chatcli

README

ChatCLI

Unified AI platform for terminal, gRPC server, and Kubernetes.
_{14 providers · 14 autonomous agents · 7-pattern quality pipeline · one binary.}

Português • Full documentation • Architecture • Observability

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ChatCLI connects the industry's leading LLMs to a single, extensible interface — from chatcli -p in your terminal to a Kubernetes operator with an autonomous AIOps pipeline, passing through a production-ready gRPC server with authentication, failover, and Prometheus metrics.

Highlights

Multi-provider with failover	14 LLM providers (OpenAI · OpenAI Responses · Anthropic · Bedrock · Google · xAI · ZAI · MiniMax · Moonshot (Kimi) · Copilot · GitHub Models · StackSpot · OpenRouter · Ollama) with intelligent error classification, exponential backoff, and per-provider cooldown.
Autonomous agents	14 built-in workers coordinated by a ReAct engine (Reason + Act): 12 orchestration specialists run in parallel + 2 quality agents (refiner, verifier), plus a 7-pattern quality pipeline.
Quality pipeline	Self-Refine, Chain-of-Verification (CoVe), Reflexion, RAG + HyDE, Plan-and-Solve (ReWOO), cross-provider reasoning backbone — all composed via a thread-safe state machine with circuit breakers and hot reload.
Scheduler (Chronos)	Durable scheduling with cron + wait-until + DAG + daemon mode. /schedule, /wait, /jobs + @scheduler tool for agents. CRC32 WAL, snapshots, rate limiter, circuit breakers, JSONL audit, 13 Prometheus metrics. Jobs survive crashes and CLI exit.
Durable Reflexion	WAL-backed queue with worker pool, dead letter queue, boot replay, exponential retry with jitter — lessons survive process crashes.
Semantic convergence	char → Jaccard → embedding cosine cascade for Self-Refine, with LRU/TTL cache and quality regression detection.
Production-ready	gRPC + TLS 1.3, JWT + RBAC, AES-256-GCM, rate limiting, audit logging, 50+ Prometheus metrics.
Kubernetes-native	Operator with 17 CRDs and an autonomous AIOps pipeline (54+ remediation actions), SLO monitoring, post-mortems.
Extensible	Plugins with Ed25519 signature verification, multi-registry skills (skills.sh, ClawHub, ChatCLI.dev), lifecycle hooks, MCP client (stdio + SSE).

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Installation

# Homebrew (macOS / Linux)
brew tap diillson/chatcli && brew install chatcli

# Go install
go install github.com/diillson/chatcli@latest

# Pre-built, cosign-signed binaries
# https://github.com/diillson/chatcli/releases

Build from source

git clone https://github.com/diillson/chatcli.git && cd chatcli
go mod tidy && go build -o chatcli

# With version metadata injected via ldflags
VERSION=$(git describe --tags --always --dirty 2>/dev/null || echo "dev")
go build -ldflags "-X github.com/diillson/chatcli/version.Version=${VERSION}" -o chatcli

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Quick Setup

LLM_PROVIDER=OPENAI    # OPENAI, CLAUDEAI, BEDROCK, GOOGLEAI, XAI, ZAI, MINIMAX, MOONSHOT,
                       # COPILOT, GITHUB_MODELS, OLLAMA, STACKSPOT, OPENROUTER
OPENAI_API_KEY=sk-xxx

Full provider configuration reference

Provider	API Key	Model	Extras
OpenAI	OPENAI_API_KEY	OPENAI_MODEL	OPENAI_MAX_TOKENS, OPENAI_USE_RESPONSES
Anthropic	ANTHROPIC_API_KEY	ANTHROPIC_MODEL	ANTHROPIC_MAX_TOKENS
AWS Bedrock	IAM / Profile / credentials chain	BEDROCK_MODEL	AWS_REGION, BEDROCK_CROSS_REGION
Google Gemini	GOOGLEAI_API_KEY	GOOGLEAI_MODEL	GOOGLEAI_MAX_TOKENS
xAI	XAI_API_KEY	XAI_MODEL	XAI_MAX_TOKENS
ZAI	ZAI_API_KEY	ZAI_MODEL	ZAI_MAX_TOKENS
MiniMax	MINIMAX_API_KEY	MINIMAX_MODEL	MINIMAX_MAX_TOKENS
Moonshot (Kimi)	MOONSHOT_API_KEY	MOONSHOT_MODEL	MOONSHOT_MAX_TOKENS, MOONSHOT_THINKING
GitHub Copilot	GITHUB_COPILOT_TOKEN	COPILOT_MODEL	or /auth login github-copilot
GitHub Models	GITHUB_TOKEN	GITHUB_MODELS_MODEL	GH_TOKEN, GITHUB_MODELS_TOKEN
StackSpot	CLIENT_ID, CLIENT_KEY	—	STACKSPOT_REALM, STACKSPOT_AGENT_ID
OpenRouter	OPENROUTER_API_KEY	—	OPENROUTER_MAX_TOKENS, OPENROUTER_FALLBACK_MODELS
Ollama	—	OLLAMA_MODEL	OLLAMA_ENABLED=true, OLLAMA_BASE_URL
OpenAI (Responses API)	OPENAI_API_KEY	OPENAI_MODEL	OPENAI_RESPONSES_API_URL

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Three Modes of Operation

Interactive CLI AI-powered terminal with a Bubble Tea TUI, project context, tool calling, and autonomous agents. chatcli chatcli -p "Explain this repo" git diff | chatcli -p "Summarize"

gRPC Server Shared backend with TLS 1.3, JWT/RBAC, failover, Prometheus metrics, MCP, and plugin discovery. chatcli server --port 50051 \ --token my-token chatcli connect \ --server host:50051 \ --token my-token

Kubernetes Operator Autonomous AIOps pipeline with 17 CRDs, 54+ remediation actions, SLO monitoring, and post-mortems. helm install chatcli-operator \ oci://ghcr.io/diillson/charts/chatcli-operator \ --namespace chatcli-system \ --create-namespace

Autonomous scheduler (Chronos)

The scheduler runs embedded in the CLI and optionally as a daemon. Jobs survive restarts via WAL + snapshot.

# Fire a command in 30s
/schedule ping --when +30s --do "/run curl https://api.example.com/health"

# Daily cron with retry
/schedule backup --cron "0 2 * * *" --do "shell: ./backup.sh" --max-retries 3

# Deploy + K8s wait + trigger smoke
/schedule deploy --when +0s --do "shell: terraform apply -auto-approve" \
  --wait "k8s:deployment/prod/api:Available" --timeout 15m \
  --triggers smoke-tests

# Daemon to keep running with the CLI closed
chatcli daemon start --detach
chatcli daemon status

# List / inspect / cancel
/jobs list
/jobs show <id>
/jobs tree
/jobs cancel <id>

Agents get the @scheduler tool and can pause themselves waiting on conditions — see Cookbook: scheduler automation and the feature doc.

Context commands (CLI mode)

Inject environment data directly into your prompt:

Command	Description
@git	Status, branches, and recent commits
@file <path>	File or directory contents
@env	Environment variables
@history	Recent shell commands
@command <cmd>	Execute a command and inject its output

Kubernetes manifest example (Instance CRD)

apiVersion: platform.chatcli.io/v1alpha1
kind: Instance
metadata:
  name: chatcli-prod
spec:
  provider: ZAI
  model: glm-5
  replicas: 2
  fallback:
    enabled: true
    providers:
      - name: OPENAI
        model: gpt-5.4
      - name: MINIMAX
        model: MiniMax-M2.7

helm install chatcli oci://ghcr.io/diillson/charts/chatcli \
  --namespace chatcli --create-namespace \
  --set llm.provider=OPENAI --set secrets.openaiApiKey=sk-xxx

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Supported Providers

14 providers with a unified interface. Automatic failover with intelligent error classification, cross-provider extended thinking, and prompt caching where available.

Provider	Default Model	Tool Calling	Vision	Reasoning / Thinking
OpenAI	gpt-5.4	Native	Yes	reasoning_effort (o-series / gpt-5)
Anthropic (Claude)	claude-sonnet-4-6	Native	Yes	Extended thinking with cache
AWS Bedrock	claude-sonnet-4-5	Native	Yes	Thinking budget (Anthropic models)
Google Gemini	gemini-2.5-flash	Native	Yes	—
xAI (Grok)	grok-4-1	XML fallback	—	—
ZAI (Zhipu AI)	glm-5	Native	Yes	—
MiniMax	MiniMax-M2.7	Native	Yes	—
Moonshot (Kimi)	kimi-k2.6	Native	Yes	MOONSHOT_THINKING=enabled|disabled|auto
GitHub Copilot	gpt-4o	Native	Yes	—
GitHub Models	gpt-4o	Native	Yes	—
StackSpot AI	StackSpotAI	—	—	—
OpenRouter	openai/gpt-5.2	Native	Yes	Passthrough
Ollama	(local)	XML fallback	—	<thinking> tag normalization
OpenAI (Responses API)	gpt-5.4	Native	Yes	reasoning_effort

# Configurable fallback chain
CHATCLI_FALLBACK_PROVIDERS=OPENAI,CLAUDEAI,BEDROCK,ZAI,MINIMAX,MOONSHOT,OPENROUTER

/thinking on|off|auto enables extended thinking / reasoning_effort on any provider that supports it — the cross-provider mapping is automatic.

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Autonomous Agents

ReAct engine (Reason + Act) with 14 built-in agents: 12 orchestration specialists running in parallel (file, coder, shell, git, search, planner, reviewer, tester, refactor, diagnostics, formatter, deps) + 2 quality-harness agents (refiner, verifier).

/coder "Refactor the auth module to use JWT"
chatcli -p "Create tests for the utils package" --agent-auto-exec

Agent	Responsibility
File	File reading, writing, and manipulation
Coder	Code generation and editing
Shell	System command execution
Git	Version control operations
Search	Code and file search
Planner	Complex task decomposition (Plan-and-Solve / ReWOO)
Reviewer	Automated code review
Tester	Test generation and execution
Refactor	Safe code refactoring
Diagnostics	Problem analysis and debugging
Formatter	Formatting and linting
Deps	Dependency management
Refiner	Self-Refine post-hook (critique → revise)
Verifier	Chain-of-Verification (questions + final answer)

Workers are coordinated by the dispatcher with a configurable semaphore (CHATCLI_AGENT_MAX_WORKERS), retry policy, and FileLockManager synchronization.

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Harness/Quality Pipeline

Seven prompting/execution patterns composed via a pluggable pipeline with state machine, hot reload, and per-hook isolation.

#	Pattern	Status	Opt-in
1	ReAct (Reason + Act)	✅ agent core	—
2	Plan-and-Solve / ReWOO	✅	/plan, CHATCLI_QUALITY_PLAN_FIRST_MODE
3	Reflexion (with durable queue)	✅	on by default
4	RAG + HyDE	✅	CHATCLI_QUALITY_HYDE_ENABLED=1
5	Self-Refine (with semantic convergence)	✅	CHATCLI_QUALITY_REFINE_ENABLED=1
6	Chain-of-Verification (CoVe)	✅	CHATCLI_QUALITY_VERIFY_ENABLED=1
7	Cross-provider reasoning backbone	✅	CHATCLI_QUALITY_REASONING_MODE=auto

Pipeline Architecture

• State machine (Active → Draining → Closed) with atomic CAS transitions.
• Copy-on-Write via atomic.Pointer[snapshot] — AddPre/AddPost/SwapConfig are atomic, zero locks on the hot path.
• Per-hook isolation: panic recovery, timeout enforcement (default 30s), circuit breaker (5 failures → open for 30s).
• Priority-based ordering via optional Prioritized interface (backward-compatible — unmarked hooks default to 100).
• Short-circuit sentinels: ErrSkipExecution (cache-hit before agent.Execute) and ErrSkipRemainingHooks (ensemble patterns).
• Graceful shutdown via DrainAndClose(timeout) honoring in-flight calls.

Durable Reflexion (WAL + DLQ)

Reflexion triggers (error, hallucination flagged by CoVe, low quality) flow through a lesson queue with enterprise guarantees — lessons survive process crashes:

• WAL with double CRC32, atomic rename, dir fsync — torn writes detected automatically.
• Worker pool (default 2) with per-job timeout, exponential backoff with jitter, configurable MaxAttempts.
• Persistent DLQ (same WAL format) with /reflect failed, /reflect retry <id>, /reflect purge <id>.
• Drain-on-boot: pending lessons from a previous session are reprocessed automatically.
• Idempotency via sha256(task | trigger | attempt) — re-triggering the same situation is a no-op.
• Stale discard (default 7d) — old lessons dropped at replay time.

/reflect list              # current queue + DLQ
/reflect failed            # DLQ with last error per entry
/reflect retry <job-id>    # re-queue a failed lesson
/reflect purge <job-id>    # permanently remove a DLQ entry
/reflect drain             # force WAL replay

Semantic Convergence (Self-Refine)

Self-Refine uses a char → Jaccard → embedding cascade to detect when to stop iterating. Catches "same meaning, different words" that the char-level heuristic missed:

Stage	Cost	When it fires
Char	μs	Always. Early-exit when sim > 0.99 (identical) or sim < 0.3 (diverged)
Jaccard	ms	Borderline, normalized token sets with EN/PT stop-words
Embedding	ms + $	Borderline after Jaccard. Opt-in via CHATCLI_QUALITY_REFINE_CONVERGENCE_EMBEDDING=1

• LRU cache with TTL (default 256 entries / 5min) avoids re-embedding identical text.
• Per-scorer circuit breaker — provider outage degrades to Jaccard without blocking refine.
• Quality regression detection: when pass N gets worse (>15% sim loss vs best) → revert to best draft + set refine_rolled_back metadata so Reflexion can learn.
• Strict mode: refuses to declare convergence without embedding when stakes are high.

Full quality pipeline config

# Master switch
CHATCLI_QUALITY_ENABLED=true

# Self-Refine (#5) + semantic convergence
CHATCLI_QUALITY_REFINE_ENABLED=false            # opt-in
CHATCLI_QUALITY_REFINE_MAX_PASSES=1
CHATCLI_QUALITY_REFINE_CONVERGENCE_ENABLED=true
CHATCLI_QUALITY_REFINE_CONVERGENCE_EMBEDDING=false
CHATCLI_QUALITY_REFINE_CONVERGENCE_STRICT=false

# Chain-of-Verification (#6)
CHATCLI_QUALITY_VERIFY_ENABLED=false
CHATCLI_QUALITY_VERIFY_NUM_QUESTIONS=3
CHATCLI_QUALITY_VERIFY_REWRITE=true

# Reflexion (#3) + durable queue
CHATCLI_QUALITY_REFLEXION_ENABLED=true
CHATCLI_QUALITY_REFLEXION_QUEUE_ENABLED=true    # WAL + worker pool + DLQ
CHATCLI_QUALITY_REFLEXION_QUEUE_WORKERS=2
CHATCLI_QUALITY_REFLEXION_QUEUE_MAX_ATTEMPTS=5
CHATCLI_QUALITY_REFLEXION_QUEUE_STALE_AFTER=168h

# Plan-and-Solve / ReWOO (#2)
CHATCLI_QUALITY_PLAN_FIRST_MODE=auto             # off|auto|always

# HyDE (#4)
CHATCLI_QUALITY_HYDE_ENABLED=false
CHATCLI_QUALITY_HYDE_USE_VECTORS=false

# Reasoning backbone (#7)
CHATCLI_QUALITY_REASONING_MODE=auto              # off|on|auto
CHATCLI_QUALITY_REASONING_BUDGET=8000

All exposed via /config quality with runtime state (registered hooks, queue depth, DLQ size).

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Observability

End-to-end Prometheus integration in the chatcli namespace. 50+ metrics covering LLM, agents, pipeline, queue, and lesson queue.

chatcli server --port 50051 --metrics-port 9090
curl http://localhost:9090/metrics | grep chatcli_
curl http://localhost:9090/healthz

Key metrics

Subsystem	Metric	Type
chatcli_llm_*	requests_total, request_duration_seconds, tokens_used_total, errors_total	Counter, Histogram
chatcli_quality_pipeline_*	dispatch_total, hook_duration_seconds, hook_errors_total, hook_circuit_state, generation	Counter, Histogram, Gauge
chatcli_lessonq_*	enqueue_total, queue_depth, dlq_size, processing_duration_seconds, wal_corruption_total, retry_total	Counter, Gauge, Histogram
chatcli_session_*	duration, commands executed, signals	Counter, Gauge
chatcli_grpc_*	unary + stream interceptors	Counter, Histogram

Standard Go runtime and process_* collectors are registered automatically.

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Enterprise Security

Security is not a feature flag. It is the foundation of every layer of ChatCLI.

Authentication & authorization JWT with RBAC (admin / user / readonly) OAuth PKCE + Device Flow (RFC 8628) Automatic token refresh per provider Encryption AES-256-GCM for credentials at rest TLS 1.3 for gRPC communication Encrypted session store on disk Network Built-in SSRF prevention Per-client rate limiting Operator webhook validation

Plugin & agent security Ed25519 plugin signature verification Agent command allowlist (150+ approved commands) Schema validation during plugin discovery Auditing & compliance Structured audit logging (JSON Lines) Per-session cost tracking per provider Prometheus metrics for observability CI/CD security govulncheck + gosec on every PR Automated Trivy image scanning Cosign-signed releases + CycloneDX SBOM

Built-in OAuth

/auth login openai-codex       # OAuth PKCE + local callback
/auth login anthropic          # OAuth PKCE + manual code
/auth login github-copilot     # Device Flow (RFC 8628)
/auth status                   # All provider status

Credentials are stored with AES-256-GCM at ~/.chatcli/auth-profiles.json.

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Command Reference

Category	Commands
Core	/help · /version · /reload · /exit · /reset
Sessions	/session {save,load,list,delete,new,fork,search} · /export · /newsession · /rewind
Context	/context {create,attach,list,remove} · @git · @file · @env · @history · @command
Config	/config [section] · /status · /settings · /switch <provider|model>
Agent mode	/agent [task] · /run · /coder · /plan [query] · /moa <prompt>
Quality pipeline	/thinking [on|off|auto] · /refine [draft] · /verify [answer] · /reflect [list|failed|retry|purge|drain|<text>]
Memory & graph	/memory {longterm,list,profile,facts,remember,forget,profile set,compact} · @memory (remember/recall/forget/profile/neighbors/map) — profile with lifecycle: list fields upsert (restating an item supersedes instead of duplicating) and _replace/_done/_remove key suffixes rewrite (e.g. goals_done= removes the finished goal; record milestone= and certifications= alongside); new interests, directives (hard rules vs preferences; per-project scope with "[scope:<project>] rule" — injected only when the matching workspace is active), milestone (dated timeline), stance (technical position with its why, "position :: reason") and env_<key> (structured environment) fields; per-field provenance+freshness (user vs extraction, re-affirmation bumps confirmed_at, aging fields get flagged as possibly stale) and a privacy tier (finance/health/family keys auto-tagged [sensitive]: they personalize answers but never enter code/examples/artifacts; sensitive_mark/sensitive_unmark); daily notes consolidate into weekly and monthly digests (Trajectory section in context); profile updates also work in chat (sanctioned exception, /config chat memory, CHATCLI_CHAT_MEMORY) · /graph [subject] · /compact [ratio]
Extensibility	/mcp {init,list,invoke,config} · /plugin {list,load,unload} · /skill <name> · /hooks {list,enable,disable,test}
Messaging & Servers	/gateway {start,status} (Telegram/Slack/Discord/WhatsApp/webhook) · chatcli mcp-server · chatcli acp
Remote	/auth {login,logout,status} · /connect <server> · /disconnect
Tools	/watch {pid|file} · /worktree {create,list,remove} · /channel {create,switch} · /websearch <query> · /lsp <file>
Scheduler	/schedule <name> --when <t> --do <a> · /wait --until <cond> · /jobs {list,show,tree,cancel,pause,resume,logs,daemon} · chatcli daemon {start,stop,status,ping,install}
Diagnostics	/metrics · /cost · /ratelimit (/limits)

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Core Features

Every feature is designed to compose with the others. Plugins discover skills. Hooks drive tools. Contexts feed agents.

Feature	Description
Native tool calling	Native APIs from OpenAI, Anthropic, Bedrock, Google, ZAI, MiniMax, Moonshot, OpenRouter. ephemeral cache for Anthropic. Automatic XML fallback for providers without native support.
MCP (Model Context Protocol)	Client via stdio and SSE for expanded context. Server (chatcli mcp-server) exposes chat, agent, coder and built-in tools; ACP mode (chatcli acp) for editors.
Chat Gateway	Runs as a messaging daemon (Telegram, Slack, Discord, WhatsApp, webhook): each message runs through the agent loop and progress is streamed back to the chat. Voice messages are transcribed (local-first whisper) and answered in voice by default (CHATCLI_GATEWAY_VOICE_REPLY=auto|always|never); each conversation controls it by asking in natural language ("answer me in audio" / "stop sending audio") via the @voice tool, with the preference persisted.
Embedded voice (TTS)	CHATCLI_TTS_PROVIDER=embedded — offline Kokoro neural voice, no API key and no cgo: downloads the sherpa-onnx engine + model once (~150MB) and works the same on Linux/macOS/Windows. Routes pt-BR/English by reply language (CHATCLI_TTS_VOICE=bm_george, CHATCLI_TTS_VOICE_PT=pm_alex); the other backends (say/espeak, self-hosted, OpenAI/Groq/Gemini) remain available.
Embedded transcription (STT)	Offline multilingual Whisper via sherpa-onnx, no API key and no cgo — and the automatic fallback: with nothing configured, the gateway downloads the engine + an ONNX model once (~200MB for base; CHATCLI_TRANSCRIPTION_MODEL=tiny|base|small|…) at startup and transcribes voice notes auto-detecting the spoken language. OGG/Opus voice notes (Telegram/WhatsApp) decode in pure Go — no ffmpeg needed; only residual formats (mp3/m4a) require ffmpeg, and the gateway preflight + /gateway status warn with your platform's install command. CHATCLI_TRANSCRIPTION_PROVIDER=embedded forces it over the other backends (local whisper CLI, self-hosted, Groq/OpenAI), which remain available.
Mixture-of-Agents	/moa — several models propose in parallel and an aggregator synthesizes (Wang et al., 2406.04692). Every participant gets the same briefing as a chat turn (attached contexts, workspace memory, skills) plus read-only knowledge retrieval, CCR recall and long-term memory recall.
LSP diagnostics	/lsp <file> — compiler errors/warnings via the Language Server Protocol (gopls, pyright, rust-analyzer, clangd, …).
Rate limits	/ratelimit — provider limits parsed from x-ratelimit-* headers (requests/tokens, % used, reset).
Trajectory export	/export — current conversation as ShareGPT JSONL for fine-tuning/analysis.
Persistent contexts	/context create, /context attach — inject whole projects into the system prompt with cache hints.
Knowledge base (keyless RAG)	/context create docs corpus.jsonl --mode knowledge — documentation corpora (e.g. JSONL from the builtin @docs-flatten tool, which flattens local or git-repo Markdown/MDX docs) become a knowledge base: attaching injects only an index card (~900 fixed tokens, even at 6MB+) and relevant passages are retrieved per turn via pure-Go BM25 (no API key) + embeddings when configured. The @knowledge tool (search/get/toc) interrogates the base iteratively in agent/coder and also in chat (read-only exception, /config chat knowledge) — including authoring skills from the docs with @skill.
Bootstrap & Memory	SOUL.md, USER.md, IDENTITY.md, RULES.md + long-term memory with facts (confidence + provenance + contradiction reconciliation), topics with rolling summaries, and decay.
Self-evolution	Skills author and evolve themselves on the memory extraction pass (no extra LLM call): reusable procedures become auto-activating skills; an insight evolves an existing skill by additive merge, with a reversible backup (@skill restore). CHATCLI_SELFEVOLVE_MODE=off|suggest|auto; observability under /config selfevolve.
Knowledge graph (Obsidian in the core)	Facts, topics, projects, skills and tags become an on-demand graph: @memory neighbors <subject> / map pull backlinks and related notes, a tiny index card rides each turn, and /graph [subject] renders the graph to an image (embedded go-graphviz). CHATCLI_GRAPH_INDEX=on|off.
Plugins	Auto-detection, schema validation, Ed25519 signatures, remote plugins.
Skills	Self-authoring (@skill), multi-registry (skills.sh, ClawHub, ChatCLI.dev), fuzzy search, security audits, source preferences, atomic install.
Custom personas	Markdown with YAML frontmatter (model, tools, skills).
Hooks	PreToolUse, PostToolUse, SessionStart/End, UserPromptSubmit, Pre/PostCompact — shell or webhook.
WebFetch / WebSearch	DuckDuckGo + fetch with text extraction.
Cost tracking	Per-session cost with per-provider pricing tables.
Git Worktrees	Isolated work on parallel branches.
K8s Watcher	Multi-target: metrics, logs, events, Prometheus scraping.
i18n	Portuguese and English with automatic detection.
Session management	Save, load, fork, export.

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Architecture

chatcli/
  cli/
    agent/
      quality/              7-pattern pipeline (state machine + COW snapshots)
        convergence/        Semantic convergence (char → jaccard → embedding)
        lessonq/            Reflexion durable queue (WAL + worker pool + DLQ)
      workers/              14 agents + dispatcher + FileLockManager
    hooks/                  Lifecycle events (shell/webhook)
    mcp/                    MCP client (stdio + SSE)
    plugins/                Plugin manager + signature verification
    scheduler/              Chronos — durable scheduler (WAL + cron + DAG + daemon)
      condition/            10 evaluators (shell, http, k8s, docker, tcp, llm, ...)
      action/               8 executors (slash, shell, agent, webhook, ...)
      builtins/             Aggregated registry for evaluators + executors
    workspace/memory/       Facts, topics, patterns, vector index (HyDE)
    tui/                    Bubble Tea adapters
  llm/
    openai/  openai_responses/  openai_assistant/
    claudeai/  bedrock/
    googleai/  xai/  zai/  minimax/
    copilot/  github_models/  stackspotai/  openrouter/  ollama/
    fallback/  catalog/  registry/  token/  toolshim/  embedding/
  metrics/                  Prometheus registry + /metrics + /healthz
  server/                   gRPC + TLS + JWT + MCP + plugin discovery
  operator/                 Kubernetes Operator (17 CRDs, AIOps pipeline)
  k8s/                      Watcher (collectors, store, summarizer)
  models/                   ToolDefinition, ToolCall, LLMResponse, Message
  auth/                     OAuth PKCE, Device Flow, AES-256-GCM store
  config/                   ConfigManager with versioned migration
  i18n/                     embed.FS + golang.org/x/text (PT / EN)

Design principle: each package declares its own interfaces and self-registers. The llm/ registry lets you add a new provider by implementing a single interface. The quality pipeline is pluggable via AddPre/AddPost with atomic swap. The operator coordinates independent CRDs via the controller pattern.

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CI/CD & Releases

• CI (.github/workflows/1-ci.yml): golangci-lint, gofmt, go vet, go test -race -coverprofile, coverage HTML as artifact.
• Security scan (security-scan.yml): continuous Trivy image scanning.
• Release automation (release-please + publish-release.yml): multi-platform builds, cosign signatures, CycloneDX SBOM, ArtifactHub publishing.
• Makefile: make build, make test, make lint, make install with Version, CommitHash, BuildDate injected via ldflags.

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Contributing

1. Fork the repository
2. Create a branch from main: git checkout -b feature/my-feature
3. Commit and push
4. Open a Pull Request

See docs/ for detailed architecture, quality pipeline, and operator guides.

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License

Apache License 2.0

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Documentation • Releases • Helm Charts • Go Reference • Issues

Documentation

Overview

ChatCLI is a multi-provider AI platform for terminal, server, and Kubernetes.

It connects 11 LLM providers (OpenAI, Anthropic Claude, Google Gemini, xAI Grok, ZAI, MiniMax, GitHub Copilot, GitHub Models, StackSpot AI, Ollama, and OpenAI Assistants) to a unified interface with autonomous agents, native tool calling, automatic provider failover, and a full AIOps pipeline.

Three Modes of Operation

• Interactive CLI: Terminal-based TUI (Bubble Tea) with context injection, 12 specialized agents running in parallel, and tool calling.
• gRPC Server: Centralized server with JWT + RBAC authentication, TLS 1.3, rate limiting, Prometheus metrics, MCP integration, and plugin/agent discovery.
• Kubernetes Operator: 17 CRDs powering an autonomous AIOps pipeline with anomaly detection, AI-driven root cause analysis, 54+ automated remediation actions, approval workflows, SLO monitoring, and auto-generated post-mortems.

Key Features

• Multi-provider with automatic failover and exponential backoff
• ReAct engine with 12 specialized agents (File, Coder, Shell, Git, Search, Planner, Reviewer, Tester, Refactor, Diagnostics, Formatter, Deps)
• Native tool calling via OpenAI, Anthropic, Google, ZAI, and MiniMax APIs
• MCP (Model Context Protocol) for extending LLM capabilities
• OAuth PKCE + Device Flow authentication with AES-256-GCM encrypted storage
• Plugin system with Ed25519 signature verification
• Persistent contexts, bootstrap files (SOUL.md, USER.md), and long-term memory
• Session management with AES-256-GCM encryption at rest
• Cost tracking per session and provider
• Internationalization (Portuguese and English)

Enterprise Security

• JWT authentication with RBAC roles (admin, user, readonly)
• AES-256-GCM encryption for credentials and sessions
• TLS 1.3 enforcement with mTLS support
• SSRF prevention blocking private IPs and cloud metadata endpoints
• Per-client token-bucket rate limiting
• Ed25519 plugin signature verification
• Agent command allowlist (150+ pre-approved commands)
• Structured audit logging (JSON lines)
• Automated security scanning (govulncheck, gosec, Dependabot)

Installation

brew tap diillson/chatcli && brew install chatcli

Or:

go install github.com/diillson/chatcli@latest

Full documentation: https://chatcli.edilsonfreitas.com

Source code: https://github.com/diillson/chatcli

• ChatCLI - Command Line Interface for LLM interaction
• Copyright (c) 2024 Edilson Freitas
• License: Apache-2.0