Not a copilot. A crew of autonomous agents that understand, navigate, and evolve your codebase — together.
Most AI coding tools share a fundamental flaw: they treat code as text, not structure.
| Traditional Tools | CodeActor |
|---|---|
| Flat text pattern matching | 🧠 Structural code understanding via AST + call graphs + semantic vectors |
| Single agent, working alone | 🤖 Hub-and-Spoke multi-agent: Director orchestrates, six specialized agents execute |
| Static capabilities | 🧬 Meta-Agent: designs & registers new agents at runtime — the system evolves |
| Keyword-only search | 🔍 Natural-language semantic search — "find where auth logic is implemented" |
| Agent | Role | Core Capability |
|---|---|---|
| 🎼 Director | Orchestrator | Task decomposition, dynamic planning, delegation, review |
| 🔬 Repo-Agent | Code Archaeologist | AST parsing, semantic search, call graphs, code skeletons |
| ✏️ Coding-Agent | Staff Engineer | 22+ tools, autonomous coding, self-correction |
| 🌐 Browser-Agent | Web Researcher | Headless Chrome, page navigation, data extraction |
| 🔧 DevOps-Agent | SRE | Shell execution, environment diagnostics, process management |
| 💬 Chat-Agent | Technical Advisor | General Q&A, technical explanations |
| 🧬 Meta-Agent | Agent Factory | Runtime agent design & registration |
User Interface (TUI / HTTP+WebSocket)
│
🎼 Director
Task Decomposition · Dynamic Planning · Review
│
┌────────┼────────┬────────┬────────┬────────┐
│ │ │ │ │ │
🔬Repo ✏️Coding 💬Chat 🔧DevOps 🌐Browser 🧬Meta
Code Intel Editing Q&A Shell Web Agent
(Rust) (22 tools) Ops Research Factory
The Repo-Agent is backed by a Rust engine with Tree-sitter AST parsing, LanceDB vector embeddings, and Petgraph call-graph analysis. It understands code like a senior engineer — cross-file impact analysis, cycle detection, semantic search.
- 7 language ASTs: Rust · Python · JavaScript · TypeScript · Java · C++ · Go
- Semantic search: find code by meaning, not keywords
- Call graph analysis: real-time "who calls this function?" and "what will this change break?"
- Auto-indexing: file watcher with 20s debounce keeps the model in sync
This is CodeActor's most unique capability. When the Director encounters a task beyond built-in agents, the Meta-Agent:
- 🎨 Designs — auto-generates a new agent's system prompt and toolset
- ⚡ Executes — immediately runs the new agent to complete the task
- 🔧 Registers — permanently adds it to the available tool pool
Example: auto-creates
delegate_security_auditorfor full-repo security audits, ordelegate_performance_profilerfor bottleneck analysis.
Built-in headless Chrome (go-rod) navigates the web autonomously — documentation, GitHub issues, Stack Overflow. When local context is insufficient, the Director delegates web research automatically.
"Find the latest FastAPI middleware docs and summarize CORS setup" — without leaving the terminal.
Automatically fetches recent commits → LLM generates structured summaries → LanceDB vector storage → semantic matching on user queries → relevant history auto-injected into context. The AI always knows your project's latest evolution.
Traditional code search tells you where keywords match. CodeActor finds the code, then automatically analyzes the structural world around it.
User Query
│
├─→ Stage 1: Hybrid Search (Dual-Channel High Recall)
│ ├── 🧠 Dense: LanceDB Vector Search (Qwen3-Embedding-4B, 2560-dim)
│ └── 🔤 Sparse: Tantivy BM25 Full-Text Search (CodeTokenizer for snake_case/CamelCase)
│ └── 🔗 RRF Fusion: Reciprocal Rank Fusion merges both channels
│
├─→ Stage 2: Code Graph Expansion (Structural Context Injection)
│ └── PetCodeGraph BFS Traversal: from seed functions, auto-expand callers/callees
│ └── Cross-file context: place isolated code blocks back into their architectural position
│
└─→ Stage 3: Cross-Encoder Rerank (Precision Refinement)
└── Optional Reranker API for Query-Document cross-encoding rerank
Pure vector search treats code blocks as isolated islands — it computes semantic similarity but has no idea what the function calls, who calls it, or what module it belongs to.
CodeActor's breakthrough: Hybrid retrieval + code graph expansion = a leap from "found" to "understood".
| Aspect | Pure Vector Search | CodeActor Hybrid + Graph Expansion |
|---|---|---|
| Recall | ❌ Semantic matches with different keywords → missed | ✅ BM25 + Vector dual-channel covers both semantics and exact match |
| Precision | ❌ Short text / noise often ranks high | ✅ RRF fusion + short-code penalty + Cross-Encoder triple filtering |
| Context | ❌ Returns isolated code blocks with no call relationships | ✅ PetCodeGraph auto-expands call chains, restores architectural context |
| Code-Aware | ❌ Generic tokenizers don't understand code naming | ✅ Custom CodeTokenizer designed for snake_case & CamelCase |
| Robustness | ❌ Single point of failure | ✅ Triple degradation: BM25 fails→dense-only, Reranker fails→RRF, one channel→other |
Download the latest all-in-one release for your platform from the GitHub Releases page. The binary bundles the codeseek intelligence engine (Rust), fzf (fuzzy finder), and ripgrep (regex search) — everything you need is included. Just extract and run ./codeactor — zero dependencies, zero configuration.
- Go 1.24+
ripgrep(full-text regex search)
git clone https://github.com/iohub/codeactor-agent.git
cd codeactor-agent
go build -o codeactor .Create ~/.codeactor/config/config.toml:
[global.llm]
use_provider = "siliconflow"
[global.llm.providers.siliconflow]
model = "deepseek-ai/DeepSeek-V3.2"api_base_url = "https://api.siliconflow.cn/v1"api_key = "your-api-key"temperature = 0.0max_tokens = 23000# TUI mode
./codeactor tui
# With a task file
./codeactor tui --taskfile TASK.md
# HTTP server mode (default :9080)
./codeactor http| Document | Description |
|---|---|
| ARCHITECTURE.md | System architecture, modules, data flow |
| Agent_Design.md | Multi-agent design rationale |
| Agent_Reference.md | API reference & configuration |
| Browser_Agent_Design.md | Browser agent architecture |
We welcome all contributions — bug reports, feature requests, docs, and code.