta posteriors: skills, canaries, holdout)         │
 │                         durable debounced jobs (multi-replica safe, budgets)                       │
 │ REFLECT  outcome signal (verifier > feedback > label > LLM judge) → per-run diagnosis →            │
 │          helpful/harmful tags on injected rules → memory reconcile → CURATE bounded delta ops      │
 │ EVOLVE   GEPA: Pareto parents, minibatch pre-gate, strict held-out gate, rejected-edit buffer      │
 │ CURATE   dedup-merge (KEEP remembered), stale→archived lifecycle, evidence-based nomination        │
 │ GOVERN   changeset → safety scan + lint → risk tier → policy/human → CANARY on live traffic →       │
 │          promote/reject by posterior → immutable version → rollback                                │
 └─────────────────────── SQLite (dev) · PostgreSQL (prod) · Prometheus · OpenTelemetry ─────────────┘

Related MCP server: engramia

Why it's better than each alternative

What each mechanism is, and where it comes from

Knowledge model

• Skills are sets of atomic bullets ("When , ", 15–50 words) grouped as steps, pitfalls, heuristics, verification, snippets and notes, and exported as agentskills.io SKILL.md. ACE's ablation: delta updates instead of whole rewrites were worth +17 vs +3.6 points. Rewriting causes "context collapse" (18k tokens fell to 122 and accuracy dropped below baseline).

• Bounded edits per changeset (SkillOpt's "textual learning rate"). Unbounded rewrites underperform.

• Lint blocks incident-diary-shaped rules (Hermes) and over-long rules. Rules must generalise, with names replaced by placeholders, and a broad rule can replace narrow ones only with ≥3 instances across ≥2 tasks (Kayba).

Signals and credit

• Outcome hierarchy: verifier/tests > user feedback or correction > reported outcome > LLM self-judge (ReasoningBank: judge accuracy of about 73% was enough). Evidence gating: runs without a reliable signal can add memories but never change skills. ACE and Dynamic Cheatsheet both fell below baseline when learning from unreliable feedback. Letta found verifier feedback added +6.7 points on top of trajectory-only learning.

• Reflector diagnoses each run, successes included (ACE, Trace2Skill), and tags only the rules that were actually injected (Kayba). Optional citation mode ([b-xxxxxxxx]) gives sharper attribution.

• Beta posteriors on every skill and canary arm, with fractional, re-creditable rewards (feedback after a judged outcome replaces the old credit instead of double counting).

Improvement

• Curator returns only ops for what is missing or wrong. An empty list is the expected answer for most batches. Previously rejected edits are shown to it so they aren't proposed again (SkillOpt's buffer).

• Evolution:

  • GEPA Pareto parent selection (+12.4 vs +6.1 for select-best in GEPA's ablation).

  • A minibatch pre-gate (b=3) before the expensive held-out evaluation.

  • A strict gate: ties lose, and the improvement must clear a margin.

  • A brevity guard that rejects children dropping more than 30% of the rules.

  • The output is a reviewable net diff.

• Canary: a changed proven skill is served beside the incumbent by Thompson sampling with an exploration floor. Assignment is sticky per session. The canary auto-promotes at P(better) ≥ 0.95 and auto-rejects at ≤ 0.05 or when the budget runs out. This guards against the limit every paper names: offline judges are imperfect.

• Holdout: by default 5% of runs get no learned skills, and the would-be-served skills are recorded so the comparison is like for like. /insights reports the lift with a credible interval: the ROI number.

Memory

• Reconcile each candidate against its nearest memories with ADD / UPDATE / SUPERSEDE / NOOP (Mem0). Exact duplicates cost no LLM call.

• Bi-temporal: superseded facts get invalid_at and superseded_by and are never deleted by learning (Zep). as_of gives point-in-time recall for audits.

• Profile cards per user, rebuilt in the background (Honcho peer card, Hermes USER.md). The current user's preferences always apply.

Upkeep

• Dedup proposes merges for near-duplicate rules. KEEP decisions are remembered and merges sum the evidence counters (ACE, Kayba).

• Lifecycle moves unused skills active → stale (14 d) → archived (30 d). Nothing is deleted, and pinned skills are exempt (Hermes curator).

• Nomination: poor posteriors or rules that keep being tagged harmful queue an evolution run. Counters nominate but never delete; Kayba removed auto-delete at harmful≥3 for good reason.

Retrieval

• BM25 plus optional embeddings, fused with RRF, with the skill posterior as a prior, then MMR for diversity (Zep, Mem0 2026).

• Progressive disclosure: the top-k skills are injected in full and the rest appear as a name index loaded with skill_view (Hermes).

• Injected text is ordered deterministically, so the prompt prefix caches (ACE served 91.8% of input from cache).

Quick start

uv sync --extra server --extra maf
echo "ANTHROPIC_API_KEY=..." >> .env   # learning model (default claude-opus-5-5); read from env or .env
MYELINATE_ENV=dev uv run myelinate serve --port 8080   # dev: no credentials, tenant "dev"
open http://localhost:8080/docs

Without MYELINATE_ENV=dev the gateway is secure by default: it refuses to start without a bootstrap admin key or OIDC configuration.

Production: cp .env.example .env, edit it, then docker compose --env-file .env -f deploy/docker-compose.yml up -d. That runs the gateway, a worker pool and Postgres. Mint scoped keys with POST /v1/admin/api-keys.

Plugging in

Microsoft Agent Framework, as a context provider:

learner = SelfLearningProvider(GatewayClient("https://learner.internal", api_key="myl_..."), agent_id="support-bot",
                               verify_fn=lambda ctx: Verification(passed=ticket_resolved(ctx)))  # optional ground truth
agent = Agent(client=chat_client, instructions="...", context_providers=[learner])

OpenClaw, as a plugin (adapters/openclaw/): the before_prompt_build hook injects learned context, agent_end records the run, and it adds the tools skill_view (credited to the turn), remember, and rate_last_turn (with a correction).

Zero code for any framework that emits OpenTelemetry GenAI spans (MAF, ADK, Strands, Pydantic AI, OpenClaw diagnostics-otel, or LangGraph/CrewAI through OpenInference):

OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=https://learner.internal/v1/traces
OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf          # http/json works too; gzip is accepted
OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer myl_...,X-Agent-Id=support-bot"

Spans of one trace that arrive in separate exports are merged into one episode. gen_ai.skill.name spans credit skills automatically. Set the span attributes myelinate.outcome / myelinate.verification.passed to add labels. Recall still needs one call, or the MCP tool, for the agent to use what was learned.

Any MCP client (Claude, Hermes, Cursor...): https://learner.internal/mcp, with the tools recall, skill_view, remember, record_episode, feedback and ask.

Anything else (LangGraph, CrewAI, OpenAI Agents SDK...):

async with learner.turn(task, user_id="u1") as t:
    out = await run_my_agent(system=BASE + t.instructions, task=task)
    t.add_message("assistant", out)
    t.verify(passed=tests_ok, details=test_log)

Bring existing skills: POST /v1/skills/import with any SKILL.md (Anthropic, Hermes, agentskills.io). Bulk-import history with POST /v1/episodes:batch, then POST /v1/agents/{id}/replay to warm-start learning offline (ACE's offline warm-up lifted its online score from 56.1 to 59.5).

API

Enterprise controls

• Identity: tenant API keys (hashed, revocable) or OIDC (Entra ID, Okta, Auth0, Keycloak). Scopes are learn, review and admin.

• Isolation: every query is tenant-scoped. Knowledge is scoped per agent, with tenant-shared skills under agent_id="*".

• Safety:

  • PII and secrets are redacted before storage.

  • Every changeset is scanned for secrets, prompt injection and oversize content. Flagged changes never auto-apply.

  • Risk tiers: additive = low, edits = medium, removals and routing changes = high (always human).

• Change control:

  • Immutable version snapshots and optimistic concurrency (stale changesets fail instead of clobbering).

  • Canary before full rollout, and one-call rollback.

  • The rejected-change history is kept and learned from.

• Privacy:

  • Right to erasure.

  • Episode retention (myelinate purge).

  • Hard memory delete for data-subject requests. Learning itself never hard-deletes.

• Cost: a per-tenant daily token budget. Over-budget jobs are deferred, not failed. Usage is broken down by purpose for chargeback.

• Reliability:

  • A durable job queue with leases, heartbeats, retries and debounce, safe across replicas. Only one job per agent reflects at a time.

  • Atomic SQL counter updates, so concurrent runs never lose credit.

  • Idempotency keys (set by the SDKs and derived from the OTel trace id), so retried uploads never double count.

  • Adapters fail open, so a gateway outage never breaks the agent.

  • Canaries that can't reach a verdict expire and keep the incumbent. Queued changes are re-evaluated, never silently rebased.

• Observability: Prometheus metrics, including learning-cost and canary-verdict counters. JSON logs carry request and tenant IDs, and OTel tracing is available.

Layout

src/myelinate/
  engine.py       facade: recall / record / credit / feedback / ask / insights / export
  learning.py     outcome signal, reflector, evidence gating, curator
  evolution.py    GEPA-style evolution with gates
  governance.py   changesets, policy, canary experiments, apply, versions, rollback
  memory.py       reconcile, bi-temporal store, profile cards
  maintenance.py  dedup, lifecycle, nomination, profile refresh
  skills.py       delta ops, simulation, diff, SKILL.md render/parse, lint
  experiments.py  Beta posteriors, Thompson sampling, lift
  retrieval.py    BM25 + dense RRF + MMR      embeddings.py  optional /v1/embeddings client
  otel.py         OTLP → episodes             prompts.py     role prompts + strict schemas
  core.py         metered LLM, budgets, jobs, audit           worker.py  durable job worker
  gateway/        FastAPI app, auth/RBAC, MCP server          adapters/  maf.py, generic.py
adapters/openclaw TypeScript plugin + reusable TS client
tests/            60 tests: loop, gating, tagging, canary, GEPA, memory, dedup, lift, OTel, import, auth, MCP, budget,
                  concurrency, idempotency, SDK request shape, regressions

Verification status

Not covered here: load and performance testing, Kubernetes manifests (only Compose ships), and Entra ID / Okta SSO against a real identity provider (JWT validation is implemented, but was not tested against one).

Honest limits

• Learning quality is bounded by signal quality. Wire verify_fn / t.verify() / feedback to real checks where you can. Without them the system stays conservative (memories only).

• The LLM judge used in evolution is a proxy. That is why changed proven skills are canaried on live traffic. Canaries need traffic: with few runs per skill, keep MYELINATE_AUTO_APPROVE_MEDIUM_RISK=false and review.

• Migrations. Tables are created at startup. Add Alembic before your first production schema change.

• Scale. Lexical retrieval over a few thousand skills per agent is fine. Beyond that, configure embeddings and move them to pgvector. The rate limiter is per process; put a shared limiter in front of multiple replicas.

• OTLP is accepted over HTTP (protobuf or JSON, gzip or not). gRPC needs a Collector in front.

• The OpenClaw plugin targets openclaw@2026.10.4 (Node ≥24.16 <25 or ≥26.1). Re-run npx tsc against newer SDK versions when upgrading.

Contributing and security

Issues and pull requests are welcome. CI runs the test suite on SQLite and PostgreSQL, type-checks the OpenClaw plugin and builds the Docker image. Please report vulnerabilities privately; see SECURITY.md.

License

MIT