Muster — the AI agent harness you can audit

Open-source agent runtime with a token-waste ledger, leak-proof scoped memory, eval-gated learning, and integrity verification. Works with Claude, OpenAI, Gemini, Grok, Kimi, DeepSeek, Ollama, and 20+ providers. TypeScript, MIT, self-hosted.

Self-improving agents are easy. Provably governed agents are Muster: every memory scoped, every skill eval-gated, every token on a ledger. Does your agent pass muster?

pnpm dlx @musterhq/cli init && muster demo

See it work — muster demo

One command provisions a throwaway workspace and a local model service, then runs the full governed pipeline: scoped-memory recall → token ledger → integrity check.

muster demo — provisioned an isolated workspace and a live stub model service.

> Where do we deploy?
  (recalled 1 scoped memory)
  Muster deploys to uat-erp.example.com (recalled from scoped memory).

run            model                        in       out      est  cost$    waste   session
----------------------------------------------------------------------------------------------
287bde9c-eb19- demo/demo-model              38       17       ~    -        -       -
653b434a-0924- demo/demo-model              7        18       ~    -        -       -

totals by model              runs   in         out        cost$      waste-runs
--------------------------------------------------------------------------------
demo/demo-model              2      45         35         -          0

integrity check at 2026-06-12: OK
store      lines    corrupt
episodes   2        0
memory     3        0
tokens     2        0

Proof, not promises — muster benchmark

The Token Waste Index measures what Muster's immutable-transcript renderer and never-wedge compactor actually save versus a naive replay-everything harness. Deterministic — no model calls, fully reproducible.

scenario                          turns  naive    muster   reduction  replay-overhead
--------------------------------------------------------------------------------------
codebase-refactor-20              21     82.6k    40.7k    50.7%      90.5%
incident-triage-30                31     140.4k   56.2k    59.9%      93.6%
erp-data-audit-40                 41     197.8k   72.4k    63.4%      95.1%
research-synthesis-25             26     156.8k   64.6k    58.8%      92.3%
long-support-thread-50            51     268.8k   93.8k    65.1%      96.1%
--------------------------------------------------------------------------------------
AGGREGATE                         170    846.4k   327.9k   61.3%      94.2%

~61% fewer tokens on long agent sessions, and the saving grows with session length. Full methodology + table: benchmark/RESULTS.md.

Features

| | | |---|---| | 🪙 Token ledger | Every run recorded; replay-waste flagged with the exact ratio. muster tokens | | 🔒 Scoped memory | Tenant / workspace / user / role / session lanes. Cross-user leakage is a failing test, not a hope. | | 🎓 Eval-gated skills | Skills promote only after an eval suite converges — no self-certified learning. | | 🛡️ Integrity verify | Corruption, duplicate runs, silent model drift, stale-narrative poisoning. muster verify | | ♻️ Never-wedge compactor | A session can always take a turn — no compaction deadlock. | | 🔁 Recursive self-test | muster evolve runs real tasks, adjudicates against evidence, converges. | | 🌊 Flow engine | Tool/agent/gate steps, preflight, durable runs, replay/diff, flow loop --cron. | | 📡 One gateway, every chat app | Telegram · Slack · Discord · WhatsApp · Google Chat · Teams + a zero-dep web client. | | 🔌 MCP client | Per-server isolation, circuit breakers, capped results. | | 🧰 20+ providers | Claude (Fable 5), OpenAI, Gemini, Grok, Kimi, DeepSeek, Groq, Ollama, vLLM… zero lock-in. | | 💓 Pulse scheduler | Heartbeat that feels alive at ~5% of the token cost — zero-LLM preflight + daily budget. | | 👥 Pull-based subagents | Durable run store, exactly-once results, no zombie processes. |

Everyday commands

muster provider add anthropic                 # or kimi / ollama / add-openai-compatible <any-url>
muster run "where do we deploy?"              # governed run: memory recall + ledger + evidence
muster tokens                                 # per-run cost table, replay-waste flags
muster verify                                 # store integrity
muster sessions search "leave balance"        # FTS search across past sessions
muster evolve evolve-suites/core-capabilities.json   # recursive self-test
muster pulse add "0 9 * * 1-5" --kind task --prompt "summarize open work"
muster benchmark                              # the Token Waste Index, live

Everything renders plain-text tables in your terminal. No web dashboard required.

Architecture

prompt ──> router ──> [agent rules + recalled scoped memory] ──> runtime
                                                                  ├─ Pi SDK (embedded)
  scoped memory lanes                                             ├─ Claude Code CLI
  tenant/workspace/user/role/session                              ├─ Codex CLI
        │                                                         └─ any HTTP provider
        ▼
  episode store ──> token ledger ──> feedback adjudication ──> eval fixtures
        │                 │                                         │
        └──── muster verify (integrity) ◄──── muster evolve (self-test loop)

Built on the pi.dev coding-agent SDK as bedrock — embedded sessions, tools, and TUI — with the governance layer Muster adds on top.

How it compares

| | Muster | OpenClaw | Hermes | crewAI | |---|---|---|---|---| | Token ledger + waste detection | ✅ | ❌ | ❌ | ❌ | | Scoped memory (leak = CI failure) | ✅ | partial | ❌ (single MEMORY.md) | ❌ | | Eval-gated learning | ✅ | ❌ | ❌ (promotes on use) | ❌ | | Governed fallback (evidence, never silent) | ✅ | ❌ (#65646) | ❌ | ❌ | | Session integrity verification | ✅ | ❌ (#75235) | ❌ (#5563) | ❌ | | Channels & web embeds (one governed envelope) | ✅ Slack, Discord, Telegram, WhatsApp, GChat, Teams, any web app | ✅ 20+ bespoke | ✅ | ❌ | | Maturity / ecosystem | v0 | huge | large | large |

Honest table: they have breadth and ecosystems we don't (yet). We have the governance core they demonstrably lack — each ❌ above links to their own issue tracker.

Use cases

• AI agents for business systems: the Frappe/ERPNext capability pack ships permission-scoped tools where every action executes as the real user — see capability-packs/frappe/. Built from a production deployment serving thousands of employees.
• Cost-controlled agent fleets: per-profile ledgers, per-flow budgets, waste alerts.
• Regulated / BFSI / air-gapped: local models (Ollama, vLLM, SGLang), no cloud required, full audit trail.
• Agent CI: muster evolve as a pipeline gate — your agent's behavior is regression-tested like code.

Keywords

AI agent framework · LLM agent harness · agent memory · token cost tracking · agent observability · eval-driven development · agentic workflows · Claude agent SDK · OpenAI agents · Ollama agents · self-hosted AI agent · AI governance · agent audit trail · ERPNext AI · Frappe AI assistant · multi-provider LLM routing

Maturity — v0.1, feature-complete core

Muster is v0.1: the governed core is feature-complete and test-covered, the public API may still shift before 1.0. (For reference, the largest open agent frameworks still version in the v0.x / date-based range — v0.x here means "pre-1.0 stability," not "incomplete.")

Mapped against the mid-2026 production bar for agent harnesses:

| Production-bar capability | Muster | |---|---| | MCP client | ✅ per-server isolation, circuit breakers, capped results | | Eval-gated learning | ✅ skills promote only through a converged suite | | Per-run cost / token tracking | ✅ token ledger with replay-waste detection | | Layered, deterministic permissions | ✅ scoped-memory lanes + hook bus, leak = failing test | | Memory: working / episodic / scoped | ✅ scoped lanes + SQLite session store (FTS5) | | Strategic (not reactive) compaction | ✅ immutable transcript renderer + never-wedge compactor | | One protocol for CLI / desktop / web | ✅ JSON-RPC gateway with ledger.tick live cost | | OpenTelemetry tracing | 🔜 planned | | Desktop apps | 🔜 Tauri over the RPC protocol |

Claude Fable 5 ready: the Anthropic preset defaults to claude-fable-5 (1M context, adaptive thinking via effort). The token ledger and scoped tool exposure align with Fable 5's deferred-tool-loading and task-budget direction. First-class stop_reason: "refusal" handling is on the roadmap.

Independence: Muster is operator-governed and MIT — no foundation, no single-vendor entanglement. You run it, you audit it.

Next: OTEL tracing, Tauri desktop apps, channel approval round-trips, npm publish, and a Token Waste Index benchmark. See docs/SDLC_KANBAN.md and docs/FEATURE_PARITY_PLAN.md.

License

MIT. Open source, community-driven. Contributions welcome — start with good first issue.