# MicroResolve
[](https://crates.io/crates/microresolve)
[](https://pypi.org/project/microresolve/)
[](https://www.npmjs.com/package/microresolve)
[](https://docs.rs/microresolve)
[](https://github.com/gladius/microresolve/actions/workflows/ci.yml)
[](#license)
A reflex layer for LLM agents. Your agent makes the same routing
decisions on every call — which tool, which intent, is this PII,
is this safe. MicroResolve handles those repeated decisions in tens of
microseconds and learns continuously from your existing LLM calls, so
your LLM only thinks about the parts that actually need thinking.
> **v0.1 — early release.** API may change before 1.0. Pin exact versions
> in production.
[**Documentation**](https://gladius.github.io/microresolve/) ·
[**Benchmarks & methodology**](benchmarks/) ·
[**Changelog**](CHANGELOG.md) ·
[**Contributing**](CONTRIBUTING.md)
```text
"the dispute on order 1834 came back invalid — refund the customer,
mark the dispute resolved, then post in #ops that we lost it"
→ create_refund (confirmed, high)
→ update_dispute (confirmed, high)
→ slack_send_message (confirmed, medium)
→ relation: sequential
~92 µs · narrowed 129 tools to top-3
for the LLM, with no LLM call yet
```
LLMs do everything: reason, compose, generate, choose tools, handle
context. MicroResolve only handles the *repeated routing decisions* —
which tool, which intent, what's PII, is this safe. It **learns continuously from your existing LLM calls** —
every misroute the LLM corrects flows back into the index, so the
candidate set sharpens from production traffic alone.
**Use it for:**
- **LLM-agent tool prefiltering** — narrow 100+ tools to the top 3 the
LLM actually needs to see this turn. Cuts prompt tokens, cuts latency,
scales to large catalogs.
- **Customer-support triage** — route incoming tickets / chat messages
to the right queue or workflow before the LLM gets involved (or
without an LLM at all for the easy cases).
- **Intent classification** — single-utterance bucketing for
conversational interfaces, IVR-style menus, search-query routing.
- **Slash / chat command routing** — recognize the user's command from
free-form phrasing without retraining a model every time the catalog
changes.
- **Workflow / decision routing** — multi-intent decomposition with
relation detection (sequential / conditional / parallel / negation)
for steps that need to fan out or chain.
- **Permission and risk gating** — classify a request into a risk tier
before paying for an LLM round-trip.
| Latency | 200–2000 ms | 10–50 ms | **30–90 µs** |
| Cost / query | per-token | GPU / API | **$0** |
| Setup | prompt engineering | training data + GPU | seed phrases or one OpenAPI / MCP import |
| Continuous learning| retrain pipeline | full retrain | **incremental, in place** |
| Multi-intent | prompt-dependent | separate model | **native** |
| Dependencies | API key | PyTorch / ONNX | **none at runtime** — Rust core with Python and Node bindings |
> [!IMPORTANT]
> **MicroResolve is a prefilter, not an absolute classifier.** It returns
> ranked candidates — the LLM is the final picker. Your agent prompt
> **must** include a `confirm_full_catalog` fallback tool so the LLM can
> reach the full catalog when none of the candidates fit. That fallback
> is part of the design, not an optional safety net. See
> [Confirm-turn pattern](#confirm-turn-pattern-system-1--system-2).
## Benchmarks
**Agent tool routing** — 129 real tools imported from 5 production MCP
servers (Stripe / Linear / Notion / Slack / Shopify), single namespace,
scored as a prefilter for an LLM picker:
| Cold start (LLM-seeded import) | 76.5 % | 88.2 % | 40.9 % | 87 µs |
| **+ auto-learn from corrections** | **88.2 %** | **97.1 %** | **76.6 %** | **64 µs** |
`+ auto-learn` = incremental Hebbian + LLM-judged phrase ingestion from
production corrections. No retraining pipeline; the data updates in place.
Reproduce: `python3 benchmarks/agent_tools_bench.py` (~$0.55 / ~10 min on
Haiku 4.5).
> Methodology, datasets, and reproduction scripts live in
> [`benchmarks/`](benchmarks/). What's *not* yet benchmarked: out-of-scope
> rejection (see [Confirm-turn pattern](#confirm-turn-pattern-system-1--system-2)),
> adversarial robustness, drift over multi-week production traffic.
**Single-utterance intent classification** (academic baselines):
| CLINC150 | 150 | 50 | 84.0 % | 85.8 % | 94.2 % | 22 µs |
| CLINC150 | 150 | 100 | 87.5 % | 96.4 % | 95.9 % | 24 µs |
| BANKING77 | 77 | 50 | 81.9 % | 85.0 % | 94.1 % | 21 µs |
| BANKING77 | 77 | 130 | 85.5 % | 92.8 % | 96.0 % | 23 µs |
## Quick start
Embedded (Rust):
```rust
use microresolve::MicroResolve;
let engine = MicroResolve::open("./data")?;
let ns = engine.namespace("agent")?;
ns.add_intent("cancel_subscription", &["cancel my plan", "stop the recurring billing"])?;
let r = ns.resolve("end my subscription right now");
// r.ranked[0].id == "cancel_subscription"
```
HTTP server (with the optional Studio UI):
```bash
cp .env.example .env # set LLM_API_KEY
cargo run --release --bin server --features server -- --data ./data
cd ui && npm install && npm run dev # http://localhost:3000
```
Bootstrap intents from an existing spec:
```bash
curl -X POST http://localhost:3001/api/import/mcp/apply \
-H "Content-Type: application/json" \
-H "X-Namespace-ID: agent" \
-d '{"tools_json": "<MCP tools/list response>", "selected": ["..."], "domain": ""}'
```
Bindings: [`microresolve` on crates.io](https://crates.io/crates/microresolve),
[`microresolve` on PyPI](https://pypi.org/project/microresolve/),
[`microresolve` on npm](https://www.npmjs.com/package/microresolve).
## How it works
A query passes through five layers, all in-process, all in-memory:
```text
raw query
→ L0 typo correction (character n-gram Jaccard)
→ L1 vocabulary bridging (morphology / abbreviation, OOV-gated)
→ L2 intent scoring (IDF-weighted sparse term graph)
→ L3 confidence + disposition
→ L4 cross-provider tiebreak
→ result
failures → review queue → auto-learn worker → L1 + L2 updated
```
L0 fixes typos against the known vocabulary. L1 substitutes morphological
variants (`canceling → cancel`) and abbreviations (`pr → pull request`)
**only when the source word is out-of-vocabulary**, so distinctive user
input is never rewritten. L2 is the core scorer: each intent has a learned
sparse vector of IDF-weighted terms; multi-intent uses a token-consumption
pass to confirm one intent then re-score the rest. L3 classifies the score
distribution as `confident` / `low_confidence` / `escalate`. L4 breaks ties
when the same action exists across multiple providers.
Every confirmed routing reinforces term weights. Every correction
shifts weights away from the wrong intent. No retraining; the data
updates in place.
## Confirm-turn pattern (System 1 → System 2)
MicroResolve returns ranked candidates; the LLM picks one or falls back
to the full catalog when nothing fits. **Always include a
`confirm_full_catalog` fallback tool in your agent prompt** — without it,
out-of-scope queries and novel phrasing route to the wrong tool:
```text
Candidate tools (from the prefilter):
- tool_a (...description...)
- tool_b (...description...)
- tool_c (...description...)
If one of these clearly applies, call it. Otherwise — unsure, out of
scope, or candidates look unrelated — call `confirm_full_catalog` to
receive every tool, then pick.
```
The prefilter shrinks 150 tools to 3 in ~60 µs; the LLM is the final
picker. Every confirmed call flows back as a corrected example, so the
candidate set sharpens over time.
## HTTP API
Send `X-Namespace-ID: my-namespace` to isolate intents per namespace. The
core endpoints are `/api/route_multi`, `/api/intents`, `/api/training/{review,apply}`,
and `/api/import/mcp/{search,fetch,apply}`. Full reference in the
[server API docs](https://gladius.github.io/microresolve/server/api/).
## Multi-intent, projection, and multilingual
**Native multi-intent** — a single query can confirm several intents in
one call, with the relation between them detected:
```text
"cancel my order and update my address"
→ cancel_order (confirmed, high)
→ update_address (confirmed, medium)
→ relation: parallel
```
Detected relations: `sequential`, `conditional`, `negation`, `parallel`.
**Projected context** — co-occurrence tracking discovers what auxiliary
intents typically fire alongside the primary one, so your orchestrator
can pre-fetch them in parallel without an extra LLM round-trip:
```text
"I want a refund"
→ refund_order (confirmed, high)
→ projected: check_balance (21 %), warranty_lookup (13 %)
```
These relationships emerge from accumulated routing — they're not
configured.
**Multilingual** — Latin scripts via whitespace tokenization, CJK
(Chinese / Japanese / Korean) via Aho-Corasick automaton + character
bigrams, all in the same namespace. Per-language seed phrases per intent.
Multi-intent decomposition runs after tokenization, so a Chinese query
like "取消订阅然后退款" decomposes the same way an English query does.
## Import formats
- **MCP tools/list** (and Smithery registry passthrough)
- **OpenAPI / Swagger** — each endpoint becomes an intent
- **OpenAI function-calling schemas**
- **LangChain tools**
## License
Dual-licensed under **MIT** or **Apache-2.0** at your option — the
standard Rust ecosystem licensing. Both are fully permissive and allow
commercial use.
- [LICENSE-MIT](LICENSE-MIT)
- [LICENSE-APACHE](LICENSE-APACHE) — adds an explicit patent grant
### Contribution
Unless you state otherwise, any contribution intentionally submitted for
inclusion in this work shall be dual-licensed as above, without any
additional terms or conditions.
## Commercial support
Maintained by Gladius Thayalarajan — consulting on custom integrations,
multilingual tuning, and agent-stack tool routing: [gladius.thayalarajan@gmail.com](mailto:gladius.thayalarajan@gmail.com).