Quick Start · Features · Integrations · SDK · CLI · Architecture · Docs · Community · Contributing

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81% fewer planning steps  ·  72% less tokens  ·  on real GDPVal knowledge-work tasks,
on top of what a SOTA self-improving Hermes agent already learns on its own.
See the benchmark →

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What is Reflexio?

Reflexio is an AI agent self-improvement harness that enables your AI agents to continuously learn from real user interactions. It turns user corrections into persisted behavioral improvements for agents and captures successful execution paths for reuse.

User-specific learnings stay scoped to that user; lessons that recur across users can be aggregated into shared agent playbooks and approved for reuse.

As your agent is used more, it becomes smarter, faster, and more effective at solving domain-specific tasks. The moat for AI agents is what your agent learns from every interaction it handles.

Our vision is that AI systems should get better with every interaction.

Benchmarked on GDPVal: on 4 of 5 real knowledge-work tasks from OpenAI's public GDPVal benchmark, Reflexio cuts a median −81% planning steps and −72% tokens on a Hermes agent running minimax/MiniMax-M2.7 — measured against a warm baseline: the same agent re-running the task after it has already learned from itself. In other words, Reflexio's savings come on top of what a SOTA self-improving agent has learnt on its own. See the full writeup → benchmark/gdpval/RESULTS.md.

flowchart LR
    A[AI Agent] -->|conversations| B[Reflexio]
    G[Human Expert] -->|ideal responses| B
    B --> C[User Profiles]
    B --> D[Playbook Extraction]
    D --> E[Playbook Aggregation]
    B --> F[Success Evaluation]

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Publish conversations from your agent, and Reflexio closes the self-improvement loop:

• Never Repeat the Same Mistake: Transforms user corrections and interaction signals into improved decision-making processes — so agents adapt their behavior and avoid repeating the same mistakes.
• Lock In What Works: Persists successful strategies and workflows so your agent reuses proven paths instead of starting from scratch.
• Transfer Learning Across Users: Corrections and successful strategies that recur across users can be aggregated into shared agent playbooks; approved playbooks improve the agent for everyone without retraining.
• Learn from Human Experts: Publish expert-provided ideal responses alongside agent responses — Reflexio automatically extracts actionable playbooks from the differences.

For developers: See developer.md for project structure, environment setup, testing, and coding guidelines.

Table of Contents

• Demo
• Quick Start
• Features
• Integrations
• SDK Usage
• Architecture
• Documentation
• Community
• Contributing
• Star History
• License

Demo

Quick Start

Prerequisites

Tool	Description
Python >= 3.12	Required for PyPI and source installs
uv	Required when running from source
Python's linked SQLite runtime	>= 3.35.0 (required for local SQLite storage; not the standalone sqlite3 CLI)
Node.js >= 18	Required only for the local docs site in a source checkout

Check the SQLite runtime linked to Python with:

python -c "import sqlite3; print(sqlite3.sqlite_version_info)"

Setup

Option A — Install from PyPI (fastest, for users):

pip install reflexio-ai

# start/stop services. data saved under ~/.reflexio
reflexio services start           # API (8061), inference (8069), SQLite storage
reflexio services stop            # Stop all services

The PyPI package does not ship the local docs site. Use the hosted documentation, or clone the repository if you want to run the docs locally.

Option B — Clone from source (for contributors):

# clone the repo
git clone https://github.com/ReflexioAI/reflexio.git
cd reflexio

# configure: copy env template, then set at least one LLM API key (OpenAI, Anthropic, etc.)
cp .env.example .env

# install dependencies
uv sync                                    # Python (includes workspace packages)
npm --prefix docs install                  # API docs

# start/stop services. data saved under ~/.reflexio
uv run reflexio services start             # API (8061), Docs (8062), inference (8069), SQLite
uv run reflexio services stop              # Stop all services

Source-checkout alternatives: python -m reflexio.cli services start or ./run_services.sh

The standalone server listens on loopback by default and needs no API key for local use. To allow network clients, set REFLEXIO_API_KEY, start with --backend-host 0.0.0.0, and send Authorization: Bearer <key> on data requests. Use TLS for network access. Without a key, remote clients and nonlocal browser origins cannot access data endpoints. See standalone access.

When the backend is selected, the launcher also starts the local inference service on port 8069 unless REFLEXIO_EMBEDDING_SERVICE_URL points to a remote service. It serves embeddings and the optional cross-encoder reranker; cloud embedding configurations still use it for reranking. The deployment-wide REFLEXIO_RERANK_ENABLED flag defaults to true. Set it to false to skip reranker model loading/prewarm and disable reranker requests. Automatic unified-search relevance flooring (which includes reranking) remains separately off by default through retrieval_floor.enabled. If the loopback inference service is unavailable, automatic reranking is silently skipped and retrieval order is preserved. An unavailable remote/internal service is reported while search still fails open.

The default local model, local/minilm-l6-v2, uses ONNX Runtime, tokenizers, and NumPy directly; ChromaDB is not required. It retains the existing 384-dimensional MiniLM vectors padded to 512 dimensions and the cache at ~/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx. A cold cache downloads the ~80 MB model archive and verifies its SHA-256 before extraction. Prepopulate this cache for offline use; existing MiniLM databases do not need reindexing.

For a source checkout, open http://localhost:8062 to interactively browse and try out the API.

Try it in 30 seconds (CLI)

Reflexio ships a first-class CLI — the fastest way to see the loop end-to-end with no code. Publish a real multi-turn conversation where the user corrects the agent (that's the signal Reflexio learns from), then search for what was extracted. In a source checkout, prefix these commands with uv run.

reflexio publish --user-id alice --session-id deploy-demo-1 --wait --data '{
  "interactions": [
    {"role": "user",      "content": "Deploy the new service."},
    {"role": "assistant", "content": "Starting deployment to us-east-1..."},
    {"role": "user",      "content": "Wait — we never deploy production to us-east-1. Always use us-west-2."},
    {"role": "assistant", "content": "Understood. Switching to us-west-2."}
  ]
}'

# Search the extracted profiles and playbooks
reflexio search "deployment region" --user-id alice

Depending on the configured model and extraction gates, this conversation can produce a user profile (production region is us-west-2) and a user playbook (confirm region before deploying). Agent playbooks are created later by aggregating recurring user playbooks across users and enter the approval workflow. See the CLI reference for all input modes (inline JSON, --file, --stdin) and the full command list.

Integrate with the Python SDK

import reflexio

client = reflexio.ReflexioClient(
    url_endpoint="http://localhost:8061/"
)

# A conversation is a session. Bind it once, then every call you make
# through `session` carries it — so the searches that fed the agent and
# the turns you publish stay linked to each other.
session = client.for_session("deploy-demo-1")

# Publish a multi-turn conversation where the user corrects the agent —
# Reflexio can extract a profile ("prod region = us-west-2") and a playbook
# ("confirm region before deploying").
session.publish_interaction(
    user_id="alice",
    interactions=[
        {"role": "user",      "content": "Deploy the new service."},
        {"role": "assistant", "content": "Starting deployment to us-east-1..."},
        {"role": "user",      "content": "Wait — we never deploy production to us-east-1. Always use us-west-2."},
        {"role": "assistant", "content": "Understood. Switching to us-west-2."},
    ],
)

By default, Reflexio queues profile and playbook extraction in the background; the configured models and extraction gates determine which artifacts are produced.

Features

Profile Generation

• Extracts stable facts about users and their environments with one configurable profile extractor
• Supports versioning (current → pending → archived) with upgrade/downgrade workflows
• Supports global extraction windows and strides with profile-specific overrides

Read more about user profiles →

Playbook Extraction & Aggregation

• Extracts playbooks from user behavior patterns
• Clusters similar entries and aggregates with LLM (with change detection to skip unchanged clusters)
• Approval workflow: review and approve/reject agent playbooks

An extractor can opt into the independently audited consolidator with UserPlaybookExtractorConfig.deduplication_config.strategy="audited". It drafts Create/Replace/Remove decisions, audits the complete proposed memory, and repairs affected decisions before saving. Replace can produce multiple playbooks. One 24-request allowance covers draft, read-only searches, audits, repairs and provider fallback attempts. Exhaustion or failed audits preserve committed memory; drafts are never saved as a fallback. Durable continuation retains bounded tool history and validation-repair progress within each stage, as well as validated stage results, without resetting the request allowance.

The default "legacy" keeps the existing four-action behavior and is the rollback setting. First audited rollouts require new candidates with persisted, owner-scoped source evidence. Expert examples with persisted source observations are supported; manual and older extraction flows without that evidence should explicitly retain "legacy". Missing new evidence causes audited consolidation to abstain. Missing historical evidence alone does not invalidate an existing playbook. The offline component comparison supports this method choice, but does not establish online task improvement; measure that separately after rollout.

Read more about agent playbooks →

Expert Learning

• Publish human-expert ideal responses alongside agent responses via the expert_content field
• Reflexio automatically compares agent vs. expert responses, focusing on substantive differences (missing info, incorrect approach, reasoning gaps) while ignoring stylistic ones
• Generates actionable playbooks as trigger/instruction/pitfall SOPs that teach the agent what to do differently

Read more about interactions & expert content →

Agent Success Evaluation

• Session-level evaluation sampled automatically (5% by default) and scheduled 10 minutes after the session's last request
• Per-turn head-to-head comparison when an assistant interaction includes shadow_content
• Tool usage analysis for blocking issue detection
• Source-set comparison groups evaluated sessions by the first request's source; it supports a causal claim only when sessions are assigned randomly

Read more about evaluation →

Search & Retrieval

• Hybrid search (vector + full-text) across profiles and playbooks
• Optional LLM-powered query reformulation for improved recall
• Unified search across all entity types in parallel
• Fast at scale: unified search across ~3,000 indexed rows (profile + user playbook + agent playbook, ~1,000 rows each, queried in parallel) runs at ~57 ms p50 / ~73 ms p95 — measured service-layer with local SQLite on an Apple Silicon MacBook, 30 trials × 20 fixed queries. See the full benchmark report or reproduce with reflexio.benchmarks.retrieval_latency.

Multi-Provider LLM Support

• OpenAI and Azure OpenAI, Anthropic, OpenRouter, Google Gemini, MiniMax, DeepSeek, DashScope/Qwen, Zhipu AI/GLM, Moonshot/Kimi, xAI/Grok, and custom OpenAI-compatible endpoints
• Powered by LiteLLM — configure your preferred provider via API keys or custom endpoints

SDK Usage

For detailed API documentation, see the full API reference.

Install the package:

pip install reflexio-ai

Basic usage

import reflexio

client = reflexio.ReflexioClient(
    url_endpoint="http://localhost:8061/"
)

# Bind the conversation's session id once. Recommended: it keeps the
# searches that retrieve context and the turns you publish correlated,
# which is what lets Reflexio tell whether a learning actually helped.
session = client.for_session("session-abc")

# Retrieve context for this turn
context = session.search(query="deployment region preference", user_id="user-123")

# Publish interactions
session.publish_interaction(
    user_id="user-123",
    interactions=[
        {"role": "user",      "content": "..."},
        {"role": "assistant", "content": "..."},
    ],
    agent_version="v1",       # optional: track agent versions
)

# Search profiles
profiles = client.search_user_profiles(
    user_id="user-123",
    query="deployment region preference",
)

# Search agent playbooks
playbooks = client.get_agent_playbooks(agent_version="v1")

Configuration

# Apply a targeted configuration change without resending the full Config.
client.update_config({
    "window_size": 20,
    "stride_size": 10,
})

Use set_config() only when replacing the complete configuration, including its required storage_config.

Integrations

Reflexio integrates with popular AI agent frameworks out of the box:

• OpenClaw -- Native integration with the OpenClaw agent framework.
• mem0 -- Drop-in wrapper for the mem0 managed-platform client.

mem0 drop-in wrapper

Already using mem0? Install the extra and change one import — no other code changes:

pip install 'reflexio-ai[mem0]'

# Before
from mem0 import MemoryClient
# After
from reflexio.mem0 import MemoryClient

client = MemoryClient(api_key="your-mem0-key")

Hosted sync and async add() calls still run mem0 first, then best-effort publish the same conversation to Reflexio. Normal search() is exactly mem0: it makes no Reflexio call and returns mem0's original object. Opt in when you want both result sets:

result = client.search(
    query,
    filters={"user_id": "user-123", "agent_id": "support-bot"},
    include_reflexio=True,
)
memories = result["results"]
learnings = result["reflexio"]

learnings contains a stable status, reason, profiles, user playbooks, and agent playbooks. Reflexio never rewrites the query or injects these values into a prompt; the application decides how to validate and format retrieved text as prompt context. Reflexio credentials come from REFLEXIO_API_KEY and REFLEXIO_URL, or can be passed directly as reflexio_api_key= with optional reflexio_url_endpoint=. Advanced callers can instead inject reflexio_client=ReflexioClient(...). Wrapper-created clients use a five-second timeout. Reflexio failures never change a successful mem0 add() result and are represented safely in opted-in search results.

client.reflexio exposes scoped Reflexio cleanup methods. Inherited mem0 delete* methods and reset() remain mem0-only. MemoryClient and AsyncMemoryClient are wrapped; local Memory and AsyncMemory remain exact mem0 exports. The integration supports mem0ai>=2.0,<2.1.

Migration from the removed LangChain integration. The reflexio.integrations.langchain package and its optional extra have been removed. To inject Reflexio context into a LangChain chain or agent, call the Reflexio client's search API directly and add the formatted results to your prompt (e.g. as a system message) — no framework-specific glue is required.

Architecture

Client (SDK / CLI / HTTP API)
  → FastAPI Backend
    ├─ ProfileGenerationService  → ProfileExtractor → Consolidator → Storage
    ├─ PlaybookGenerationService → UserPlaybookExtractor → Consolidator
    │                              → User playbooks (Storage)
    │                              → Aggregator → Agent playbooks (Storage)
    ├─ GroupEvaluationScheduler  → AgentSuccessEvaluator → Storage
    │                              (sampled; deferred 10 min)
    ├─ ShadowComparisonWorker    → Per-turn judge → Storage
    └─ UnifiedSearchService      → Profiles + user/agent playbooks

See developer.md for project structure, supported LLM providers, and development setup.

Documentation

For comprehensive guides, examples, and API reference, visit the Reflexio Documentation.

For coding agents adding Reflexio to another agent, use the portable integrate-reflexio skill. Read Integrating an AI Agent with Reflexio for supporting concepts and background. Copy the skill into the agent application's repository for Codex, Claude Code, or Cursor, or point a coding agent at the linked skill while it works in that application repository. The skill is not intended to modify Reflexio itself. It checks for supported tracing already in use and helps choose between connecting that data source and publishing interactions directly, while integrating retrieval for either method.

For agents already logging to Braintrust, use Connect a data source to prepare and validate importing through the setup API. The developer reviews the saved configuration and activates in Reflexio; no plugin or additional publish flow is needed.

Community

Join the Reflexio community on Discord: discord.gg/7fnCxahase.

Contributing

We welcome contributions! Please see developer.md for guidelines.

License

This project is currently licensed under Apache License 2.0.

Durable extraction catch-up

Publishing commits interactions before returning success. Background workers process configured sliding windows in admission order, regardless of request boundaries. Different users can extract concurrently within REFLEXIO_PUBLISH_LEARNING_WORKERS (default 4 per process); each user's profile/playbook cursors retry independently. A partial window is evaluated after an optional completion hint or six hours of session inactivity by default. Both paths retain the usefulness judge; a negative judge result covers the input without generating learnings. Normal full windows and strides run as before. Later messages in a finalized session remain valid.

client.publish_interaction(user_id="u", session_id="s", interactions=messages,
                           session_finished=True)
# Or signal completion after messages were already saved:
client.finalize_session(user_id="u", session_id="s")

Completion returns acceptance, not extraction completion. The async and client.for_session("s") APIs support the same operation. Set Config's extraction_idle_timeout_seconds (default 21600; 0 disables fallback for new publications). This is a configurable low-urgency policy, not proof that a session ended. Activity is scoped by organization/project/user/session; other sessions do not postpone its deadline. Each publication captures its timeout, so edits apply to subsequent publications. Existing pre-upgrade admissions have no inferred idle deadline; explicitly finalize those surviving sessions when needed. The library scheduler needs a live process and reachable Reflexio handle; reopening resumes durable deadlines without another publish. Provider fallback remains enabled. Existing extracted data is preserved, and interactions stored before stream admission are not automatically replayed.

Overflow extraction meters source input using the complete original primary fields once, rejoining their pieces before canonical cl100k_base tokenization. Neighbor context, evidence framing and repeated model calls do not multiply this source-input count. Provider prompt, output and cache totals separately retain all observed calls, and the durable billing snapshot survives delivery retries. Legacy extraction keeps its existing input formatter and billing behavior.

A caller waiting for coverage — publish_interaction(), or POST /api/publish_interaction?wait_for_response=true — is never held for a window only new input can close. It returns as soon as every eligible cursor is either covered or waiting for input, reporting learning_status="deferred" with learning_reason="waiting_for_window". Poll GET /api/learning_status with the returned request_id to see coverage complete later. Note the practical consequence of the default window_size of 10: a brand-new user's first window normally needs ten eligible interactions, so an incomplete first publish returns uncovered until more input, an optional completion hint, or its idle deadline. Reserve force_extraction for an explicit manual bypass of the judge.

Nomic dependency compatibility

The full package requires Transformers 5.6.2 or newer and uses its native Nomic implementation with remote model code disabled. There is no Transformers upper cap in Reflexio's requirement. The checkpoint, task prefixes, pooling, 8192-token sequence limit and normalized 512-dimensional vectors remain the same; serialized access to the shared model is preserved.

For release qualification, install the built full wheel into a fresh environment and run scripts/verify_nomic_artifact.py with that environment's Python from outside the checkout. It rejects editable imports and exercises real short and long inference, checking the native model and finite normalized vectors. Model weights must be cached or downloadable. Run with OMP_NUM_THREADS=1 and OPENBLAS_NUM_THREADS=1; the verifier caps Torch at two threads. This is keyless, not a paid LLM test. Loading weights alone is insufficient qualification.

Native/legacy diagnostic parity matched on the sampled short/medium inputs and one 2,346-token input before this migration. This establishes bounded evidence, not equivalence for every possible input. The opt-in real-model concurrency and long-input tests fail on load/inference errors when enabled:

RUN_LOW_PRIORITY=1 nice -n 10 uv run pytest tests/server/llm/test_nomic_embedding_realmodel_concurrency.py -o addopts=

Optional isolated query embeddings

Deployments may set REFLEXIO_QUERY_EMBEDDING_SERVICE_URL to a separate query-only worker. The ordinary embedding endpoint continues to handle stored documents and reranking. LiteLLMClient.get_embedding and get_embeddings accept a keyword-only purpose="query"; the default is "document". Storage supplies this purpose explicitly, independently of text prefixes or request priority. Cloud embedding models retain their normal provider route. Both local endpoints must advertise identical immutable model, revision and dimension contracts at /health/model, and the query endpoint must advertise workload="query". An empty query URL retains shared routing. Changing an endpoint's model requires restarting API workers; a failed query endpoint never fails over to shared CPU.

When constructing workers with create_embedding_app, supply a model_contract callback returning the loaded encoder's model, pinned revision, and dimensions on both apps. The factory exposes /health/model and derives the embedding workload from admission policy. Without that callback it returns 503 rather than inventing a revision. Additional metadata such as device and thread count can be returned by the callback.

Search priority on a shared CPU worker

Explicit query embedding requests always forward purpose="query", including when routed to the shared inference endpoint. Full-search reranking forwards priority="search"; generic profile reranking retains interactive priority. The embedding service passes these classifications to its injected inference scheduler. An older reranking service ignores the new optional field and continues its existing interactive behavior.

The enterprise CPU scheduler uses the same models and worker for all work. Search gets priority between model chunks, with a turn for other queued interactive work after at most four search chunks and a bulk turn after at most 32 foreground chunks. Each foreground queue keeps earliest-deadline ordering; cancellation, request deadlines, vector contents, and rerank policy are unchanged. This needs no additional worker or load balancer. Deploy the updated worker before the API, and verify full-search latency under competing interactive and bulk work before production promotion.

Optional reserved search reranking

create_embedding_app(workload="search", ...) keeps query-only embedding admission and enables the configured reranker. Its /health/model still reports workload="query", so existing embedding clients can coexist during a rolling upgrade; /health separately advertises the search workload and reranker readiness. Legacy query_only=True remains embedding-only and rejects reranking. Search workers require a successful reranker prewarm before serving. If a later prediction fails, /health and /health/rerank can retry one warmup under a one-second request budget. Recovery uses the existing inference scheduler when supplied; overlapping health probes never run overlapping warmups, including when a slow prediction continues after its health request times out. The worker remains unready until a real prediction succeeds.

Set REFLEXIO_SEARCH_RERANK_SERVICE_URL to the same endpoint as REFLEXIO_QUERY_EMBEDDING_SERVICE_URL to route retrieval relevance scoring there. The client verifies matching immutable vector contracts, the same configured reranker as the shared worker, and a ready search workload. Generic score_pairs/score_pairs_with_model calls keep shared routing; retrieval uses the explicit score_search_pairs helpers. Unset search routing preserves the existing path. Failed isolated calls do not retry against the shared worker; retrieval retains its existing unavailable-reranker behavior and deadlines.