Parallax
A modular, model-agnostic AI coding-agent runtime designed to power CLI, desktop, and web applications.
Parallax is being built as a serious AI engineering project rather than as a single-purpose coding CLI.
The central idea is to separate the agent runtime from the interface that users interact with. The runtime should understand how to reason over a task, interact with a repository, use tools, manage context, call different models, coordinate subagents, interact with MCP servers, execute code safely, and maintain run state. The CLI, desktop application, and web application should all consume the same core runtime.
Project Vision
Parallax aims to become a reusable AI agent runtime rather than merely another coding-agent interface.
The runtime should eventually be capable of taking a task such as:
Fix the failing authentication tests in this repository.
and autonomously:
- Understand the task.
- Inspect the repository.
- Search for relevant code.
- Read files and configuration.
- Form a plan.
- Modify files.
- Run tests or other verification commands.
- Inspect failures.
- Iterate.
- Produce a final result with a useful summary of what changed.
The same runtime should be usable through multiple interfaces:
┌─────────────────────────┐
│ Parallax Core │
│ │
│ Agent Runtime │
│ Model Layer │
│ Tool System │
│ Context Engine │
│ Repository Intel │
│ MCP │
│ Subagents │
│ Sandbox │
│ Persistence │
│ Evals │
│ Observability │
└────────────┬────────────┘
│
┌───────────────────┼───────────────────┐
│ │ │
▼ ▼ ▼
CLI Desktop Web
Python first Tauri + TS/ Next.js + TS
Rust later
The core is the product's technical foundation. The interfaces are consumers of that foundation.
Why Parallax Exists
AI coding agents are no longer simply chat interfaces with code generation.
A capable coding agent needs to operate inside an environment:
- repositories
- files
- terminals
- Git
- tests
- build systems
- package managers
- documentation
- external tools
- model providers
- MCP servers
- multiple agents
- persistent sessions
- context limits
- permissions and security boundaries
This makes the interesting engineering problem much larger than:
prompt → LLM → answer
Parallax is intended to explore the complete system:
User Task
↓
Agent Runtime
↓
Context + Repository State
↓
Model
↓
Tool Calls
↓
Execution Environment
↓
Tool Results
↓
State Update
↓
Context Rebuild
↓
Model
↓
...
↓
Verified Result
The project is also intentionally designed as a learning vehicle for modern AI engineering.
The goal is not to hide the mechanics behind an agent framework from day one. The core runtime will initially be implemented directly so that the underlying concepts are understood before abstractions are introduced.
Core Thesis
The central architectural principle is:
Parallax Core should not know or care whether it is being used by a CLI, desktop application, web application, or another program.
The core should provide APIs such as:
from parallax import Agent
agent = Agent(
model="...",
tools="auto",
)
result = await agent.run(
"Fix the failing authentication tests."
)
The interface consuming the runtime decides how that result is displayed.
For example:
CLI
└── renders AgentEvents in the terminal
Desktop
└── renders AgentEvents in a graphical UI
Web
└── streams AgentEvents over WebSocket/SSE
This prevents UI concerns from leaking into the runtime.
Architecture
┌─────────────────────────────────────────────────────────────┐
│ Interfaces │
│ │
│ CLI Desktop Web │
└───────────────┬───────────────┬───────────────┬─────────────┘
│ │ │
└───────────────┼───────────────┘
│
Runtime API
│
┌───────────────────────────────▼─────────────────────────────┐
│ PARALLAX CORE │
│ │
│ Agent Runtime │
│ ├── State │
│ ├── Execution Loop │
│ ├── Termination │
│ ├── Budgets │
│ ├── Retries │
│ └── Cancellation │
│ │
│ Model Layer │
│ ├── Provider abstraction │
│ ├── Streaming │
│ ├── Tool calling │
│ └── Usage │
│ │
│ Tool System │
│ ├── File tools │
│ ├── Search tools │
│ ├── Shell tools │
│ ├── Git tools │
│ └── Test tools │
│ │
│ Context Engine │
│ ├── Context budgeting │
│ ├── History │
│ ├── Compaction │
│ └── Repository instructions │
│ │
│ Repository Intelligence │
│ ├── File discovery │
│ ├── Symbol search │
│ ├── AST analysis │
│ └── Code indexing │
│ │
│ MCP │
│ ├── Servers │
│ ├── Tools │
│ └── Resources │
│ │
│ Agents │
│ ├── Subagents │
│ ├── Delegation │
│ └── Parallel execution │
│ │
│ Sandbox │
│ ├── Process isolation │
│ ├── Filesystem boundaries │
│ └── Network permissions │
│ │
│ Persistence │
│ ├── Sessions │
│ ├── Runs │
│ ├── Messages │
│ └── Checkpoints │
│ │
│ Evals + Observability │
└───────────────────────────────┬─────────────────────────────┘
│
Optional Platform
│
┌───────────────────────────────▼─────────────────────────────┐
│ PARALLAX PLATFORM │
│ │
│ API │
│ Authentication / OAuth │
│ Users / Organizations │
│ Model access policies │
│ Usage / quotas │
│ Billing │
│ Metrics │
│ Cloud persistence │
└─────────────────────────────────────────────────────────────┘
Repository Structure
Current Codebase (src/ Package Layout)
Parallax is organized as an installable package (src/parallax) providing a headless agent runtime decoupled from user interfaces:
parallax/
├── src/
│ └── parallax/ # Main installable package
│ ├── __init__.py # Public exports (Agent, LocalEnvironment, BashTool, etc.)
│ └── core/ # Headless agent runtime
│ ├── agent.py # Agent coordinator & high-level run methods
│ ├── run.py # Asynchronous TaskGroup execution loop
│ ├── llm.py # LLM client with retry & full exponential jitter
│ ├── environment.py # LocalEnvironment runtime abstraction
│ ├── tool.py # Tool decorator & registry system
│ ├── types.py # Typed dataclasses (Message, AgentEvent, etc.)
│ ├── errors.py # Domain error hierarchy
│ ├── tools/ # Core tools (bash, editor, search)
│ └── context/ # Token estimation, compactor, manager
├── tests/ # Comprehensive test suite (131+ unit & e2e tests)
├── pyproject.toml # Dependencies, packaging & build configuration
├── PLAN.md # Roadmap and execution tracking
├── LEARNING.md # Engineering study guide & applied learnings
└── README.md
Intended Long-Term Monorepo Evolution
As interfaces and services expand (CLI, Web, Desktop), the architecture will evolve into a workspace structure:
parallax/
│
├── packages/
│ ├── parallax-core/ # Headless runtime package
│ │ └── src/
│ │ └── parallax/
│ │ ├── runtime/
│ │ ├── models/
│ │ ├── tools/
│ │ ├── context/
│ │ ├── repository/
│ │ ├── mcp/
│ │ ├── agents/
│ │ ├── persistence/
│ │ └── evals/
│ │
│ └── parallax-cli/ # Rich interactive terminal interface
│ └── src/
│ └── parallax_cli/
│
├── apps/
│ ├── web/ # Next.js web application
│ └── desktop/ # Tauri / Rust desktop client
│
├── services/
│ └── api/ # Headless API service
│
├── evals/
├── docs/
├── README.md
├── pyproject.toml
└── uv.lock
The project grows incrementally, keeping the runtime clean and headless at every stage.
Core Runtime
The runtime is the most important component in Parallax.
Conceptually:
class Agent:
async def run(self, task: str) -> AgentResult:
...
The runtime is responsible for:
- maintaining execution state
- constructing model requests
- exposing available tools
- executing tool calls
- feeding results back to the model
- enforcing iteration limits
- handling failures
- managing context
- tracking usage
- emitting events
- supporting cancellation
- deciding when the run terminates
- producing a final result
A useful mental model is:
Agent =
Model
+ State
+ Tools
+ Environment
+ Context Policy
+ Execution Loop
+ Termination Policy
Agent Execution Model
The fundamental loop is:
┌──────────────┐
│ User Task │
└──────┬───────┘
│
▼
┌─────────────────┐
│ Build Context │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Model │
└────────┬────────┘
│
┌─────────┴─────────┐
│ │
▼ ▼
Final Response Tool Calls
│ │
│ ▼
│ ┌─────────────┐
│ │ Execute │
│ │ Tools │
│ └──────┬──────┘
│ │
│ ▼
│ ┌─────────────┐
│ │ Tool Result │
│ └──────┬──────┘
│ │
│ ▼
│ Update State
│ │
│ └──────────┐
│ │
└──────────────────────────────┘
│
▼
Continue / Stop
A simplified runtime can eventually look like:
class Agent:
async def run(self, task: str):
state = AgentState(task=task)
while not self.should_stop(state):
response = await self.model.generate(
messages=self.context.build(state),
tools=self.tools.definitions(),
)
state.record(response)
if response.is_final:
break
results = await self.execute_tools(
response.tool_calls
)
state.record(results)
return state.to_result()
Agent vs Agent Run
A key design distinction:
Agent
Reusable configuration:
model
system prompt
tool registry
context manager
policies
runtime configuration
AgentRun
One execution:
task
messages
tool calls
tool results
iteration count
usage
events
status
checkpoints
final result
Conceptually:
Agent
├── Model
├── Tools
├── Context Manager
└── Policies
│
├── Run #1
├── Run #2
└── Run #3
This prevents conversation state from being permanently attached to the agent configuration.
Model Layer
Parallax should be model-agnostic.
The runtime should not be tightly coupled to one provider.
A model abstraction should eventually provide:
generate()
stream()
tool calling
structured output
token usage
context limits
model metadata
Conceptually:
class Model:
async def generate(
self,
messages,
tools=None,
) -> ModelResponse:
...
Potential providers may include:
- OpenRouter
- OpenAI-compatible APIs
- OpenAI
- Anthropic
- local models
- other compatible providers
The model layer should expose capabilities such as:
provider
model_id
context_window
supports_tools
supports_streaming
supports_vision
input_cost
output_cost
Tool System
Tools are the bridge between the model and the environment.
A tool should have:
name
description
input schema
function
execution policy
result
metadata
Example:
@tool
def read_file(path: str) -> str:
"""Read a file from the repository."""
...
The runtime should transform this into a provider-compatible schema:
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a file from the repository.",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string"
}
},
"required": ["path"]
}
}
}
Tool execution should eventually support:
- validation
- timeouts
- cancellation
- permissions
- structured results
- error reporting
- execution metadata
- logging
- sandboxing
A tool failure should normally become model-visible feedback rather than crashing the entire agent.
Initial Coding Tools
read_file
write_file
edit_file
list_files
search_code
shell
git
run_tests
Later:
inspect_ast
search_symbols
find_references
apply_patch
run_linter
run_formatter
inspect_dependencies
query_docs
Repository Intelligence
A coding agent needs more than raw text retrieval.
Parallax should eventually understand repositories through multiple layers.
Layer 1 --- Filesystem
directories
files
file metadata
.gitignore
configuration
Layer 2 --- Text Search
grep-like search
regex search
keyword search
filename search
Layer 3 --- Structural Search
functions
classes
methods
imports
symbols
references
Layer 4 --- AST
Tree-sitter and similar parsing technologies can provide language-aware structure.
Layer 5 --- Repository Graph
file
├── imports
├── defines
├── references
├── tests
└── dependencies
This allows Parallax to reason about a repository structurally instead of repeatedly reading arbitrary files.
Context Engineering
Context is one of the central engineering problems in coding agents.
The runtime cannot simply append everything it sees to the prompt forever.
Parallax should eventually manage:
- context budgets
- message history
- tool results
- relevant files
- repository instructions
- summaries
- compaction
- retrieval
- prioritization
- progressive disclosure
The context manager should answer:
What information does the model need right now to make the next decision?
Conceptually:
Repository
│
├── relevant files
├── symbols
├── search results
├── previous actions
├── tool results
└── task information
│
▼
Context Manager
│
▼
Model Context
RAG
RAG will primarily support repository intelligence and contextual retrieval.
Potential pipeline:
Repository
↓
Parse / Chunk
↓
Metadata
↓
Embeddings
↓
Vector Index
↓
Hybrid Retrieval
↓
Reranking
↓
Context Manager
↓
LLM
Parallax should not depend exclusively on vector search.
Coding repositories benefit from:
- lexical search
- symbol search
- AST-aware search
- graph relationships
- vector similarity
Therefore the long-term approach should support hybrid retrieval.
Potential technologies:
- PostgreSQL
- pgvector
- local vector stores
- BM25/lexical retrieval
- Tree-sitter
- embedding APIs or local embedding models
MCP
Model Context Protocol will be an important interoperability layer.
Parallax should eventually support MCP servers for capabilities such as:
documentation
databases
browser tools
issue trackers
internal tools
developer services
The MCP subsystem should handle:
server discovery
connection lifecycle
tool discovery
tool execution
resources
prompts
transport
authorization
errors
Parallax should use an established MCP implementation rather than reimplementing the protocol itself.
The runtime's responsibility is to integrate MCP capabilities into its own tool/context model.
Subagents
Complex tasks can eventually be delegated to specialized agents.
Example:
Main Agent
│
┌─────────┼─────────┐
│ │ │
▼ ▼ ▼
Research Coding Testing
Agent Agent Agent
Potential responsibilities:
Research Agent
└── investigate repository / documentation
Coding Agent
└── implement changes
Testing Agent
└── run and analyze tests
Review Agent
└── inspect resulting changes
The system should support:
- delegation
- handoffs
- isolated state
- shared context
- result passing
- parallel execution
- cancellation
- budgets
- failure handling
Subagents should be introduced only after the single-agent runtime is stable.
Sandboxing and Security
A coding agent can execute arbitrary commands and modify arbitrary files.
Security is therefore a first-class concern.
Potential controls:
filesystem restrictions
working-directory restrictions
command allowlists
environment isolation
network restrictions
resource limits
process isolation
secret protection
permission prompts
A future sandbox may use containerization or another isolated execution environment.
Security concerns to address include:
- prompt injection
- malicious repository content
- untrusted MCP servers
- accidental destructive commands
- secret exfiltration
- network access
- arbitrary code execution
- tool permission escalation
Tool execution should be treated as a security boundary rather than assuming model-generated commands are trustworthy.
Sessions and Persistence
Sessions should eventually become first-class objects.
A session may contain:
user
repository
working directory
model
configuration
messages
runs
tool calls
checkpoints
usage
state
Conceptual hierarchy:
User
└── Session
├── Run
│ ├── Messages
│ ├── Tool Calls
│ ├── Events
│ └── Result
│
├── Run
└── Run
Local mode may initially use:
SQLite
Platform mode may use:
PostgreSQL
The runtime should not require a cloud database to operate locally.
Events and Streaming
The runtime should be event-driven.
Possible events:
run_started
model_started
model_delta
model_completed
tool_started
tool_output
tool_completed
tool_failed
file_changed
test_started
test_completed
agent_message
agent_error
run_completed
run_cancelled
Example:
async for event in agent.run_stream(task):
...
The CLI could render these events directly.
The desktop application could subscribe through a local process boundary.
The web application could receive the same event stream through WebSocket or SSE.
This keeps the runtime independent of presentation.
Evals
Parallax should eventually contain its own evaluation system.
For coding agents, useful evaluation signals include:
task completion
tests passing
files correctly modified
regressions
tool errors
iterations
latency
token usage
cost
human intervention
A benchmark task might look like:
Repository: example-project
Task: Fix issue #42
Expected:
- tests pass
- regression test exists
- unrelated files unchanged
The evaluation harness should make it possible to compare:
model A vs model B
prompt A vs prompt B
runtime version A vs runtime version B
tool strategy A vs tool strategy B
without relying only on subjective manual testing.
Observability
Agent systems are difficult to debug without structured telemetry.
Parallax should eventually track:
run duration
model latency
time to first token
input tokens
output tokens
cached tokens
estimated cost
tool latency
tool failures
iterations
context size
subagent activity
MCP activity
Tracing should make it possible to understand:
Run
├── Model call
│ ├── context size
│ ├── latency
│ └── usage
│
├── Tool call
│ ├── command
│ ├── duration
│ └── result
│
├── Model call
│
└── Final result
OpenTelemetry is a possible foundation for standardized tracing.
Interfaces
CLI
The CLI is the first interface.
Initial goal:
parallax "Fix the authentication tests"
Potential commands:
parallax
parallax run "Fix this bug"
parallax inspect
parallax session
parallax config
parallax models
parallax mcp
parallax eval
The CLI should remain thin.
It should primarily:
- Parse user input.
- Configure the runtime.
- Subscribe to runtime events.
- Render output.
- Forward user interaction to the runtime.
Desktop
The desktop application is a future interface.
Potential architecture:
Tauri
├── Rust host
├── TypeScript/React UI
└── Parallax Core process
Potential capabilities:
- repository explorer
- agent chat
- diff viewer
- terminal
- task history
- session management
- model selection
- tool permissions
- MCP configuration
- agent activity visualization
The desktop application should consume the same core rather than implementing another agent runtime.
Web
The web application may eventually use:
Next.js
TypeScript
FastAPI
Parallax Core
PostgreSQL
Conceptually:
Browser
│
▼
Next.js
│
▼
API
│
▼
Parallax Runtime
│
├── Model Providers
├── Tools
├── MCP
└── Repository
The web application introduces additional platform concerns such as:
- authentication
- authorization
- sessions
- organizations
- usage limits
- model policies
- cloud persistence
- job execution
- billing
- rate limiting
These concerns should not be embedded into the local core.
Platform Layer
The platform layer is optional and sits outside the core runtime.
Potential responsibilities:
API
Authentication
OAuth / OIDC
Users
Organizations
Permissions
Model access policies
Usage tracking
Quotas
Billing
Cloud persistence
Metrics
Rate limiting
For example, a hosted Parallax service could decide:
User A
└── can use models X and Y
User B
└── can use models X, Y and Z
Organization C
└── has a monthly token quota
The core runtime should instead receive the effective configuration and execute the run.
This separation allows:
Local Parallax
↓
user's API key
user's repository
local execution
Hosted Parallax
↓
platform authentication
platform model gateway
cloud persistence
remote execution
Both can use the same runtime architecture.
Usage and Model Policies
A future platform model gateway can track:
user
session
run
provider
model
input tokens
output tokens
cached tokens
cost
latency
This enables policies such as:
allowed models
token limits
rate limits
usage quotas
provider restrictions
organization policies
The runtime itself should remain capable of running independently.
Technology Direction
Core
Primary language:
Python
Python is the initial choice because of its AI/LLM ecosystem, async support, rapid iteration, and broad developer tooling.
CLI
Initial:
Python
Typer
Rich
Potential future native CLI:
Rust
Rust should not be introduced into the runtime prematurely.
A native Rust CLI may eventually make sense for startup performance, distribution, native system integration, or tighter process management. Until then, a Python CLI keeps the architecture simpler.
Desktop
Potential:
Tauri
Rust
React
TypeScript
Web
Potential:
Next.js
React
TypeScript
Tailwind CSS
FastAPI
Data
Local:
SQLite
Platform:
PostgreSQL
Potential vector search:
pgvector
Redis should be introduced only when a real workload requires it.
Infrastructure
Potential:
Docker
Docker Compose
GitHub Actions
Vercel
Cloud infrastructure as required
Infrastructure should not become the focus before the runtime itself is mature.
Design Principles
1. Core first
Build the runtime before building multiple UIs.
Core → CLI → Desktop/Web
not:
CLI + Web + Desktop + Core simultaneously
2. Interface independence
The core should not contain React, Next.js, terminal formatting, HTTP route handlers, or browser-specific logic.
It should expose clean APIs and events.
3. Model independence
Do not hardcode the runtime around one provider.
4. Local-first
The runtime should work with:
local repository
local filesystem
user-owned API keys
local persistence
without requiring a hosted Parallax account.
5. Explicit state
Agent state should be inspectable and structured.
Avoid hiding important runtime behavior behind opaque abstractions.
6. Async by design
The runtime will eventually need:
streaming
parallel tools
MCP
subagents
cancellation
concurrent operations
Therefore asynchronous execution should be adopted early.
7. Events over UI coupling
The runtime emits events. Clients decide how those events are rendered.
8. Security by design
Any capability that can execute commands, modify files, access networks, or read secrets must be treated as privileged.
9. Evaluate important behavior
New agent capabilities should be measurable.
10. Build understanding before abstractions
Early versions should intentionally implement core mechanics directly. Frameworks can be evaluated and introduced after the underlying architecture is understood.
Development Roadmap
Phase 0 --- Foundation
Python
uv
project structure
environment management
typing
dataclasses
Pydantic
pytest
asyncio
Phase 1 --- Minimal Agent Runtime
Implement:
LLM request
message state
tool schemas
tool registry
tool execution
tool results
iteration loop
termination
Initial runtime:
LLM
↓
Tool Call
↓
Tool
↓
Result
↓
LLM
Phase 2 --- Runtime Robustness
Add:
async execution
max iterations
timeouts
tool errors
structured ToolResult
retry handling
Agent
AgentRun
cancellation
Phase 3 --- Coding Agent
Add:
read_file
write_file
edit_file
list_files
search_code
shell
git
test runner
Phase 4 --- Events and Streaming
Add:
AgentEvent
streaming model output
tool lifecycle events
file change events
execution timing
usage tracking
structured logs
Phase 5 --- Context Engineering
Add:
context manager
token budgets
history management
repository instructions
context compaction
summarization
progressive disclosure
Phase 6 --- Repository Intelligence
Add:
Tree-sitter
symbol indexing
AST analysis
reference search
repository graph
hybrid retrieval
Phase 7 --- RAG
Add:
embeddings
vector search
lexical retrieval
hybrid retrieval
reranking
repository indexing
Phase 8 --- MCP
Add:
MCP client
server configuration
tool discovery
resource discovery
tool execution
permissions
Phase 9 --- Subagents
Add:
delegation
agent roles
parallel agents
handoffs
shared state
subagent budgets
Phase 10 --- Sandboxing
Add:
process isolation
filesystem isolation
network policy
resource limits
permission model
secret protection
Phase 11 --- Persistence
Add:
sessions
runs
checkpoints
SQLite
run recovery
history
Phase 12 --- Evals
Add:
benchmark tasks
automated graders
regression suite
runtime comparisons
model comparisons
tool effectiveness measurements
Phase 13 --- Observability
Add:
structured logging
metrics
tracing
token accounting
cost tracking
latency
tool analytics
Phase 14 --- Interfaces
Build:
CLI
↓
Desktop
↓
Web
while keeping the core unchanged.
Phase 15 --- Platform
Eventually introduce:
FastAPI
authentication
OAuth/OIDC
users
organizations
model gateway
usage policies
quotas
cloud persistence
billing
This phase should happen only after the core runtime is mature.
Current Implementation
The initial prototype demonstrates:
OpenRouter model call
↓
tool schema generation
↓
tool registration
↓
LLM tool calling
↓
tool execution
↓
tool result
↓
LLM continuation
The prototype uses Python and an OpenRouter client.
The current code is a learning implementation and is expected to be
refactored into the parallax-core package.
Important early improvements include:
fix tool registration
fix provider-compatible message serialization
introduce a proper Tool abstraction
introduce model abstraction
make runtime asynchronous
add max iterations
add tool error handling
add structured ToolResult
separate Agent from AgentRun
The prototype should not be discarded. It is the seed from which the runtime is being built.
Recommended Initial Core API
A long-term API may look approximately like:
from parallax import Agent
agent = Agent(
model="provider/model",
tools="auto",
)
result = await agent.run(
"Fix the failing authentication tests."
)
print(result.output)
For streaming:
async for event in agent.run_stream(
"Fix the failing authentication tests."
):
print(event)
The exact API is expected to evolve as the runtime becomes more capable.
Learning Objectives
Parallax is deliberately designed to develop practical AI engineering skills.
Python systems engineering
typing
dataclasses
Pydantic
asyncio
subprocess
filesystem APIs
packaging
testing
logging
LLM engineering
messages
tool calling
structured output
streaming
context windows
tokens
model capabilities
provider abstraction
Agent engineering
agent loops
state machines
tool execution
termination
retries
context management
planning
delegation
subagents
Coding-agent engineering
Git
patches
diffs
repository search
ASTs
tests
build systems
code indexing
Retrieval
embeddings
vector search
BM25
hybrid retrieval
reranking
RAG
Systems engineering
processes
sandboxing
IPC
concurrency
cancellation
persistence
event systems
Backend engineering
Later:
REST
WebSockets
SSE
authentication
OAuth
sessions
databases
rate limiting
quotas
Production AI engineering
observability
tracing
evaluations
cost tracking
model routing
security
reliability
What Parallax Will Not Reinvent
The project should build important AI-agent abstractions itself for learning purposes, but should not waste time reimplementing mature foundational technology.
Build in-house
agent execution loop
agent state
tool registry abstraction
runtime policies
context management
event system
agent orchestration
subagent orchestration
usage accounting
run persistence
evaluation harness
model abstraction
Use mature libraries/protocols
HTTP
TLS
cryptography
Git
Tree-sitter
Docker
MCP protocol
OAuth/OIDC
database drivers
tokenizers
LLM inference
provider SDKs
The objective is to understand the architecture without unnecessarily rebuilding foundational infrastructure.
Long-Term Vision
PARALLAX
│
┌──────────────┴──────────────┐
│ │
Parallax Core Parallax Platform
│ │
Agent Runtime API / Auth
Model Layer Users / Orgs
Tool System Usage / Quotas
Context Engine Model Gateway
Repository Intel Cloud Persistence
MCP Billing
Subagents Metrics
Sandbox
Persistence
Evals
Observability
│
┌───────┼────────┐
│ │ │
CLI Desktop Web
The final system should make it possible to use the same agent runtime in multiple environments:
Local developer
↓
Parallax CLI
↓
Parallax Core
or:
Developer
↓
Parallax Desktop
↓
Parallax Core
or:
Developer
↓
Parallax Web
↓
Parallax API
↓
Parallax Core
The runtime remains the common foundation.
Project Philosophy
Parallax is not being built simply to produce another chatbot or another wrapper around an LLM API.
The project is intended to answer a deeper engineering question:
What does it take to build a robust, model-agnostic AI agent runtime capable of operating inside real software repositories?
That means understanding and implementing the systems around the model:
Models
+
Tools
+
State
+
Context
+
Environment
+
Repository Intelligence
+
Memory
+
MCP
+
Subagents
+
Security
+
Evaluation
+
Observability
The LLM is one component of the system.
Parallax is the system around it.
Status
⚡ Core Runtime & CLI Operational (Phases 0–5 & Phase 14 Complete)
The core headless runtime and flagship terminal CLI are fully implemented, tested, and packaged:
- Phase 0 (Foundation): Dataclasses, JSON schemas, typed exceptions,
LocalEnvironmentabstraction. - Phase 1 (Tools): Tool decorator & registry,
BashTool(with 30k char head+tail windowing),EditorTool,SearchTool. - Phase 2 (Async Agent Loop): Asynchronous task group runner, queue-based event streaming (
AgentEvent,EventType), cooperative cancellation. - Phase 3 (Hardening & Resilience): Retry loop with full exponential jitter, 429/500/502/503 handling, HTTP error classification, safe tool dispatch.
- Phase 4 (Packaging & Architecture): Modular
src/parallax/layout cleanly decoupling core runtime (parallax.core) from UI layers. - Phase 5 (Context Management & Compaction): Exact tiktoken token estimation, dynamic sliding context windowing, turn-atomic compaction (preserving OpenAI tool call/response pairs).
- Phase 14 (Interactive Terminal CLI): Flagship terminal interface via Typer + Rich (
parallaxCLI entrypoint):- Single-shot execution:
parallax "Fix failing tests in auth" - Interactive multi-turn REPL:
parallax/parallax chatwith slash commands (/help,/model,/context,/compact,/tools,/clear,/exit) - Diagnostics & tools:
parallax doctorandparallax tools - Premium developer UX: strictly zero emojis, live typing/progress indicators, collapsible tool receipts, and bounded syntax diff previews.
- Single-shot execution:
Next Milestone: Process-Level Execution Control & Phase 11 (Local SQLite Persistence).
License
License to be decided.
1 maintainer
Metadata
1 maintainer
Release files for parallax-agent 0.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| parallax_agent-0.1.3.tar.gz | 102.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| parallax_agent-0.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 169.0 kB
Release files / parallax_agent-0.1.3.tar.gz
| Download URL | parallax_agent-0.1.3.tar.gz |
|---|---|
| Size | 102.7 kB |
| Tags | Source |
|
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Release files / parallax_agent-0.1.3-py3-none-any.whl
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